Search

Background: In many therapeutic areas, individual patient markers have been identified that are associated with differential treatment response. These markers include both baseline characteristics, as well as short-term changes following treatment. Using such predictive markers to select subjects for inclusion in randomized clinical trials could potentially result in more targeted studies and reduce the number of subjects to recruit., Methods: This study compared three trial designs on the sample size needed to establish treatment efficacy across a range of realistic scenarios. A conventional parallel group design served as the point of reference, while the alternative designs selected subjects on either a baseline characteristic or an early improvement after a short active run-in phase. Data were generated using a model that characterized the effect of treatment on survival as a combination of a primary effect, an interaction with a baseline marker and/or an early marker improvement. A representative scenario derived from empirical data was also evaluated., Results: Simulations showed that an active run-in design could substantially reduce the number of subjects to recruit when improvement during active run-in was a reliable predictor of differential treatment response. In this case, the baseline selection design was also more efficient than the parallel group design, but less efficient than the active run-in design with an equally restricted population. For most scenarios, however, the advantage of the baseline selection design was limited., Conclusions: An active run-in design could substantially reduce the number of subjects to recruit in a randomized clinical trial. However, just as with the baseline selection design, generalizability of results may be limited and implementation could be difficult.

Two-stage clinical trial designs may be efficient in pharmacogenetics research when there is some but inconclusive evidence of effect modification by a genomic marker. Two-stage designs allow to stop early for efficacy or futility and can offer the additional opportunity to enrich the study population to a specific patient subgroup after an interim analysis. This study compared sample size requirements for fixed parallel group, group sequential, and adaptive selection designs with equal overall power and control of the family-wise type I error rate. The designs were evaluated across scenarios that defined the effect sizes in the marker positive and marker negative subgroups and the prevalence of marker positive patients in the overall study population. Effect sizes were chosen to reflect realistic planning scenarios, where at least some effect is present in the marker negative subgroup. In addition, scenarios were considered in which the assumed 'true' subgroup effects (i.e., the postulated effects) differed from those hypothesized at the planning stage. As expected, both two-stage designs generally required fewer patients than a fixed parallel group design, and the advantage increased as the difference between subgroups increased. The adaptive selection design added little further reduction in sample size, as compared with the group sequential design, when the postulated effect sizes were equal to those hypothesized at the planning stage. However, when the postulated effects deviated strongly in favor of enrichment, the comparative advantage of the adaptive selection design increased, which precisely reflects the adaptive nature of the design., (Copyright © 2013 John Wiley & Sons, Ltd.)

Introduction: Continuous trial outcomes are often dichotomized into 'response' and 'non-response' categories prior to statistical analysis. This facilitates the interpretation of results, but generally reduces statistical power. Exceptions may occur when response in the study population is heterogeneous, and outcomes are bimodally distributed. We explore whether bimodality is present in antidepressant trial data and whether dichotomizing then indeed results in more powerful statistical tests., Methods: The distributions of relative changes from baseline (rCFB) on the Hamilton depression rating scale (HAM-D) were estimated using pooled data from nine antidepressant trials. T-tests on rCFB scores and chi-square tests on dichotomized outcomes were compared to assess the consequences of dichotomization, using both the commonly applied cutoff (i.e. rCFB > 50%) and an estimated cutoff that provided optimal separation of the mixture of two normal distributions that best fitted the pooled placebo outcomes. The power of both tests was also evaluated for simulated scenario's that varied the degree of bimodality and the treatment effect and sample size., Results: Placebo and treatment groups showed evidence of bimodality. The estimated cutoff closely matched the commonly applied cutoff. Nevertheless, t-tests generally yielded smaller p-values than chi-square tests. Simulations showed that dichotomization only provides superior power when bimodality was considerably more marked than observed in the empirical data., Conclusion: Antidepressant trial outcomes showed bimodality, suggesting differential response among patient groups. This heterogeneity in outcome distributions should be reported more often, since a comparison of means does not adequately summarize the differences between treatment groups. However, simply dichotomizing outcomes is not an appropriate alternative as it reduces statistical power., (Copyright © 2012 Elsevier Ltd. All rights reserved.)