This document discusses meta-analysis of ordinal data and some of the challenges involved. It notes that ordinal outcomes are common in Cochrane reviews of stroke interventions, but are typically analyzed as dichotomous or continuous data rather than using methods suited for ordinal scales. Dichotomizing or treating ordinal data as continuous can discard important information. The document recommends using proportional odds modeling for ordinal data, which makes no distributional assumptions and can provide a single odds ratio summarizing the treatment effect across the full ordinal scale. It provides examples of how this method can be applied and discusses some remaining challenges like assessing model assumptions.
This document discusses selective outcome reporting bias (ORB), which occurs when researchers select a subset of original outcomes to report based on statistical significance. ORB threatens the validity of systematic reviews and meta-analyses. The document describes different types of ORB and methods to assess risk of bias. It proposes the ORBIT classification system to code incomplete outcome reporting in trials. Sensitivity analyses can estimate the potential impact of ORB on review conclusions. While awareness of ORB is growing, more needs to be done to address this issue through improved trial registration, reporting and access to protocols and outcomes.
This document outlines the concepts and methods of multiple-treatments meta-analysis (MTM). MTM allows for the simultaneous comparison of multiple interventions for a condition by combining both direct and indirect evidence from randomized controlled trials. Key advantages of MTM include the ability to rank treatments, comprehensively use all available data, and compare interventions not directly compared in trials. The document discusses MTM approaches using frequentist meta-regression and Bayesian statistics.
This document discusses investigating heterogeneity in meta-analyses through subgroup analysis and meta-regression. It outlines when and how to use these techniques to explore reasons for variability in study results. Key challenges include having enough studies, selecting explanatory variables carefully to avoid false positives, and accounting for confounding and aggregation bias in study-level data. Meta-regression allows for random effects but interpretation requires caution given observational relationships between study characteristics and effects.
This document discusses biases that can arise in randomized controlled trials and meta-analyses. It notes that biases can be introduced in the design, conduct, analysis, and reporting of trials. Various empirical studies are presented that demonstrate biases from lack of allocation concealment and blinding in trials. Risk of bias assessments are recommended over quality scores for evaluating biases in individual trials and meta-analyses.
This document discusses potential sources of missing data in meta-analyses, including studies not being found, outcomes not being fully reported, missing standard deviations or other information needed for the meta-analysis, and missing participants. It also covers concepts related to missing data like whether it is missing completely at random, missing at random, or informatively missing. Strategies for dealing with missing data include simple or multiple imputation as well as sensitivity analyses. Specific examples discussed include imputing missing standard deviations or correlation coefficients.
This document provides an overview of multiple treatment meta-analysis (MTMA) or network meta-analysis. It discusses Bayesian pairwise meta-analysis models as well as extensions to multiple treatments. Key assumptions of MTMA including consistency are explained. Computational details using Markov Chain Monte Carlo are covered. Measures of model fit such as residual deviance and model comparison using Deviance Information Criteria are also summarized. Examples from cardiovascular treatment meta-analyses are provided.
2010 smg training_cardiff_day1_session1(3 of 3)beyenergveroniki
This document summarizes a presentation on using the Ratio of Means (RoM) as an alternative effect measure for meta-analyzing continuous outcomes. Through simulation studies and an empirical analysis of Cochrane reviews, the RoM was found to have statistical performance comparable to the Mean Difference and Standardized Mean Difference. Specifically:
1) Simulation studies found the RoM to have similar bias, coverage, power, and ability to estimate heterogeneity as the Mean Difference and Standardized Mean Difference in most scenarios.
2) An empirical analysis of over 200 Cochrane reviews found no significant differences in treatment effect sizes or heterogeneity between the RoM, Mean Difference, and Standardized Mean Difference.
3) The RoM was proposed
This document discusses selective outcome reporting bias (ORB), which occurs when researchers select a subset of original outcomes to report based on statistical significance. ORB threatens the validity of systematic reviews and meta-analyses. The document describes different types of ORB and methods to assess risk of bias. It proposes the ORBIT classification system to code incomplete outcome reporting in trials. Sensitivity analyses can estimate the potential impact of ORB on review conclusions. While awareness of ORB is growing, more needs to be done to address this issue through improved trial registration, reporting and access to protocols and outcomes.
This document outlines the concepts and methods of multiple-treatments meta-analysis (MTM). MTM allows for the simultaneous comparison of multiple interventions for a condition by combining both direct and indirect evidence from randomized controlled trials. Key advantages of MTM include the ability to rank treatments, comprehensively use all available data, and compare interventions not directly compared in trials. The document discusses MTM approaches using frequentist meta-regression and Bayesian statistics.
