title: Evaluating predictors of competing risk outcomes when censoring depends on time-dependent covariates, with application to safety and efficacy of HIV treatment.
abstract: We propose a prediction model for the cumulative incidence functions of competing risks, based on a logit link. Because of a concern about censoring potentially depending on time-varying covariates in our motivating human immunodeficiency virus (HIV) application, we describe an approach for estimating the parameters in the prediction models using inverse probability of censoring weighting under a missingness at random assumption. We then illustrate the application of this methodology to identify predictors of the competing outcomes of virologic failure, an efficacy outcome, and treatment limiting adverse event, a safety outcome, among human immunodeficiency virus-infected patients first starting antiretroviral treatment. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Department of Biostatistics, Harvard T.H. Chan School of Public Health, Huntington Avenue, Boston, MA, 65502115, U.S.A.
authors.forename: Judith J
authors.initials: JJ
authors.lastname: Lok
authors.affiliation: Department of Biostatistics, Harvard T.H. Chan School of Public Health, Huntington Avenue, Boston, MA, 65502115, U.S.A.
authors.forename: Michael D
authors.initials: MD
authors.lastname: Hughes
mesh_terms: D019380:Anti-HIV Agents / Q000009:adverse effects / Q000627:therapeutic use*; D003627:Data Interpretation, Statistical; D015658:HIV Infections / Q000188:drug therapy*; D006801:Humans; D015233:Models, Statistical; D011336:Probability; D018570:Risk Assessment; D013997:Time Factors; D017211:Treatment Failure; D016896:Treatment Outcome
chemical_list: D019380:Anti-HIV Agents
keywords: HIV/AIDS; competing risks; logit link; missing at random; personalized medicine; safety and efficacy