This document provides a summary of a meta-analysis presented by Preethi Rai on November 12, 2013. It defines meta-analysis as a quantitative approach that systematically combines the results of previous research studies in order to arrive at conclusions about the body of research. The summary explains that meta-analysis increases the overall sample size and statistical power to better understand treatment effects. It also addresses how meta-analysis can help resolve controversies, identify areas needing more research, and generalize study results. Limitations including publication bias and inability to improve original study quality are also noted.
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.
Introduction to meta-analysis (1612_MA_workshop)Ahmed Negida
This document provides an overview of a meta-analysis workshop. It will introduce descriptive and inferential statistics, the concept of meta-analysis, and meta-analysis software and models. The workshop covers new topics like quality effects meta-analysis, heterogeneity models, and assessment of publication bias. It explains that simply averaging study results is incorrect, and meta-analysis statistically combines studies while weighting them by size and power to provide a single pooled effect estimate. Meta-analysis has advantages like larger power but must address heterogeneity and differences between studies.
Systematic reviews and meta-analyses aim to summarize all available evidence on a topic. A systematic review collects and analyzes results from relevant studies, while a meta-analysis uses statistical methods to combine results into a pooled estimate. Meta-analyses can determine if an effect exists and its direction, but are subject to biases from unpublished or missing studies. They provide more reliable conclusions than individual studies but also have limitations like heterogeneity between studies.
Meta analysis: Made Easy with Example from RevManGaurav Kamboj
This document provides an overview of meta-analysis, including:
1) Meta-analysis allows researchers to quantitatively combine the results of multiple studies on a topic to arrive at overall conclusions about the body of research.
2) The key steps of conducting a meta-analysis include developing a research protocol, performing a comprehensive literature search, selecting studies, assessing study quality, extracting data, analyzing data, and addressing heterogeneity and publication bias.
3) Funnel plots and statistical tests can be used to examine potential biases like publication bias in a meta-analysis. Addressing these biases helps ensure the meta-analysis provides an accurate summary of the evidence.
1. A meta-analysis systematically combines data from multiple studies to identify patterns among study results, increase statistical power, and resolve uncertainties in areas where individual studies may be too narrow.
2. Key steps include defining the question, reviewing literature and extracting data, computing effect sizes, determining average effect sizes and confidence intervals, and looking for associations that may explain variability among studies.
3. Factors like study quality and publication bias must be considered, as missing or unpublished studies could change conclusions. Meta-analyses aim to synthesize evidence from diverse studies and elucidate general patterns.
Meta-analysis is defined as quantitatively combining and integrating the findings of multiple research studies on a particular topic. It was coined by Glass in 1976 and refers to analyzing the results of several studies that address a shared research hypothesis. The key steps in a meta-analysis involve defining a hypothesis, locating relevant studies, inputting empirical data, calculating an overall effect size by standardizing statistics, and analyzing any moderating variables if heterogeneity exists. An example provided is a meta-analysis on coping behaviors of cancer patients that would statistically analyze results from quantitative studies with similar age groups.
This document outlines the steps involved in conducting a systematic review and meta-analysis on the prevalence of elder abuse. It discusses how 52 studies from around the world were analyzed using comprehensive meta-analysis software. The key findings were that the pooled prevalence of elder abuse was 15.7%. While systematic reviews have strengths like being comprehensive and transparent, they also have limitations such as reliance on the quality of primary studies and risk of publication bias.
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.
Introduction to meta-analysis (1612_MA_workshop)Ahmed Negida
This document provides an overview of a meta-analysis workshop. It will introduce descriptive and inferential statistics, the concept of meta-analysis, and meta-analysis software and models. The workshop covers new topics like quality effects meta-analysis, heterogeneity models, and assessment of publication bias. It explains that simply averaging study results is incorrect, and meta-analysis statistically combines studies while weighting them by size and power to provide a single pooled effect estimate. Meta-analysis has advantages like larger power but must address heterogeneity and differences between studies.
Systematic reviews and meta-analyses aim to summarize all available evidence on a topic. A systematic review collects and analyzes results from relevant studies, while a meta-analysis uses statistical methods to combine results into a pooled estimate. Meta-analyses can determine if an effect exists and its direction, but are subject to biases from unpublished or missing studies. They provide more reliable conclusions than individual studies but also have limitations like heterogeneity between studies.
Meta analysis: Made Easy with Example from RevManGaurav Kamboj
This document provides an overview of meta-analysis, including:
1) Meta-analysis allows researchers to quantitatively combine the results of multiple studies on a topic to arrive at overall conclusions about the body of research.
2) The key steps of conducting a meta-analysis include developing a research protocol, performing a comprehensive literature search, selecting studies, assessing study quality, extracting data, analyzing data, and addressing heterogeneity and publication bias.
3) Funnel plots and statistical tests can be used to examine potential biases like publication bias in a meta-analysis. Addressing these biases helps ensure the meta-analysis provides an accurate summary of the evidence.
1. A meta-analysis systematically combines data from multiple studies to identify patterns among study results, increase statistical power, and resolve uncertainties in areas where individual studies may be too narrow.
2. Key steps include defining the question, reviewing literature and extracting data, computing effect sizes, determining average effect sizes and confidence intervals, and looking for associations that may explain variability among studies.
3. Factors like study quality and publication bias must be considered, as missing or unpublished studies could change conclusions. Meta-analyses aim to synthesize evidence from diverse studies and elucidate general patterns.
Meta-analysis is defined as quantitatively combining and integrating the findings of multiple research studies on a particular topic. It was coined by Glass in 1976 and refers to analyzing the results of several studies that address a shared research hypothesis. The key steps in a meta-analysis involve defining a hypothesis, locating relevant studies, inputting empirical data, calculating an overall effect size by standardizing statistics, and analyzing any moderating variables if heterogeneity exists. An example provided is a meta-analysis on coping behaviors of cancer patients that would statistically analyze results from quantitative studies with similar age groups.
This document outlines the steps involved in conducting a systematic review and meta-analysis on the prevalence of elder abuse. It discusses how 52 studies from around the world were analyzed using comprehensive meta-analysis software. The key findings were that the pooled prevalence of elder abuse was 15.7%. While systematic reviews have strengths like being comprehensive and transparent, they also have limitations such as reliance on the quality of primary studies and risk of publication bias.
This meta-analysis examined the relationship between body mass index (BMI) and incident asthma. It identified 2006 relevant studies and included 12 prospective cohort studies. Inclusion criteria required adult subjects, asthma as the primary outcome, BMI measurement, minimum 1-year follow up of 70%, and BMI data categorized by standard ranges. Random effects models were used to generate summary odds ratios. Results showed overweight individuals had a 38% higher odds of developing asthma compared to normal weight, and obese individuals had 92% higher odds. When stratified by sex, the association was stronger for women. The analysis provided evidence that higher BMI is a risk factor for incident asthma.
This document provides an overview of how to conduct a systematic review and meta-analysis. It describes the key steps: (1) asking a focused clinical question using PICO, (2) acquiring relevant studies through database searches, (3) appraising the quality of included studies, (4) analyzing the data using statistical methods to obtain an overall treatment effect size, and (5) reporting results typically in a forest plot. Meta-analyses provide increased statistical power over individual studies but are not without limitations such as potential bias that must be considered when interpreting results.
Introduction to Systematic Review & Meta-Analysis Hasanain Ghazi
The document discusses systematic reviews and meta-analyses. It defines systematic reviews as a summary of available healthcare studies that provides high-level evidence on healthcare interventions. Meta-analyses use statistical methods to quantitatively summarize results across multiple studies. The document outlines the steps in conducting systematic reviews, including developing a protocol, searching for evidence, assessing risk of bias, and synthesizing findings. It also discusses how meta-analyses can help determine the strength and consistency of effects across studies.
Meta-analysis is a statistical technique used to synthesize the results of multiple scientific studies. It provides a high-level overview of the key steps in conducting a meta-analysis, which include: formulating the research question, performing a literature search and selecting studies based on eligibility criteria, extracting relevant data from the studies, using statistical methods like fixed or random effects models to calculate an overall effect, and conducting sensitivity analyses to evaluate the robustness of the results. Meta-analysis allows researchers to obtain a better understanding of how an intervention works by combining results from several studies while accounting for variability between the studies.
This document discusses different study designs used in research. It defines a study design as a specific plan for conducting a study that allows the investigator to translate a conceptual hypothesis into an operational one. The document outlines different types of study designs including descriptive studies, analytical observational studies like cross-sectional studies, case-control studies, and cohort studies, as well as experimental/interventional studies. For each study design, it provides details on the unit of study, study question, direction of inquiry, and key aspects of the design.
Basics of Systematic Review and Meta-analysis: Part 3Rizwan S A
A 4 part lecture series on the basics of Systematic Review and Meta-analysis, Part 3 discusses the software needed and analytical techniques used for this purpose.
An introduction on how to go about a meta-analysis. Primarily designed for people with non statistical background. Heavily borrows from Cochrane Handbook of Systematic Reviews of Interventions.
Randomization is the process by which allocation of subjects to treatment groups is done by chance, without the ability to predict who is in what group. It is done in clinical trials. This presentation describes the methods of randmization used in clinical trials.
This document describes different types of epidemiological study designs, including observational studies like cross-sectional, case-control, cohort, and experimental studies like randomized controlled trials. It provides details on descriptive versus analytical epidemiology and cross-sectional studies specifically. Cross-sectional studies measure prevalence at a single point in time by surveying exposures and disease status simultaneously in a population cross-section. They are useful for assessing disease burden, comparing prevalence between populations, and examining trends over time.
A systematic review is a rigorous analysis of published research on a focused question that collects and summarizes the evidence. It contrasts with an overview, which may include non-research articles and be influenced by other evidence. Meta-analysis uses statistical methods to combine results from multiple studies. To ensure validity, meta-analyses must have a well-defined methodology, including comprehensive searches and duplicate screening and data extraction to reduce bias. Important factors include assessing whether all relevant studies were found and the sources searched, as well as controlling for biases such as from selective data extraction or funding influences.
The document discusses different study designs used in research, including observational studies like case reports, case series, cross-sectional studies, and cohort studies, as well as experimental studies like randomized controlled trials. It describes the key characteristics and advantages and disadvantages of each design. The highest level of evidence comes from randomized controlled trials, while observational studies are useful for initial hypothesis generation.
Methods of randomisation in clinical trialsAmy Mehaboob
Randomized clinical trials are the gold standard for evaluating medical treatments. Randomization involves randomly assigning participants to treatment groups using chance to prevent bias. Common randomization methods include simple randomization by shuffling envelopes, block randomization which assigns participants in blocks to balance groups, and stratification which randomizes within subgroups. Sample size must be adequately powered and randomization methods should conceal group assignments to prevent bias and ensure validity.
This document discusses bias and validity in clinical research. It defines clinical epidemiology as the study of health-related states and events in populations to control health problems. It describes how epidemiologic studies compare outcomes like disease rates between exposed and unexposed groups. Validity is important, with internal validity indicating good construct free from bias/errors, and external validity showing generalizability. Bias and confounding can threaten validity and lead to erroneous associations if not avoided or controlled for.
Cross-sectional studies examine the relationship between a disease and exposure in a population at a single point in time. They provide a snapshot of disease prevalence and exposure prevalence simultaneously. While they can describe disease burden and identify potential risk factors, the temporal relationship between exposure and disease is unclear since they involve simultaneous rather than longitudinal measurement.
Bias in research can occur at any stage from study design to publication. There are several types of bias including selection bias, information bias, and confounding bias. Selection bias occurs when the study sample is not representative of the target population. Information bias results from errors in measuring or classifying exposure and outcome variables. Confounding bias is introduced when a third variable is associated with both the exposure and outcome. Researchers should employ techniques like randomization, matching, and restriction to minimize bias.
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.
Randomisation is a process that randomly assigns participants in a clinical trial to treatment groups in order to prevent bias. It distributes characteristics of participants evenly across groups and ensures comparability. Common randomisation methods include simple randomisation using a coin flip or computer generation, block randomisation which assigns participants in blocks to balance group sizes, and stratified randomisation which divides participants with prognostic factors into subgroups before randomisation. Bias can still occur if the randomisation process is not properly implemented or if those involved in the trial are aware of participant group assignments.
