Swiss Medical Weekly, 2012

Meta-analyses overcome the limitation of small sample sizes or rare outcomes by pooling results from a number of individual studies to generate a single best estimate. As long as a meta-analysis is not limited by poor quality of included trials, unexplainable heterogeneity and/or reporting bias of individual trials, meta-analyses can be instrumental in reliably demonstrating benefit or harm of an intervention when results of individual randomised controlled trials are conflicting or inconclusive. Therefore meta-analyses should be conducted as part of a systematic review, i.e., a systematic approach to answer a focused clinical question. Important features of a systematic review are a comprehensive, reproducible search for primary studies, selection of studies using clear and transparent eligibility criteria, standardised critical appraisal of studies for quality, and investigation of heterogeneity among included studies. Cumulative meta-analysis may prevent delays in the introduction of effective treatments and may allow for early detection of harmful effects of interventions. As opposed to meta-analysis based on aggregate study data, individual patient data meta-analyses offer the advantage to use standardised criteria across trials and reliably investigate subgroup effects of interventions. Network meta-analysis allows the integration of data from direct and indirect comparisons in order to compare multiple treatments in a comprehensive analysis and determine the best treatment among several options. We conclude that meta-analysis has become a popular, versatile, and powerful tool. If rigorously conducted as part of a systematic review, it is essential for evidence-based decision making in clinical practice as well as on the health policy level.

Controlled Clinical Trials, 1986

This paper examines eight published reviews each reporting results from several related trials. Each review pools the results from the relevant trials in order to evaluate the efficacy of a certain treatment for a specified medical condition. These reviews lack consistent assessment of homogeneity of treatment effect before pooling. We discuss a random effects approach to combining evidence from a series of experiments comparing two treatments. This approach incorporates the heterogeneity of effects in the analysis of the overall treatment efficacy. The model can be extended to include relevant covariates which would reduce the heterogeneity and allow for more specific therapeutic recommendations. We suggest a simple noniterative procedure for characterizing the distribution of treatment effects in a series of studies.

PloS one, 2014

Background: ''Cumulative meta-analysis'' describes a statistical procedure to calculate, retrospectively, summary estimates from the results of similar trials every time the results of a further trial in the series had become available. In the early 1990s, comparisons of cumulative meta-analyses of treatments for myocardial infarction with advice promulgated through medical textbooks showed that research had continued long after robust estimates of treatment effects had accumulated, and that medical textbooks had overlooked strong, existing evidence from trials. Cumulative meta-analyses have subsequently been used to assess what could have been known had new studies been informed by systematic reviews of relevant existing evidence and how waste might have been reduced.

Statistics in Medicine, 1987

Meta-analysis is an important method of bridging the gap between undersized randomized control trials and the treatment of patients. However, as in any retrospective study, the opportunities for bias to distort the results are widespread. Attempts must be made to introduce the controls found in prospective studies by blinding the selection of papers and extraction of data and making blinded duplicate determinations. Informal and personalized methods of obtaining data are probably more liable to error and bias than employing only published data. Publication bias is a serious problem requiring further research. There also need to be more comparisons of meta-analysed small studies with large cooperative trials. KEY WORDS Meta-analysis Randomized control trials Random allocation bias Double-blind method Cooperative trials.

Controlled Clinical Trials, 1995

From 1983 to 1987, the Department of Veterans Affairs (DVA) Cooperative Studies Program (CSP) conducted a multicenter clinical trial (CSP #207) to determine whether four different antiplatelet regimens compared to placebo could prevent the occlusion of grafts following coronary artery bypass surgery. The study showed that all of the active regimens tended to be better than placebo and that the three regimens containing aspirin were statistically significantly better. A cumulative meta-analysis of 12 trials performed shortly before the end of CSP # 207 raised the issue as to whether the meta-analysis, if done earlier, would have changed the conduct of the trial. At the start of the planning period, one trial of size n = 37 had been published with a nonsignifcant odds ratio (OR) of 0.74 (95% CI: 0.18, 3.12). At the time that CSP #207 was approved by the DVA Cooperative Studies Evaluation Committee, two trials had been published (cumulative n = 150, OR = 0.44,95% CI 0.19,0.99). At the time patient intake started, five trials showed cumulative n = 769, OR = 0.42,95% CI = 0.26,0.68. Although the first 6-month CSP #207 progress report showed no treatment effect, by the time of the 12-month review by the Data Monitoring Board (DMB) a trend was developing in favor of active treatment. If the results of the meta-analysis had been available to the DMB at that time, conceivably the Board would have recommended stopping the placebo arm because of a convincing treatment effect based on the totality of the evidence. Cumulative meta-analysis could be useful as an adjunct in the planning, conduct, and final analysis of a clinical trial. It could also be used as one piece of evidence in the monitoring of the ongoing phase of a trial.

Journal of Clinical Epidemiology, 2014

Background: There is evidence to suggest that component randomized controlled trials (RCTs) within systematic reviews may be biased. It is important that these reviews are identified to prevent erroneous conclusions influencing health care policies and decisions.

Journal of nephrology

Meta-analyses are frequently criticized because in most cases they are compiled from quite heterogeneous studies. In spite of this limitation meta-analyses are increasingly published because in many areas of clinical research the results of individual studies are devoid of statistical power and end up with conflicting results. Metaanalyses, if performed with a rigorous and exhaustive search of all accountable information on a specific topic, have the potential of overcoming the drawbacks of single studies and, in addition, of adjusting for publication bias and interstudy variability. These strengths of metaanalyses can be exploited to provide conclusive answers on diagnostic and therapeutic issues being debated, which in turn may help guide doctors toward more rational decisions.

2013

Objective To assess the influence of trial sample size on treatment effect estimates within meta-analyses.

Journal of Clinical Epidemiology, 2011

Objective: Many systematic reviews include only a few studies. It is unclear whether recommendations based on these will be correct in the longer term; hence, this article explores whether meta-analyses give reliable results after only a few studies. Study Design and Setting: Cumulative meta-analysis of data from 65 meta-analyses from 18 Cochrane systematic reviews was carried out. Various measures of closeness to the pooled estimate from all trials after three and five trials were included. Changes during the accumulation of evidence were noted. Results: The 95% confidence interval included the final estimate in 72% of meta-analyses after three studies and in 83% after five studies. It took a median of four (interquartile range: 1.25e6) studies to get within 10% of the final point estimate. Agreement between the results at three and five studies and the final estimate was not predicted by the number of participants, the number of events, t 2 , or I 2. Estimates could still change substantially after many trials were included. Conclusion: Many of the conclusions drawn from systematic reviews with small numbers of included studies will be correct in the long run, but it is not possible to predict which ones.

British Journal of Surgery, 2000

S u m m a r y B a c k g r o u n d The Quality of Reporting of Meta-analyses (QUOROM) conference was convened to address standards for improving the quality of reporting of meta-analyses of clinical randomised controlled trials (RCTs).