The Cochrane Handbook for Systematic Reviews of Interventions is the official document that describes in detail the process of preparing and maintaining Cochrane systematic reviews on the effects of healthcare interventions.

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Cochrane Handbook for Systematic Reviews of Interventions

Background. Qualitative research explores complex phenomena encountered by clinicians, health care providers, policy makers and consumers. Although partial checklists are available, no consolidated reporting framework exists for any type of qualitative design. Objective. To develop a checklist for explicit and comprehensive reporting of qualitative studies (indepth interviews and focus groups). Methods. We performed a comprehensive search in Cochrane and Campbell Protocols, Medline, CINAHL, systematic reviews of qualitative studies, author or reviewer guidelines of major medical journals and reference lists of relevant publications for existing checklists used to assess qualitative studies. Seventy-six items from 22 checklists were compiled into a comprehensive list. All items were grouped into three domains: (i) research team and reflexivity, (ii) study design and (iii) data analysis and reporting. Duplicate items and those that were ambiguous, too broadly defined and impractical to assess were removed. Results. Items most frequently included in the checklists related to sampling method, setting for data collection, method of data collection, respondent validation of findings, method of recording data, description of the derivation of themes and inclusion of supporting quotations. We grouped all items into three domains: (i) research team and reflexivity, (ii) study design and (iii) data analysis and reporting. Conclusions. The criteria included in COREQ, a 32-item checklist, can help researchers to report important aspects of the research team, study methods, context of the study, findings, analysis and interpretations.

Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups

The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.

https://www.bmj.com/content/bmj/372/bmj.n71.full.pdf

The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

The quality assessment of non-randomized studies is an important component of a thorough meta-analysis of nonrandomized studies. Low quality studies can lead to a distortion of the summary effect estimate. Recent guidelines for the reporting of meta-analyses of observational studies recommend the assessment of the study quality (MOOSE) [1]. In principal, three categories of quality assessments tools are available: scales, simple checklists, or checklists with a summary judgment (for details see Sanderson et al. 2007 [2]). The results of the quality assessment can be used in several ways such as forming inclusion criteria for the meta-analysis, informing a sensitivity analysis or metaregression, weighting studies, or highlighting areas of methodological quality poorly addressed by the included studies [3]. It has been criticized that the use of summary scores involve inherent weighting of component items including items that may not be related to the validity of the study findings [2]. Sanderson et al. [2] recently identified overall 86 tools for assessing the quality of non-randomized studies. Their review "highlighted the lack of a single obvious candidate tool for assessing quality of observational epidemiological studies" [2]. In the field of randomized trials, it has been shown that the choice of quality scale can dramatically influence the interpretation of meta-analyses, and can even reverse conclusions regarding the effectiveness of an intervention [4]. Wells et al. [5] proposed a scale for assessing the quality of published non-randomized studies in meta-analyses,

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Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses

An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models Multivariate techniques, including classification and ordination, are then introduced Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature The book is supported by a website that provides all data sets, questions for each chapter and links to software

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Experimental Design and Data Analysis for Biologists

ObjectiveBecause of the pressure for timely, informed decisions in public health
and clinical practice and the explosion of information in the scientific literature,
research results must be synthesized. Meta-analyses are increasingly used
to address this problem, and they often evaluate observational studies. A
workshop was held in Atlanta, Ga, in April 1997, to examine the reporting
of meta-analyses of observational studies and to make recommendations to aid
authors, reviewers, editors, and readers.ParticipantsTwenty-seven participants were selected by a steering committee, based
on expertise in clinical practice, trials, statistics, epidemiology, social
sciences, and biomedical editing. Deliberations of the workshop were open
to other interested scientists. Funding for this activity was provided by
the Centers for Disease Control and Prevention.EvidenceWe conducted a systematic review of the published literature on the
conduct and reporting of meta-analyses in observational studies using MEDLINE,
Educational Research Information Center (ERIC), PsycLIT, and the Current Index
to Statistics. We also examined reference lists of the 32 studies retrieved
and contacted experts in the field. Participants were assigned to small-group
discussions on the subjects of bias, searching and abstracting, heterogeneity,
study categorization, and statistical methods.Consensus ProcessFrom the material presented at the workshop, the authors developed a
checklist summarizing recommendations for reporting meta-analyses of observational
studies. The checklist and supporting evidence were circulated to all conference
attendees and additional experts. All suggestions for revisions were addressed.ConclusionsThe proposed checklist contains specifications for reporting of meta-analyses
of observational studies in epidemiology, including background, search strategy,
methods, results, discussion, and conclusion. Use of the checklist should
improve the usefulness of meta-analyses for authors, reviewers, editors, readers,
and decision makers. An evaluation plan is suggested and research areas are
explored.

https://jamanetwork.com/journals/jama/articlepdf/192614/JST00003.pdf

Meta-analysis of observational studies in epidemiology - A proposal for reporting

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Meta-analysis of Observational Studies in Epidemiology

A meta-analysis examined data from 36 studies linking physical activity to well-being in older adults without clinical disorders. The weighted mean-change effect size for treatment groups (d(C). = 0.24) was almost 3 times the mean for control groups (d(C). = 0.09). Aerobic training was most beneficial (d(C). = 0.29), and moderate intensity activity was the most beneficial activity level (d(C). = 0.34). Longer exercise duration was less beneficial for several types of well-being, though findings are inconclusive. Physical activity had the strongest effects on self-efficacy (d(C).= 0.38), and improvements in cardiovascular status, strength, and functional capacity were linked to well-being improvement overall. Social-cognitive theory is used to explain the effect of physical activity on well-being.

https://psycnet.apa.org/journals/pag/20/2/272.pdf

Physical activity and psychological well-being in advanced age: a meta-analysis of intervention studies.

A new approach is presented for the meta-analysis of data from pre-test-post-test designs. With this approach, data from studies using different designs may be compared directly and studies without control groups need not be omitted. The approach is based on a ‘standardized mean-change’ measure, computed for each sample within a study, and involves analysis of the standardized mean changes and differences in the standardized mean changes. Analyses are illustrated using results of studies of the effectiveness of mental practice on motor-skill development.

Synthesizing standardized mean‐change measures

One of the most frequently cited reasons for conducting a meta-analysis is the increase in statistical power that it affords a reviewer. This article demonstrates that fixed-effects meta-analysis increases statistical power by reducing the standard error of the weighted average effect size (T.) and, in so doing, shrinks the confidence interval around T.. Small confidence intervals make it more likely for reviewers to detect nonzero population effects, thereby increasing statistical power. Smaller confidence intervals also represent increased precision of the estimated population effect size. Computational examples are provided for 3 effect-size indices: d (standardized mean difference), Pearson's r, and odds ratios. Random-effects meta-analyses also may show increased statistical power and a smaller standard error of the weighted average effect size. However, the authors demonstrate that increasing the number of studies in a random-effects meta-analysis does not always increase statistical power.

Betsy Jane Becker

Papers

Meta-analysis of observational studies in epidemiology - A proposal for reporting

Meta-analysis of Observational Studies in Epidemiology

Physical activity and psychological well-being in advanced age: a meta-analysis of intervention studies.

Synthesizing standardized mean‐change measures

How meta-analysis increases statistical power.