Research Methodology - Case control studyRizwan S A
This document discusses case control studies, an observational study design that compares individuals with a disease or condition (cases) to individuals without the disease or condition (controls) to determine associations between exposures and disease outcomes. It provides an overview of key elements of case control studies, including the selection and matching of cases and controls, measurement of exposure, analysis using odds ratios, potential biases, advantages and disadvantages compared to cohort studies, and examples of case control studies conducted.
This document provides an overview of meta-analysis, including:
1) Meta-analysis is a statistical method for combining results from multiple studies to obtain a single estimate of effect. It provides a more precise estimate than individual studies.
2) Proper meta-analyses require a detailed protocol and eligibility criteria. Studies must be carefully selected and data extracted by multiple independent reviewers.
3) Results are typically reported as odds ratios, risk ratios, or mean differences along with confidence intervals. Forest plots visually display results and heterogeneity between studies.
Meta-analysis in Epidemiology is:
Useful tool for epidemiological studies which investigates the relationships between certain risk factors and disease.
Useful tool to improve animal well-being and productivity
Despite of a wealth of suitable studies it is relatively underutilized in animal and veterinary science.
Meta-analysis can provide reliable results about diseases occurrence, pattern and impact in livestock.
It is utmost essential to take benefit of this statistical tool for produce. more reliable estimates of concern effects in animal and veterinary science data.
Meta analysis - qualitative research designDinesh Selvam
Meta-analysis is a statistical technique that combines the results of multiple quantitative studies on a topic to draw overall conclusions. Key studies are entered into a database and analyzed similarly to other data to test hypotheses. Meta-analysis provides a systematic overview that can increase power, resolve uncertainty, and address questions not originally posed. It involves carefully selecting and evaluating relevant studies, extracting common measures, and performing analyses to interpret overall results. Meta-analysis is appropriate when multiple studies test similar hypotheses or produce contradictory findings.
This meta-analysis examined the relationship between body mass index (BMI) and incident asthma. It identified 2006 relevant studies and included 12 prospective cohort studies. Inclusion criteria required adult subjects, asthma as the primary outcome, BMI measurement, minimum 1-year follow up of 70%, and BMI data categorized by standard ranges. Random effects models were used to generate summary odds ratios. Results showed overweight individuals had a 38% higher odds of developing asthma compared to normal weight, and obese individuals had 92% higher odds. When stratified by sex, the association was stronger for women. The analysis provided evidence that higher BMI is a risk factor for incident asthma.
This document provides an overview of how to conduct a systematic review and meta-analysis. It describes the key steps: (1) asking a focused clinical question using PICO, (2) acquiring relevant studies through database searches, (3) appraising the quality of included studies, (4) analyzing the data using statistical methods to obtain an overall treatment effect size, and (5) reporting results typically in a forest plot. Meta-analyses provide increased statistical power over individual studies but are not without limitations such as potential bias that must be considered when interpreting results.
Introduction to Systematic Review & Meta-Analysis Hasanain Ghazi
The document discusses systematic reviews and meta-analyses. It defines systematic reviews as a summary of available healthcare studies that provides high-level evidence on healthcare interventions. Meta-analyses use statistical methods to quantitatively summarize results across multiple studies. The document outlines the steps in conducting systematic reviews, including developing a protocol, searching for evidence, assessing risk of bias, and synthesizing findings. It also discusses how meta-analyses can help determine the strength and consistency of effects across studies.
Meta-analysis is a statistical technique used to synthesize the results of multiple scientific studies. It provides a high-level overview of the key steps in conducting a meta-analysis, which include: formulating the research question, performing a literature search and selecting studies based on eligibility criteria, extracting relevant data from the studies, using statistical methods like fixed or random effects models to calculate an overall effect, and conducting sensitivity analyses to evaluate the robustness of the results. Meta-analysis allows researchers to obtain a better understanding of how an intervention works by combining results from several studies while accounting for variability between the studies.
This document discusses different study designs used in research. It defines a study design as a specific plan for conducting a study that allows the investigator to translate a conceptual hypothesis into an operational one. The document outlines different types of study designs including descriptive studies, analytical observational studies like cross-sectional studies, case-control studies, and cohort studies, as well as experimental/interventional studies. For each study design, it provides details on the unit of study, study question, direction of inquiry, and key aspects of the design.
Basics of Systematic Review and Meta-analysis: Part 3Rizwan S A
A 4 part lecture series on the basics of Systematic Review and Meta-analysis, Part 3 discusses the software needed and analytical techniques used for this purpose.
An introduction on how to go about a meta-analysis. Primarily designed for people with non statistical background. Heavily borrows from Cochrane Handbook of Systematic Reviews of Interventions.
Randomization is the process by which allocation of subjects to treatment groups is done by chance, without the ability to predict who is in what group. It is done in clinical trials. This presentation describes the methods of randmization used in clinical trials.
This document describes different types of epidemiological study designs, including observational studies like cross-sectional, case-control, cohort, and experimental studies like randomized controlled trials. It provides details on descriptive versus analytical epidemiology and cross-sectional studies specifically. Cross-sectional studies measure prevalence at a single point in time by surveying exposures and disease status simultaneously in a population cross-section. They are useful for assessing disease burden, comparing prevalence between populations, and examining trends over time.
A systematic review is a rigorous analysis of published research on a focused question that collects and summarizes the evidence. It contrasts with an overview, which may include non-research articles and be influenced by other evidence. Meta-analysis uses statistical methods to combine results from multiple studies. To ensure validity, meta-analyses must have a well-defined methodology, including comprehensive searches and duplicate screening and data extraction to reduce bias. Important factors include assessing whether all relevant studies were found and the sources searched, as well as controlling for biases such as from selective data extraction or funding influences.
The document discusses different study designs used in research, including observational studies like case reports, case series, cross-sectional studies, and cohort studies, as well as experimental studies like randomized controlled trials. It describes the key characteristics and advantages and disadvantages of each design. The highest level of evidence comes from randomized controlled trials, while observational studies are useful for initial hypothesis generation.
