title: Evaluating surrogate marker information using censored data.
abstract: Given the long follow-up periods that are often required for treatment or intervention studies, the potential to use surrogate markers to decrease the required follow-up time is a very attractive goal. However, previous studies have shown that using inadequate markers or making inappropriate assumptions about the relationship between the primary outcome and surrogate marker can lead to inaccurate conclusions regarding the treatment effect. Currently available methods for identifying and validating surrogate markers tend to rely on restrictive model assumptions and/or focus on uncensored outcomes. The ability to use such methods in practice when the primary outcome of interest is a time-to-event outcome is difficult because of censoring and missing surrogate information among those who experience the primary outcome before surrogate marker measurement. In this paper, we propose a novel definition of the proportion of treatment effect explained by surrogate information collected up to a specified time in the setting of a time-to-event primary outcome. Our proposed approach accommodates a setting where individuals may experience the primary outcome before the surrogate marker is measured. We propose a robust non-parametric procedure to estimate the defined quantity using censored data and use a perturbation-resampling procedure for variance estimation. Simulation studies demonstrate that the proposed procedures perform well in finite samples. We illustrate the proposed procedures by investigating two potential surrogate markers for diabetes using data from the Diabetes Prevention Program. Copyright © 2017 John Wiley & Sons, Ltd.
authors.affiliation: RAND Corporation, 1776 Main Street, Santa Monica, 90401, CA, U.S.A.
authors.forename: Layla
authors.initials: L
authors.lastname: Parast
authors.affiliation: Department of Biostatistics, Harvard University, Boston, 02115, MA, U.S.A.
authors.forename: Tianxi
authors.initials: T
authors.lastname: Cai
authors.affiliation: Department of Health, Research and Policy, Stanford University, Stanford, 94305, CA, U.S.A.
authors.forename: Lu
authors.initials: L
authors.lastname: Tian
mesh_terms: D015415:Biomarkers / Q000032:analysis*; D015984:Causality; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D011336:Probability; D018709:Statistics, Nonparametric; D016896:Treatment Outcome
chemical_list: D015415:Biomarkers
keywords: non-parametric methods; robust procedures; smoothing; survival analysis