In isolation, the P value may be dangerous and misleading as it does not provide the directionality, magnitude or variability of treatment effects. Careful study design and statistical analysis planning will help reduce the overuse of P values while making the presentation of results more meaningful by complementing P values with effect estimates and confidence intervals will help mitigate misuse of P values.
Recent position statements on the misuse of p-values and significance testing have led to a reassessment of how study results are reported in journals. Increased use of point
estimates and confidence intervals can help avoid the misinterpretation encountered with significance testing. Greater use of confidence intervals can lead to more criticallyand clinically-relevant discussions of study results
When designing a clinical study, a fundamental aspect is the sample size. In this article, we describe the rationale for sample size calculations, when it should be calculated and describe the components necessary to calculate it. For simple studies, standard formulae can be
used; however, for more advanced studies, it is generally necessary to use specialized statistical software programs and consult a biostatistician. Sample size calculations for non-randomized studies are also discussed and two clinical examples are used for illustration
Common Statistical Concerns in Clinical TrialsClin Plus
Statistics are a major part of clinical trials. This article breaks down how they are used, and things that people think about when recording statistical data.
Most medical research around the world is
empirical and uses data to derive a result. Many
researchers substantially depend on statistical
evidence such as P values to decide that an effect
of a specific factor is present or not. Now, there is a
storm around the world, and the P value, particularly
the resulting statistical significance, has been not
just questioned but also sought to be abolished
altogether. Abandoning statistical significance has
the potential to change research in empirical sciences
such as medicine forever. This article discusses the
arguments in favour and against this contention and
pleads that medical scientists present a balanced
picture in their articles where P values have a role
but not as dominant as is currently seen in most
publications. The following discussion would also
make medical researchers aware of this raging
controversy, help them to understand the involved
nuances and equip them to prepare a better report of
their research
This document provides an overview of essentials for manuscript review, including how to organize a manuscript, use statistics, identify types of studies and levels of evidence, address bias, interpret results, and write an abstract, introduction, methods, discussion, and conclusion. It discusses key aspects of each section and how to effectively review a submitted manuscript.
hypothesis-Meaning need for hypothesis qualities of good hypothesis type of hypothesis null and alternative hypothesis sources of hypothesis formulation of hypothesis, hypothesis testing
a small and well descriptive presentation on hypothesis topic. it has all the information you need to know about the topic covering the types, characteristics and contributions you want to know about hypothesis.
Recent position statements on the misuse of p-values and significance testing have led to a reassessment of how study results are reported in journals. Increased use of point
estimates and confidence intervals can help avoid the misinterpretation encountered with significance testing. Greater use of confidence intervals can lead to more criticallyand clinically-relevant discussions of study results
When designing a clinical study, a fundamental aspect is the sample size. In this article, we describe the rationale for sample size calculations, when it should be calculated and describe the components necessary to calculate it. For simple studies, standard formulae can be
used; however, for more advanced studies, it is generally necessary to use specialized statistical software programs and consult a biostatistician. Sample size calculations for non-randomized studies are also discussed and two clinical examples are used for illustration
Common Statistical Concerns in Clinical TrialsClin Plus
Statistics are a major part of clinical trials. This article breaks down how they are used, and things that people think about when recording statistical data.
Most medical research around the world is
empirical and uses data to derive a result. Many
researchers substantially depend on statistical
evidence such as P values to decide that an effect
of a specific factor is present or not. Now, there is a
storm around the world, and the P value, particularly
the resulting statistical significance, has been not
just questioned but also sought to be abolished
altogether. Abandoning statistical significance has
the potential to change research in empirical sciences
such as medicine forever. This article discusses the
arguments in favour and against this contention and
pleads that medical scientists present a balanced
picture in their articles where P values have a role
but not as dominant as is currently seen in most
publications. The following discussion would also
make medical researchers aware of this raging
controversy, help them to understand the involved
nuances and equip them to prepare a better report of
their research
This document provides an overview of essentials for manuscript review, including how to organize a manuscript, use statistics, identify types of studies and levels of evidence, address bias, interpret results, and write an abstract, introduction, methods, discussion, and conclusion. It discusses key aspects of each section and how to effectively review a submitted manuscript.
hypothesis-Meaning need for hypothesis qualities of good hypothesis type of hypothesis null and alternative hypothesis sources of hypothesis formulation of hypothesis, hypothesis testing
a small and well descriptive presentation on hypothesis topic. it has all the information you need to know about the topic covering the types, characteristics and contributions you want to know about hypothesis.
This document defines key concepts related to hypothesis testing. It explains that a hypothesis is a temporary assumption made to conduct research. The two main types of hypotheses are the null hypothesis, which assumes no difference or relationship, and the alternative hypothesis, which assumes a significant difference or relationship. The document outlines the steps to test a hypothesis, which include setting the hypotheses, significance level, test statistic, critical region, calculating and comparing test statistics, and making a decision to accept or reject the null hypothesis. Common test statistics and significance levels are also defined.
This document provides guidance on how to write an abstract for an academic paper or research study. It discusses the key components and structure of an abstract, including the title, authors, introduction/background, methods, results, and conclusions sections. The introduction explains the purpose of an abstract is to concisely summarize the entire paper in a short space. The body provides tips for writing each section of an abstract and examples of well-structured abstracts. It emphasizes the abstract should answer why the study was conducted, what methods were used, what results were found, and what the overall conclusions and implications are.
The document discusses key concepts related to formulating and testing hypotheses, including:
- Null and alternative hypotheses, which are mutually exclusive statements tested through sample analysis.
- Type I and Type II errors that can occur when making decisions to accept or reject the null hypothesis.
- The level of significance, critical region, and test statistics used to determine whether to reject the null hypothesis.
- The differences between one-tailed and two-tailed tests, parametric vs. non-parametric tests, and one-sample vs. two-sample tests.
The document discusses the general procedure for testing hypotheses. It involves:
1) Stating the null and alternative hypotheses.
2) Choosing a significance level, usually 1% or 5%.
3) Calculating a test statistic from the sample data.
4) Comparing the test statistic to critical values to determine whether to reject or fail to reject the null hypothesis based on the pre-defined significance level and decision rule.
5) Drawing a conclusion about whether the alternative or null hypothesis is supported.
This document provides guidance on selecting appropriate study designs for evaluating population health and health service interventions. It emphasizes that choosing an effective study design is critical for producing high-quality evaluations that can credibly demonstrate the impact of an intervention. The document outlines key initial steps in evaluation planning, such as developing a program logic model and generating evaluation questions. It then describes experimental, quasi-experimental, and non-experimental quantitative study designs and provides examples of when each may be suitable. The intended audience is NSW Health staff involved in planning, implementing, overseeing or using results from evaluations.
The document discusses hypothesis testing in research. It defines a hypothesis as a proposition that can be tested scientifically. The key points are:
- A hypothesis aims to explain a phenomenon and can be tested objectively. Common hypotheses compare two groups or variables.
- Statistical hypothesis testing involves a null hypothesis (H0) and alternative hypothesis (Ha). H0 is the initial assumption being tested, while Ha is what would be accepted if H0 is rejected.
- Type I errors incorrectly reject a true null hypothesis. Type II errors fail to reject a false null hypothesis. Hypothesis tests aim to control the probability of type I errors.
- The significance level is the probability of a type I error,
Brm (one tailed and two tailed hypothesis)Upama Dwivedi
This document discusses one-tailed and two-tailed hypothesis tests. It defines a hypothesis as an assumption made about the probable results of research. The null hypothesis assumes a parameter takes a certain value, while the alternative hypothesis expresses how the parameter may deviate. A one-tailed test examines if a parameter falls on one side of the distribution, while a two-tailed test looks at both sides. Two-tailed tests are more conservative since they require more extreme test statistics to reject the null hypothesis. Examples are provided to illustrate the difference between one-tailed and two-tailed tests.
Okay, let me try to analyze this step-by-step:
1) Null Hypothesis (H0): The advertisement had no effect on sales.
2) Alternative Hypothesis (H1): The advertisement increased sales.
3) We can test this using a paired t-test, since we have sales data from the same shops before and after.
4) Calculate the mean difference between before and after sales for each shop. Then take the average of those differences.
5) Use the t-statistic to determine if the average difference is significantly greater than 0, which would indicate the advertisement increased sales.
So in summary, a paired t-test can be used to determine if the advertisement
The document provides guidance on statistical methods for publishing medical research papers, with a focus on three key areas:
1. The responsibilities of statistical reviewers include ensuring authors clearly spell out the limitations of their conclusions based on the study design, data collection, and analyses. Reviewers should also help authors improve their manuscripts.
2. When describing statistical methods, authors should provide enough detail to allow other researchers to verify the reported results. They should also quantify findings and present uncertainty where possible.
3. For clinical trials, authors must register trials in a public registry and adhere to reporting guidelines like CONSORT for randomized controlled trials.
This document discusses sample size estimation and informed consent for research studies. It explains the importance of calculating an appropriate sample size to avoid interpreting studies with inadequate samples or wasting resources. The key factors for determining sample size are outlined, including the type of study, primary outcome, acceptable margin of error, and desired power. The document also covers sampling methods, types of errors, effect sizes, and adjusting sample sizes. It emphasizes the need for informed consent and outlines the necessary elements and information to include in participant information sheets to properly consent participants.