Methods of randomisation in clinical trialsAmy Mehaboob
Randomized clinical trials are the gold standard for evaluating medical treatments. Randomization involves randomly assigning participants to treatment groups using chance to prevent bias. Common randomization methods include simple randomization by shuffling envelopes, block randomization which assigns participants in blocks to balance groups, and stratification which randomizes within subgroups. Sample size must be adequately powered and randomization methods should conceal group assignments to prevent bias and ensure validity.
This document discusses bias and validity in clinical research. It defines clinical epidemiology as the study of health-related states and events in populations to control health problems. It describes how epidemiologic studies compare outcomes like disease rates between exposed and unexposed groups. Validity is important, with internal validity indicating good construct free from bias/errors, and external validity showing generalizability. Bias and confounding can threaten validity and lead to erroneous associations if not avoided or controlled for.
Cross-sectional studies examine the relationship between a disease and exposure in a population at a single point in time. They provide a snapshot of disease prevalence and exposure prevalence simultaneously. While they can describe disease burden and identify potential risk factors, the temporal relationship between exposure and disease is unclear since they involve simultaneous rather than longitudinal measurement.
Bias in research can occur at any stage from study design to publication. There are several types of bias including selection bias, information bias, and confounding bias. Selection bias occurs when the study sample is not representative of the target population. Information bias results from errors in measuring or classifying exposure and outcome variables. Confounding bias is introduced when a third variable is associated with both the exposure and outcome. Researchers should employ techniques like randomization, matching, and restriction to minimize bias.
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.
Randomisation is a process that randomly assigns participants in a clinical trial to treatment groups in order to prevent bias. It distributes characteristics of participants evenly across groups and ensures comparability. Common randomisation methods include simple randomisation using a coin flip or computer generation, block randomisation which assigns participants in blocks to balance group sizes, and stratified randomisation which divides participants with prognostic factors into subgroups before randomisation. Bias can still occur if the randomisation process is not properly implemented or if those involved in the trial are aware of participant group assignments.
Research Methodology - Case control studyRizwan S A
This document discusses case control studies, an observational study design that compares individuals with a disease or condition (cases) to individuals without the disease or condition (controls) to determine associations between exposures and disease outcomes. It provides an overview of key elements of case control studies, including the selection and matching of cases and controls, measurement of exposure, analysis using odds ratios, potential biases, advantages and disadvantages compared to cohort studies, and examples of case control studies conducted.
This document provides an overview of meta-analysis, including:
1) Meta-analysis is a statistical method for combining results from multiple studies to obtain a single estimate of effect. It provides a more precise estimate than individual studies.
2) Proper meta-analyses require a detailed protocol and eligibility criteria. Studies must be carefully selected and data extracted by multiple independent reviewers.
3) Results are typically reported as odds ratios, risk ratios, or mean differences along with confidence intervals. Forest plots visually display results and heterogeneity between studies.
Meta-analysis in Epidemiology is:
Useful tool for epidemiological studies which investigates the relationships between certain risk factors and disease.
Useful tool to improve animal well-being and productivity
Despite of a wealth of suitable studies it is relatively underutilized in animal and veterinary science.
Meta-analysis can provide reliable results about diseases occurrence, pattern and impact in livestock.
It is utmost essential to take benefit of this statistical tool for produce. more reliable estimates of concern effects in animal and veterinary science data.
Meta analysis - qualitative research designDinesh Selvam
Meta-analysis is a statistical technique that combines the results of multiple quantitative studies on a topic to draw overall conclusions. Key studies are entered into a database and analyzed similarly to other data to test hypotheses. Meta-analysis provides a systematic overview that can increase power, resolve uncertainty, and address questions not originally posed. It involves carefully selecting and evaluating relevant studies, extracting common measures, and performing analyses to interpret overall results. Meta-analysis is appropriate when multiple studies test similar hypotheses or produce contradictory findings.
This document provides guidance on how to conduct a meta-analysis. It outlines the basic 4 step process: 1) identifying relevant studies, 2) determining study eligibility, 3) abstracting data from eligible studies, and 4) analyzing the data statistically. Statistical analysis includes calculating effect sizes, confidence intervals, heterogeneity tests, and creating forest and funnel plots. Limitations of meta-analyses like bias and model selection are also discussed. Finally, it lists popular databases for searching literature and statistical software options for conducting the analyses.
Modern B2B Marketing in the Era of the Empowered BuyerScott Levine
In the past 20 years, B2B marketing has changed exponentially more than it has in the past 100 years. Today, buyers possess infinite choice and infinite power.
We live in an “instant” world. Whether it’s instant access, information, gratification, justification or rationalization, all of these “instants” have impacted the way that businesses who market to businesses, think, act and react.
Modern Business-to-Business Marketing in the Era of the Empowered Buyer is causing many organizations to rethink their strategies. We are hopeful that some of the information we have shared with you here will enable you to understand the current and possible future state of B-to-B marketing, and will help you to put your organization in a position to best deal with complexities caused by the Modern Empowered B-to-B Buyer.
Amanda burls ppt teaching materials for publication biasAmanda Burls
Teaching materials explaining publication bias. For use with students with no prior knowlede or previous training in critical appraisal skills. It explains both failure to report and selective reporting. The notes beneath each slide give suggestions of how the materials can be used in class. The examples are real example e.g. a trial of activia yoghurt versus yoghurt without live probiotics for irritable bowel syndrome (IBS),
Basic slides will take only 5 minutes in a presentation or the materials can be used in a full one hour interactive workshop. These were put together in 2016 for www.testingtreatments.org which helps explain a fair test of treatment. They are particularly useful for teaching the critical appraisal of systematic reviews.
Systematic Review Of Observational Studies By Yusuf Abdu MisauYusuf Misau
This document provides background information on a proposed systematic review and meta-analysis being conducted by Dr. Yusuf Abdu Misau on factors associated with delayed testing and presentation among antiretroviral naive HIV patients. It outlines the objectives to assess causes and effects of late presentation, describes the study design as a systematic review and meta-analysis of observational studies, and discusses the public health significance of identifying factors leading to late presentation to improve HIV patient prognosis and prevention.
Summary slides for "Systematic Review and Meta-Analysis Course for Healthcare Professionals", January 8-9, 2013, King Abdullah Medical City, Makkah, Saudi Arabia
http://KAMCResearch.org
This document appears to be the board for a trivia game where players take turns throwing a dice and answering questions related to where they land on the board. The rules state that players who answer correctly can move ahead additional spaces, multiple players can occupy the same space, and the objective is to be the first player to reach the finish space while following the movement instructions on each space.
