Joint modeling of multiple longitudinal patient-reported outcomes and survival

Laura A. Hatfield, Mark E. Boye, Brad Carlin

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

Researchers often include patient-reported outcomes (PROs) in Phase III clinical trials to demonstrate the value of treatment from the patient's perspective. These data are collected as longitudinal repeated measures and are often censored by occurrence of a clinical event that defines a survival time. Hierarchical Bayesian models having latent individual-level trajectories provide a flexible approach to modeling such multiple outcome types simultaneously. We consider the case of many zeros in the longitudinal data motivating a mixture model, and demonstrate several approaches to modeling multiple longitudinal PROs with survival in a cancer clinical trial. These joint models may enhance Phase III analyses and better inform health care decision makers.

Original languageEnglish (US)
Pages (from-to)971-991
Number of pages21
JournalJournal of Biopharmaceutical Statistics
Volume21
Issue number5
DOIs
StatePublished - Sep 2011

Keywords

  • Cancer
  • Failure time
  • Multivariate analysis
  • Random effects model
  • Repeated measures

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