Analysis of randomized comparative clinical trial data for personalized treatment selections.
Suppose that under the conventional randomized clinical trial setting, a new therapy is compared with a standard treatment. In this article, we propose a systematic, 2-stage estimation procedure for the subject-level treatment differences for future patient's disease management and treatment selections. To construct this procedure, we first utilize a parametric or semiparametric method to estimate individual-level treatment differences, and use these estimates to create an index scoring system for grouping patients. We then consistently estimate the average treatment difference for each subgroup of subjects via a nonparametric function estimation method. Furthermore, pointwise and simultaneous interval estimates are constructed to make inferences about such subgroup-specific treatment differences. The new proposal is illustrated with the data from a clinical trial for evaluating the efficacy and toxicity of a 3-drug combination versus a standard 2-drug combination for treating HIV-1-infected patients.
Cai
Tianxi
T
Department of Biostatistics, Harvard University, Boston, MA 02115, USA. tcai@hsph.harvard.edu
Tian
Lu
L
Wong
Peggy H
PH
Wei
L J
LJ
D000367:Age Factors; D000465:Algorithms; D044966:Anti-Retroviral Agents / Q000008:administration & dosage / Q000009:adverse effects / Q000627:therapeutic use; D018791:CD4 Lymphocyte Count; D003198:Computer Simulation; D004359:Drug Therapy, Combination / Q000009:adverse effects; D015658:HIV Infections / Q000188:drug therapy; D015497:HIV-1; D006801:Humans; D015233:Models, Statistical; D057285:Precision Medicine / Q000379:methods*; D012367:RNA, Viral / Q000097:blood; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D016896:Treatment Outcome
D044966:Anti-Retroviral Agents; D012367:RNA, Viral