HEALTHCARE RESEARCH METHODS: Secondary and tertiary StudiesDr. Khaled OUANES
Secondary analyses are based on the use of pre-existing data sets and usually the researcher conducting the statistical analysis has not had any contact with the participants whose data are being examined.
A systematic review is, on the other hand, the thorough compilation and summary of all publications relevant to a particular research topic.
Chapter 2: Legal English. Features and exercisesegonzalezlara
This document discusses legal English and its translation into Spanish. It notes that legal English is characterized by density and obscurity, making it difficult for ordinary people to understand contracts, wills, and legal decisions. This has led to the "Plain English Campaign" which advocates making legal texts understandable to all. The document then outlines some of the key linguistic features of legal English, including its use of Latin and French words, formal register, redundancies, and complex syntax. It also discusses "false friends" where words have different meanings in English and Spanish legal contexts.
This document provides a 10 minute lecture on forest plots, which are visual representations of statistical analyses that allow results from multiple studies to be compared. The lecture discusses how forest plots can be used in different contexts such as analyzing the effectiveness of pre-exposure prophylaxis for HIV prevention, comparing survival rates of cancer patients using different treatments, and assessing the sensitivity and specificity of commercial PCR tests for tuberculosis meningitis. Examples of cumulative meta-analysis plots are presented from studies on these topics.
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.
The document discusses some key characteristics of legal English discourse. It notes that legal discourse involves specialized language used in professional and institutional settings like courts. There are different types of legal discourse depending on context, such as language between lawyers and clients, language used in courts, and language found in legal documents and academic texts. Some other characteristics include the use of archaic words, Latin terms, repetitive structures, long complex sentences, passive voice constructions, and an impersonal style.
This document discusses how to interpret a forest plot used in a meta-analysis. A forest plot visually displays the results of individual studies and the overall meta-analysis. It shows the odds or risk ratio for each study with confidence intervals, along with a diamond representing the combined results. The location of the diamond in relation to the line of no effect indicates whether the overall effect is statistically significant. Heterogeneity between studies is also assessed using the forest plot and quantitative measures.
This document provides information about conducting and appraising a meta-analysis on the use of prophylactic antibiotics for pancreatic necrosis. It outlines the steps of formulating the clinical question using PICO, acquiring relevant studies through database searches and hand searches, appraising study quality, collecting and recording study data, analyzing results using both individual and pooled treatment effects, and reporting findings in a forest plot. Key aspects of meta-analysis methodology are discussed including biases that can affect results.
This workshop is meant to be an introduction to the systematic review process. Further information about systematic reviews was available through a research guide. http://libguides.ucalgary.ca/content.php?pid=593664
A systematic review is a literature review focused on answering a specific question by identifying, appraising, selecting, and synthesizing high-quality research evidence relevant to that question. It follows a rigorous methodology to overcome bias, including formulating a research question, conducting a comprehensive literature search, applying inclusion/exclusion criteria, assessing study quality, and analyzing results. The results are often combined using meta-analysis to provide a quantitative summary of effects across multiple studies.
How to handle discrepancies while you collect data for systemic review – pubricaPubrica
1. Population specification error:
2. Sample error:
3. Selection error:
4. Non- response error:
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A meta-analysis is the use of statistical methods to summaries the results of the studies. Meta-analyses are conducted to assess the strength of evidence present on a disease and treatment. The results of a meta-analysis can improve precision of estimates of effect, answer questions not posed by the individual studies, settle controversies arising from apparently conflicting studies, and generate new hypotheses. In particular, the examination of heterogeneity is vital to the development of new hypotheses.
Systematic reviews employ rigorous systematic methods to identify and synthesize data from multiple studies to obtain a quantitative summary of the effects of an intervention. This involves formulating clear objectives and criteria for inclusion of studies, assessing methodological quality, extracting data, and presenting results both descriptively and through meta-analysis to obtain a pooled effect estimate. Conducting systematic reviews using these standardized methods helps establish whether research findings are consistent and generalizable across studies.
Systematic reviews, rapid reviews, and scoping reviews are all types of literature reviews but differ in their methods and objectives. Systematic reviews have a narrow question and use comprehensive searches and selection criteria to minimize bias. They analyze available studies to answer a specific question. Rapid reviews have time constraints and omit some systematic review stages to provide timely information. Scoping reviews have broader questions and identify the nature and scope of research on a topic, including identifying gaps. They involve iterative searches and selection and usually do not critically appraise studies.
This document provides an overview of research methods and statistical concepts. It discusses research design types including descriptive, historical, and experimental. Experimental design can be true experiments or quasi-experiments. It also discusses quantitative and qualitative research approaches and mixed methods. Key statistical concepts are defined, such as population, sample, probability and non-probability sampling, and levels of measurement. Common statistical tests are introduced along with important assumptions. The document provides guidance on how to measure learning experimentally using different research designs. It also discusses how to determine appropriate sample sizes and select statistical analyses based on the research questions.
Pandemic Preparedness Results and Recommendations.pdfbkbk37
This chapter discusses the findings and recommendations from a study on pandemic preparedness. The study used a cohort study design to assess preparedness levels in local hospitals. A questionnaire was administered to emergency management coordinators to collect data on facility planning, workforce capacity, and surge capacity. Qualitative data was also collected through interviews. The results showed both strengths and limitations in pandemic plans and capacity. Recommendations include continued planning and identification of gaps to improve readiness for future pandemics.
Advanced Statistical Methods in Meta-analysis Enhancing Accuracy, Reliability...pubrica101
Meta-analysis is a powerful statistical method that combines the results of multiple studies to provide more reliable estimates of the effects of various interventions or treatments. If you're looking for expert meta-analysis services in the pharmaceutical industry, Pubrica is here to help.
Advanced Statistical Methods in Meta-analysis Enhancing Accuracy, Reliability...pubrica101
meta-analysis is a valuable research approach that can provide insightful findings. By using advanced statistical techniques such as network meta-analysis, subgroup analysis, and sensitivity analysis, researchers can seek hidden trends and identify sources of heterogeneity. Researchers need to stay up to date with the latest advancements in statistical methods to incorporate them into their meta-analysis studies. With the help of these techniques, researchers can achieve greater accuracy and reliability in their findings, ultimately contributing to the advancement of their respective fields.
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 and Qualitative Approaches.pdfssuser504dda
This document provides an overview of quantitative and qualitative research approaches. It defines quantitative research as deductive, using numeric data from large samples to test hypotheses and analyze relationships between variables objectively. Qualitative research is defined as inductive, relying on words from smaller samples to understand participant experiences subjectively and identify themes in the data. The key differences between the two approaches are described in terms of identifying research problems, reviewing literature, specifying research purposes and questions, collecting and analyzing data, and reporting results. The document also discusses research design and types of quantitative, qualitative, and mixed methods designs.
Pubrica's team of researchers and authors develop Scientific and medical research papers that can act as an indispensable tool to the practitioner/authors. Here is how we help.
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.
This document outlines the key steps and considerations for writing a dissertation in data analytics, including identifying patterns in data, deriving insights, developing predictive models, and using models to make decisions. It emphasizes that dissertations should apply these analytical activities to address real-world problems or opportunities. The dissertation should demonstrate that the stated research was actually conducted and convincingly report the solutions found. Various research methods, tools, types of data, and analytical project types are also discussed.
PUB- Advanced Statistical Methods in Meta-analysis Enhancing Accuracy, Reliab...pubrica101
Researchers use meta-analysis to combine data from various studies to gain a complete understanding of a topic. This approach enhances the reliability of conclusions, as it involves information from multiple sources.
This methodological guidance article discusses the elements of a high-quality meta-analysis that is conducted within the context of a systematic review.
Meta-analysis, a set of statistical techniques for synthesizing the results of
multiple studies, is used when the guiding research question focuses on a
quantitative summary of study results. In this guidance article, we discuss the
systematic review methods that support high-quality meta-analyses and outline best practice meta-analysis methods for describing the distribution of
effect sizes in a set of eligible studies. We also provide suggestions for transparently reporting the methods and results of meta-analyses to influence
practice and policy. Given the increasing use of meta-analysis for important
policy decisions, the methods and results of meta-analysis should be both
transparent and reproducible.
Keywords: meta-analysis, systematic review
This document discusses different methods of data collection. It defines data collection as the process of systematically gathering and measuring information on variables of interest in order to answer research questions and test hypotheses. The two main types of data are qualitative and quantitative. Qualitative data is non-numerical, descriptive data often in the form of words, while quantitative data is numerical and can be mathematically computed. Common qualitative methods include interviews and focus groups, while quantitative methods include surveys, experiments, and observational studies. The document also discusses mixed methods research, which combines qualitative and quantitative approaches.
This document provides an introduction to critical appraisal. It defines critical appraisal as systematically weighing the quality and relevance of research to inform decision making. The document outlines different types of research studies including systematic reviews, randomized controlled trials, cohort studies, and case-control studies. It discusses how to critically appraise studies by assessing their validity, results, and relevance. Key aspects of appraising randomized controlled trials are described such as randomization, blinding, accounting for all participants, and interpreting results including p-values and confidence intervals. The goal is to help readers gain skills to critically evaluate research.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
2. USE OF MOBILE
PHONES LEAD TO
NEUROMASGERMANY
DRINKING RED WINE
IS BENEFICIAL TO
HEALTH-US STUDY
CHOCOLATES
ARE HARMFUL
SOFT DRINKS
ARE SAFE
SOFT DRINKS
CONTAIN HARMFUL
PESTICIDES
RED WINE IS
HARMFUL-US
CHOCOLATES ARE
GOOD FOR HEART
MOBILE PHONES
CAUSE NO HARMEUROPE
4. Meta-analysis
A meta-analysis is a quantitative study wherein a
set of statistical procedure is used to summarize
and synthesize results of a number of
independently conducted research studies. If
done well it can be very valuable to a researchers
, because it provides an extensive bibliography of
existing research on a topic ,while also providing
a combined analysis of a number of study.
Meta-analysis
is
a
very
time
consuming, understanding and usually conducted
by a team of researchers.
5. Thus, Meta Analysis is A Quantitative approach
For
systematically combining
the results of
previous researches
to
arrive at conclusions
about
the body of research.
6. What does it mean?
Quantitative
: numbers
Systematic
: methodical
Combining
: putting
together
Previous research
: already
done
Conclusions
: new
7. Important
1- MA falls under a broader classification of
reviews known as Systematic Reviews
2- There are two types of SRs –
(a) Qualitative
(b) Quantitative (meta-analysis)
8. Important
Both follow the same rigorous steps, EXCEPT that
a qualitative review does not combine the
endpoints for statistical analysis, usually
because it’s not appropriate to combine them
into any type of common Metric.
9. The Etymology
"Meta" implies something occurring
later, more comprehensive.
Alternative terms are less specific — for
example, "overview" is also used for
traditional reviews, & "pooling" incorrectly
implies that the source data are merged .
10. Historical notes
Karl Pearson (1904) - Use of formal
techniques to combine data from
different samples.
Glass (1976) coined the term metaanalysis
11. Rationale
“by combining the samples
of the individual studies,
the overall sample size is increased, thereby
improving the statistical power of the analysis
as well as the
precision of the estimates
of treatment effects”
12. Objectives
The benefits or hazards that might not
be detected in small studies can be
found .
Integrating the findings.
Identifying the treatment effect (or
effect size) when it is consistent from
one study to the next.
Identifying the reason for the variation
when the effect varies from one study
to the next .
13. Importance MA
Too much scientific information.
A Well-informed clinical decision is difficult to
reach, time consuming & cost-ineffective.
Decisions about the utility of an intervention or
the validity of a hypothesis can’t be based on
results of a single study.
Information from many studies with less effort
& hassle .
14. Advantages of MA
Saves effort and time
Increases sample size – Gain in
statistical power by reducing Random
errors.
Enhances reliability (precision) &
accuracy (validity).
Explores & Reduces bias.
15.
Resolves controversies &conflicting
reports
Identifies crucial areas & questions
that have not been adequately
addressed with past research.
Generalizes study results.
May explain heterogeneity & its
sources between the results of individual
studies.
16.