The document discusses business research design and provides details on key concepts related to research design such as dependent and independent variables, extraneous variables, experimental and control groups, and treatments. It explains the need for research design to facilitate efficient research. Research design involves consideration of data collection techniques, the population being studied, and data analysis methods. The document also distinguishes between exploratory and conclusive research designs.
Combination of informative biomarkers in small pilot studies and estimation ...LEGATO project
Background:
Biomarker candidates are defined as measurable molecules found in biological media. According to Biomarkers Definitions Working Group, 2001, biomarkers cover a rather wide range of parameters. Recently, biomarkers are used widely in medical researches, where single biomarkers may not possess the desired cause-effect association for disease classification and outcome prediction. Therefore the efforts of the researchers currently is to combine biomarkers. By new technologies like microarrays, next generation sequencing and mass spectrometry, researchers can obtain many biomarker candidates that can exceed tens of thousands. To avoid wasting money and time, it is suggested to control the number of patients strictly. However, pilot studies usually have low statistical power which reduces the chance of detecting a true effect .
This document discusses hypothesis testing and related statistical concepts. It defines a hypothesis as a statement that can be tested, and distinguishes between the null hypothesis (H0) and alternative hypothesis (Ha). It explains how to select a significance level, commonly 1%, 5% or 10%, and how this relates to Type I and Type II errors. It also covers one-tailed and two-tailed tests, and when to use z-tests or t-tests to test hypotheses about population means based on sample data. The document provides examples of hypotheses for research topics and how to carry out the statistical tests.
The document discusses key concepts related to research methodology and hypothesis testing. It defines the following:
- Null and alternative hypotheses, with the null hypothesis representing what is being tested and the alternative representing other possibilities.
- Type I and Type II errors in hypothesis testing, with Type I being rejection of a true null hypothesis and Type II being acceptance of a false null hypothesis.
- Significance levels which determine the probability of a Type I error, with common values being 0.10, 0.05, and 0.01.
- Power which is the probability of correctly rejecting a false null hypothesis and can be increased by raising the significance level, increasing sample size, or considering alternatives further from the null.
This document discusses hypothesis construction in research methodology. It defines a hypothesis as a proposed explanation or relationship between variables that is testable but unproven. Hypotheses provide focus and direction for a study by indicating what data to collect. There are two main types: research hypotheses that predict a relationship, stated as equality; and alternative hypotheses that predict an alternative if the null is false, stated as inequality. Hypotheses must be simple, verifiable, related to existing knowledge, and operationalizable. Errors can occur if the study design, sampling, data collection, analysis, or conclusions are flawed. Qualitative research does not strictly require or test hypotheses but may formulate them to highlight phenomena and relationships.
The document provides guidance on improving the chances of getting a manuscript accepted for publication. It discusses key methodological concepts like uncertainty of measurement and sampling, statistical assumptions, multiplicity, and proper reporting of different study types including case reports, experiments, observational studies, and randomized trials. The key recommendations are to 1) clearly describe statistical methods and sample sizes, 2) present both data and interpretation of results considering clinical and statistical significance, and 3) properly adjust for confounding and comply with reporting standards for different study designs.
Types of Hypothesis-Advance Research MethodologyRehan Ehsan
This Presentation states the details of Hypothesis for students to get help in advance research methodology. Rearchers may also get help from this work.
This document provides guidelines for statistical reporting in medical journals. It recommends including details of statistical methods, results, and analyses to allow readers to evaluate evidence and verify results. Key guidelines include:
1. Describe statistical design, population, analyses, and software in the Methods.
2. Report patient characteristics, outcomes, p-values, and comparisons precisely in Results.
3. Do not overinterpret non-significant p-values or use terms like "trend"; only conclude observed differences, not equivalence.
The guidelines aim to improve clarity and reproducibility of statistical methods and findings reported in medical literature.
The authors propose modifying the PICO framework for formulating clinical research questions to PICOS by adding an "S" for statistical analysis. The PICOS framework would help authors more robustly and reproducibly present the methodology of scientific studies. It would also serve as a checklist for reviewers to thoroughly evaluate manuscripts. The PICOS components are: P - patient population, I - intervention, C - comparative controls, O - outcomes, S - statistical analysis. Adopting the PICOS framework could help ensure scientific thoroughness and objectivity in reporting methodology and improve reproducibility and comparability of studies.
This document defines key concepts related to hypothesis testing. It explains that a hypothesis is a temporary assumption made to conduct research. The two main types of hypotheses are the null hypothesis, which assumes no difference or relationship, and the alternative hypothesis, which assumes a significant difference or relationship. The document outlines the steps to test a hypothesis, which include setting the hypotheses, significance level, test statistic, critical region, calculating and comparing test statistics, and making a decision to accept or reject the null hypothesis. Common test statistics and significance levels are also defined.
This document provides guidance on how to write an abstract for an academic paper or research study. It discusses the key components and structure of an abstract, including the title, authors, introduction/background, methods, results, and conclusions sections. The introduction explains the purpose of an abstract is to concisely summarize the entire paper in a short space. The body provides tips for writing each section of an abstract and examples of well-structured abstracts. It emphasizes the abstract should answer why the study was conducted, what methods were used, what results were found, and what the overall conclusions and implications are.
The document discusses key concepts related to formulating and testing hypotheses, including:
- Null and alternative hypotheses, which are mutually exclusive statements tested through sample analysis.
- Type I and Type II errors that can occur when making decisions to accept or reject the null hypothesis.
- The level of significance, critical region, and test statistics used to determine whether to reject the null hypothesis.
- The differences between one-tailed and two-tailed tests, parametric vs. non-parametric tests, and one-sample vs. two-sample tests.
The document discusses the general procedure for testing hypotheses. It involves:
1) Stating the null and alternative hypotheses.
2) Choosing a significance level, usually 1% or 5%.
3) Calculating a test statistic from the sample data.
4) Comparing the test statistic to critical values to determine whether to reject or fail to reject the null hypothesis based on the pre-defined significance level and decision rule.
5) Drawing a conclusion about whether the alternative or null hypothesis is supported.
This document provides guidance on selecting appropriate study designs for evaluating population health and health service interventions. It emphasizes that choosing an effective study design is critical for producing high-quality evaluations that can credibly demonstrate the impact of an intervention. The document outlines key initial steps in evaluation planning, such as developing a program logic model and generating evaluation questions. It then describes experimental, quasi-experimental, and non-experimental quantitative study designs and provides examples of when each may be suitable. The intended audience is NSW Health staff involved in planning, implementing, overseeing or using results from evaluations.
The document discusses hypothesis testing in research. It defines a hypothesis as a proposition that can be tested scientifically. The key points are:
- A hypothesis aims to explain a phenomenon and can be tested objectively. Common hypotheses compare two groups or variables.
- Statistical hypothesis testing involves a null hypothesis (H0) and alternative hypothesis (Ha). H0 is the initial assumption being tested, while Ha is what would be accepted if H0 is rejected.
- Type I errors incorrectly reject a true null hypothesis. Type II errors fail to reject a false null hypothesis. Hypothesis tests aim to control the probability of type I errors.
- The significance level is the probability of a type I error,
Brm (one tailed and two tailed hypothesis)Upama Dwivedi
This document discusses one-tailed and two-tailed hypothesis tests. It defines a hypothesis as an assumption made about the probable results of research. The null hypothesis assumes a parameter takes a certain value, while the alternative hypothesis expresses how the parameter may deviate. A one-tailed test examines if a parameter falls on one side of the distribution, while a two-tailed test looks at both sides. Two-tailed tests are more conservative since they require more extreme test statistics to reject the null hypothesis. Examples are provided to illustrate the difference between one-tailed and two-tailed tests.
Okay, let me try to analyze this step-by-step:
1) Null Hypothesis (H0): The advertisement had no effect on sales.
2) Alternative Hypothesis (H1): The advertisement increased sales.
3) We can test this using a paired t-test, since we have sales data from the same shops before and after.
4) Calculate the mean difference between before and after sales for each shop. Then take the average of those differences.
5) Use the t-statistic to determine if the average difference is significantly greater than 0, which would indicate the advertisement increased sales.
So in summary, a paired t-test can be used to determine if the advertisement
The document provides guidance on statistical methods for publishing medical research papers, with a focus on three key areas:
1. The responsibilities of statistical reviewers include ensuring authors clearly spell out the limitations of their conclusions based on the study design, data collection, and analyses. Reviewers should also help authors improve their manuscripts.
2. When describing statistical methods, authors should provide enough detail to allow other researchers to verify the reported results. They should also quantify findings and present uncertainty where possible.
3. For clinical trials, authors must register trials in a public registry and adhere to reporting guidelines like CONSORT for randomized controlled trials.
This document discusses sample size estimation and informed consent for research studies. It explains the importance of calculating an appropriate sample size to avoid interpreting studies with inadequate samples or wasting resources. The key factors for determining sample size are outlined, including the type of study, primary outcome, acceptable margin of error, and desired power. The document also covers sampling methods, types of errors, effect sizes, and adjusting sample sizes. It emphasizes the need for informed consent and outlines the necessary elements and information to include in participant information sheets to properly consent participants.
The document discusses business research design and provides details on key concepts related to research design such as dependent and independent variables, extraneous variables, experimental and control groups, and treatments. It explains the need for research design to facilitate efficient research. Research design involves consideration of data collection techniques, the population being studied, and data analysis methods. The document also distinguishes between exploratory and conclusive research designs.
