Volume 69, Issue 2
ORIGINAL ARTICLE

Mark‐Specific Hazard Ratio Model with Multivariate Continuous Marks: An Application to Vaccine Efficacy

M. Juraska

Corresponding Author

Department of Biostatistics, University of Washington, Seattle, Washington 98195, U.S.A.

email: mjuraska@u.washington.eduSearch for more papers by this author
P. B. Gilbert

Department of Biostatistics, University of Washington, Seattle, Washington 98195, U.S.A.

Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, U.S.A.

Search for more papers by this author
First published: 19 February 2013
Citations: 10

Abstract

Summary

In randomized placebo‐controlled preventive HIV vaccine efficacy trials, an objective is to evaluate the relationship between vaccine efficacy to prevent infection and genetic distances of the exposing HIV strains to the multiple HIV sequences included in the vaccine construct, where the set of genetic distances is considered as the continuous multivariate “mark” observed in infected subjects only. This research develops a multivariate mark‐specific hazard ratio model in the competing risks failure time analysis framework for the assessment of mark‐specific vaccine efficacy. It allows improved efficiency of estimation by employing the semiparametric method of maximum profile likelihood estimation in the vaccine‐to‐placebo mark density ratio model. The model also enables the use of a more efficient estimation method for the overall log hazard ratio in the Cox model. In addition, we propose testing procedures to evaluate two relevant hypotheses concerning mark‐specific vaccine efficacy. The asymptotic properties and finite‐sample performance of the inferential procedures are investigated. Finally, we apply the proposed methods to data collected in the Thai RV144 HIV vaccine efficacy trial.

Number of times cited according to CrossRef: 10

  • A hybrid approach for the stratified mark‐specific proportional hazards model with missing covariates and missing marks, with application to vaccine efficacy trials, Journal of the Royal Statistical Society: Series C (Applied Statistics), 10.1111/rssc.12417, 69, 4, (791-814), (2020).
  • A unified evaluation of differential vaccine efficacy, Biometrics, 10.1111/biom.13211, 0, 0, (2020).
  • Prediction of VRC01 neutralization sensitivity by HIV-1 gp160 sequence features, PLOS Computational Biology, 10.1371/journal.pcbi.1006952, 15, 4, (e1006952), (2019).
  • Viral genetic diversity and protective efficacy of a tetravalent dengue vaccine in two phase 3 trials, Proceedings of the National Academy of Sciences, 10.1073/pnas.1714250115, 115, 36, (E8378-E8387), (2018).
  • Sieve analysis using the number of infecting pathogens, Biometrics, 10.1111/biom.12833, 74, 3, (1023-1033), (2017).
  • Sieve analysis of breakthrough HIV-1 sequences in HVTN 505 identifies vaccine pressure targeting the CD4 binding site of Env-gp120, PLOS ONE, 10.1371/journal.pone.0185959, 12, 11, (e0185959), (2017).
  • Estimation of Stratified Mark-Specific Proportional Hazards Models Under Two-Phase Sampling with Application to HIV Vaccine Efficacy Trials, Statistics in Biosciences, 10.1007/s12561-016-9177-5, 9, 1, (259-283), (2016).
  • Predicting Overall Vaccine Efficacy in a New Setting by Re-calibrating Baseline Covariate and Intermediate Response Endpoint Effect Modifiers of Type-Specific Vaccine Efficacy, Epidemiologic Methods, 10.1515/em-2015-0007, 5, 1, (2016).
  • Mark-specific hazard ratio model with missing multivariate marks, Lifetime Data Analysis, 10.1007/s10985-015-9353-9, 22, 4, (606-625), (2015).
  • Leaky Vaccines Protect Highly Exposed Recipients at a Lower Rate: Implications for Vaccine Efficacy Estimation and Sieve Analysis, Computational and Mathematical Methods in Medicine, 10.1155/2014/813789, 2014, (1-12), (2014).

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