Answers questions about whether
an overall study result varies
among subgroups—for
example, among men and
women, older and younger
patients, or subjects with different
degrees of severity of disease.
Reproducible numerical values –
no place of unhelpful descriptors such
as "no relation," "some evidence of a
trend”, "a weak relation," and "a
17. Applications OF M A
Clinicians & applied researchers - to
determine which interventions
work, and which ones work best.
Basic research - to evaluate the
evidence in diverse areas.
Planning new studies.
Some funding agencies now require it
as part of the grant application to fund
new research.
18. CAUTION
Not used or meaningless, when –
1. Studies are different in terms of their
population, intervention or how outcomes were
measured.
2. Treatments, evaluated in the individual
are different.
studies
3. The findings of individual studies differ significantly
– because combining widely differing results to
produce an average effect would fail to represent the
great variation in the outcomes .
19. Types of MA
1. Literature-based MA (LBMA)
- most frequent type of MA
- may be misleading as Data extraction
&
analysis may be less accurate
2. Individual patient data MA (IPDMA)
- Gold-standard, but has problems
- inability of investigators to supply data
- the increased costs
20. Principles of MA
1-The need to consider the totality of evidence.
2-Requirement for Reproducibility
Transparent, explicit & systematic approach.
3-Principles of reliable detection of the effects of
health care interventions
21. The Process
The process simply involves:
1. Calculation of the treatment effect i.e. OR/RR.
2. Calculation of the 95% Confidence interval
around the individual OR/RR.
3. Giving a weight to the individual OR/RR (shown
as the size of the box in the forest plot). The
weight is calculated as the inverse of the square
of the standard error of each OR/RR (1/SE2).
22. How do we conduct MA
1.
2.
3.
4.
5.
6.
7.
Write a protocol – the blueprint
Locating relevant studies
Selecting & appraising studies for inclusion
Data extraction from selected studies
Statistical methods to combine the effect
measures extracted from primary studies
Addressing biases and limitations
Results in graphical form – the Forest Plot
23. How do we conduct MA
1.
2.
3.
4.
5.
6.
7.
Write a protocol – the blueprint
Locating relevant studies
Selecting & appraising studies for inclusion
Data extraction from selected studies
Statistical methods to combine the effect
measures extracted from primary studies
Addressing biases and limitations
Results in graphical form – the Forest Plot
24. Recipe of a good protocol
1.
2.
3.
4.
5.
6.
7.
8.
Purpose of meta-analysis.
Design a research question.
Search for studies.
Specify study selection (inclusion & exclusion) &
appraisal criteria.
Decide data extraction procedures (including statistical
reanalysis).
Select an analytical strategy (use of models & sensitivity
analysis).
Anticipate systematic errors (biases)/limitations.
Present & disseminate results .
25. How do we conduct MA
1.
2.
3.
4.
5.
6.
7.
Write a protocol – the blueprint
Locating relevant studies
Selecting & appraising studies for inclusion
Data extraction from selected studies
Statistical methods to combine the effect
measures extracted from primary studies
Addressing biases and limitations
Results in graphical form – the Forest Plot
26. Locating relevant studies
Systematic approach.
Primary objective – Strategically locate as
much of the completed research on the topic
as possible.
Document strategy in sufficient detail to allow
others to critique it’s quality.
Usually include e-databases
(MEDLINE, CINAHL, Psyclit, Embase, Cochrane
Library) and others
27. How do we conduct MA
1.
2.
3.
4.
5.
6.
7.
Write a protocol – the blueprint.
Locating relevant studies.
Selecting & appraising studies for inclusion .
Data extraction from selected studies.
Statistical methods to combine the effect
measures extracted from primary studies
Addressing biases and limitations.
Results in graphical form – the Forest Plot.
28. Selecting & appraising studies for
inclusion
Selecting - Judge the relevance of the studies to the
review question.
Appraising - Judge numerous features of design &
analysis .
Methodical, impartial and reliable strategies are
necessary as MA are retrospective exercises & are
therefore susceptible to both random & systematic
sampling errors .
Rationale - by excluding lesser quality studies
the risk of error/bias will be lessened.
29. How do we conduct MA
1.
2.
3.
4.
5.
6.
7.
Write a protocol – the blueprint.
Locating relevant studies.
Selecting & appraising studies for inclusion.
Data extraction from selected studies.
Statistical methods to combine the effect
measures extracted from primary studies.
Addressing biases and limitations.
Results in graphical form – the Forest Plot.
30. Data extraction
Eligibility criteria for inclusion of data .
Data collection in standardized record form.
2 independent observers extract the data, to
avoid errors.
Blinding observers to the names of the
authors, their institutions, the names of the
journals, sources of funding, and
acknowledgments leads to more consistent
scores.
31. How to Extract Data
Create a spreadsheet (Excel)
For each study, create the following columns:
name
of the study
name of the author, year published
number of participants who received intervention
number of participants who were in control arm
number who developed outcomes in intervention
number who developed outcomes in control arm
32. We got like 22 studies to do our meta analysis, after all
33. How do we conduct MA
1.
2.
3.
4.
5.
6.
7.
Write a protocol – the blueprint
Locating relevant studies
Selecting & appraising studies for inclusion
Data extraction from selected studies
Statistical methods to combine the effect
measures extracted from primary studies
Addressing biases and limitations
Results in graphical form – the Forest Plot
34. Statistical Methods
They attempt to answer basic questions:
(a) Are results of different studies similar?
-Check for heterogeneity.
(b) To what extent that they are similar?
-Calculate the amount of heterogeneity.
(c) What is the best overall estimate?
- Combine the effect measures using suitable model
& calculate the summary effect size & its CI.
(d) How precise & robust is this estimate?
- Do Sensitivity Analysis.
(e) Finally, can dissimilarities be explained?
35.
Heterogeneity
if
present, should not simply be
ignored after a statistical test is
applied; rather, it should be
scrutinized and explained.
More weight is given to –
(a) larger trials
(b) Studies with narrow CI
36. • Assess the heterogeneity of effect size
across the studies
• Decide the type of model for combining
the effect size of all studies.
• 2 models to adjust the potential
confounding effects of study –
(1) Fixed Effect model .
(When the combined trials are a homogeneous set)
(2) Random Effect model.
