## 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 language | English (US) |
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Pages (from-to) | 685-693 |

Number of pages | 9 |

Journal | Contemporary Clinical Trials |

Volume | 32 |

Issue number | 5 |

DOIs | |

State | Published - Sep 2011 |

## Keywords

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

## ASJC Scopus subject areas

- Pharmacology (medical)
- Medicine(all)