title: Optimal treatment allocation for placebo-treatment comparisons in trials with discrete-time survival endpoints.
abstract: In many randomized controlled trials, treatment groups are of equal size, but this is not necessarily the best choice. This paper provides a methodology to calculate optimal treatment allocations for longitudinal trials when we wish to compare multiple treatment groups with a placebo group, and the comparisons may have unequal importance. The focus is on trials with a survival endpoint measured in discrete time. We assume the underlying survival process is Weibull and show that values for the parameters in the Weibull distribution have an impact on the optimal treatment allocation scheme in an interesting way. Additionally, we incorporate different cost considerations at the subject and measurement levels and determine the optimal number of time periods. We also show that when many events occur at the beginning of the trial, fewer time periods are more efficient. As an application, we revisit a risperidone maintenance treatment trial in schizophrenia and use our proposed methodology to redesign it and compare merits of our optimal design.
authors.affiliation: Department of Methodology and Statistics, Utrecht University, Utrecht, The Netherlands.
authors.forename: Mirjam
authors.initials: M
authors.lastname: Moerbeek
authors.affiliation: Department of Biostatistics, University of California at Los Angeles, U.S.A.
authors.forename: Weng-Kee
authors.initials: WK
authors.lastname: Wong
mesh_terms: D014150:Antipsychotic Agents / Q000627:therapeutic use; D023381:Endpoint Determination / Q000706:statistics & numerical data; D006801:Humans; D008137:Longitudinal Studies; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic; D012107:Research Design; D018967:Risperidone / Q000627:therapeutic use*; D018401:Sample Size; D012559:Schizophrenia / Q000188:drug therapy; D016019:Survival Analysis
chemical_list: D014150:Antipsychotic Agents; D018967:Risperidone
keywords: Cox regression model; design efficiency; longitudinal study; multiple-objective optimal design