The relative efficiency of time-to-threshold and rate of change in longitudinal data

M. C. Donohue, A. C. Gamst, R. G. Thomas, R. Xu, Laurel A Beckett, R. C. Petersen, M. W. Weiner, P. Aisen

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

Randomized, placebo-controlled trials often use time-to-event as the primary endpoint, even when a continuous measure of disease severity is available. We compare the power to detect a treatment effect using either rate of change, as estimated by linear models of longitudinal continuous data, or time-to-event estimated by Cox proportional hazards models. We propose an analytic inflation factor for comparing the two types of analyses assuming that the time-to-event can be expressed as a time-to-threshold of the continuous measure. We conduct simulations based on a publicly available Alzheimer's disease data set in which the time-to-event is algorithmically defined based on a battery of assessments. A Cox proportional hazards model of the time-to-event endpoint is compared to a linear model of a single assessment from the battery. The simulations also explore the impact of baseline covariates in either analysis.

Original languageEnglish (US)
Pages (from-to)685-693
Number of pages9
JournalContemporary Clinical Trials
Volume32
Issue number5
DOIs
StatePublished - Sep 2011

Keywords

  • Linear mixed models
  • Longitudinal data
  • Marginal linear models
  • Power
  • Survival analysis

ASJC Scopus subject areas

  • Pharmacology (medical)
  • Medicine(all)

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