Combination of informative biomarkers in small pilot studies and estimation ...LEGATO project
Background:
Biomarker candidates are defined as measurable molecules found in biological media. According to Biomarkers Definitions Working Group, 2001, biomarkers cover a rather wide range of parameters. Recently, biomarkers are used widely in medical researches, where single biomarkers may not possess the desired cause-effect association for disease classification and outcome prediction. Therefore the efforts of the researchers currently is to combine biomarkers. By new technologies like microarrays, next generation sequencing and mass spectrometry, researchers can obtain many biomarker candidates that can exceed tens of thousands. To avoid wasting money and time, it is suggested to control the number of patients strictly. However, pilot studies usually have low statistical power which reduces the chance of detecting a true effect .
This document discusses hypothesis testing and related statistical concepts. It defines a hypothesis as a statement that can be tested, and distinguishes between the null hypothesis (H0) and alternative hypothesis (Ha). It explains how to select a significance level, commonly 1%, 5% or 10%, and how this relates to Type I and Type II errors. It also covers one-tailed and two-tailed tests, and when to use z-tests or t-tests to test hypotheses about population means based on sample data. The document provides examples of hypotheses for research topics and how to carry out the statistical tests.
The document discusses key concepts related to research methodology and hypothesis testing. It defines the following:
- Null and alternative hypotheses, with the null hypothesis representing what is being tested and the alternative representing other possibilities.
- Type I and Type II errors in hypothesis testing, with Type I being rejection of a true null hypothesis and Type II being acceptance of a false null hypothesis.
- Significance levels which determine the probability of a Type I error, with common values being 0.10, 0.05, and 0.01.
- Power which is the probability of correctly rejecting a false null hypothesis and can be increased by raising the significance level, increasing sample size, or considering alternatives further from the null.
This document discusses hypothesis construction in research methodology. It defines a hypothesis as a proposed explanation or relationship between variables that is testable but unproven. Hypotheses provide focus and direction for a study by indicating what data to collect. There are two main types: research hypotheses that predict a relationship, stated as equality; and alternative hypotheses that predict an alternative if the null is false, stated as inequality. Hypotheses must be simple, verifiable, related to existing knowledge, and operationalizable. Errors can occur if the study design, sampling, data collection, analysis, or conclusions are flawed. Qualitative research does not strictly require or test hypotheses but may formulate them to highlight phenomena and relationships.
The document provides guidance on improving the chances of getting a manuscript accepted for publication. It discusses key methodological concepts like uncertainty of measurement and sampling, statistical assumptions, multiplicity, and proper reporting of different study types including case reports, experiments, observational studies, and randomized trials. The key recommendations are to 1) clearly describe statistical methods and sample sizes, 2) present both data and interpretation of results considering clinical and statistical significance, and 3) properly adjust for confounding and comply with reporting standards for different study designs.
Types of Hypothesis-Advance Research MethodologyRehan Ehsan
This Presentation states the details of Hypothesis for students to get help in advance research methodology. Rearchers may also get help from this work.
This document provides guidelines for statistical reporting in medical journals. It recommends including details of statistical methods, results, and analyses to allow readers to evaluate evidence and verify results. Key guidelines include:
1. Describe statistical design, population, analyses, and software in the Methods.
2. Report patient characteristics, outcomes, p-values, and comparisons precisely in Results.
3. Do not overinterpret non-significant p-values or use terms like "trend"; only conclude observed differences, not equivalence.
The guidelines aim to improve clarity and reproducibility of statistical methods and findings reported in medical literature.
The authors propose modifying the PICO framework for formulating clinical research questions to PICOS by adding an "S" for statistical analysis. The PICOS framework would help authors more robustly and reproducibly present the methodology of scientific studies. It would also serve as a checklist for reviewers to thoroughly evaluate manuscripts. The PICOS components are: P - patient population, I - intervention, C - comparative controls, O - outcomes, S - statistical analysis. Adopting the PICOS framework could help ensure scientific thoroughness and objectivity in reporting methodology and improve reproducibility and comparability of studies.
The document discusses choosing appropriate statistical tests for analyzing medical research studies. It provides an overview of commonly used statistical tests such as the t-test, chi-square test, Fisher's exact test, analysis of variance, and Wilcoxon rank sum test. The document outlines the key factors to consider when selecting a statistical test, such as the scale of measurement (continuous, categorical, binary) and study design (paired or unpaired). Algorithms and tables are provided to help readers identify the proper statistical test based on these characteristics.
Method
Understanding
Statistical
Significance
Matthew J. Hayat
b Background: Statistical significance is often misinterpreted as proof or scientific evi-
dence of importance. This article addresses the most common statistical reporting
error in the biomedical literature, namely, confusing statistical significance with
clinical importance.
b Objective: The aim of this study was to clarify the confusion between statistical sig-
nificance and clinical importance by providing a historical perspective of significance
testing, presenting a correct understanding of the information given by p values and
significance testing, and offering recommendations for the correct use and reporting
of statistical results.
b Approach: The correct interpretation of p values and statistical significance is given,
and the recommendations provided include a description of the current recom-
mended guidelines for statistical reporting of the size of an effect.
b Results: This article provides a comprehensive overview of p values and significance
testing and an understanding of the need for measures of importance and
magnitude in statistical reporting.
b Discussion: Statistical significance is not an objective measure and does not provide
an escape from the requirement for the researcher to think carefully and judge the
clinical and practical importance of a study’s results.
b Key Words: effect size & p value & statistical significance
Statistical significance often is usedmistakenly as the standard for as-
sessing the importance of a measured
effect. This practice has a long history
and is a consequence of the desire for
an objective method for stating im-
portance. However, significance test-
ing is not an objective procedure and
does not alleviate the need for careful
thought and judgment relevant to the
subject matter being studied. The
search for an objective measure of im-
portance is a daunting task. The re-
searcher makes subjective decisions
about a research topic to the best of
his or her ability given the circum-
stances, resources, and design options.
Each research study is limited by sub-
jectivity related to these factors, as
well as time and decisions about what
to measure. In the midst of this sub-
jectivity, the researcher often uses sta-
tistics to claim proof and scientific
breakthrough in the form of statistical
significance. It is common practice to
consider a p value less than .05 as a
form of objective scientific evidence of
an effect. Unfortunately, this judgment
is subjective and flawed and leads to
erroneous conclusions and interpreta-
tions (Ziliak & McCloskey, 2008).
Confusing statistical significance
with clinical importance is the most
common statistical reporting error in
the biomedical literature (Lang, 2007).
Significance is defined by Merriam-
Webster (2009) as ‘‘the quality of being
important,’’ but statistical significance
is not a measure of importance.
The purpose of this article was to
provide a historical per ...
This document provides an overview of meta-analysis and summarizes its key aspects and statistical methods. It discusses how meta-analysis can combine results from multiple studies to obtain a single estimate of treatment effect. It also summarizes the steps involved in planning and conducting a meta-analysis, including defining the question, inclusion criteria, searching strategies, and statistical methods for analyzing different types of outcomes. Finally, it reviews several software options available for performing meta-analyses.
184 Deutsches Ärzteblatt International⏐⏐Dtsch Arztebl Int 2009.docxhyacinthshackley2629
184 Deutsches Ärzteblatt International⏐⏐Dtsch Arztebl Int 2009; 106(11): 184–9
M E D I C I N E
M edical research studies can be split into fivephases—planning, performance, documenta-
tion, analysis, and publication (1, 2). Aside from finan-
cial, organizational, logistical and personnel questions,
scientific study design is the most important aspect of
study planning. The significance of study design for
subsequent quality, the relability of the conclusions,
and the ability to publish a study are often underestimated
(1). Long before the volunteers are recruited, the study
design has set the points for fulfilling the study objec-
tives. In contrast to errors in the statistical evaluation,
errors in design cannot be corrected after the study has
been completed. This is why the study design must be
laid down carefully before starting and specified in the
study protocol.
The term "study design" is not used consistently in
the scientific literature. The term is often restricted to
the use of a suitable type of study. However, the term
can also mean the overall plan for all procedures in-
volved in the study. If a study is properly planned, the
factors which distort or bias the result of a test procedure
can be minimized (3, 4). We will use the term in a
comprehensive sense in the present article. This will
deal with the following six aspects of study design:
the question to be answered, the study population, the
type of study, the unit of analysis, the measuring tech-
nique, and the calculation of sample size—, on the
basis of selected articles from the international litera-
ture and our own expertise. This is intended to help
the reader to classify and evaluate the results in publi-
cations. Those who plan to perform their own studies
must occupy themselves intensively with the issue of
study design.
Question to be answered
The question to be answered by the research is of
decisive importance for study planning. The research
worker must be clear about the objectives. He must
think very carefully about the question(s) to be
answered by the study. This question must be opera-
tionalized, meaning that it must be converted into a
measurable and evaluable form. This demands an
adequate design and suitable measurement parameters.
A distinction must be made between the main questions
to be answered and secondary questions. The result of
the study should be that open questions are answered
R E V I E W A RT I C L E
Study Design in Medical Research
Part 2 of a Series on the Evaluation of Scientific Publications
Bernd Röhrig, Jean-Baptist du Prel, Maria Blettner
SUMMARY
Background: The scientific value and informativeness of
a medical study are determined to a major extent by the
study design. Errors in study design cannot be corrected
afterwards. Various aspects of study design are discussed
in this article.
Methods: Six essential considerations in the planning and
evaluation of medical research studies are presented and
discussed in the light.
