Half-day course taught by Alex Dmitrienko at the ASA Biopharmaceutical Section Regulatory-Industry Statistics Workshop in September 2016.
This half-day course focuses on a broad class of statistical problems related to optimizing the design and analysis of Phase II and III trials (Dmitrienko and Pulkstenis, 2016). This general topic has attracted much attention across the clinical trial community due to increasing pressure to reduce implementation costs and shorten timelines. The Clinical Scenario Evaluation (CSE) framework (Benda et al., 2010) will be described in this short course to formulate a general approach to clinical trial optimization and decision making. The CSE framework facilitates the comparison of competing options for clinical development programs and clinical trial designs/analyses. The concept includes three different elements, namely, the set of underlying assumptions (data models), the options to be assessed (analysis models) and the metrics used for the assessment (evaluation models).
Using the CSE approach, main objectives of clinical trial optimization will be formulated, including selection of clinically relevant optimization criteria, identification of sets of optimal and nearly optimal values of the parameters of interest, and sensitivity assessments. Key principles of clinical trial optimization will be illustrated using a number of problems that often arise in Phase II and III clinical trials. This includes optimal selection of analysis strategies that involve multiplicity adjustments (Dmitrienko et al., 2009; Dmitrienko, D’Agostino and Huque, 2013), optimal selection design elements in adaptive clinical trials (Dmitrienko et al., 2016) and selection of patient subgroups in enrichment designs. The short course will focus on a frequentist perspective but will also introduce the Bayesian approach to clinical trial optimization (including probability of success or assurance calculations).
Clinical trial optimization methods make heavy use of clinical trial simulations. Simulation-based approaches to evaluating trial designs and analysis methods will be discussed. Practical solutions that the participants can quickly apply to address real-life challenges in clinical trials will be emphasized throughout this short course.
Multiple case studies based on real Phase II and III trials will be used, e.g., clinical trials with multiple endpoints and dose-placebo comparisons, trials with adaptive and enrichment designs. Software tools for applying optimization methods will be presented, including R software (Mediana package) and Windows application with a graphical user interface.
For more information about the Mediana package, please visit the package’s page.
Download the Mediana code used in Case study 1 (direct optimization algorithm for selecting an optimal multiplicity adjustment in a Phase III clinical trial with two patient populations).
Benda, N., Branson, M., Maurer, W., Friede, T. Aspects of modernizing drug development using clinical scenario planning and evaluation. Drug Information Journal. 44, 299-315, 2010.
Dmitrienko, A., Tamhane, A.C., Bretz, F. (editors). Multiple Testing Problems in Pharmaceutical Statistics. Chapman and Hall/CRC Press, New York, 2009.
Dmitrienko, A., D’Agostino, R.B., Huque, M.F. Key multiplicity issues in clinical drug development. Statistics in Medicine. 32, 1079-1111, 2013.
Dmitrienko, A., Paux, G., Brechenmacher, T. (2015). Power calculations in clinical trials with complex clinical objectives. Journal of the Japanese Society of Computational Statistics. 28, 15-50.
Dmitrienko, A., Paux, G., Pulkstenis, E., Zhang, J. (2016). Tradeoff-based optimization criteria in clinical trials with multiple objectives and adaptive designs. Journal of Biopharmaceutical Statistics. 26, 120-140.
Dmitrienko, A., Pulkstenis, E. (editors). Clinical Trial Optimization Using R. Chapman and Hall/CRC Press, New York (expected to be published in 2017).
Friede, T., Nicholas, R., Stallard, N., Todd, S., Parsons, N. R., Valdes-Marquez, E., Chataway, J. (2010). Refinement of the clinical scenario evaluation framework for assessment of competing development strategies with an application to multiple sclerosis. Drug Information Journal. 44, 713-718.