This document discusses investigating heterogeneity in meta-analyses through subgroup analysis and meta-regression. It outlines when and how to use these techniques to explore reasons for variability in study results. Key challenges include having enough studies, selecting explanatory variables carefully to avoid false positives, and accounting for confounding and aggregation bias in study-level data. Meta-regression allows for random effects but interpretation requires caution given observational relationships between study characteristics and effects.
This document discusses biases that can arise in randomized controlled trials and meta-analyses. It notes that biases can be introduced in the design, conduct, analysis, and reporting of trials. Various empirical studies are presented that demonstrate biases from lack of allocation concealment and blinding in trials. Risk of bias assessments are recommended over quality scores for evaluating biases in individual trials and meta-analyses.
This document discusses potential sources of missing data in meta-analyses, including studies not being found, outcomes not being fully reported, missing standard deviations or other information needed for the meta-analysis, and missing participants. It also covers concepts related to missing data like whether it is missing completely at random, missing at random, or informatively missing. Strategies for dealing with missing data include simple or multiple imputation as well as sensitivity analyses. Specific examples discussed include imputing missing standard deviations or correlation coefficients.
This document provides an overview of multiple treatment meta-analysis (MTMA) or network meta-analysis. It discusses Bayesian pairwise meta-analysis models as well as extensions to multiple treatments. Key assumptions of MTMA including consistency are explained. Computational details using Markov Chain Monte Carlo are covered. Measures of model fit such as residual deviance and model comparison using Deviance Information Criteria are also summarized. Examples from cardiovascular treatment meta-analyses are provided.
2010 smg training_cardiff_day1_session1(3 of 3)beyenergveroniki
This document summarizes a presentation on using the Ratio of Means (RoM) as an alternative effect measure for meta-analyzing continuous outcomes. Through simulation studies and an empirical analysis of Cochrane reviews, the RoM was found to have statistical performance comparable to the Mean Difference and Standardized Mean Difference. Specifically:
1) Simulation studies found the RoM to have similar bias, coverage, power, and ability to estimate heterogeneity as the Mean Difference and Standardized Mean Difference in most scenarios.
2) An empirical analysis of over 200 Cochrane reviews found no significant differences in treatment effect sizes or heterogeneity between the RoM, Mean Difference, and Standardized Mean Difference.
3) The RoM was proposed
Imran rizvi statistics in meta analysisImran Rizvi
This document discusses statistics used in meta-analyses. It explains that meta-analyses statistically combine results from multiple studies on a topic. Effect measures are calculated for individual studies and then combined to find an overall effect. For dichotomous outcomes, common effect measures are risk ratio, odds ratio, and absolute risk reduction. Random effects models account for heterogeneity between studies, while fixed effect models assume one true effect. Forest plots visually display individual study results and the overall effect, allowing readers to assess consistency and precision.
2010 JSM - Meta Stat Issue Medical DevicesTerry Liao
This document summarizes statistical issues that commonly arise in meta-analyses of drug-eluting stent data. It discusses key topics like using fixed effect versus random effects models, strategies for handling zero event rates, and approaches for incorporating time-to-event data like Kaplan-Meier curves. The document provides examples and references to illustrate important considerations for conducting meta-analyses and addressing heterogeneity between studies.
This document discusses meta-analysis techniques for systematically reviewing and statistically combining results from multiple clinical trials. It covers the history of meta-analysis, methodology for combining test statistics and assessing heterogeneity, software for conducting meta-analyses, and current issues including how to handle different study designs. Examples are provided to illustrate meta-analysis of randomized controlled trials comparing treatments for stroke, myocardial infarction, and other conditions.
1) Meta-analysis is a statistical technique that combines the results of multiple studies on a topic and produces a single estimate of the overall effect. It aims to increase power by pooling data.
2) The first meta-analysis was conducted in 1904, and the term was coined in 1976. Meta-analysis is now often called a "systematic review."
3) Meta-analysis can help clinicians and policymakers integrate research findings and determine if relationships are consistent across studies. It increases precision and statistical power compared to individual studies.
This document summarizes a simulation study comparing the performance of different meta-analysis methods when assumptions of normality are violated. The study generated simulated datasets with various distributions for true effects and degrees of heterogeneity. It then compared methods like fixed effects, DerSimonian-Laird, maximum likelihood, and permutations in terms of coverage, power, and confidence interval estimation. The results showed that some methods are more robust to non-normal data, with profile likelihood and permutations generally performing best, while other methods like fixed effects and DerSimonian-Laird showed poorer performance.
Overview of systematic review and meta analysisDrsnehas2
Systematic reviews and meta-analyses aim to summarize research evidence on a topic. This document provides an overview of how to conduct systematic reviews and meta-analyses, including formulating a question, identifying relevant studies, extracting data, assessing bias, synthesizing data through meta-analysis if appropriate, interpreting results, and updating reviews. Key steps involve developing eligibility criteria, searching multiple databases, assessing risk of bias, addressing heterogeneity, and evaluating for publication bias. Conducting reviews using standardized methods helps provide reliable conclusions to inform clinical practice and policy-making.
Network meta-analysis with integrated nested Laplace approximationsBurak Kürsad Günhan
This document discusses network meta-analysis (NMA) models for combining data from multiple treatment comparisons. It provides an overview of NMA terminology and models, including the Lu-Ades and Jackson models. It also demonstrates the application of these models to sample datasets on tuberculosis vaccine trials and smoking cessation interventions using Bayesian inference with integrated nested Laplace approximations (INLA). The key contributions are the INLA implementation of the Jackson NMA model and an R function for fitting various pairwise and network meta-analysis models.
This document discusses biostatistics in cancer clinical trials. It provides an overview of cancer research and regulations for clinical trials. Pivotal phase III cancer trials usually evaluate efficacy endpoints like survival and progression-free survival using randomized controlled trial designs like superiority, non-inferiority, and equivalence trials. Sample size calculations are important for these trials and require considering the scientific questions, distributions, hypotheses, and desired power. Key elements in the statistical analysis of cancer data include evaluating time-to-event endpoints, response rates, and addressing missing tumor data.
This document discusses meta-analysis, which involves systematically combining results from multiple studies to derive conclusions about a body of research. It describes the key steps in conducting a meta-analysis, including writing a research question and protocol, performing a comprehensive literature search, selecting studies, assessing study quality, extracting data, and analyzing data. Statistical methods for pooling results across studies using fixed and random effects models are also outlined. The document highlights strengths and limitations of meta-analysis for providing more precise estimates of treatment effects and identifying areas needing further research.
演講-Meta analysis in medical research-張偉豪Beckett Hsieh
This document provides an overview of meta-analysis. It defines meta-analysis as a quantitative approach to systematically combining results from previous studies to arrive at conclusions about the body of research. It discusses key aspects of planning and conducting a meta-analysis such as defining the research question, searching for relevant literature, determining study eligibility, extracting data, analyzing effect sizes, assessing heterogeneity, and addressing publication bias. Software for performing meta-analyses and specific effect sizes like risk ratio and odds ratio are also mentioned.
This document provides an overview of statistics used in meta-analysis. It discusses key concepts like odds ratios, relative risk, confidence intervals, heterogeneity, and fixed and random effects models. It also summarizes different types of meta-analyses including realist reviews, meta-narrative reviews, and network meta-analyses. Software for performing meta-analyses and potential pitfalls in systematic reviews are also briefly covered.
This document discusses evidence-based medicine (EBM) and key concepts in evaluating medical evidence. It defines EBM as the conscientious use of current best evidence in patient care. Randomized controlled trials are considered the gold standard for evaluating new therapies or tests. However, observational studies can also provide valuable evidence when RCTs are not possible or ethical. Systematic reviews provide a critical summary of all relevant randomized trials on a topic to determine the state of evidence and guide clinical practice and policy.
Parmetric and non parametric statistical test in clinical trailsVinod Pagidipalli
The document discusses parametric and non-parametric statistical tests used in clinical trials. Parametric tests like the z-test, t-test, ANOVA, and correlation tests are used when data follows a normal distribution. Non-parametric tests like the chi-square test, Fisher's exact test, and binomial test are used when data cannot be assumed to be normally distributed. Several statistical tests are described, including how to apply them in clinical trials to compare treatment groups, analyze associations between variables, and test hypotheses about population proportions.
This document provides an overview of meta-analysis, including what it is, why and when it should be conducted, and how to perform one. It defines meta-analysis as using statistical techniques to combine results from multiple studies on a topic to produce a single estimate. It describes when meta-analysis is appropriate, how to assess heterogeneity between studies, account for publication bias, and estimate summary effects. Statistical tests and graphs are presented to evaluate heterogeneity and bias. The document concludes by listing some programs and techniques used for meta-analysis.
This document discusses network meta-analysis (NMA), which synthesizes both direct and indirect evidence from randomized controlled trials (RCTs) that compare multiple interventions. NMA allows for comparisons between interventions that have not been directly compared in RCTs. It provides treatment relative rankings and effect estimates. Assumptions of NMA include similarity of trials, homogeneity within comparisons, and consistency between direct and indirect evidence. Tests for heterogeneity and inconsistency help evaluate if these assumptions are valid. Software like Addis, WinBUGS, NetMetaXL, and RevMan can be used to conduct NMA.
