A discrete-time survival model with random effects for designing and analyzing repeated low-dose challenge experiments

Chaeryon Kang, Ying Huang, Chris J Miller

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Repeated low-dose (RLD) challenge designs are important in HIV vaccine research. Current methods for RLD designs rely heavily on an assumption of homogeneous risk of infection among animals, which, upon violation, can lead to invalid inferences and underpowered study designs. We propose to fit a discrete-time survival model with random effects that allows for heterogeneity in the risk of infection among animals and allows for predetermined challenge dose changes over time. Based on this model, we derive likelihood ratio tests and estimators for vaccine efficacy. A two-stage approach is proposed for optimizing the RLD design under cost constraints. Simulation studies demonstrate good finite sample properties of the proposed method and its superior performance compared to existing methods. We illustrate the application of the heterogeneous infection risk model on data from a real simian immunodeficiency virus vaccine study using Rhesus Macaques. The results of our study provide useful guidance for future RLD experimental design.

Original languageEnglish (US)
Pages (from-to)295-310
Number of pages16
JournalBiostatistics
Volume16
Issue number2
DOIs
StatePublished - Jul 29 2015

Keywords

  • Discrete-time survival model with random effects: Heterogeneous infection risk: HIV vaccine/prevention research: Repeated low
  • dose challenge experiment: Sample size calculation

ASJC Scopus subject areas

  • Medicine(all)
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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