· Reflect on the four peer-reviewed articles you critically apprai.docxVannaJoy20
· Reflect on the four peer-reviewed articles you critically appraised in Module 4, related to your clinical topic of interest and PICOT.
· Reflect on your current healthcare organization and think about potential opportunities for evidence-based change, using your topic of interest and PICOT as the basis for your reflection.
· Consider the best method of disseminating the results of your presentation to an audience.
The Assignment: (Evidence-Based Project)
Part 4: Recommending an Evidence-Based Practice Change
Create an 8- to 9-slide
narrated PowerPoint presentation in which you do the following:
· Briefly describe your healthcare organization, including its culture and readiness for change. (You may opt to keep various elements of this anonymous, such as your company name.)
· Describe the current problem or opportunity for change. Include in this description the circumstances surrounding the need for change, the scope of the issue, the stakeholders involved, and the risks associated with change implementation in general.
· Propose an evidence-based idea for a change in practice using an EBP approach to decision making. Note that you may find further research needs to be conducted if sufficient evidence is not discovered.
· Describe your plan for knowledge transfer of this change, including knowledge creation, dissemination, and organizational adoption and implementation.
· Explain how you would disseminate the results of your project to an audience. Provide a rationale for why you selected this dissemination strategy.
· Describe the measurable outcomes you hope to achieve with the implementation of this evidence-based change.
· Be sure to provide APA citations of the supporting evidence-based peer reviewed articles you selected to support your thinking.
· Add a lessons learned section that includes the following:
· A summary of the critical appraisal of the peer-reviewed articles you previously submitted
· An explanation about what you learned from completing the Evaluation Table within the Critical Appraisal Tool Worksheet Template (1-3 slides)
Zeinab Hazime
Nurs 6052
10/16/2022
Evaluation Table
Use this document to complete the
evaluation table requirement of the Module 4 Assessment,
Evidence-Based Project, Part 3A: Critical Appraisal of Research
Full
APA formatted citation of selected article.
Article #1
Article #2
Article #3
Article #4
Abraham, J., Kitsiou, S., Meng, A., Burton, S., Vatani, H., & Kannampallil, T.
(2020). Effects of CPOE-based medication ordering on outcomes: an overview of systematic reviews.
BMJ Quality & Safety, 29(10), 1-2.
Alanazi, A. (2020). The effect of computerized physician order entry on mortality rates in pediatric and neonatal care setting: Meta-analysis.
Informatics in Medicine
Unlocked, 19, 100308. https.
Biostatistics in clinical research involves the application of statistical methods to analyze and interpret data from clinical trials. It plays a crucial role in study design, sample size determination, data analysis, and result interpretation. Biostatisticians ensure that clinical research findings are valid, reliable, and meaningful, contributing to evidence-based medicine. Their expertise helps researchers make informed decisions, assess treatment efficacy, and draw accurate conclusions about the safety and effectiveness of interventions.
The document summarizes a research methods article based on the CONSORT guidelines. It finds that the article's introduction adequately describes the study aims and background but omits some details. The methods describe the trial design, sample characteristics, data collection and analysis appropriately but omit some elements like sample size calculations and blinding. The results report baseline data using tables and include inferential statistics like p-values. The discussion analyzes applicability and generalizability but omits limitations. Overall, the article follows many CONSORT guidelines but is missing some recommended elements.
Nursing Practice Literature Evaluation Table Article Paper.pdfbkbk37
This document discusses the creation of 10 information models from electronic health record (EHR) flowsheet data to enable secondary use of the data for quality improvement and research. Flowsheet data contains structured assessments, interventions, and other clinical information but is challenging for secondary use due to lack of standardization. The study used a consensus-based approach to map 1,552 unique flowsheet measures from one health system's EHR data to 557 concepts within 10 information models related to quality measures and physiological systems. The models reduce duplication and enable standardized analysis of flowsheet data across settings and disciplines.
Rationale: Biostatistics continues to play an essential role in cardiovascular investigations, but successful implementation can be complex.
Objective: To present the rationale behind statistical applications and review useful tools for cardiology research.
Methods and Results: Prospective declaration of the research question, clear methodology, and adherence to protocol serve as the critical foundation. Parametric and distribution-free measures are presented along with t-testing, ANOVA, regression analyses, survival analysis, logistic regression, and interim monitoring. Finally, common weaknesses are considered.
Conclusions: Biostatistics can be productively applied to cardiovascular research if investigators (1) develop and rely on a well-written protocol and analysis plan, (2) consult bi
How Randomized Controlled Trials are Used in Meta-Analysis Pubrica
Randomized Controlled Trials (RCTs) are a commonly used research design in medical and scientific studies to assess the effectiveness of interventions or treatments. Meta-analysis, on the other hand, is a statistical technique used to combine and analyze the results of multiple studies on a particular topic to draw more robust conclusions.
Continue reading @ https://pubrica.com/academy/meta-analysis/how-randomized-controlled-trials-are-used-in-meta-analysis/
For all your research assistance visit us @ https://pubrica.com/services/research-services/
·Quantitative Data Analysis StatisticsIntroductionUnd.docxlanagore871
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Quantitative Data Analysis: Statistics
Introduction
Understanding the use of basic statistical strategies is part of being a critical consumer of published research literature. Unless they plan to conduct research themselves, it is not as important for counselors to understand the mathematical calculations of the statistical techniques as it is to be able to recognize the names of the common ones and what kind of information they provide. There are several commercially-available software packages for analyzing quantitative data, one of which is described in detail in Chapter 14 of
Counseling Research: Quantitative, Qualitative, and Mixed Methods
.
Descriptive and Inferential Statistics
In quantitative studies, statistical techniques are used for data analysis. The two main categories of statistics are descriptive and inferential. Descriptive statistics are used to summarize the data. Some common descriptive statistics are the measures of central tendency: the mean, median, and mode. They provide information about where the middle is in distribution of scores. On the normal distribution, the mean, median, and mode are the same. Distributions are said to be skewed when extreme scores draw the mean away from the middle of the distribution. Measures of variability, such as the range, variance, and standard deviation, provide information about how widely a distribution of scores is dispersed (Erford, 2015, p. 250). The standard deviation is a measure of how the scores cluster around the mean. The greater the standard deviation, the greater the spread of scores.
Toggle DrawerHide Full Introduction
Inferential statistics are used to make inferences from the sample to the population. All inferential statistical procedures are based on probability theory. They are used to test hypotheses. Three commonly used inferential statistics are chi square, t-test, and analysis of variance (ANOVA). Chi square is used with nominal data to determine if the observed expected frequency differs significantly from the expected frequency. A t-test is used to determine whether there is a statistically significant difference between the means of two groups. ANOVA is used to determine whether there is a statistically significant difference between the means of three or more groups.
Statistical Significance
When a quantitative study tests a hypothesis, it is technically the null hypothesis being tested. The null hypothesis says there is no difference between the groups, or relationship between the variables (depending on the research design). If the statistical procedure indicates there is statistical significance, the null hypothesis is rejected, meaning that the probability is high that there really is a group difference or strong relationship between the variables.
Rejecting the null hypothesis is not equivalent to proving the research or alternative hypothesis. Researchers can embrace the research hypothesis as one plausible explanation, but because only .
Respond to this Statistical Significance as described by Was.docxcwilliam4
Respond to this
Statistical Significance as described by Wasserstein & Lazar (2016) is the “P” value of less than .05, which Pstands for probability. The probability of the research that was not due to chance and that something significant has occurred. The value of the “P” in the study denotes that the true difference between the control and experimental compared groups is as big or if not bigger than reported in the study. The “p” value depends on three factors such as the degree of the effect or the performance difference between the selected group; the number of participants; and the data distribution which commonly measured as variance and standard deviation (Pintea, 2010).
Clinical significance of results or clinical importance is used to assess and evaluate the effects ofinterventions or outcomes that may be relevant to the target subject. To assess clinical significance, it isimportant to perform a systematic and complete evaluation between the advantagesand disadvantages effects of the study (Guyatt et.al, 2013). It is the difference between the two therapyresults that are proven to justify, modify or change the standard of care.
In my opinion, there is a possibility that research study results can support the acceptance of the nullhypothesis and develop clinical significance. An example that I came across was the study conducted byLockyer, Hodgson, Dumville, and Cullum (2013) on wound care. The study reported that 74% (32/43) of thereviewed articles have no clear and solid primary outcome and included a finding that was not significant forthe study. This can happen if the selected sample of the research was too small and missed in describing orestablishing the relationship and difference between the groups being studied. It can also happen if thestatistical test was weak, and the measure utilized during the research was defective or unreliable. Andlastly, if the method that was applied to develop an intervention missed identifying the significant variables orideas of the study (Polit & Beck, 2014).
I believe that questioning the credibility of the result from a qualitative study may contribute to clinicalsignificance in my practice area. Credibility refers to facts and certainty of data collected and theresearcher’s interpretation and understanding of results (Polit and Beck, 2014). Qualitative research is focused on exploring ones’ experiences, establishing trust and rapport and describing a comprehensive understanding of a phenomenon. The researcher in a qualitative study must demonstrate engagement withthe participants/informants to validate if the data and conclusion were accurately interpreted.
An example is a qualitative inquiry of patients diagnosed with breast cancer (Pederson et.al,2013). Thequalitative study in the discovery of emotions, experiences, and challenges of cancer patients throughout thedisease process helps to recognize the needed support and care from the health care providers (Cope,2014). Measuring .
