Semiparametric Transformation Rate Model for Recurrent Event Data.

TitleSemiparametric Transformation Rate Model for Recurrent Event Data.
Publication TypeJournal Article
Year of Publication2011
AuthorsZeng, Donglin, Douglas E. Schaubel, and Jianwen Cai
JournalStat Biosci
Volume3
Issue2
Pagination187-207
Date Published2011 Dec 01
ISSN1867-1764
Abstract

In this article, we propose a class of semiparametric transformation rate models for recurrent event data subject to right-censoring and potentially stopped by a terminating event (e.g., death). These transformation models include both additive rates model and proportional rates model as special cases. Respecting the property that no recurrent events can occur after the terminating event, we model the conditional recurrent event rate given survival. Weighted estimating equations are constructed to estimate the regression coefficients and baseline rate function. In particular, the baseline rate function is approximated by wavelet function. Asymptotic properties of the proposed estimators are derived and a data-dependent criterion is proposed for selecting the most suitable transformation. Simulation studies show that the proposed estimators perform well for practical sample sizes. The proposed methods are used in two real-data examples: a randomized trial of rhDNase and a community trial of Vitamin A.

DOI10.1007/s12561-011-9043-4
Alternate JournalStat Biosci
Original PublicationSemiparametric transformation rate model for recurrent event data.
PubMed ID22505954
PubMed Central IDPMC3325027
Grant ListP01 CA142538-03 / CA / NCI NIH HHS / United States
R01 HL057444 / HL / NHLBI NIH HHS / United States
R01 DK070869-07 / DK / NIDDK NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
R01 HL057444-13 / HL / NHLBI NIH HHS / United States
R01 DK070869 / DK / NIDDK NIH HHS / United States
Project: