Systematic review and meta analysis is considered as the highest body of evidence in research evidence hierarchy. Often misunderstood or skipped over, this powerful tool can broaden our understanding on a specific topic and form basis of practicing evidence based medicine for us.
I presented systematic review and meta analysis as part of my PG seminar and got a good feedback. Now I wanted to share the presentation for a broader audience.
Any kind of constructive feedback is welcome.
Dr. Anik Chakraborty
JR3, Dept. Of Community Medicine
Pt. B. D. Sharma PGIMS, Rohtak
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.
The document discusses odds ratios, which are used to measure the association between an exposure and an outcome. An odds ratio is calculated by dividing the odds of an event in one group (e.g. exposed to a drug) by the odds of the event in another unexposed group. Odds ratios can be calculated in both cohort and case-control studies. While relative risk can only be calculated in cohort studies, odds ratios are commonly used to approximate relative risk in case-control studies when the outcome is rare. The document provides examples of how to calculate odds ratios from 2x2 contingency tables and interprets what different values mean.
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.
Observational descriptive study: case report, case series & ecological studyPrabesh Ghimire
This document discusses different types of research designs, including observational and intervention designs. It focuses on non-intervention designs like case reports, case series, and cross-sectional studies. Case reports describe the occurrence, diagnosis, treatment and follow-up of an individual patient, especially unusual cases. Case series describe aspects of a disease or treatment by following a group of patients with common characteristics. Both case reports and case series are useful for generating hypotheses but have limitations due to lack of a control group.
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.
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.
This document discusses different types of epidemiologic study designs including descriptive studies, analytical studies, and experimental studies. It provides details on descriptive epidemiology, analytic epidemiology, and different types of observational and experimental study designs such as cohort studies, case-control studies, randomized controlled trials, and ecological studies. Key aspects of cohort and case-control study designs are outlined including their advantages and disadvantages. Potential sources of error and bias in epidemiologic studies are also reviewed.
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.
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.
The document discusses odds ratios, which are used to measure the association between an exposure and an outcome. An odds ratio is calculated by dividing the odds of an event in one group (e.g. exposed to a drug) by the odds of the event in another unexposed group. Odds ratios can be calculated in both cohort and case-control studies. While relative risk can only be calculated in cohort studies, odds ratios are commonly used to approximate relative risk in case-control studies when the outcome is rare. The document provides examples of how to calculate odds ratios from 2x2 contingency tables and interprets what different values mean.
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.
Observational descriptive study: case report, case series & ecological studyPrabesh Ghimire
This document discusses different types of research designs, including observational and intervention designs. It focuses on non-intervention designs like case reports, case series, and cross-sectional studies. Case reports describe the occurrence, diagnosis, treatment and follow-up of an individual patient, especially unusual cases. Case series describe aspects of a disease or treatment by following a group of patients with common characteristics. Both case reports and case series are useful for generating hypotheses but have limitations due to lack of a control group.
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.
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.
This document discusses different types of epidemiologic study designs including descriptive studies, analytical studies, and experimental studies. It provides details on descriptive epidemiology, analytic epidemiology, and different types of observational and experimental study designs such as cohort studies, case-control studies, randomized controlled trials, and ecological studies. Key aspects of cohort and case-control study designs are outlined including their advantages and disadvantages. Potential sources of error and bias in epidemiologic studies are also reviewed.
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.
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 nested case-control studies, case-cohort studies, and case-crossover studies. It provides examples and discusses the advantages and disadvantages of each study design. Nested case-control studies select controls from within a prospective cohort study. Case-cohort studies select a random subcohort of controls from the entire cohort. Case-crossover studies use individuals as their own controls by comparing exposure during case periods to control periods.
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.
This document summarizes a presentation on case-control studies. It defines epidemiology and different types of studies. It then discusses the key aspects of case-control studies including:
- They proceed backwards from the effect (disease) to the potential cause (exposure).
- Cases and controls are selected and their exposure status is determined. Exposure rates, relative risk, and odds ratios can then be estimated.
- Important steps include properly defining cases and controls, selecting controls, matching, measuring exposure, and analyzing for bias. Case-control studies are useful for investigating rare diseases and establishing causal relationships.
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.
The document discusses bias in epidemiology. It defines bias as systematic error that results in a mistaken estimate of an exposure's effect. It describes several types of bias including selection bias, information bias, and confounding. Selection bias can occur if cases and controls are selected in a way that distorts the exposure-disease association. Information bias arises from inadequate means of obtaining information. Confounding occurs when a third factor is associated with both exposure and outcome. The document outlines various specific biases like self-selection, recall bias, and healthy worker effect. It emphasizes the importance of minimizing bias through proper study design, conduct, and analysis to obtain valid results.