TITLE OF THE PAPER121Report on GeriatricsProTakishaPeck109
TITLE OF THE PAPER 1
21
Report on Geriatrics
Professor of Course
First name, middle initial(s), last name. Omit all professional titles and/or degrees (e.g. Dr., Rev., PhD, MA).
Joseph A. Snider
DeVoe School of Business, Indiana Wesleyan University
Author Note
2
A paper submitted in partial fulfillment of the requirements for the degree of Masters of Business Administration.
Table of Contents
Report on Geriatrics 3
Project Background 3
Purpose of the Study 3
Context of the Problem, Challenge Opportunity, or Issue 3
Objectives of the Study 3
Limitations of the Study 4
Assumptions of the Study 4
Significance of the Study 4
Goals of the Study 4
Significance of the Topic to the Writer 4
Significance of the Topic to Stakeholders 5
Industry implications 5
Global implications 5
Information and Literature Review 6
Brief Summary of the Literature on the Subject 6
Systematic Review of the Literature 7
Descriptive Statistics 8
Descriptive Graphs 9
Project Analysis 14
Analysis of the Literature Review Research Findings 14
Simple Linear Regression Analysis 14
Single Sample Hypothesis Test of the Mean 14
Chi-Square Analysis of Age and Principal Payer 16
Project Summary 17
Conclusions 17
Specific Recommendations 17
Suggestions for Future Research 17
References 18
Appendix A 19
Data Set 19
Appendix B 22
Pictures of Analysis 22
Report on Geriatrics 3
Project Background 3
Purpose of the Study 3
Context of the Problem, Challenge Opportunity, or Issue 3
Objectives of the Study 3
Limitations of the Study 4
Assumptions of the Study 4
Significance of the Study 4
Goals of the Study 4
Significance of the Topic to the Writer 4
Significance of the Topic to Stakeholders 5
Broader Implications of the Topic 5
INFORMATION and LITERATURE REVIEW 6
Brief Summary of the Literature on the Subject 6
Systematic Review of the Literature 7
Descriptive Statistics 7
Descriptive Graphs 9
Project Analysis 13
Analysis of the Literature Review Research Findings 13
Simple Linear Regression Analysis 13
Single Sample Hypothesis Test of the Mean 14
Chi-Square Analysis of Age and Principal Payer 15
Project Summary 16
Conclusions 16
Specific Recommendations 16
Suggestions for Future Research 16
Ethical Considerations 17
References 18
Appendix A 19
Data Set 19
Appendix B 22
Pictures of Analysis 22
Report on Geriatrics Comment by Wise, Jay: The APA 7th Edition Publication Manual’s sample professional and student papers both include at least one paragraph that serves as an introduction. APA does not use the word Introduction as a heading. Based on the headings used here, I would expect to read a short introduction that outlines the paper’s major topics and prepares me for the kinds of information I will interact with in the paper.Additionally, the Author Note includes the phrase “in partial fulfillment,” which I commonly see on theses or dissertations. This leads me to expect an Abstract. Comment by Snider, Joseph: Took out author note to not b ...
Title of the paper121 report on geriatricsproraju957290
This document appears to be a report analyzing geriatric patient data from Freedom Hospital. The objectives are to describe statistics, explore data visually through graphs, and test hypothetical claims using the sample data. Specific analyses included simple linear regression, a single sample test of the mean, and a chi-square test. The report discusses limitations, assumptions, and significance of studying geriatric healthcare costs. It provides context on rising healthcare costs and reviews literature on cost estimation methods. The implications on industry and globally are also addressed. In summary, this report analyzes geriatric patient data to better understand healthcare costs and support business and policy decisions.
Evidence Synthesis for Sparse Evidence Base, Heterogeneous Studies, and Disco...InsideScientific
Standard models in evidence synthesis work well in settings characterized by a large evidence base, the absence of effect modifiers, and connected networks. Handling sparse data, substantial between-study heterogeneity and disconnected studies, however, poses challenges to researchers and requires advanced methodology.
In the absence of head-to-head studies, evidence synthesis is a well-established technique to indirectly compare novel and established interventions in various disease areas. In standard settings, the most established methods for various outcome types work well and result in realistic effect estimates. However, there are a variety of situations when standard methods may no longer be sufficient:
- if there is only a sparse network of evidence
- if there is a large amount of between-study heterogeneity
- if the network is disconnected
Key Topics Include:
- General introduction into the objectives of conducting evidence synthesis
- Description of typical situations of “non-standard” data, including sparse networks of evidence, a large amount of between-study heterogeneity, or disconnected networks
- Advanced methods to address non-standard data, including the use of informative priors, subgroup analyses, meta-regression and multi-level meta regression, and matching-adjusted indirect comparisons (MAICs)
- Case studies illustrating how these advanced methods of evidence synthesis are applied on actual data
Similar to improving the utilization and presentation of p values (20)
A 57-year-old woman was admitted to the hospital with chest pain. Electrocardiograms and troponin levels were normal. Intravascular ultrasound was performed before placing a stent in the left main coronary artery and left anterior descending artery to treat a blockage. The minimum lumen area increased to 4.24mm x 4.13mm after stenting.
Congenital defects can put a strain on the heart, causing it to work harder. To stop your heart from getting weaker with this extra work, your doctor may try to treat you with medications. They are aimed at easing the burden on the heart muscle. You need to control your blood pressure if you have any type of heart problem.
Changing your lifestyle can help control and manage high blood pressure. Your health care provider may recommend that you make lifestyle changes including:
Eating a heart-healthy diet with less salt
Getting regular physical activity
Maintaining a healthy weight or losing weight
Limiting alcohol
Not smoking
Getting 7 to 9 hours of sleep daily
CRISPR technologies have progressed by leaps and bounds over the past decade, not only having a transformative effect on
biomedical research but also yielding new therapies that are poised to enter the clinic. In this review, I give an overview of (i)
the various CRISPR DNA-editing technologies, including standard nuclease gene editing, base editing, prime editing, and epigenome editing, (ii) their impact on cardiovascular basic science research, including animal models, human pluripotent stem
cell models, and functional screens, and (iii) emerging therapeutic applications for patients with cardiovascular diseases, focusing on the examples of Hypercholesterolemia, transthyretin amyloidosis, and Duchenne muscular dystrophy.
This case report describes a patient who underwent seven operations over one year to treat recurrent pacemaker pocket infections. The patient had undergone a splenectomy seven years prior due to a splenic rupture from a traffic accident. This left the patient immunocompromised and at higher risk for infection. The patient later required a pacemaker implantation for complete heart block. The pacemaker pocket developed repeated infections, likely due to the patient's asplenic state impairing immunity. The infections were difficult to treat due to multiple complicating factors, including an abandoned pacemaker lead and reuse of a sterilized pacemaker. This highlights the influence of patient factors like asplenia on procedural outcomes like pacemaker implantation.
Transcatheter closure of patent ductus arteriosus (PDA) is feasible in low-birth-weight infants. A female baby was born prematurely with a birth weight of 924 g. She had a PDA measuring 3.7 mm. She was dependent on positive pressure ventilation for congestive heart failure in addition to the heart failure medications. She could not be discharged from the hospital even after 79 days of birth, and even though her weight reached 1.9 kg in the neonatal intensive care unit. We attempted to plug the PDA using an Amplatzer Piccolo Occluder, but the device failed to anchor. Then, the PDA was plugged using a 4-6 Amplatzer Duct Occluder using a 6-Fr sheath which was challenging.
Accidental misplacement of the limb lead electrodes is a common cause of ECG abnormality and may simulate pathology such as ectopic atrial rhythm, chamber enlargement or myocardial ischaemia and infarction
A Case of Device Closure of an Eccentric Atrial Septal Defect Using a Large D...Ramachandra Barik
Device closure of an eccentric atrial septal defect can be challenging and needs technical modifications to avoid unnecessary complications. Here, we present a case of a 45-year-old woman who underwent device closure of an eccentric defect with a large device. The patient developed pericardial effusion and left-sided pleural effusion due to injury to the junction of right atrium and superior vena cava because of the malalignment of the delivery sheath and left atrial disc before the device was pulled across the eccentric defect despite releasing the left atrial disc in the left atrium in place of the left pulmonary vein. These two serious complications were managed conservatively with close monitoring of the case during and after the procedure.
1) Bradycardia can be caused by abnormalities in the conduction system or autonomic nervous system. The conduction system includes the sinus node, AV node, His-Purkinje system and different types of heart block can occur when impulses are blocked at different locations.
2) There are three main types of AV block - first degree, second degree (Mobitz types I and II), and third degree. High grade AV block involves blockage of two or more consecutive impulses.
3) Third degree or complete heart block results in complete dissociation between the atria and ventricles with independent pacemakers. It can occur at the AV node or below in the His-Purkin
1. Bradycardia is defined as a resting heart rate below 50 beats per minute. It can be physiological or pathological.
2. Sinus bradycardia originates from the sinus node and has a normal P wave morphology with a prolonged PR interval. It can be caused by increased vagal tone, medications, or hypothyroidism.
3. Sick sinus syndrome is characterized by sinus bradycardia, sinus arrest, or combinations of sinus node and AV node dysfunction. It may involve intermittent bradycardia and tachycardia. Pacemaker implantation is usually treatment.
This document discusses ventricular arrhythmias including their origins, characteristics, classifications, and causes. It provides details on:
- The sites of origin for supraventricular tachycardia (SVT) and ventricular arrhythmias.