The document discusses non-parametric tests and provides information about when to use them. Non-parametric tests make fewer assumptions about the distribution of population values and can be used when sample sizes are small or the data is ordinal. Examples of non-parametric tests provided include the sign test, chi-square test, Mann-Whitney U test, and Kruskal-Wallis test. The general steps to perform a non-parametric test are also outlined.
This powerpoint presentation gives a brief explanation about the biostatic data .this is quite helpful to individuals to understand the basic research methodology terminologys
Imran rizvi statistics in meta analysisImran Rizvi
This document discusses statistics used in meta-analyses. It explains that meta-analyses statistically combine results from multiple studies on a topic. Effect measures are calculated for individual studies and then combined to find an overall effect. For dichotomous outcomes, common effect measures are risk ratio, odds ratio, and absolute risk reduction. Random effects models account for heterogeneity between studies, while fixed effect models assume one true effect. Forest plots visually display individual study results and the overall effect, allowing readers to assess consistency and precision.
2010 JSM - Meta Stat Issue Medical DevicesTerry Liao
This document summarizes statistical issues that commonly arise in meta-analyses of drug-eluting stent data. It discusses key topics like using fixed effect versus random effects models, strategies for handling zero event rates, and approaches for incorporating time-to-event data like Kaplan-Meier curves. The document provides examples and references to illustrate important considerations for conducting meta-analyses and addressing heterogeneity between studies.
This document discusses meta-analysis techniques for systematically reviewing and statistically combining results from multiple clinical trials. It covers the history of meta-analysis, methodology for combining test statistics and assessing heterogeneity, software for conducting meta-analyses, and current issues including how to handle different study designs. Examples are provided to illustrate meta-analysis of randomized controlled trials comparing treatments for stroke, myocardial infarction, and other conditions.
1) Meta-analysis is a statistical technique that combines the results of multiple studies on a topic and produces a single estimate of the overall effect. It aims to increase power by pooling data.
2) The first meta-analysis was conducted in 1904, and the term was coined in 1976. Meta-analysis is now often called a "systematic review."
3) Meta-analysis can help clinicians and policymakers integrate research findings and determine if relationships are consistent across studies. It increases precision and statistical power compared to individual studies.
This document summarizes a simulation study comparing the performance of different meta-analysis methods when assumptions of normality are violated. The study generated simulated datasets with various distributions for true effects and degrees of heterogeneity. It then compared methods like fixed effects, DerSimonian-Laird, maximum likelihood, and permutations in terms of coverage, power, and confidence interval estimation. The results showed that some methods are more robust to non-normal data, with profile likelihood and permutations generally performing best, while other methods like fixed effects and DerSimonian-Laird showed poorer performance.
Overview of systematic review and meta analysisDrsnehas2
Systematic reviews and meta-analyses aim to summarize research evidence on a topic. This document provides an overview of how to conduct systematic reviews and meta-analyses, including formulating a question, identifying relevant studies, extracting data, assessing bias, synthesizing data through meta-analysis if appropriate, interpreting results, and updating reviews. Key steps involve developing eligibility criteria, searching multiple databases, assessing risk of bias, addressing heterogeneity, and evaluating for publication bias. Conducting reviews using standardized methods helps provide reliable conclusions to inform clinical practice and policy-making.
Network meta-analysis with integrated nested Laplace approximationsBurak Kürsad Günhan
This document discusses network meta-analysis (NMA) models for combining data from multiple treatment comparisons. It provides an overview of NMA terminology and models, including the Lu-Ades and Jackson models. It also demonstrates the application of these models to sample datasets on tuberculosis vaccine trials and smoking cessation interventions using Bayesian inference with integrated nested Laplace approximations (INLA). The key contributions are the INLA implementation of the Jackson NMA model and an R function for fitting various pairwise and network meta-analysis models.
This document discusses biostatistics in cancer clinical trials. It provides an overview of cancer research and regulations for clinical trials. Pivotal phase III cancer trials usually evaluate efficacy endpoints like survival and progression-free survival using randomized controlled trial designs like superiority, non-inferiority, and equivalence trials. Sample size calculations are important for these trials and require considering the scientific questions, distributions, hypotheses, and desired power. Key elements in the statistical analysis of cancer data include evaluating time-to-event endpoints, response rates, and addressing missing tumor data.
This document discusses meta-analysis, which involves systematically combining results from multiple studies to derive conclusions about a body of research. It describes the key steps in conducting a meta-analysis, including writing a research question and protocol, performing a comprehensive literature search, selecting studies, assessing study quality, extracting data, and analyzing data. Statistical methods for pooling results across studies using fixed and random effects models are also outlined. The document highlights strengths and limitations of meta-analysis for providing more precise estimates of treatment effects and identifying areas needing further research.