This document summarizes a seminar presentation on case control studies. It begins by defining epidemiological study cycles and analytical study types such as case control and cohort studies. It then focuses on case control studies, defining them, discussing their history, design, outcomes, limitations, advantages and applications. Examples of notable case control studies are provided, such as Lane Claypon's 1926 breast cancer study and studies from the 1950s linking smoking to lung cancer. Key aspects of case control study methodology like selection of cases and controls, and matching to control for confounding variables are explained.
This document discusses cross-sectional studies, which measure exposure and health outcomes at the same point in time. It notes that cross-sectional studies can be descriptive, providing prevalence rates, or analytic, examining associations between exposures and outcomes. While able to generate hypotheses, cross-sectional studies cannot determine causation due to their inability to assess temporal relationships. The document also briefly touches on case reports and case series, which lack control groups for formally assessing relationships.
This document describes a cross-sectional study and its methodology. A cross-sectional study involves collecting data on exposure and outcome variables from a population at a single point in time. The document discusses the differences between descriptive and analytical cross-sectional studies. Descriptive studies measure prevalence, while analytical studies test associations between exposures and outcomes. The document provides examples of cross-sectional study design, biases, advantages, and guidelines for evaluating validity.
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.
This document compares cohort and case-control studies. Cohort studies follow groups forward in time from exposure to outcome, while case-control studies trace backwards from outcome to exposure. Cohort studies are better for studying incidence and natural history, but can be time-consuming and expensive. Case-control studies are efficient but prone to selection and recall bias. Key factors to consider in cohort studies include minimizing selection, information, and loss to follow-up bias. Case-control studies must carefully select cases and controls and address recall and confounding biases.
This document discusses kappa statistics, which measure interrater reliability beyond chance agreement. Kappa statistics are useful when multiple raters are interpreting subjective data, such as radiology images. The kappa statistic formula calculates observed agreement between raters compared to expected chance agreement. Examples show how to calculate kappa when two raters are assessing whether a biomarker is present or absent in samples. Confidence intervals for kappa are determined using 1.96 as a constant to generate a 95% confidence level.
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 discusses different risk measures used in epidemiology, including relative risk, odds ratio, and attributable risk. Relative risk measures the strength of association between an exposure and disease based on prospective studies. Odds ratio is used similarly in case-control studies when relative risk cannot be directly calculated. Attributable risk determines how much disease can be attributed to a specific exposure by comparing disease rates in exposed and unexposed groups. These measures provide important information for evaluating disease causation and determining potential disease prevention through reducing exposures.
This document discusses randomized controlled trials (RCTs) and non-randomized trials. It defines non-randomized trials as studies where participants are assigned to treatment groups by a non-random method controlled by the investigator. The document outlines sources of bias in non-randomized studies, statistical adjustment methods, and appropriate uses of non-randomized designs. It compares RCTs and non-randomized trials, noting similarities in measuring outcomes but differences in potential for bias, validity, and cost-effectiveness.
This document provides an overview of randomized controlled trials (RCTs). It begins with an introduction to RCTs, describing them as the gold standard for evaluating health care technologies. It then covers key aspects of designing and conducting RCTs, including developing a protocol, selecting and randomizing study populations, manipulating variables, follow-up, assessment, types of RCTs based on design and use, and ethical considerations. RCTs aim to provide scientific evidence of causal relationships and evaluate interventions through random assignment and control groups.
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.
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.
This document provides an overview of how to conduct a systematic review. It defines what a systematic review is and compares it to a traditional narrative review. Some key aspects covered include developing a protocol with a review team, formulating an answerable question using PICO, systematically searching literature and selecting studies, assessing study quality, extracting and analyzing data, and interpreting results. The document provides examples and outlines the major steps to follow in a high-quality systematic review.
Systematic literature review | Meta analysis | Retrospective versusPubrica
Systematic review for prospective studies is a meticulous and essential process ensuring research findings’ reliability and validity. The key to success lies in adhering to a well-structured methodology that includes defining the research question, developing a comprehensive search strategy, screening studies based on pre-defined criteria, and critically appraising the selected articles.
Read more @ https://pubrica.com/academy/manuscript-editing/conduct-a-systematic-review-for-prospective-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 nested case-control studies, case-cohort studies, and case-crossover studies. It provides examples and discusses the advantages and disadvantages of each study design. Nested case-control studies select controls from within a prospective cohort study. Case-cohort studies select a random subcohort of controls from the entire cohort. Case-crossover studies use individuals as their own controls by comparing exposure during case periods to control periods.
