title: The relative efficiency of time-to-threshold and rate of change in longitudinal data.
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.
authors.affiliation: Division of Biostatistics and Bioinformatics, Department of Family and Preventive Medicine, University of California, San Diego, CA, United States. mdonohue@ucsd.edu
authors.forename: M C
authors.initials: MC
authors.lastname: Donohue
authors.forename: A C
authors.initials: AC
authors.lastname: Gamst
authors.forename: R G
authors.initials: RG
authors.lastname: Thomas
authors.forename: R
authors.initials: R
authors.lastname: Xu
authors.forename: L
authors.initials: L
authors.lastname: Beckett
authors.forename: R C
authors.initials: RC
authors.lastname: Petersen
authors.forename: M W
authors.initials: MW
authors.lastname: Weiner
authors.forename: P
authors.initials: P
authors.lastname: Aisen
mesh_terms: D000544:Alzheimer Disease / Q000473:pathology*; D018450:Disease Progression; D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies / Q000379:methods*; D016016:Proportional Hazards Models; D012107:Research Design; D012680:Sensitivity and Specificity; D013995:Time