演講-Meta analysis in medical research-張偉豪Beckett Hsieh
This document provides an overview of meta-analysis. It defines meta-analysis as a quantitative approach to systematically combining results from previous studies to arrive at conclusions about the body of research. It discusses key aspects of planning and conducting a meta-analysis such as defining the research question, searching for relevant literature, determining study eligibility, extracting data, analyzing effect sizes, assessing heterogeneity, and addressing publication bias. Software for performing meta-analyses and specific effect sizes like risk ratio and odds ratio are also mentioned.
This document provides an overview of statistics used in meta-analysis. It discusses key concepts like odds ratios, relative risk, confidence intervals, heterogeneity, and fixed and random effects models. It also summarizes different types of meta-analyses including realist reviews, meta-narrative reviews, and network meta-analyses. Software for performing meta-analyses and potential pitfalls in systematic reviews are also briefly covered.
This document discusses evidence-based medicine (EBM) and key concepts in evaluating medical evidence. It defines EBM as the conscientious use of current best evidence in patient care. Randomized controlled trials are considered the gold standard for evaluating new therapies or tests. However, observational studies can also provide valuable evidence when RCTs are not possible or ethical. Systematic reviews provide a critical summary of all relevant randomized trials on a topic to determine the state of evidence and guide clinical practice and policy.
Parmetric and non parametric statistical test in clinical trailsVinod Pagidipalli
The document discusses parametric and non-parametric statistical tests used in clinical trials. Parametric tests like the z-test, t-test, ANOVA, and correlation tests are used when data follows a normal distribution. Non-parametric tests like the chi-square test, Fisher's exact test, and binomial test are used when data cannot be assumed to be normally distributed. Several statistical tests are described, including how to apply them in clinical trials to compare treatment groups, analyze associations between variables, and test hypotheses about population proportions.
This document provides an overview of meta-analysis, including what it is, why and when it should be conducted, and how to perform one. It defines meta-analysis as using statistical techniques to combine results from multiple studies on a topic to produce a single estimate. It describes when meta-analysis is appropriate, how to assess heterogeneity between studies, account for publication bias, and estimate summary effects. Statistical tests and graphs are presented to evaluate heterogeneity and bias. The document concludes by listing some programs and techniques used for meta-analysis.
This document discusses network meta-analysis (NMA), which synthesizes both direct and indirect evidence from randomized controlled trials (RCTs) that compare multiple interventions. NMA allows for comparisons between interventions that have not been directly compared in RCTs. It provides treatment relative rankings and effect estimates. Assumptions of NMA include similarity of trials, homogeneity within comparisons, and consistency between direct and indirect evidence. Tests for heterogeneity and inconsistency help evaluate if these assumptions are valid. Software like Addis, WinBUGS, NetMetaXL, and RevMan can be used to conduct NMA.
The document discusses non-parametric tests and provides information about when to use them. Non-parametric tests make fewer assumptions about the distribution of population values and can be used when sample sizes are small or the data is ordinal. Examples of non-parametric tests provided include the sign test, chi-square test, Mann-Whitney U test, and Kruskal-Wallis test. The general steps to perform a non-parametric test are also outlined.
This powerpoint presentation gives a brief explanation about the biostatic data .this is quite helpful to individuals to understand the basic research methodology terminologys
Statistics.pdf.pdf for Research Physiotherapy and Occupational TherapySakhileKhoza2
This document discusses statistical concepts and how statisticians can assist with research studies. It begins by noting that statistical analysis is common in health research and that medical practitioners need a basic understanding of statistics. It then discusses how statisticians can help with all stages of a study design, ensuring results are comparable and generalizable. The document outlines different types of data - categorical, numerical, count - and how data can be summarized using proportions, rates, and ratios. It provides examples of summarizing binary outcome data from studies using tables, risks, risk differences, risk ratios, and odds ratios. Statisticians are emphasized as important consultants early in planning studies to optimize design and analysis.
Dive into our students' innovative project leveraging machine learning for heart disease prediction. Discover how advanced analytics and predictive modeling can revolutionize healthcare, providing early detection and personalized interventions for better patient outcomes. To learn more, do check out https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/.
General principles of research methodology. Terms frequently used in this chapter. It is a course subject for fourth Pharm D in The Tamilnadu Dr.MGR. Medical University, Chennai.
This document provides an overview of basic statistical concepts and techniques for analyzing data that are important for oncologists to understand. It covers topics such as types of data, measures of central tendency and variability, theoretical distributions, sampling, hypothesis testing, and basic techniques for analyzing categorical and numerical data, including t-tests, ANOVA, chi-square tests, correlation, and regression. The goal is to equip oncologists with fundamental statistical knowledge for handling, describing, and making inferences from medical data.
Study of the distribution and determinants of
health-related states or events in specified populations and the application of this study to control health problems.
John M. Last, Dictionary of Epidemiology
Common statistical tests and applications in epidemiological literatureKadium
This document provides an overview of common statistical tests used in epidemiological literature, including their appropriate applications and calculations. It describes the three main types of data - nominal, ordinal, and continuous - and how they are characterized. Key concepts discussed include hypothesis testing, null and alternative hypotheses, Type I and Type II errors, alpha and power. Specific statistical tests covered are the Student's t-test for comparing group means and chi-square analysis for examining associations between categorical variables. Examples are provided to illustrate how these tests are applied and interpreted.
Critical appraisal of randomized clinical trialsSamir Haffar
The document discusses key concepts in randomized clinical trials (RCTs), including:
1) RCTs are considered the gold standard for evaluating the effectiveness of interventions due to their ability to minimize bias through randomization and blinding.
2) Proper randomization aims to create comparable treatment and control groups, conceal allocation to prevent bias, and may involve simple, stratified or blocked methods.
3) Blinding (masking) of participants, investigators and assessors can decrease observation bias and is important for RCT validity, though full blinding is not always possible.
4) Intention-to-treat analysis includes all randomized patients to preserve comparable groups and prevent bias from non-compliance.
BASIC STATISTICS AND THEIR INTERPRETATION AND USE IN EPIDEMIOLOGY 050822.pdfAdamu Mohammad
This document provides an introduction to basic statistical concepts and their use in epidemiology. It discusses different types of data including categorical, quantitative, discrete, and continuous data. It also covers measures of central tendency (mean, median, mode) and dispersion (range, variance, standard deviation). The document introduces the concepts of skewness and the normal distribution. It then discusses inferential statistics, hypothesis testing, and parametric vs non-parametric tests. Key statistical tests are outlined depending on whether populations are related or independent. The overall goal is to provide health professionals with foundational statistical knowledge for investigating medical science.
Common statistical tests and applications in epidemiological literatureKadium
This document provides an overview of common statistical tests and applications in epidemiological literature. It describes the different types of data, including nominal, ordinal and continuous data. It also discusses describing data through distributions and other characteristics. Hypothesis testing and the concepts of null and alternative hypotheses are explained. Types of errors in statistical testing like Type I and Type II errors are defined. Specific statistical tests like the student's t-test and chi-square analysis are outlined along with examples of their applications. Practice questions related to hypothesis testing and p-values are also included.
Common statistical tests and applications in epidemiological literatureKadium
This document provides an overview of common statistical tests and applications in epidemiological literature. It describes the different types of data, such as nominal, ordinal, and continuous data. It also discusses describing data through distributions and other characteristics. Hypothesis testing and the concepts of null and alternative hypotheses are explained. Types of errors in statistical testing like Type I and Type II errors are defined. Specific statistical tests like the Student's t-test and chi-square analysis are outlined along with examples of their applications. Practice questions related to hypothesis testing and p-values are provided at the end.
When to use, What Statistical Test for data Analysis modified.pptxAsokan R
This document discusses choosing the appropriate statistical test for data analysis. It begins by defining key terminology like independent and dependent variables. It then discusses the different types of variables, including quantitative, categorical, and their subtypes. Hypothesis testing and its key steps are explained. The document outlines assumptions that statistical tests make and categorizes common parametric and non-parametric tests. It provides guidance on choosing a test based on the research question, data structure, variable type, and whether the data meets necessary assumptions. Specific statistical tests are matched to questions about differences between groups, association between variables, and agreement between assessment techniques.
Randomization aims to equally distribute participant characteristics between treatment groups to prevent bias. There are several types of randomization including simple, block, and stratified block randomization. Blinding, such as double or triple blinding, helps prevent performance, detection, and other biases by keeping parties unaware of treatment assignments. Bias can still occur through factors like selection, performance, detection, laboratory, or sample size biases if randomization and blinding are not properly implemented.
Measuring the right outcomes in mental healthJohn Brazier
This talk presents the findings of an MRC study on whether the generic health measures of EQ-5D and SF-36 are valid in mental health. It uses mixed methods research (including interviews with service users) to show that these measures miss important ways in which mental health impacts on people's lives. It proposes 7 themes that seem to capture the important domains of recovery for people with mental health problems that provide the basis for a new generic outcome measure for mental health.
N.B. These slides were presented at the 20th Anniversary of the Centre for Mental and Physical Health Economics, 7th November 2013.