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.
This document summarizes a presentation on case-control studies. It defines epidemiology and different types of studies. It then discusses the key aspects of case-control studies including:
- They proceed backwards from the effect (disease) to the potential cause (exposure).
- Cases and controls are selected and their exposure status is determined. Exposure rates, relative risk, and odds ratios can then be estimated.
- Important steps include properly defining cases and controls, selecting controls, matching, measuring exposure, and analyzing for bias. Case-control studies are useful for investigating rare diseases and establishing causal relationships.
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.
The document discusses bias in epidemiology. It defines bias as systematic error that results in a mistaken estimate of an exposure's effect. It describes several types of bias including selection bias, information bias, and confounding. Selection bias can occur if cases and controls are selected in a way that distorts the exposure-disease association. Information bias arises from inadequate means of obtaining information. Confounding occurs when a third factor is associated with both exposure and outcome. The document outlines various specific biases like self-selection, recall bias, and healthy worker effect. It emphasizes the importance of minimizing bias through proper study design, conduct, and analysis to obtain valid results.
This document summarizes a seminar presentation on case control studies. It begins by defining epidemiological study cycles and analytical study types such as case control and cohort studies. It then focuses on case control studies, defining them, discussing their history, design, outcomes, limitations, advantages and applications. Examples of notable case control studies are provided, such as Lane Claypon's 1926 breast cancer study and studies from the 1950s linking smoking to lung cancer. Key aspects of case control study methodology like selection of cases and controls, and matching to control for confounding variables are explained.
This document discusses cross-sectional studies, which measure exposure and health outcomes at the same point in time. It notes that cross-sectional studies can be descriptive, providing prevalence rates, or analytic, examining associations between exposures and outcomes. While able to generate hypotheses, cross-sectional studies cannot determine causation due to their inability to assess temporal relationships. The document also briefly touches on case reports and case series, which lack control groups for formally assessing relationships.
This document describes a cross-sectional study and its methodology. A cross-sectional study involves collecting data on exposure and outcome variables from a population at a single point in time. The document discusses the differences between descriptive and analytical cross-sectional studies. Descriptive studies measure prevalence, while analytical studies test associations between exposures and outcomes. The document provides examples of cross-sectional study design, biases, advantages, and guidelines for evaluating validity.
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.
This document compares cohort and case-control studies. Cohort studies follow groups forward in time from exposure to outcome, while case-control studies trace backwards from outcome to exposure. Cohort studies are better for studying incidence and natural history, but can be time-consuming and expensive. Case-control studies are efficient but prone to selection and recall bias. Key factors to consider in cohort studies include minimizing selection, information, and loss to follow-up bias. Case-control studies must carefully select cases and controls and address recall and confounding biases.
This document discusses kappa statistics, which measure interrater reliability beyond chance agreement. Kappa statistics are useful when multiple raters are interpreting subjective data, such as radiology images. The kappa statistic formula calculates observed agreement between raters compared to expected chance agreement. Examples show how to calculate kappa when two raters are assessing whether a biomarker is present or absent in samples. Confidence intervals for kappa are determined using 1.96 as a constant to generate a 95% confidence level.
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 discusses different risk measures used in epidemiology, including relative risk, odds ratio, and attributable risk. Relative risk measures the strength of association between an exposure and disease based on prospective studies. Odds ratio is used similarly in case-control studies when relative risk cannot be directly calculated. Attributable risk determines how much disease can be attributed to a specific exposure by comparing disease rates in exposed and unexposed groups. These measures provide important information for evaluating disease causation and determining potential disease prevention through reducing exposures.
This document discusses randomized controlled trials (RCTs) and non-randomized trials. It defines non-randomized trials as studies where participants are assigned to treatment groups by a non-random method controlled by the investigator. The document outlines sources of bias in non-randomized studies, statistical adjustment methods, and appropriate uses of non-randomized designs. It compares RCTs and non-randomized trials, noting similarities in measuring outcomes but differences in potential for bias, validity, and cost-effectiveness.
This document provides an overview of randomized controlled trials (RCTs). It begins with an introduction to RCTs, describing them as the gold standard for evaluating health care technologies. It then covers key aspects of designing and conducting RCTs, including developing a protocol, selecting and randomizing study populations, manipulating variables, follow-up, assessment, types of RCTs based on design and use, and ethical considerations. RCTs aim to provide scientific evidence of causal relationships and evaluate interventions through random assignment and control groups.