- Characteristics that distinguish SVT from ventricular arrhythmias such as QRS width.
- Classifications of ventricular arrhythmias including premature ventricular complexes, ventricular tachycardia, fibrillation, and electrical storm.
- Causes and characteristics of different types of ventricular tachycardia such as monomorphic VT, polymorphic VT, and torsades de pointes.
- Investigations and treatments for ventricular arrhythmias including cardiac imaging
This document provides information on supraventricular tachycardia (SVT), including:
- The anatomy and conduction system of the heart that is relevant to SVT.
- The mechanisms that can cause cardiac arrhythmias, including disorders of impulse formation, conduction, and combinations of the two.
- Characteristics used to classify different types of arrhythmias based on rate, rhythm, site of origin, and QRS morphology.
- Specific types of SVT like atrial fibrillation, AV nodal reentry tachycardia, and accessory pathway mediated tachycardias.
- Methods for diagnosing and treating SVT such as electrophysiology studies, catheter ablation
Trio of Rheumatic Mitral Stenosis, Right Posterior Septal Accessory Pathway a...Ramachandra Barik
A 57-year-old male presented with recurrent palpitations. He was diagnosed with rheumatic mitral stenosis, right posterior septal accessory pathway and atrial flutter. An electrophysiological study after percutaneous balloon mitral valvotomy showed that the palpitations were due to atrial flutter with right bundle branch aberrancy. The right posterior septal pathway was a bystander because it had a higher refractory period than the atrioventricular node.
This document discusses anticoagulation therapy options during pregnancy for different cardiac conditions. It notes that vitamin K antagonists (VKAs) should be avoided in the first trimester due to risk of embryopathy but can be used in the second and third trimester with risks of 0.7-2% of foetopathy. Unfractionated heparin does not cross the placenta but its use throughout pregnancy is not recommended due to risk of foetopathy. Low molecular weight heparin is considered the safest option for anticoagulation in weeks 6-12 when risk of embryopathy is a concern and has not been associated with risk of foetopathy. Fondaparinux use should be limited
Percutaneous balloon dilatation, first described by
Andreas Gruentzig in 1979, was initially performed
without the use of guidewires.1 The prototype
balloon catheter was developed as a double lumen
catheter (one lumen for pressure monitoring or
distal perfusion, the other lumen for balloon inflation/deflation) with a short fixed and atraumatic
guidewire at the tip. Indeed, initially the technique
involved advancing a rather rigid balloon catheter
freely without much torque control into a coronary
artery. Bends, tortuosities, angulations, bifurcations,
and eccentric lesions could hardly, if at all, be negotiated, resulting in a rather frustrating low procedural success rate whenever the initial limited
indications (proximal, short, concentric, noncalcified) were negated.2 Luck was almost as
important as expertise, not only for the operator,
but also for the patient. It is to the merit of
Simpson who, in 1982, introduced the novelty of
advancing the balloon catheter over a removable
guidewire, which had first been advanced in the
target vessel.3 This major technical improvement
resulted overnight in a notable increase in the procedural success rate. Guidewires have since evolved
into very sophisticated devices.
Optical coherence tomography-guided algorithm for percutaneous coronary intervention. Vessel diameter should be assessed using the external elastic lamina (EEL)-EEL diameter at the reference segments, and rounded down to select interventional devices (balloons, stents). If the EEL cannot be identified, luminal measures are used and rounded up to 0.5 mm larger for selection of the devices. Optical coherence tomography (OCT)-guided optimisation strategies post stent implantation per EEL-based diameter measurement and per lumen-based diameter measurement are shown. For instance, if the distal EEL-EEL diameter measures 3.2 mm×3.1 mm (i.e., the mean EEL-based diameter is 3.15 mm), this number is rounded down to the next available stent size and post-dilation balloon to be used at the distal segment. Thus, a 3.0 mm stent and non-compliant balloon diameter is selected. If the proximal EEL cannot be visualised, the mean lumen diameter should be used for device sizing. For instance, if the mean proximal lumen diameter measures 3.4 mm, this number is rounded up to the next available balloon diameter (within up to 0.5 mm larger) for post-dilation. MLA: minimal lumen area; MSA: minimal stent area;NC: non-compliant
Brugada syndrome (BrS) is an inherited cardiac disorder,
characterised by a typical ECG pattern and an increased
risk of arrhythmias and sudden cardiac death (SCD).
BrS is a challenging entity, in regard to diagnosis as
well as arrhythmia risk prediction and management.
Nowadays, asymptomatic patients represent the majority
of newly diagnosed patients with BrS, and its incidence
is expected to rise due to (genetic) family screening.
Progress in our understanding of the genetic and
molecular pathophysiology is limited by the absence
of a true gold standard, with consensus on its clinical
definition changing over time. Nevertheless, novel
insights continue to arise from detailed and in-depth
studies, including the complex genetic and molecular
basis. This includes the increasingly recognised
relevance of an underlying structural substrate. Risk
stratification in patients with BrS remains challenging,
particularly in those who are asymptomatic, but recent
studies have demonstrated the potential usefulness
of risk scores to identify patients at high risk of
arrhythmia and SCD. Development and validation of
a model that incorporates clinical and genetic factors,
comorbidities, age and gender, and environmental
aspects may facilitate improved prediction of disease
expressivity and arrhythmia/SCD risk, and potentially
guide patient management and therapy. This review
provides an update of the diagnosis, pathophysiology
and management of BrS, and discusses its future
perspectives.
The Human Developmental Cell Atlas (HDCA) initiative, which is part of the Human Cell Atlas, aims to create a comprehensive reference map of cells during development. This will be critical to understanding normal organogenesis, the effect of mutations, environmental factors and infectious agents on human development, congenital and childhood disorders, and the cellular basis of ageing, cancer and regenerative medicine. Here we outline the HDCA initiative and the challenges of mapping and modelling human development using state-of-the-art technologies to create a reference atlas across gestation. Similar to the Human Genome Project, the HDCA will integrate the output from a growing community of scientists who are mapping human development into a unified atlas. We describe the early milestones that have been achieved and the use of human stem-cell-derived cultures, organoids and animal models to inform the HDCA, especially for prenatal tissues that are hard to acquire. Finally, we provide a roadmap towards a complete atlas of human development.
The treatment of patients with advanced acute heart failure is still challenging.
Intra-aortic balloon pump (IABP) has widely been used in the management of
patients with cardiogenic shock. However, according to international guidelines, its
routinary use in patients with cardiogenic shock is not recommended. This recommendation is derived from the results of the IABP-SHOCK II trial, which demonstrated
that IABP does not reduce all-cause mortality in patients with acute myocardial infarction and cardiogenic shock. The present position paper, released by the Italian
Association of Hospital Cardiologists, reviews the available data derived from clinical
studies. It also provides practical recommendations for the optimal use of IABP in
the treatment of cardiogenic shock and advanced acute heart failure.
Left ventricular false tendons (LVFTs) are fibromuscular
structures, connecting the left ventricular
free wall or papillary muscle and the ventricular
septum.
There is some discussion about safety issues during
intense exercise in athletes with LVFTs, as these
bands have been associated with ventricular arrhythmias
and abnormal cardiac remodelling. However,
presence of LVFTs appears to be much more common
than previously noted as imaging techniques
have improved and the association between LVFTs
and abnormal remodelling could very well be explained
by better visibility in a dilated left ventricular
lumen.
Although LVFTsmay result in electrocardiographic abnormalities
and could form a substrate for ventricular
arrhythmias, it should be considered as a normal
anatomic variant. Persons with LVFTs do not appear
to have increased risk for ventricular arrhythmias or
sudden cardiac death.
Osteoporosis - Definition , Evaluation and Management .pdfJim Jacob Roy
Osteoporosis is an increasing cause of morbidity among the elderly.
In this document , a brief outline of osteoporosis is given , including the risk factors of osteoporosis fractures , the indications for testing bone mineral density and the management of osteoporosis
Basavarajeeyam is a Sreshta Sangraha grantha (Compiled book ), written by Neelkanta kotturu Basavaraja Virachita. It contains 25 Prakaranas, First 24 Chapters related to Rogas& 25th to Rasadravyas.
Rasamanikya is a excellent preparation in the field of Rasashastra, it is used in various Kushtha Roga, Shwasa, Vicharchika, Bhagandara, Vatarakta, and Phiranga Roga. In this article Preparation& Comparative analytical profile for both Formulationon i.e Rasamanikya prepared by Kushmanda swarasa & Churnodhaka Shodita Haratala. The study aims to provide insights into the comparative efficacy and analytical aspects of these formulations for enhanced therapeutic outcomes.
TEST BANK For Community Health Nursing A Canadian Perspective, 5th Edition by...Donc Test
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Histololgy of Female Reproductive System.pptxAyeshaZaid1
Dive into an in-depth exploration of the histological structure of female reproductive system with this comprehensive lecture. Presented by Dr. Ayesha Irfan, Assistant Professor of Anatomy, this presentation covers the Gross anatomy and functional histology of the female reproductive organs. Ideal for students, educators, and anyone interested in medical science, this lecture provides clear explanations, detailed diagrams, and valuable insights into female reproductive system. Enhance your knowledge and understanding of this essential aspect of human biology.
