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Cluster randomized controlled trials (cluster RCTs), also known as parallel-arm group-randomized trials, are trials in which the randomized units are groups of participants, as opposed to individual participants. These trials have largely been implemented to address broad public health issues, but with the growing interest in use of real-world data in the regulatory setting, this design may be increasingly considered for industry trials. The key difference between cluster RCTs and traditional RCTs is the intraclass correlation coefficient (ICC) that needs to be considered in cluster RCTs. In this article, we discuss some key practical considerations that are related to ICC in the design, conduct, analysis, and report stages of a cluster RCT. These key considerations related to ICC can lead to improvement in how we translate research findings from cluster RCTs into practices in the biopharmaceutical industry.

Background Two methods that are commonly used in most clinical trials for evaluating the effect of treatment on hot flashes are frequency only and severity score methods. However, in these crude methods, composite recurrent hot flash data on a patient are reduced to a one-dimensional summary measure, an approach which may not be optimal. In addition, the duration of a hot flash event provides additional information, which is not accounted for in the two methods mentioned above. Purpose We propose two new methods in the analysis of hot flash data in this paper. Methods The first method utilizes a generalization of the continuation ratio model to model the severity of a hot flash event. The model accounts for individual random effects and dependence of recurrent events within an individual. The other method that accounts for the duration of a hot flash event is a 3-Dimensional (3D) score method, which incorporates hot flash information on frequency, severity, and duration. Results Simulation studies were conducted to evaluate the properties of our proposed methods. The results indicate that our proposed generalized continuation ratio model has appreciably high power in detecting small treatment effects on severity compared with the two commonly used methods. The proposed 3D score, on the other hand, is sensitive in detecting treatment effects on both frequency and duration, but has low power in detecting treatment effects on severity alone. Conclusions Our proposed generalized continuation ratio model approach provides a valuable and powerful alternative to the two commonly used methods in the analysis of treatment effects on hot flash severity. Our proposed 3D score method incorporates the additional information hot flash duration provides, and is another analysis option for the practitioners to employ.

Clinical trials often collect longitudinal measurements of a disease marker and time to a major clinical event of the disease to assess treatment effects. It makes sense to combine the treatment effects on both the longitudinal disease marker and the time to clinical event, especially when the clinical event is also mediated through the disease marker over time. In this paper we apply a joint modeling approach in the assessment of the treatment effects in treating benign prostatic hyperplasia (BPH) for the ProscarTM Long-Term Efficacy and Safety Study (PLESS).