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.
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.
This document provides an overview of how to conduct a systematic review. It defines what a systematic review is and compares it to a traditional narrative review. Some key aspects covered include developing a protocol with a review team, formulating an answerable question using PICO, systematically searching literature and selecting studies, assessing study quality, extracting and analyzing data, and interpreting results. The document provides examples and outlines the major steps to follow in a high-quality systematic review.
Systematic literature review | Meta analysis | Retrospective versusPubrica
Systematic review for prospective studies is a meticulous and essential process ensuring research findings’ reliability and validity. The key to success lies in adhering to a well-structured methodology that includes defining the research question, developing a comprehensive search strategy, screening studies based on pre-defined criteria, and critically appraising the selected articles.
Read more @ https://pubrica.com/academy/manuscript-editing/conduct-a-systematic-review-for-prospective-studies/
This document provides an overview of how to conduct a systematic review. It begins by defining what a systematic review is and why they are important for evidence-based practice. It then outlines the key steps in conducting a systematic review, including formulating an answerable question using PICO(T), performing a comprehensive literature search, selecting studies and extracting data in an unbiased manner, critically appraising the evidence, and synthesizing the data. The document emphasizes that systematic reviews need to follow a structured, systematic process and make all methods explicit to minimize bias. It also discusses challenges that can arise in systematic reviews like database, publication, and language biases.
Efficacy of Information interventions in reducing transfer anxiety from a cri...Ambika Rai
Efficacy of Information interventions in reducing transfer anxiety from a critical care setting to a general ward: A systematic review and a meta-analysis
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.
The document provides an introduction to systematic reviews and meta-analyses. It discusses that systematic reviews aim to reduce bias by comprehensively identifying, appraising, and synthesizing all relevant studies on a topic. They often include a meta-analysis to statistically synthesize data from multiple studies. Systematic reviews use a predefined protocol and search strategy to find all studies, whereas traditional reviews may not consider study quality or report how conclusions follow from evidence. The key steps in a systematic review are developing a protocol, conducting a comprehensive literature search, assessing study eligibility, extracting data, critically appraising studies, and synthesizing results.
This document provides an overview of systematic reviews and meta-analysis. It defines a systematic review as a review that uses explicit and reproducible methods to minimize bias in identifying, appraising, and synthesizing results from relevant studies. It notes key characteristics of systematic reviews include having a focused question, comprehensive literature search strategies, explicit inclusion/exclusion criteria, quality assessment of studies, and qualitative or quantitative data synthesis. Meta-analysis is defined as a statistical approach to pooling quantitative results from multiple studies. The document outlines the steps involved in conducting systematic reviews and meta-analyses, including developing a protocol, searching for evidence, screening studies, extracting and analyzing data, and reporting results. It highlights the importance of systematic reviews in providing reliable evidence to inform
The critical appraisal process examines research to judge its validity and relevance. It involves summarizing key aspects of research articles like the introduction, methods, results and discussion sections. Important tools for appraisal include the PICO method to assess the research question, and CASP checklists tailored to different study designs. Proper appraisal helps identify clinically relevant papers and supports evidence-based decision making.
This document outlines a lecture on systematic reviews and meta-analyses. It discusses the rationale for systematic reviews in healthcare, the steps to conduct one, and how meta-analyses aggregate and statistically analyze results. Advantages include providing the best evidence and reducing bias compared to traditional reviews. Disadvantages include more effort required and insufficient high-quality studies. Heterogeneity between studies must be assessed and addressed. Publication bias can skew results if smaller negative studies are not published.
This document provides an overview of systematic reviews and meta-analyses. It defines key terms like systematic review and meta-analysis. It describes the steps involved in conducting a systematic review, including developing a protocol and research question, searching for studies, assessing study quality, extracting and synthesizing data, and interpreting findings. It notes that a meta-analysis allows for quantitative synthesis by statistically combining study results, while a systematic review without meta-analysis provides a qualitative narrative synthesis. The document discusses factors like heterogeneity that determine whether a quantitative or qualitative synthesis is appropriate.
This document outlines the process and key stages of conducting a systematic review. It aims to define what a systematic review is, discuss the formulation of a review question and development of a protocol, and explain the stages of searching for studies, selecting studies, extracting data, and synthesizing evidence. A systematic review attempts to comprehensively and methodically identify and synthesize all empirical evidence that fits pre-specified eligibility criteria to answer a specific research question and minimize bias in order to draw reliable conclusions.