Deciphering the dilemma of parametric and nonparametric testsRamachandra Barik
This document discusses the differences between parametric and nonparametric statistical tests and provides guidance on selecting the appropriate test. Parametric tests make assumptions about the population distribution, while nonparametric tests make fewer assumptions. The key factors in deciding which test to use are the scale of measurement, population distribution, homogeneity of variances, and independence of samples. Although nonparametric tests are more flexible, parametric tests often have more statistical power. The document provides examples and guidelines to help researchers select the right test for their data and research questions.
Advice On Statistical Analysis For Circulation ResearchNancy Ideker
This document provides an overview and review of statistical methods for analyzing cardiovascular research data. It discusses common statistical errors in previous decades, such as low statistical power and inadequate analysis of repeated measures studies. It introduces several statistical methods that are useful but not always familiar to cardiologists, including power analysis, methods for analyzing repeated measures, analysis of covariance, multivariate analysis of variance, nonparametric tests, and more. The goal is to help researchers choose the appropriate statistical tests and properly interpret the results.
This document provides an overview of key concepts in experimental design and statistics. It discusses variables, statistical tests, types of statistics, basic experimental design principles, and sample size determination. The key points are:
1. Experimental design should be unbiased through randomization, blinding, and inclusion of controls. It aims for high precision through uniform samples, replication, and stratification.
2. Statistics can be descriptive or inferential. Descriptive statistics summarize data, while inferential statistics make generalizations from samples to populations through hypothesis testing, confidence intervals, and significance testing.
3. Sample size is determined based on desired power to detect a minimum clinically meaningful effect size given available resources. Larger samples increase power but come
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
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Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
How to Build a Module in Odoo 17 Using the Scaffold Method
day1(2010 smg training_cardiff)_session2b (1of 2) lewis
1. Meta-analysis of ordinal data
Steff Lewis
Edinburgh MRC clinical trials methodology
hub
(with thanks to Izzy Butcher, Gillian McHugh
and Jim Weir for examples)
2. Examples of ordinal scales in
stroke
• Modified Rankin Scale
Score Description
0 No symptoms at all
1 No significant disability despite symptoms; able to carry out all usual
duties and activities
2 Slight disability; unable to carry out all previous activities, but able to
look after own affairs without assistance
3 Moderate disability; requiring some help, but able to walk without
assistance
4 Moderately severe disability; unable to walk without assistance and
unable to attend to own bodily needs without assistance
5 Severe disability; bedridden, incontinent and requiring constant
nursing care and attention
6 Dead
4. How common are ordinal data?
• Cochrane stroke group has 118 full
reviews of the effectiveness of
interventions (12 Jan 2010).
• Approx 2/3 have an ordinal outcome
measure.
• None are analysed as ordinal data.
• They either dichotomise [approx 3/4] or
treat as continuous [approx 1/4].
5. 9.4.7 Meta-analysis of ordinal
outcomes and measurement scales
Ordinal and measurement scale outcomes
are most commonly meta-analysed as
dichotomous data or continuous data
depending on the way that the study
authors performed the original analyses.
What the Handbook says……
6. How common are ordinal outcomes
in other review groups?
• Does anyone know of any ordinal
analyses in Cochrane that use methods
other than those available in Revman?
8. Individuals who fall close to, but on different sides of the
cut-point, will be assumed by the analysis to be different,
yet they are likely to be similar.
• Modified Rankin Scale
Score Description
0 No symptoms at all
1 No significant disability despite symptoms; able to carry out all usual
duties and activities
2 Slight disability; unable to carry out all previous activities, but able to
look after own affairs without assistance
3 Moderate disability; requiring some help, but able to walk without
assistance
4 Moderately severe disability; unable to walk without assistance and
unable to attend to own bodily needs without assistance
5 Severe disability; bedridden, incontinent and requiring constant
nursing care and attention
6 Dead
9. Individuals who improve, but don’t improve past the cut-
point won’t be counted as improvers in the analysis.
• Modified Rankin Scale
Score Description
0 No symptoms at all
1 No significant disability despite symptoms; able to carry out all usual
duties and activities
2 Slight disability; unable to carry out all previous activities, but able to
look after own affairs without assistance
3 Moderate disability; requiring some help, but able to walk without
assistance
4 Moderately severe disability; unable to walk without assistance and
unable to attend to own bodily needs without assistance
5 Severe disability; bedridden, incontinent and requiring constant
nursing care and attention
6 Dead
10. It is throwing away information
• In individual studies, for continuous data:
– The loss of power in dichotomising continuous data at
the mean is equivalent to throwing away a third of the
data.
– Dichotomising away from the mean is even worse.
– Cohen J. Appl Psychol Meas 1983;7:249.
• The same concepts are true of ordinal data.
