On a Shape-Invariant Hazard Regression Model with application to an HIV Prevention Study of Mother-to-Child Transmission

Cheng Zheng () and Ying Qing Chen
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Cheng Zheng: University of Wisconsin-Milwaukee
Ying Qing Chen: Fred Hutchinson Cancer Research Center

Statistics in Biosciences, 2020, vol. 12, issue 3, No 6, 340-352

Abstract: Abstract In survival analysis, Cox model is widely used for most clinical trial data. Alternatives include the additive hazard model, the accelerated failure time (AFT) model and a more general transformation model. All these models assume that the effects for all covariates are on the same scale. However, it is possible that for different covariates, the effects are on different scales. In this paper, we propose a shape-invariant hazard regression model that allows us to estimate the multiplicative treatment effect with adjustment of covariates that have non-multiplicative effects. We propose moment-based inference procedures for the regression parameters. We also discuss the risk prediction and the goodness of fit test for our proposed model. Numerical studies show good finite sample performance of our proposed estimator. We applied our method to the HIVNET 012 study, a milestone trial of single-dose nevirapine in prevention of mother-to-child transmission of HIV. From the HIVNET 012 data analysis, single-dose nevirapine treatment is shown to improve 18-month infant survival significantly with appropriate adjustment of the maternal CD4 counts and the virus load.

Keywords: Censoring; Counting processes; Semiparametric methods; Time-to-event analysis; 62N01; 62N02; 62P10 (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s12561-019-09260-4

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