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Critical appraisal of a journal articleDrSahilKumar
This document provides guidance on critically appraising journal articles. It defines critical appraisal as systematically identifying the strengths and weaknesses of research to assess validity and usefulness. Key aspects to evaluate include relevance of the research question, appropriateness of study design, addressing biases, adherence to original protocol, statistical analyses, and conflicts of interest. Checklists like CASP, CONSORT, and STROBE provide frameworks to appraise study methodologies like randomized trials, systematic reviews, and observational studies. The goal of critical appraisal is for clinicians to identify high-quality evidence to inform clinical practice.
This document provides an overview of randomized controlled trials (RCTs), including their definition, characteristics, and critical appraisal. RCTs are prospective studies that randomly assign participants to experimental and control groups. The key characteristics discussed include randomization and allocation concealment to distribute confounding variables equally between groups, blinding to reduce bias, pre-specified outcomes, sample size calculation, and intention-to-treat analysis. Critical appraisal involves assessing the validity, relevance, and risk of bias in RCTs to determine the reliability and generalizability of results.
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.
Study designs & amp; trials presentation1 2Praveen Ganji
This document defines and describes different types of clinical research studies and trials. It discusses meta-analyses, systematic reviews, randomized controlled trials, cohort studies, case-control studies, cross-sectional studies, case reports, editorials, animal research, laboratory research, and clinical trial phases. For each type of study, it provides brief explanations of their purpose and advantages and disadvantages. It also defines key statistical concepts like p-values and standard deviation.
Systematic Review at Glance-WMB-July282022.pdfWeam Banjar
The document provides an overview of systematic reviews, defining them as attempts to collate all empirical evidence to answer a specific research question using explicit and systematic methods. It notes systematic reviews are needed whenever there are multiple primary studies on a question with uncertain findings. The document outlines the key characteristics of high-quality systematic reviews as identifying all relevant evidence, selecting studies for inclusion, assessing study quality, and synthesizing findings in an unbiased way. It lists the typical process as defining the question, searching literature, assessing studies, combining results, and placing findings in context.
how to do review research PRISMA-IS2012.pptemebetnigatu1
This document provides an overview of systematic reviews and meta-analyses, including:
1) It describes different types of reviews, specifically narrative reviews which provide an overview but can be biased, and systematic reviews which use explicit scientific methods to identify and summarize studies;
2) Key characteristics of systematic reviews are outlined, including having a focused question, comprehensive search strategy, and explicit inclusion/exclusion criteria;
3) Guidelines for conducting systematic reviews are discussed, including the Cochrane Handbook and PRISMA statement for reporting reviews.
How to structure your table for systematic review and meta analysis – PubricaPubrica
According to the, a systematic review is "a scholarly method in which all empirical evidence that meets pre-specified eligibility requirements is gathered to address a particular research question."
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A research study Writing a Systematic Review in Clinical Research – PubricaPubrica
A systematic review summarises the findings of precisely organized healthcare research (controlled trials) and gives a high degree of evidence on the efficacy of healthcare interventions. The evidence may be used to make decisions and guide healthcare recommendations.
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Systematic Review & Meta Analysis.pptx
1. Systematic Review & Meta
Analysis
Presenter: Dr. Anik Chakraborty (JR-III)
Moderator: Dr. Neelam Kumar (Professor)
Dept. of Community Medicine
Pt. B. D. Sharma PGIMS, Rohtak
2. Contents
• Introduction
• Systematic Review: Why and What
• Systematic Review: How
• Systematic Review: Quality assessment & Risk of Bias
• Meta Analysis
• Meta Analysis: Effect Size
• Meta Analysis: Heterogeneity
• Meta Analysis: Forest Plot
• Publication Bias: Funnel Plot
• Conclusion
3. Introduction
• The amount of studies published in the biomedical literature, has increased strikingly
over the last few decades.
• This massive abundance of literature makes practice of clinics or forming an opinion
increasingly complex, and knowledge from various researches is often needed to inform a
particular decision.
• Available studies are often heterogeneous with regard to their design, operational
quality, and subjects under study and may handle the research question in a different way,
which adds to the complexity of evidence and conclusion synthesis.
• Systematic review and meta-analysis focuses on how the evidence relating to a
particular research question can be summarized in order to make it accessible to
medical practitioners and inform the practice of evidence-based medicine.
4. Levels of Evidence (Hierarchy of Evidence) in research
• A systematic review collects all
possible studies related to a given
topic and design, and reviews and
analyzes their results.
• During the systematic review
process, the quality of studies is
evaluated, and a statistical meta-
analysis of the study results is
conducted on the basis of their
quality.
