Goodness-of-fit test of the stratified mark-specific proportional hazards model with continuous mark

Yanqing Sun, Mei Li and Peter B. Gilbert

Computational Statistics & Data Analysis, 2016, vol. 93, issue C, 348-358

Abstract: Motivated by the need to assess HIV vaccine efficacy, previous studies proposed an extension of the discrete competing risks proportional hazards model, in which the cause of failure is replaced by a continuous mark only observed at the failure time. However the model assumptions may fail in several ways, and no diagnostic testing procedure for this situation has been proposed. A goodness-of-fit test procedure for the stratified mark-specific proportional hazards model in which the regression parameters depend nonparametrically on the mark and the baseline hazards depend nonparametrically on both time and the mark is proposed. The test statistics are constructed based on the weighted cumulative mark-specific martingale residuals. The critical values of the proposed test statistics are approximated using the Gaussian multiplier method. The performance of the proposed tests is examined extensively in simulations for a variety of the models under the null hypothesis and under different types of alternative models. An analysis of the ‘Step’ HIV vaccine efficacy trial using the proposed method is presented. The analysis suggests that the HIV vaccine candidate may increase susceptibility to HIV acquisition.

Keywords: Asymptotics; Bandwidth selection; Competing risks; Cumulative mark-specific martingale residuals; Gaussian multiplier method; HIV vaccine efficacy trial; Immune responses; Local partial likelihood estimation; Mark-specific hazard ratio; Semiparametric failure time model; HVTN 502 Step trial (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:93:y:2016:i:c:p:348-358

DOI: 10.1016/j.csda.2014.11.012

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