SEMIPARAMETRIC REGRESSION ANALYSIS OF REPEATED CURRENT STATUS DATA.

TitleSEMIPARAMETRIC REGRESSION ANALYSIS OF REPEATED CURRENT STATUS DATA.
Publication TypeJournal Article
Year of Publication2017
AuthorsLiang, Baosheng, Xingwei Tong, Donglin Zeng, and Yuanjia Wang
JournalStat Sin
Volume27
Issue3
Pagination1079-1100
Date Published2017 Jul
ISSN1017-0405
Abstract

In many clinical studies, patients may be asked to report their medication adherence, presence of side effects, substance use, and hospitalization information during the study period. However, the exact occurrence time of these recurrent events may not be available due to privacy protection, recall difficulty, or incomplete medical records. Instead, the only available information is whether the events of interest have occurred during the past period. In this paper, we call these incomplete recurrent events as repeated current status data. Currently, there are no valid standard methods for this kind of data. We propose to use the Andersen-Gill proportional intensity assumption to analyze such data. Specifically, we propose a maximum sieve likelihood approach for inference and we show that the proposed estimators for regression coefficients are consistent, asymptotically normal and attain semiparametric efficiency bounds. Simulation studies show that the proposed approach performs well with small sample sizes. Finally, our method is applied to study medication adherence in a clinical trial on non-psychotic major depressive disorder.

DOI10.5705/ss.202014.0153
Alternate JournalStat Sin
Original PublicationSemiparametric regression analysis of repeated current status data.
PubMed ID28959115
PubMed Central IDPMC5611822
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 GM047845 / GM / NIGMS NIH HHS / United States
R01 NS073671 / NS / NINDS NIH HHS / United States
U01 NS082062 / NS / NINDS NIH HHS / United States