Systematic review and meta-analysis

Systematic reviews and meta-analyses are essential to summarize evidence relating to efficacy and safety of health care interventions accurately and reliably. The clarity and transparency of these reports, however, is not optimal. Poor reporting of systematic reviews diminishes their value to clinicians, policy makers, and other users.

Since the development of the QUOROM (QUality Of Reporting Of Meta-analysis) Statement—a reporting guideline published in 1999—there have been several conceptual, methodological, and practical advances regarding the conduct and reporting of systematic reviews and meta-analyses. Also, reviews of published systematic reviews have found that key information about these studies is often poorly reported. Realizing these issues, an international group that included experienced authors and methodologists developed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) as an evolution of the original QUOROM guideline for systematic reviews and meta-analyses of evaluations of health care interventions.

The PRISMA Statement consists of a 27-item checklist and a four-phase flow diagram. The checklist includes items deemed essential for transparent reporting of a systematic review. In this Explanation and Elaboration document, we explain the meaning and rationale for each checklist item. For each item, we include an example of good reporting and, where possible, references to relevant empirical studies and methodological literature. The PRISMA Statement, this document, and the associated Web site (http://www.prisma-statement.org/) should be helpful resources to improve reporting of systematic reviews and meta-analyses.

/pdf/the-prisma-statement-for-reporting-systematic-reviews-and-3j53e5oe33.pdf

The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration

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.

/pdf/cochrane-handbook-for-systematic-reviews-of-interventions-32sv80ql7o.pdf

Cochrane Handbook for Systematic Reviews of Interventions

Flaws in the design, conduct, analysis, and reporting of randomised trials can cause the effect of an intervention to be underestimated or overestimated. The Cochrane Collaboration’s tool for assessing risk of bias aims to make the process clearer and more accurate

https://www.bmj.com/content/bmj/343/bmj.d5928.full.pdf

The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials

/pdf/cochrane-handbook-for-systematic-reviews-of-interventions-1xbdy6uvru.pdf

Causal diagrams have a long history of informal use and, more recently, have undergone formal development for applications in expert systems and robotics. We provide an introduction to these developments and their use in epidemiologic research. Causal diagrams can provide a starting point for identifying variables that must be measured and controlled to obtain unconfounded effect estimates. They also provide a method for critical evaluation of traditional epidemiologic criteria for confounding. In particular, they reveal certain heretofore unnoticed shortcomings of those criteria when used in considering multiple potential confounders. We show how to modify the traditional criteria to correct those shortcomings.

https://www.jstor.org/stable/pdfplus/3702180.pdf

Causal diagrams for epidemiologic research.

Valentin Amrhein, Sander Greenland, Blake McShane and more than 800 signatories call for an end to hyped claims and the dismissal of possibly crucial effects. Valentin Amrhein, Sander Greenland, Blake McShane and more than 800 signatories call for an end to hyped claims and the dismissal of possibly crucial effects.

/pdf/scientists-rise-up-against-statistical-significance-3w9zi5l6z2.pdf

Scientists rise up against statistical significance

Modern Epidemiology 3rd edition

In the absence of prior knowledge about population relations, investigators frequently employ a strategy that uses the data to help them decide whether to adjust for a variable. The authors compared the performance of several such strategies for fitting multiplicative Poisson regression models to cohort data: 1) the "change-in-estimate" strategy, in which a variable is controlled if the adjusted and unadjusted estimates differ by some important amount; 2) the "significance-test-of-the-covariate" strategy, in which a variable is controlled if its coefficient is significantly different from zero at some predetermined significance level; 3) the "significance-test-of-the-difference" strategy, which tests the difference between the adjusted and unadjusted exposure coefficients; 4) the "equivalence-test-of-the-difference" strategy, which significance-tests the equivalence of the adjusted and unadjusted exposure coefficients; and 5) a hybrid strategy that takes a weighted average of adjusted and unadjusted estimates. Data were generated from 8,100 population structures at each of several sample sizes. The performance of the different strategies was evaluated by computing bias, mean squared error, and coverage rates of confidence intervals. At least one variation of each strategy that was examined performed acceptably. The change-in-estimate and equivalence-test-of-the-difference strategies performed best when the cut-point for deciding whether crude and adjusted estimates differed by an important amount was set to a low value (10%). The significance test strategies performed best when the alpha level was set to much higher than conventional levels (0.20).

Simulation Study of Confounder-Selection Strategies

Much controversy exists regarding proper methods for the selection of variables in confounder control. Many authors condemn any use of significance testing, some encourage such testing, and other propose a mixed approach. This paper presents the results of a Monte Carlo simulation of several confounder selection criteria, including change-in-estimate and collapsibility test criteria. The methods are compared with respect to their impact on inferences regarding the study factor's effect, as measured by test size and power, bias, mean-squared error, and confidence interval coverage rates. In situations in which the best decision (of whether or not to adjust) is not always obvious, the change-in-estimate criterion tends to be superior, though significance testing methods can perform acceptably if their significance levels are set much higher than conventional levels (to values of 0.20 or more).

The impact of confounder selection criteria on effect estimation

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