• A meta-analysis is a valid,
objective, and scientific method of
analyzing and combining different
results.
5. Systematic Review: Why and What
• A conventional ‘narrative’ literature review – a ‘summary of the information
available to the author from the point of view of the author’ – can be very
misleading as a basis from which to draw conclusions on the overall evidence
on a particular subject.
• Reliable reviews must be systematic if bias in the interpretation of findings is
to be avoided.
• Definition: The application of scientific strategies that limit bias by the
systematic assembly, critical appraisal and synthesis of all relevant studies on
a specific topic. (Cook et al, 1995)
Systematic Review
7. Systematic Review: How
Strict guidelines have been developed over the years for reporting of systematic
review:
• Cochrane Collaboration or Cochrane database of systematic reviews (1993)
• Quality of Reporting of Meta-analyses (QUORUM) statement (for randomized
trials) (1999)
• The Preferred Reporting Items for Systematic reviews and Meta-Analyses
(PRISMA) (2009)
• Meta-analysis Of Observational Studies in Epidemiology (MOOSE) (for
observational studies).
8.
9.
10.
11. 1. Research question:
• Should be feasible, interesting, novel, ethical, and relevant.
• Therefore, a clear, logical, and well-defined research question should be
formulated.
• Usually, two common tools are used: PICO or SPIDER.
• PICO (Population, Intervention, Comparison, Outcome) is used mostly in
quantitative evidence synthesis.
• SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, Research
type) was proposed as a method for qualitative and mixed methods search.
But how to conduct a systematic review step by step?
12. 2. Preliminary search: Validate idea, see if it has been done previously.
Propose no. of included studies
3. Inclusion & Exclusion criteria:
13. 4. Search strategy & 5. Searching databases:
• PubMed, EMBASE, Google Scholar, Scopus, Cochrane etc. According to AMSTAR guidelines, at
least two databases have to be searched.
• Boolean operators, such as “AND”, “OR”, “NOT” are used for refining search strategy.
6. Protocol writing & registration:
• Protocol registration at an early stage guarantees transparency in the research process and
protects from duplication problems.
• Besides, it is considered a documented proof of team plan of action, research question,
eligibility criteria, intervention/exposure, quality assessment, and pre-analysis plan.
• Researchers should send it to the principal investigator (PI) to revise it, then upload it to
registry sites [Proposed by Cochrane and Campbell collaborations; PROSPERO etc.]
14. 7. Title and abstract screening:
• Reviewers (2-3) decide to include or exclude any report based on criteria.
8. Full text downloading and screening
9. Manual search:
• Searching references from included studies/reviews
• Contacting authors and experts, and
• Looking at related articles/cited articles in PubMed and Google Scholar.
10. Data extraction and Quality assessment: (More on quality assessment and risk
of bias assessment later on)
11. Statistical analysis (Meta analysis) 12. Manuscript writing, revision &
Submission
15.
16. • However, well planned the systematic review or meta-analysis is, if the quality
of evidence in the studies is low, the quality of the meta-analysis decreases and
incorrect results can be obtained.
• The quality of the studies included in the systematic review determines the
certainty with which conclusions can be drawn.
• Quality assessment is the assessment of the inclusion of methodological
safeguards within a study whereas Risk of bias assessment concerns the
implication of the inclusion of such safeguards for study results.
• Many a times these two terms (quality assessment and risk of bias assessment)
is used interchangeably.
Quality Assessment and Risk of Bias
17. • Once all the relevant studies have been identified, the studies should undergo
a quality assessment. This is particularly important if there is contradictory
evidence.
• Even when using randomized studies with a high quality of evidence,
evaluating the quality of evidence precisely helps determine the strength of
recommendations in the meta- analysis.
• Various tools have been designed to check quality assessment
18. • The Jadad score (Oxford Quality Rating scale) is frequently used for quality
assessment of RCTs
20. Risk of Bias
• The study limitations are evaluated using the “risk of bias” method proposed by
Cochrane.
• The risk of bias is defined as the risk of systematic error or a deviation from reporting the
truth or an appropriate evidence finding.
• This method classifies bias in randomized studies as “low,” “high,” or “unclear” on the
basis of the presence or absence of six processes (Random sequence generation,
Allocation concealment, Blinding participants or investigators, Incomplete outcome data,
Selective reporting, and Other biases)
• Again, there are number of tools to assess risk of bias (according to different kind of
studies)
21.