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Cell Therapy Expansion and Challenges in Autoimmune DiseaseHealth Advances
There is increasing confidence that cell therapies will soon play a role in the treatment of autoimmune disorders, but the extent of this impact remains to be seen. Early readouts on autologous CAR-Ts in lupus are encouraging, but manufacturing and cost limitations are likely to restrict access to highly refractory patients. Allogeneic CAR-Ts have the potential to broaden access to earlier lines of treatment due to their inherent cost benefits, however they will need to demonstrate comparable or improved efficacy to established modalities.
In addition to infrastructure and capacity constraints, CAR-Ts face a very different risk-benefit dynamic in autoimmune compared to oncology, highlighting the need for tolerable therapies with low adverse event risk. CAR-NK and Treg-based therapies are also being developed in certain autoimmune disorders and may demonstrate favorable safety profiles. Several novel non-cell therapies such as bispecific antibodies, nanobodies, and RNAi drugs, may also offer future alternative competitive solutions with variable value propositions.
Widespread adoption of cell therapies will not only require strong efficacy and safety data, but also adapted pricing and access strategies. At oncology-based price points, CAR-Ts are unlikely to achieve broad market access in autoimmune disorders, with eligible patient populations that are potentially orders of magnitude greater than the number of currently addressable cancer patients. Developers have made strides towards reducing cell therapy COGS while improving manufacturing efficiency, but payors will inevitably restrict access until more sustainable pricing is achieved.
Despite these headwinds, industry leaders and investors remain confident that cell therapies are poised to address significant unmet need in patients suffering from autoimmune disorders. However, the extent of this impact on the treatment landscape remains to be seen, as the industry rapidly approaches an inflection point.
2. 1
Expert Opinion: Guidelines for Improving the Utilization and Presentation of P
Values
Steven J. Staffa, M.S.1,2
, David Zurakowski, M.S. Ph.D.1,2
1
Department of Surgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA,
USA
2
Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children’s Hospital,
Harvard Medical School, Boston, MA, USA
Corresponding Author: David Zurakowski, M.S. Ph. D., Department of Surgery, Boston
Children’s Hospital, 300 Longwood Avenue, Boston, MA 02115. Phone: 617-355-7839
E-mail: David.Zurakowski@childrens.harvard.edu
Conflicts of Interest and Source of Funding: NONE
Manuscript word count: 2,884
3. 2
CENTRAL MESSAGE
Careful statistical analysis planning and focus on other statistical measures can help mitigate the
misuse and overuse of P values.
PERSPECTIVE STATEMENT
In isolation, the P value may be dangerous and misleading as it does not provide the
directionality, magnitude or variability of treatment effects. Careful study design and statistical
analysis planning will help reduce the overuse of P values while making the presentation of
results more meaningful by complementing P values with effect estimates and confidence
intervals will help mitigate misuse of P values.
4. 1
Introduction1
P values were originally developed for hypothesis testing, however the utilization of the2
P value in cardiovascular and thoracic research has expanded as it has become the most3
commonly reported summary measure of statistical results. A P value measures whether or not4
the actual results of a study or trial are consistent with what would be expected by chance or if5
the results more likely indicate a real difference between the treatment groups or surgical6
approaches being studied. A small P value is often regarded as evidence that the surgeon can rule7
out the chance explanation for the observed differences between the study groups.8
Recently there has been discussion in the literature regarding P values in medical9
research as it pertains to reporting standards. 1-2
Although P values have value in providing a10
snapshot summary of the statistical evidence of group differences, treatment effects, or measures11
of association, there are times when P values have been misused or overused. The objective of12
this article is to provide a perspective regarding the utilization of P values in The Journal of13
Thoracic and Cardiovascular Surgery, and to provide guidance in how to make the best use of P14
values particularly for common statistical study designs found in studies in the Journal. We15
provide a background regarding P values and guidance regarding the optimal utilization of P16
values for 3 specific common statistical study designs of manuscript submitted to The Journal of17
Thoracic and Cardiovascular Surgery.18
Background19
The fundamental use of the P value is in the context of null hypothesis significance20
testing.3
Under the null hypothesis (H0) (in other words, assuming that the null hypothesis is21
true), the P value is an estimated probability of observing an effect as large or larger than the22
result calculated from the data. As an example, the null hypothesis may be that there is no23
5. 2
difference in the rate of 30-day readmission between two surgical groups, A and B. Using the24
study data, surgical group A has a 30-day readmission rate that is 10% larger than that of surgical25
group B. The P value, in this example, is the probability that the difference in 30-day26
readmission rates between surgical groups A and B is 10% or more, assuming that the null27
hypothesis is true (that there is no difference between groups). If the P value is very small, then28
there is a very small probability of observing a result as extreme or more extreme than the one29
observed, and therefore the result is unlikely to be due to chance and the result is declared30
statistically significant (usually the threshold of P < .05 is imposed). Very often, the goal in the31
mindset of a researcher is to be able to claim that a result is “statistically significant”.4
In some32
severe situations, investigators may be searching for statistically significant results in what is33
referred to as P-Hacking.5
34
In modern-day medical research, investigators do not always formally write null and35
alternative hypotheses for all statistical comparisons being performed. Instead, researchers most36
often consider primary and secondary research questions. In the example of 30-day readmission37
rates above, the investigator may not consider a null hypothesis that the rates are equal in the two38
groups, and an alternative hypothesis that the 30-day readmission rates are not equal in the two39
groups. Instead, the investigator would state the research question as: Is there a difference in the40
30-day readmission rates between surgical groups A and B? The investigator may proceed to41
calculate an estimated treatment effect in terms of the difference in 30-day readmission rates and42
then may also compute a corresponding 95% confidence interval and P value. However, they43
might not view that P value with the mindset that it was computed under the assumption that the44
“null hypothesis” is true. Instead, they will interpret it to represent whether or not the observed45
difference between the two groups is statistically significant. While this interpretation is not46
6. 3
faulty, it is worth noting that often investigators lose sight of the fact that using the hypothesis47
testing framework involves the assumption that the null hypothesis is true (that there is no48
difference between the study groups).49
Guidance Regarding Study Design50
The issue of overuse of P values can be potentially addressed with careful statistical and51
design planning prior to performing the study. Thorough and thoughtful statistical study design52
in specifying primary and secondary study objectives along with determining the statistical53
analyses that will be performed for planned comparisons in advance of a study will help authors54
to judiciously report P values. In statistical analysis planning with preliminary discussions with a55
statistician, the specification of primary outcomes and planned comparison will give the authors56
the knowledge of where P values will be reported in their final analyses. This will enable57
investigators, in collaboration with a statistician, to astutely preserve the study wide alpha level58
(i.e. false positive error rate, or Type I error rate) while also purposefully reporting P values and59
taking multiplicity into account (i.e. multiple testing). It should be noted that there are several60
approaches to account and adjust for multiplicity,6-7
with the most common method being the61
Bonferroni correction,8
however further detail on multiple testing is beyond the scope of this62
article. In this framework, P values may only be reported for the a priori planned comparisons63
and analyses of primary study outcomes. This will simultaneously make P values more64
meaningful with fewer reported, since each will be used for summarizing the significance of the65
key statistical results of a study, likely pertaining to the study’s Central Picture.66
67
68
69
7. 4
Accompanying the P value70
Along with purposeful and diligent study designing to help mitigate overuse, the P value71
should be presented in a comprehensive fashion in order to improve proper use of the P value.72
Sometimes, an investigator may rely heavily on P values because they are not aware or73
comfortable in interpreting alternative and additional statistics that may also be suitable. In74
isolation, the P value does not provide a comprehensive summary of the results because it does75
not show the magnitude or directionality of treatment effects or measures of association. The76
importance of reporting magnitude and directionality of treatment effects must be emphasized as77
this allows interpretations of the results to be made in terms of both statistical and clinical78
significance. Failing to include this can be dangerous and misleading to the reader. Larger79
sample sizes do not protect the investigator when reporting P values. Rather, in situations with80
large sample sizes, particularly in the situation of a large number of events for binary outcomes,81
small effects may be declared as statistically significant even if the effect itself it not clinically82
important. Therefore, it is advisable to accompany the P value with the effect estimate (i.e. odds83
ratio, risk ratio, difference in means or proportions, etc.) with a corresponding confidence84
interval.9
The point estimate and confidence interval are much more informative and85
interpretable than the P value in isolation. Going beyond the P value and using other statistical86
metrics is important for determining if results are clinically meaningful. Readers may interpret P87
< .05 for determining statistical significance; however, since P values are heavily dependent on88
sample size, a statistically significant result with P < .05 may not correspond to a clinically89
meaningful difference or effect. Furthermore, the threshold of P < .05 has been seemingly90
arbitrarily agreed upon to denote statistical significance, and investigators will provide starkly91
different interpretations of their analysis results for the scenario with P = .04 as opposed to P =92
8. 5
.06. What is considered statistically significant in terms of a P value versus what is regarded as93
clinically important to the surgeon are separate questions and must be evaluated separately when94
interpreting the results of any research study. Complementing all P values with estimated95
treatment effects and measurements of variability like confidence intervals should be a96
mandatory requirement for all submitted manuscript as this will provide improved transparency97
and more comprehensive presentation of the statistical results.98
Guidance regarding reporting of P values is presented in Figure 1, and the characteristics99
regarding misuse and overuse of P values is outlined in Figure 2 (Central Picture). The strategies100
in Figure 1 are aimed to achieve the optimal presentation of P values and a more impactful101
manuscript, as represented by the best-case scenario in the green box in Figure 2.102
