Meta-analytic stimulus for changes in clinical trials

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Abstract
The advent of meta-analysis, especially when performed cumulatively, raises many questions about how best to approach the conduct of clinical trials in the evaluation of new treatments. We need to be assured that bias is minimized by proper experimental procedures and that clinical data, on the whole and in subgroups, are presented so that they can be effectively combined in meta-analysis. We need to re-examine the idea that we should not start a randomized control trial unless sufficient patients are available to avoid reasonable type I and II errors. Meta-analyses will come to the rescue, provided trials continue to be published at the present rate. We need to perform meta-analyses before undertaking each additional trial, and we need to base estimates of trial size on past data as well as the expected control rates and the differences we do not want to miss. In clinical trials of new interventions attempting to disprove the null hypothesis may be inappropriate because past data so often suggest or even establish that it is not true. Furthermore we need to recognize that trends (p > 0.05) can be both clinically and statistically important, and we must abandon the notion that if p is not < 0.05, the treatment is ineffective. In performing meta-analyses we need to worry about minimizing bias and error and consider the differences between the random and fixed effects models and between reporting results as an odds ratio versus difference in risk, with the control rates given. Experiences with cumulative meta-analysis have required that we think about all of these problems.