22. Traffic light graph
• Low risk of bias (Green)
• Unclear risk (Orange/Yellow)
• High risk of bias (Red)
23. Few other Risk of Bias assessment tools
• AMSTAR 2: A MeaSurement Tool to Assess systematic Reviews
• GRADE: Grading of Recommendations Assessment, Development and Evaluation
• AXIS: Appraisal tool for Cross-Sectional Studies
• ROBIS: Risk Of Bias in
Systematic Reviews
• NIH checklist
24. • The statistical methods for combining the results of a number of studies
are known as meta-analysis.
• The aim of a meta-analysis is to derive a conclusion with increased power and
accuracy than what could not be able to achieve in individual studies.
• It should be emphasized that not all systematic reviews will contain a meta-
analysis; this will depend on whether the systematic review has located studies
that are sufficiently similar to make it reasonable to consider combining their
results
• Therefore, before analysis, it is crucial to evaluate the direction of effect*, size of
effect, homogeneity of effects among studies, and strength of evidence.
Meta Analysis
25. • Thereafter, the data are reviewed qualitatively and quantitatively.
• If it is determined that the different research outcomes cannot be combined,
all the results and characteristics of the individual studies are displayed in a
table or in a descriptive form; this is referred to as a qualitative review.
• A meta-analysis is a quantitative review, in which the clinical effectiveness
is evaluated by calculating the weighted pooled estimate for the interventions
in at least two separate studies.
• The pooled estimate is the outcome of the meta-analysis, and is typically
explained using a Forest plot
26. Effect Size
• SR/MA was primarily designed for RCT (Clinical trials)
• The meta-analysis result may show either a benefit or lack of benefit of a treatment
approach that will be indicated by the effect size, which is the term used to describe
the treatment effect of an intervention. Treatment effect is the gain (or loss) seen
in the experimental group relative to the control group.
• Statistically speaking, Effect size is a measure of strength of relationship between
two variables.
• Binary outcomes: Odds Ratio (OR), Relative Risk (RR)
• Continuous outcomes: Mean Difference (MD), Standardized Mean Difference (SMD)
27. • In other words, Effect size is a dimensionless estimate (i.e., a measure with
no units) that indicates both direction and magnitude of the treatment
effect
Magnitude and direction
depends upon:
Sample size
Variance
Reliability of outcome
measures
28. Heterogeneity
• Heterogeneity simply means variability among studies.
• Heterogeneity tells us, are these studies different? If yes, can we quantify it?
Should these studies be combined? If yes, how?
• Different types of heterogeneity:
A. Clinical Heterogeneity: Difference in study methods that affect the ability to
compare and/or combine data from different studies. E.g., participant
demographics, risk or severity of disease, study settings, frequency and intensity
of intervention and how outcomes were measured
B. Methodological Heterogeneity: Risk of bias assessment
C. Statistical Heterogeneity: Or simply, heterogeneity. The difference in effect size
among various studies.
29. • Heterogeneity simply tells that difference between studies are actually there and not due
to chance
• So in reality, heterogeneity will always be present among studies.
• But we should test, if that is significant or not. To what extent does it affect conclusions of
the meta analysis
Test for presence: Cochran’s Q-Test
• Cochran’s Q test is the traditional test for heterogeneity in meta-analyses. Based on a chi-
square (χ2) distribution, it generates a probability that, when large, indicates larger
variation across studies.
Quantifying heterogeneity: I2 Test
• The I2 index is a more recent approach to quantify heterogeneity in meta-analyses.
• I2 provides an estimate of the percentage of variability in results across studies that is
due to real differences and not due to chance.
30. I2 =
𝑄−𝑑𝑓
𝑄
∗ 100% Q = Cochrane’s heterogeneity stat, χ2 distribution
df = No. of studies-1
• If I2 is 20%, this would mean that 20% of the observed variation in treatment effects
cannot be attributed to chance alone
• Heterogeneity 0.25= Low | 0.5= Moderate| ≥0.75= High
• The limitation of I2 is that it provides only a measure of global heterogeneity but no
information for the factor causing heterogeneity, similar to Cochran’s Q test.
Between-study variance: Tau- squared (τ2)
τ2 =
𝑄−𝑑𝑓
𝑄
• Tau squared is the estimate of the variance of the underlying distribution of true
effect size
31. Investigating Heterogeneity: Meta regression
• Meta-regression models strive to control for and explain differences in treatment effects
in terms of study covariates.
• A meta-regression can be either a linear or a logistic regression model, and it can be
based on a fixed or random effects regression.
• The unit of the analysis is the individual study included in a systematic review or meta-
analysis.
• Predictors in the regression model are study-level characteristics such as study-level
location, sample size, length of follow-up, drop-out rates, or study quality characteristics.