– Re-analysis of ordinal data in individual stroke trials
has shown that sample sizes could be around 30%
smaller if data were analysed using the full ordinal
scale rather than by dichotomising [OAST 2008].
– Similar results occur in head injury (IMPACT team)
11. What’s wrong with analysing
ordinal data as if they are
continuous?
(using standard methods in Revman)
– There may be nonparametric methods that use
rankings that are OK (although may not give good
summary estimates for meta-analysis)
12. The data may not be Normally
distributed
0
20
40
60
80
100
120
140
160
180
0 1 2 3 4 5 Dead
modified Rankin at 6 months
Numberofpatients
FOOD trial – PEG vs NG feeding tubes in stroke patients
13. May not be a linear scale so change from 1 to 2 is not the
same as 2 to 3.
• Modified Rankin Scale
Score Description
0 No symptoms at all
1 No significant disability despite symptoms; able to carry out all usual
duties and activities
2 Slight disability; unable to carry out all previous activities, but able to
look after own affairs without assistance
3 Moderate disability; requiring some help, but able to walk without
assistance
4 Moderately severe disability; unable to walk without assistance and
unable to attend to own bodily needs without assistance
5 Severe disability; bedridden, incontinent and requiring constant
nursing care and attention
6 Dead
14. So what can we do instead?
• Proportional odds modelling
– Makes no distributional assumptions about
the outcome
15. Proportional odds model
• Proportional odds model assumes there is an
equal odds ratio for all dichotomies of the data.
• The odds ratio calculated from the proportional
odds model can be interpreted as the odds of
success on the experimental intervention
relative to control, irrespective of how the
ordered categories might be divided into
success or failure.
21. Pitfalls, etc
• IMPACT head injury investigators have
found that the proportional odds
assumption mostly holds in their trial data.
• They say even if the data deviate
considerably from proportional odds, it still
gives a useful summary.
• However, it will hide ‘kill or cure’ effects if
used without any other summary
measures.
22. Thrombolysis (tPA) for acute
ischaemic stroke
– Death during follow up
From Wardlaw JM et al. Cochrane Database of Systematic
Reviews 2009, Issue 4. Art. No.: CD000213.(Only studies that
report both death, and death and dependency included)
23. Thrombolysis (tPA) for acute
ischaemic stroke
– Death or dependency during follow up
From Wardlaw JM et al. Cochrane Database of Systematic
Reviews 2009, Issue 4. Art. No.: CD000213.(Only studies that
report both death, and death and dependency included)
25. Data of the form…
nnnnTrt = 1
nnnnTrt = 0
Good
4
Moderate
3
Severe
2
Dead/Veg
1
Glasgow outcome scale, for those with and
without active treatment
26. SAS code
proc sort;
by trial;
proc logistic order=internal;
class treatment (param=ref ref='0');
model ordscale(descending) = treatment;
weight n;
by trial;
run;
31. Collecting data
• You need the numbers of patients in each
category of the ordinal scale for each
intervention group if the proportional odds
ratio method will be used.
• Full data probably more likely for shorter
scales and more recent papers??
32. Gøtzsche paper
Optimal reporting: original ordered categories (but various scales
included). For pain on VAS, mean and SD were accepted.
35. ECASS 1 text:
• “In the ITT analysis 29.3% of patients in
the placebo arm and 35.7% of the rt-PA
treated patients had RS scores better than
2 at 90 days (Table 3)”
38. You could mix binary and ordinal
data…
• Reminder: The odds ratio calculated from the
proportional odds model can be interpreted as
the odds of success on the experimental
intervention relative to control, irrespective of
how the ordered categories might be divided into
success or failure.
• If proportional odds holds, you could combine:
– The original Rankin scale in 7 categories
– A summarised Rankin scale in 4 categories
– Binary data where the scale has been split at 0-2 vs
3-6
– Dead vs alive (category 6 on the scale vs 0-5).
39. Mixing different scales
• Methods are available for combining data from scales
that are related but have different definitions for their
categories (discussed in Anne Whitehead’s book – Meta-
analysis of controlled clinical trials, section 9.3).
40. Where next?
• An MRC project.
– Practical methods for ordinal data meta-analysis in stroke
– 1 June 2010 to 31 May 2012
a. Review the methods available for meta-analysis of
ordinal outcomes.
b. Investigate using each of these methods in real data:
• how often sufficient data are presented (or can be obtained),
• how often the available data fulfil any distributional
assumptions (and whether there are sufficient data to check
assumptions),
• how easy to understand the results are, and how much detail
they show of the way the treatment effect operates.
• assess the added statistical power gained by using ordinal and
continuous data methods over binary methods.
c. Develop a Cochrane workshop on ordinal methods.