Common Cardiovascular Surgery Examples103
Next, we will discuss P values in the context of 3 common statistical techniques that104
cardiovascular surgeons are acquainted with: time-to-event survival analysis, logistic regression,105
and propensity score matching.106
Example 1: Time-to-Event Analysis107
Survival analysis using Kaplan-Meier curves and Cox proportional hazards regression108
modeling is a cornerstone of cardiovascular and thoracic surgery clinical research studies.10-11
109
Many outcome variables in our specialty are time-to-event endpoints, with examples including110
time to mortality, re-operation, or readmission after surgery, or freedom from heart transplant.111
Traditionally, Kaplan-Meier curves are used to present the comparison of outcomes between112
groups, with the statistical comparison accomplished using the log-rank test.12
The log-rank test113
P value alone does not adequately describe the study findings in terms of which of the114
comparison groups demonstrated better survival or freedom from the event of interest, nor does115
9. 6
it describe the variability of those estimates. The log-rank test P value provides a summary of the116
statistical significance in comparing Kaplan-Meier curves across the follow-up time course, and117
while relevant it should not be presented in isolation. It should accompany the figure with the118
Kaplan-Meier curves with confidence bands and numbers at risk.13
The investigator should also119
consider presenting in the text or figure legend the Kaplan-Meier estimates at specific time120
points (e.g. 1-year, 3-years and 5-years post-surgery) with corresponding confidence intervals,121
particularly if confidence bands are visually cluttering. The log-rank test does not compare122
estimates at specific individual time point between groups, and therefore presenting numerical123
estimates with confidence intervals at time points of interest is useful. Furthermore, the log-rank124
test P value should ideally be presented only for the analysis of the primary outcome as specified125
in the study design and statistical analysis plan. These strategies can help to reduce the overuse126
of P values in time-to-event analyses.127
Cox regression analysis can be used to determine independent risk factors associated with128
the risk of the event of interest in a time-to-event analysis. Hazard ratios are obtained as point129
estimates for the measure of increased or decreased risk of the outcome associated with each130
predictor variable. Each estimated hazard ratio will have a corresponding confidence interval and131
P value. Here, the authors should save the utilization of P values in a manuscript or report for the132
adjusted hazard ratio for the primary exposure of interest (e.g. the hazard ratio associated with133
surgical group). Careful study design and statistical analysis planning regarding the primary134
exposure (predictor variable) and primary endpoint will help the researcher decide where to135
present P values in a Cox regression analysis. Furthermore, reporting P values from Cox136
regression analysis without the corresponding hazard ratio and confidence interval should be137
10. 7
avoided. P values should be reported with other statistics related to treatment evidence of risk138
factors.139
Specific Example of Misuse of P values in Time-to-Event Analysis140
To illustrate a time-to-event analysis scenario with poor statistical reporting and141
improvement needed regarding misuse of P values, consider the Kaplan-Meier curves shown in142
Figure 3. In this hypothetical analysis, the investigator is trying to determine survival over the143
course of 1 year following Fontan operation, while comparing preterm versus full term patients.144
In Figure 3, a P value is reported to compare survival between the two groups. Although P145
values are not overused in this scenario, this is an example of misuse of P values because the P146
value is not presented with the necessary accompanying statistics. There are no confidence bands147
around the Kaplan-Meier curves, which are important for showing the level of precision of the148
Kaplan-Meier survival estimates. There are no numbers at risk shown in the figure to depict the149
sample sizes of the number of patients alive and followed over the course of follow-up. In this150
analysis, the P value is presumed as being calculated using the log-rank test, however the method151
for calculating the P values is not stated on the figure itself. This specific example of P value152
misuse can be improved by providing additional statistics and including estimates of variability153
using confidence bands.154
155
Example 2: Logistic Regression156
Logistic regression analysis may be used for determining the association between a set of157
predictor variables and a dichotomous outcome variable. In cardiovascular surgery research,158
binary outcome variables are common.14-15
Examples include 30-day mortality and postoperative159
complications (without incorporating the time to event if perhaps a time window is defined or160
11. 8
time is not relevant). Similar to reporting P values in the context of Cox regression modeling, P161
values in the logistic regression framework should be reported with accompanying confidence162
intervals for the point estimates. Univariate logistic regression may be utilized as a screening for163
potentially important predictor variables, and that decision can be made using P values. P values164
may also be used in a stepwise logistic regression model building, such as backwards elimination165
for covariate removal. However, the emphasis of the presentation of study results should be166
placed on the multivariable logistic regression model and the P values pertaining to the primary167
exposure variable in this model. In addition to presenting P values, the c-index (AUC) is a useful168
metric for evaluating the performance of a multivariable logistic regression model.169
Specific Example of Misuse and Overuse of P values in Logistic Regression Analysis170
The following text is a hypothetical example misuse and overuse of P values in the171
reporting of results of multivariable logistic regression:172
“Multivariable logistic regression identified the following significant risk factors for re-173
operation following arterial switch operation: gestational age (P=.002), sex (P=.04), and cross-174
clamp time (P<.001).”175
In this hypothetical scenario, P values are overused and misused for several reasons. The176
P values are reported in isolation, without effect estimator or confidence intervals, which limits177
the ability of the reader to understand the clinical meaningfulness of the results. It is unclear if178
gestational age and cross-clamp duration are treated as continuous predictor variables, or if a179
dichotomization was performed. Since there are no effect estimates reported, it is unclear180
whether larger or smaller values for gestational age and cross-clamp time are associated with181
increased or decreased odds of reoperation. Also, it cannot be determined from what is reported182
whether males or females are at higher risk of reoperation. If the adjusted odds ratio for male sex183
12. 9
(versus female) was 1.04 with 95% confidence interval 1.02-1.07, then it could be argued that184
this statistically significant result with P < .05 is not clinically significant. In this example, P185
values are reported superfluously but without more information such as effect estimates with186
corresponding confidence intervals, the results cannot be used to generate any meaningful187
clinical insight.188
Example 3: Propensity Score Matching189
Propensity score matching is utilized in observational (non-randomized) studies to190
balance treatment groups on a set of variables in order to obtain a more valid comparison.16-17
191
The comparison of the two groups may involve any type of outcome data, such as time-to-event192
outcomes, continuous outcomes, or binary outcomes. Groups should be compared on the193
matching variables before and after propensity score matching is performed, and investigators194
often use P values as a measure of determining whether significance imbalances occur pre- and195
post-matching. However, the matched sample will necessarily have a smaller sample size than196
the unmatched sample. Since P values are heavily dependent on sample size, in propensity score197
matching studies it is advisable to use standardized mean differences to determine balance198
between the two groups for each matching covariate. Standardized mean differences with an199
absolute value less than 0.10 are often taken as indicating good balance between the two groups200
achieved by propensity score matching.18
The standardized mean difference can be calculated201
for continuous or binary variables.18-19
202
P values should be reserved for the statistical analysis based on post-matching data. As203
an example, once balance has been demonstrated on the confounders and matching variable204
between two surgical groups implying that groups are comparable, the log-rank test P value can205
13. 10
be presented along with the Kaplan-Meier curves comparing survival between two surgical206
groups using the matched dataset.207
Conclusions208
P values are misused and overused in surgical research studies as well as all other areas209
of research. Used appropriately, P values can provide information regarding the statistical210
significance of a given treatment effect. However, they are often misused and even used in211
isolation which may convey significant results which may not be clinically significant as well.212
Careful and thoughtful study design and statistical analysis planning can help investigators to213
reserve P values for the most important analysis results. While the appropriate reporting and214
utilization of P values depends on the specific study design being considered, it is important to215
include effect estimates with measures of variability (such as a confidence intervals). Presenting216
the magnitude and directionality of effect estimates is crucial for interpreting the results in terms217
statistical significance as well as clinical significance. Improvements regarding utilization of P218
values in manuscripts submitted to The Journal of Thoracic and Cardiovascular Surgery will219
lead to more robust statistical reporting and more impactful published studies in JTCVS.220
14. 1
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16. 3
FIGURE LEGENDS:
Figure 1. Guidance on Reporting P values while Integrating Other Statistical Measures. There
are several general recommendations and strategies that surgeons should integrate to help
improve the quality of their manuscripts in terms of reporting P values. These guidelines will
help with mitigating the misuse and overuse of P values in submissions to JTCVS. Good balance
of presentation of P values and other statistical measures is crucial.
Figure 2 (Central Picture). Descriptions of Four General Scenarios Based on Overuse or
Misuse of P values. We have outlined four general scenarios of statistical design and reporting in
cardiovascular surgery manuscripts based on the overuse and misuse of P values. The red box
describes the scenario where P values are overused and misused and considerable improvement
is needed in the statistical reporting of P values. The orange box highlights the situation where P
values have likely been misused. The yellow box contains reasons that are indicative of a
scenario of P value overuse but not misuse. The green box represents the ideal scenario. These
studies are the most impactful due to characteristics of robust and appropriate statistics without
overuse or misuse of P values.
Figure 3. Misuse of P values in Kaplan-Meier Curve Analysis. Here, the P value is not presented
with the necessary accompanying statistics. There are no confidence bands around the Kaplan-
Meier curves and no numbers at risk shown in the figure to depict the sample sizes of the number
of patients alive and followed over the course of follow-up period. The method for calculating
the P values is not stated on the figure itself. This specific example of P value misuse can be
improved by providing additional statistics and including estimates of variability using
confidence bands.