Evaluating marker-guided treatment selection strategies.
A potential venue to improve healthcare efficiency is to effectively tailor individualized treatment strategies by incorporating patient level predictor information such as environmental exposure, biological, and genetic marker measurements. Many useful statistical methods for deriving individualized treatment rules (ITR) have become available in recent years. Prior to adopting any ITR in clinical practice, it is crucial to evaluate its value in improving patient outcomes. Existing methods for quantifying such values mainly consider either a single marker or semi-parametric methods that are subject to bias under model misspecification. In this article, we consider a general setting with multiple markers and propose a two-step robust method to derive ITRs and evaluate their values. We also propose procedures for comparing different ITRs, which can be used to quantify the incremental value of new markers in improving treatment selection. While working models are used in step I to approximate optimal ITRs, we add a layer of calibration to guard against model misspecification and further assess the value of the ITR non-parametrically, which ensures the validity of the inference. To account for the sampling variability of the estimated rules and their corresponding values, we propose a resampling procedure to provide valid confidence intervals for the value functions as well as for the incremental value of new markers for treatment selection. Our proposals are examined through extensive simulation studies and illustrated with the data from a clinical trial that studies the effects of two drug combinations on HIV-1 infected patients.
Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts 02115, USA.
Roland A
RA
Matsouaka
Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA.
Junlong
J
Li
Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA.
Tianxi
T
Cai
D000163:Acquired Immunodeficiency Syndrome / Q000097:blood* / Q000188:drug therapy* / Q000453:epidemiology; D019380:Anti-HIV Agents / Q000008:administration & dosage*; D015415:Biomarkers / Q000097:blood; D015704:CD4 Antigens / Q000097:blood*; D003627:Data Interpretation, Statistical; D004338:Drug Combinations; D006801:Humans; D017063:Outcome Assessment, Health Care / Q000379:methods*; D015995:Prevalence; D011379:Prognosis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016896:Treatment Outcome
D019380:Anti-HIV Agents; D015415:Biomarkers; D015704:CD4 Antigens; D004338:Drug Combinations
Biomarker‐analysis design; Counterfactual outcome; Personalized medicine; Perturbation‐resampling; Predictive biomarkers; Subgroup analysis