title: Statistical considerations in evaluating pharmacogenomics-based clinical effect for confirmatory trials.
abstract: BACKGROUND
  The current practice for seeking genomically favorable patients in randomized controlled clinical trials using genomic convenience samples.
  
  
  OBJECTIVE
  To discuss the extent of imbalance, confounding, bias, design efficiency loss, type I error, and type II error that can occur in the evaluation of the convenience samples, particularly when they are small samples. To articulate statistical considerations for a reasonable sample size to minimize the chance of imbalance, and, to highlight the importance of replicating the subgroup finding in independent studies.
  
  
  METHODS
  Four case examples reflecting recent regulatory experiences are used to underscore the problems with convenience samples. Probability of imbalance for a pre-specified subgroup is provided to elucidate sample size needed to minimize the chance of imbalance. We use an example drug development to highlight the level of scientific rigor needed, with evidence replicated for a pre-specified subgroup claim.
  
  
  RESULTS
  The convenience samples evaluated ranged from 18% to 38% of the intent-to-treat samples with sample size ranging from 100 to 5000 patients per arm. The baseline imbalance can occur with probability higher than 25%. Mild to moderate multiple confounders yielding the same directional bias in favor of the treated group can make treatment group incomparable at baseline and result in a false positive conclusion that there is a treatment difference. Conversely, if the same directional bias favors the placebo group or there is loss in design efficiency, the type II error can increase substantially.
  
  
  CONCLUSIONS
  Pre-specification of a genomic subgroup hypothesis is useful only for some degree of type I error control.
  
  
  CONCLUSIONS
  Complete ascertainment of genomic samples in a randomized controlled trial should be the first step to explore if a favorable genomic patient subgroup suggests a treatment effect when there is no clear prior knowledge and understanding about how the mechanism of a drug target affects the clinical outcome of interest. When stratified randomization based on genomic biomarker status cannot be implemented in designing a pharmacogenomics confirmatory clinical trial, if there is one genomic biomarker prognostic for clinical response, as a general rule of thumb, a sample size of at least 100 patients may be needed to be considered for the lower prevalence genomic subgroup to minimize the chance of an imbalance of 20% or more difference in the prevalence of the genomic marker. The sample size may need to be at least 150, 350, and 1350, respectively, if an imbalance of 15%, 10% and 5% difference is of concern.
authors.affiliation: Office of Biostatistics, Office of Translational Sciences, Center for Drug Evaluation and Research, US FDA, Silver Spring, MD 20993, USA. suejane.wang@fda.hhs.gov
authors.forename: Sue-Jane
authors.initials: SJ
authors.lastname: Wang
authors.forename: Robert T
authors.initials: RT
authors.lastname: O'Neill
authors.forename: Hm James
authors.initials: HJ
authors.lastname: Hung
mesh_terms: D005819:Genetic Markers; D006801:Humans; D018579:Patient Selection; D010597:Pharmacogenetics; D016032:Randomized Controlled Trials as Topic / Q000379:methods* / Q000706:statistics & numerical data; D015203:Reproducibility of Results; D018401:Sample Size; D015983:Selection Bias; D013223:Statistics as Topic / Q000379:methods*
chemical_list: D005819:Genetic Markers