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Mark-specific hazard ratio model with missing multivariate marks

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  • Michal Juraska

    (Fred Hutchinson Cancer Research Center)

  • Peter B. Gilbert

    (Fred Hutchinson Cancer Research Center, and Department of Biostatistics, University of Washington)

Abstract

An objective of randomized placebo-controlled preventive HIV vaccine efficacy (VE) trials is to assess the relationship between vaccine effects to prevent HIV acquisition and continuous genetic distances of the exposing HIVs to multiple HIV strains represented in the vaccine. The set of genetic distances, only observed in failures, is collectively termed the ‘mark.’ The objective has motivated a recent study of a multivariate mark-specific hazard ratio model in the competing risks failure time analysis framework. Marks of interest, however, are commonly subject to substantial missingness, largely due to rapid post-acquisition viral evolution. In this article, we investigate the mark-specific hazard ratio model with missing multivariate marks and develop two inferential procedures based on (i) inverse probability weighting (IPW) of the complete cases, and (ii) augmentation of the IPW estimating functions by leveraging auxiliary data predictive of the mark. Asymptotic properties and finite-sample performance of the inferential procedures are presented. This research also provides general inferential methods for semiparametric density ratio/biased sampling models with missing data. We apply the developed procedures to data from the HVTN 502 ‘Step’ HIV VE trial.

Suggested Citation

  • Michal Juraska & Peter B. Gilbert, 2016. "Mark-specific hazard ratio model with missing multivariate marks," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(4), pages 606-625, October.
    • Handle: RePEc:spr:lifeda:v:22:y:2016:i:4:d:10.1007_s10985-015-9353-9
    DOI: 10.1007/s10985-015-9353-9
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    References listed on IDEAS

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    1. Xiaomin Lu & Anastasios A. Tsiatis, 2008. "Improving the efficiency of the log-rank test using auxiliary covariates," Biometrika, Biometrika Trust, vol. 95(3), pages 679-694.
    2. Yanqing Sun & Peter B. Gilbert, 2012. "Estimation of Stratified Mark‐Specific Proportional Hazards Models with Missing Marks," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 39(1), pages 34-52, March.
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    6. Peter Gilbert & Ian McKeague & Yanqing Sun, 2004. "Tests for Comparing Mark-Specific Hazards and Cumulative Incidence Functions," UW Biostatistics Working Paper Series 1032, Berkeley Electronic Press.
    7. Tan, Zhiqiang, 2006. "A Distributional Approach for Causal Inference Using Propensity Scores," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1619-1637, December.
    8. M. Juraska & P. B. Gilbert, 2013. "Mark-Specific Hazard Ratio Model with Multivariate Continuous Marks: An Application to Vaccine Efficacy," Biometrics, The International Biometric Society, vol. 69(2), pages 328-337, June.
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    Cited by:

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