Biological psychiatry
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Biological psychiatry · Jun 2006
ReviewSize of treatment effects and their importance to clinical research and practice.
In randomized clinical trails (RCTs), effect sizes seen in earlier studies guide both the choice of the effect size that sets the appropriate threshold of clinical significance and the rationale to believe that the true effect size is above that threshold worth pursuing in an RCT. That threshold is used to determine the necessary sample size for the proposed RCT. Once the RCT is done, the data generated are used to estimate the true effect size and its confidence interval. ⋯ Thus, effect sizes play an important role both in designing RCTs and in interpreting their results; but specifically which effect size? We review the principles of statistical significance, power, and meta-analysis, and commonly used effect sizes. The commonly used effect sizes are limited in conveying clinical significance. We recommend three equivalent effect sizes: number needed to treat, area under the receiver operating characteristic curve comparing T and C responses, and success rate difference, chosen specifically to convey clinical significance.
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Biological psychiatry · Jun 2006
ReviewAttrition in randomized controlled clinical trials: methodological issues in psychopharmacology.
Attrition is a ubiquitous problem in randomized controlled clinical trials (RCT) of psychotropic agents that can cause biased estimates of the treatment effect, reduce statistical power, and restrict the generalizability of results. The extent of the problem of attrition in central nervous system (CNS) trials is considered here and its consequences are examined. The taxonomy of missingness mechanisms is then briefly reviewed in order to introduce issues underlying the choice of data analytic strategies appropriate for RCTs with various forms of incomplete data. ⋯ Mixed-effects models often provide a useful data analytic strategy for attrition as do the pattern-mixture and propensity adjustments. Finally, investigators are encouraged to consider asking participants, at each assessment session, the likelihood of attendance at the subsequent assessment session. This information can be used to eliminate some of the very obstacles that lead to attrition, and can be incorporated in data analyses to reduce bias, but it will not eliminate all attrition bias.