• The advantage of meta-regression is that it determines which study-level
characteristics account for heterogeneity, rather than just providing an estimate of the
global heterogeneity.
32. Ok, so now we have explored and estimated the heterogeneity among the studies.
What if there is high heterogeneity?
Don’t pool results for meta analysis
Ignore heterogeneity and use Fixed effect model
Control for heterogeneity using Random effect model
• Fixed-effect model assumes that the effect of treatment is the same, and that
variation between results in different studies is due to random error.
• Thus, a fixed-effect model can be used when the studies are considered to have
the same design and methodology, or when the variability in results within a
study is small, and the variance is thought to be due to random error.
33. • Three common methods are used for weighted estimation in a fixed-effect model:
1) Inverse variance-weighted estimation: Small no. of studies with large sample size
2) Mantel-Haenszel estimation: Large no. of studies with small sample size
3) Peto estimation: Low event rate or one of the two groups shows zero incidence
• Random-effect model assumes heterogeneity between the studies being combined, and
these models are used when the studies are assumed different, even if a heterogeneity
test does not show a significant result.
• Unlike a fixed-effect model, a random- effect model assumes that the size of the effect of
treatment differs among studies.
• Thus, differences in variation among studies are thought to be due to not only random
error but also between-study variability in results
• Among methods for weighted estimation in a random-effect model, the Der Simonian and Laird method is
mostly used for dichotomous variables, while Inverse variance-weighted estimation is used for continuous
variables
38. Publication Bias in Meta Analysis
• In general, a study showing a beneficial effect of a new treatment is more likely to be
considered worthy of publication than one showing no effect.
• There is a considerable bias that operates at every stage of the process, with negative
trials considered to contribute less to scientific knowledge than positive ones:
Those who conducted the study are more likely to submit the results to a peer
reviewed journal;
Editors of journals are more likely to consider the study potentially worth
publishing and send it for peer review
Referees are more likely to deem the study suitable for publication.
39. • This situation has been accentuated by two factors: first that studies have often
been too small to detect a beneficial effect even if one exists and second that
there has been too much emphasis on ‘significant’ results (i.e. P < 0.05 for the
effect of interest).
• A proposed solution to the problem of publication bias is to establish registers
of all trials in a particular area, from when they are funded or established.
• It is also clear that the active discouragement of studies that do not have power
to detect a clinically important effect would alleviate the problem.
• Publication bias is a lesser problem for larger studies, for which there tends to be
general agreement that the results are of interest, whatever they are.
40. Funnel Plots to examine publication bias
• The existence of publication bias may be examined graphically by the use of ‘funnel
plots’.
• These are simple scatter plots of the study results/ treatment effects on the
horizontal (x) axis and the precision of that study (sample size or inverse SE) on the
vertical (y) axis.
• The name ‘funnel plot’ is based on the fact that the precision in the estimation of the
underlying treatment effect will increase as the sample size of component
studies increases.
• Effect estimates from small studies will therefore scatter more widely at the bottom of
the graph, with the spread narrowing among larger studies.
42. Symmetrical plot in the absence of
bias (open circles indicate smaller
studies showing no beneficial effects)
Asymmetrical plot in the presence of
publication bias (smaller studies
showing no beneficial effects are
missing)
Asymmetrical plot in the presence of bias due to
low methodological quality of smaller studies
(open circles indicate small studies of inadequate
quality whose results are biased towards larger
beneficial effects
43. • Relative measures of treatment effect (risk ratios or odds ratios) are plotted on a
logarithmic scale.
• This is important to ensure that effects of the same magnitude but opposite directions, for
example risk ratios of 0.5 and 2, are equidistant from 1 (corresponding to no effect).
• However, the statistical power of a trial is determined both by the total sample size and
the number of participants developing the event of interest.
• For example, a study with 100,000 patients and 10 events is less likely to show a
statistically significant effect of a treatment than a study with 1000 patients and 100
events.
• The standard error of the effect estimate, rather than total sample size, has therefore been
increasingly used in funnel plots
45. • Systematic reviews and meta-analysis (the quantitative analysis of such reviews) are
now accepted as an important part of medical research.
• While the analytical methods are relatively simple, there is still controversy over
appropriate methods of analysis.
• Systematic reviews are substantial undertakings, and those conducting such reviews
need to be aware of the potential biases which may affect their conclusions.
• However, the explosion in medical research information and the availability of reviews
on-line mean that synthesis of research findings in form of Systematic reviews and
Meta-analysis is likely to be of ever increasing importance to the practice of medicine.
Conclusion