Title

A hybrid approach for the stratified mark-specific proportional hazards model with missing covariates and missing marks, with application to vaccine efficacy trials

Document Type

Article

Publication Date

8-1-2020

Subject Area

ARRAY(0x55e57adb0a30)

Abstract

Deployment of the recently licensed tetravalent dengue vaccine based on a chimeric yellow fever virus, CYD-TDV, requires understanding of how the risk of dengue disease in vaccine recipients depends jointly on a host biomarker measured after vaccination (neutralization titre—neutralizing antibodies) and on a ‘mark’ feature of the dengue disease failure event (the amino acid sequence distance of the dengue virus to the dengue sequence represented in the vaccine). The CYD14 phase 3 trial of CYD-TDV measured neutralizing antibodies via case–cohort sampling and the mark in dengue disease failure events, with about a third missing marks. We addressed the question of interest by developing inferential procedures for the stratified mark-specific proportional hazards model with missing covariates and missing marks. Two hybrid approaches are investigated that leverage both augmented inverse probability weighting and nearest neighbourhood hot deck multiple imputation. The two approaches differ in how the imputed marks are pooled in estimation. Our investigation shows that nearest neighbourhood hot deck imputation can lead to biased estimation without properly selected neighbourhoods. Simulations show that the hybrid methods developed perform well with unbiased nearest neighbourhood hot deck imputations from proper neighbourhood selection. The new methods applied to CYD14 show that neutralizing antibody level is strongly inversely associated with the risk of dengue disease in vaccine recipients, more strongly against dengue viruses with shorter distances.

Publication Title

Journal of the Royal Statistical Society. Series C: Applied Statistics

Volume

69

Issue

4

First Page

791

Last Page

814

Digital Object Identifier (DOI)

10.1111/rssc.12417

ISSN

00359254

E-ISSN

14679876

Share

COinS