title: Evaluating principal surrogate endpoints with time-to-event data accounting for time-varying treatment efficacy.
abstract: Principal surrogate (PS) endpoints are relatively inexpensive and easy to measure study outcomes that can be used to reliably predict treatment effects on clinical endpoints of interest. Few statistical methods for assessing the validity of potential PSs utilize time-to-event clinical endpoint information and to our knowledge none allow for the characterization of time-varying treatment effects. We introduce the time-dependent and surrogate-dependent treatment efficacy curve, ${\mathrm {TE}}(t|s)$, and a new augmented trial design for assessing the quality of a biomarker as a PS. We propose a novel Weibull model and an estimated maximum likelihood method for estimation of the ${\mathrm {TE}}(t|s)$ curve. We describe the operating characteristics of our methods via simulations. We analyze data from the Diabetes Control and Complications Trial, in which we find evidence of a biomarker with value as a PS.
authors: Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA, USA.
authors: Erin E
authors: EE
authors: Gabriel
authors: Peter B
authors: PB
authors: Gilbert
mesh_terms: D015415:Biomarkers; D048909:Diabetes Complications / Q000517:prevention & control; D003922:Diabetes Mellitus, Type 1 / Q000188:drug therapy; D023381:Endpoint Determination; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000592:standards; D012107:Research Design / Q000592:standards*; D018570:Risk Assessment; D013997:Time Factors; D016896:Treatment Outcome
chemical_list: D015415:Biomarkers
keywords: Case–control study; Causal inference; Clinical trials; Principal stratification; Survival analysis; Treatment efficacy curve; Weibull model