Stratified Weibull Regression Model for Interval-Censored Data

Authors

Xiangdong Gu, University of Massachusetts
David Shapiro, Harvard School of Public Health
Michael D. Hughes, Harvard School of Public Health
Raji Balasubramanian, University of Massachusetts

Date of this Version

6-2014

Document Type

Article

Citation

The R Journal (June 2014) 6(1); Editor: Deepayan Sarkar

Comments

Copyright 2014, The R Foundation. Open access material. License: CC BY 3.0 Unported

Abstract

Interval censored outcomes arise when a silent event of interest is known to have occurred within a specific time period determined by the times of the last negative and first positive diagnostic tests. There is a rich literature on parametric and non-parametric approaches for the analysis of interval-censored outcomes. A commonly used strategy is to use a proportional hazards (PH) model with the baseline hazard function parameterized. The proportional hazards assumption can be relaxed in stratified models by allowing the baseline hazard function to vary across strata defined by a subset of explanatory variables. In this paper, we describe and implement a new R package straweib, for fitting a stratified Weibull model appropriate for interval censored outcomes. We illustrate the R package straweib by analyzing data from a longitudinal oral health study on the timing of the emergence of permanent teeth in 4430 children.