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When meta‐analysing intervention effects calculated from continuous outcomes, meta‐analysts often encounter few trials, with potentially a small number of participants, and a variety of trial analytical methods. It is important to know how these factors affect the performance of inverse‐variance fixed and DerSimonian and Laird random effects meta‐analytical methods. We examined this performance using a simulation study.  Meta‐analysing estimates of intervention effect from final values, change scores, ANCOVA or a random mix of the three yielded unbiased estimates of pooled intervention effect. The impact of trial analytical method on the meta‐analytic performance measures was important when there was no or little heterogeneity, but was of little relevance as heterogeneity increased. On the basis of larger than nominal type I error rates and poor coverage, the inverse‐variance fixed effect method should not be used when there are few small trials.  When there are few small trials, random effects meta‐analysis is preferable to fixed effect meta‐analysis. Meta‐analytic estimates need to be cautiously interpreted; type I error rates will be larger than nominal, and confidence intervals will be too narrow. Use of trial analytical methods that are more efficient in these circumstances may have the unintended consequence of further exacerbating these issues. © 2015 The Authors.  Research Synthesis Methods  published by John Wiley & Sons, Ltd. © 2015 The Authors.  Research Synthesis Methods  published by John Wiley & Sons, Ltd.

research synthesis methods

Impact of analysing continuous outcomes using final values, change scores and analysis of covariance on the performance of meta‐analytic methods: a simulation study