(When heterogeneity is detected)
37. How do we conduct MA
1.
2.
3.
4.
5.
6.
7.
Write a protocol – the blueprint.
Locating relevant studies.
Selecting & appraising studies for inclusion.
Data extraction from selected studies.
Statistical methods to combine the effect
measures extracted from primary studies.
Addressing biases and limitations .
Results in graphical form – the Forest Plot.
39. Publication Bias
Synthesis of published data can yield an
exaggerated effect as studies that yield relatively
large/beneficial treatment are more likely to
publish .
English language bias, citation bias, & multiple
publication bias- In English ,studies are more likely
to be cited, and more likely to be published
repeatedly.
40.
High likelihood of publishing –
- Studies sponsored by government or NGO.
- Multi-centric studies.
Many authors may not submit studies with
negative findings because they anticipate
rejection.
41. Other Biases
Selection/Inclusion Bias-Manipulation of the
inclusion criteria could lead to selective inclusion
of studies with positive findings.
Database bias – Selective inclusion of studies
from developed countries.
Citation bias – Ease of locating and contacting
authors from reference lists.
Data provision Bias – due to willingness of
investigators to make their data available.
42. Testing for bias – Funnel Plot
The presence of bias should be examined in
sensitivity analyses and funnel plots.
Funnel plot – It is graphical test for any type of
bias that is associated with sample size.
Results from small studies will scatter widely at the
bottom of the graph. The spread will narrow as
precision increases among larger studies.
In the absence of bias, the plot should thus
resemble a symmetrical inverted funnel.
If the plot shows an asymmetrical & skewed
shape, bias may be present .
43. Funnel Plot: what & how to read
To study a funnel plot, look at
its LOWER LEFT
corner, that’s where negative
or null studies are located
If EMPTY, this indicates
“PUBLICATION BIAS”
Note that here, the plot fits in
a funnel, and that the left
corner is not all that
empty, but we cannot rule out
publication bias
44.
45. How do we conduct MA
1.
2.
3.
4.
5.
6.
7.
Write a protocol – the blueprint.
Locating relevant studies.
Selecting & appraising studies for inclusion.
Data extraction from selected studies.
Statistical methods to combine the effect
measures extracted from primary studies.
Addressing biases and limitations
Results in graphical form – the Forest Plot.
46. Forest plots
Effective way of presenting results:
Studies, effect sizes, confidence intervals
Provides an overview of consistency of
effects
Summarizes an overall effect (with
confidence interval)
Useful visual model of a meta-analysis
47. Anatomy of a forest plot…
Study effect size (with C.I.)
N of study
Line of no
effect
C.I
Studies
Weighting
of study
in metaanalysis
Study
effect
size
Pooled
effect
size
Pooled effect size
48.
When individual studies are inconclusive
deficient or its not possible to do Multicentric
RCT , Money problem ,Time nahi mila.
49.
50.
51.
52.
53.
54. Weighting studies
56
More weight to the studies which give us
more information
More participants
More events
Lower variance
Weight is closely related to the width of the
study confidence interval: wider confidence
interval = less weight
55. EFFECT OF β-BLOCKADE AFTER MI
1-
3+
SG1 = MIX POP
5+
SG3 =
GERMAN POP
2+
SG2 = MIX POP
4+
SG5 = MIX POP
SG4 = MIX POP
META ANALYSIS
BENEFICIAL EFFECT OF β-BLOCKADE
13+
56. Limitations of MA
Can’t improve the quality or reporting of the
original studies.
Limitations arising from mis-applications:
- when study diversity is ignored or
mishandled , and
- when variability of patient populations’,
quality of data & potential for underlying
biases are not addressed.
Publication bias is a major limitation.
57.
Some clinicians consider it as "a tool that has
become a weapon” & which represents "the
unacceptable face of statisticism" & "should be
stifled at birth”
At the other end of the spectrum, the application
has been hailed “Newtonian”.
Some reject and see it as "mega-silliness”
58. MA continues to be controversial
technique ?
The mixed reception is not surprising
The pooling of results from a particular set of
studies may be inappropriate from a clinical
point of view, producing a population "
average" effect.
Meta-analyses of the same issue may reach
opposite conclusions.
59. Still, Meta Analyses hold promise….
If original studies of the effects of clot busters
after heart attacks had been systematically
reviewed, the benefits of therapy would have
been apparent as early as the mid-1970s.
Traditional approaches were inadequate in
summarizing the current state of knowledge &
omitted mention of effective therapies.
60. Popularity of Meta Analyses
3000
2500
Number of Publications
2000
1500
1000
500
0
93-94
94-95
95-96
96-97
97-98
98-99
99-00
Year of Publications
2000-1
2001-2
2002-3
2003-4
61.
62. Refrences
Lipsey, M.W., Wilson, D. B. Practical meta-analysis. Thousand Oaks, CA Sage; (Applied Social
Research Methods Series; 49), 2001.
Petitti, D. B. Meta-analysis, decision analysis, and cost-effectiveness analysis: Methods for
quantitative synthesis in medicine (2nd ed.). New York Oxford University Press; 2000.
Sutton AJ, Abrams KR, Jones DR, Sheldon TA, Song F. Methods of meta-analysis in
medical research. West Sussex, England Wiley, 2000. The Handbook of Research SynthesisHarris Cooper & L Hedges
Statistical Methods for Meta-Analysis – L Hedges & I OlkinLike
Practical Meta-Analysis - Mark Lipsey and David
Systematic Reviews in Health Care: Meta-Analysis in context - M Egger, G Davey-Smith, D G
Altman, Foreword by Iain
Methods for Meta-Analysis in Medical Research- AJ Sutton, K R Abrams, DR Jones, TA Sheldon, F
SongLike
Meta-Analysis in Medicine and Health Policy - DK Stangl, DBerry
Publication Bias in Meta-Analysis - H Rothstein, A Sutton, M Borenstein
How Science Takes Stock: The Story of Meta-Analysis - Morton
Methods of Meta-Analysis: Correcting Error and Bias in Research Findings - John E. Hunter and
Frank L. Schmidt
Synthesizing Research - A Guide for Literature Reviews - Harris
Meta-Analysis of Controlled Trials - Anne Whitehead