Efficient estimation of the distribution of time to composite endpoint when some endpoints are only partially observed.

TitleEfficient estimation of the distribution of time to composite endpoint when some endpoints are only partially observed.
Publication TypeJournal Article
Year of Publication2013
AuthorsDaniel, Rhian M., and Anastasios A. Tsiatis
JournalLifetime Data Anal
Volume19
Issue4
Pagination513-46
Date Published2013 Oct
ISSN1572-9249
KeywordsBiostatistics, Clinical Trials as Topic, Computer Simulation, Endpoint Determination, Humans, Linear Models, Longitudinal Studies, Models, Statistical, Proportional Hazards Models, Registries, Statistics, Nonparametric, Time Factors
Abstract

Two common features of clinical trials, and other longitudinal studies, are (1) a primary interest in composite endpoints, and (2) the problem of subjects withdrawing prematurely from the study. In some settings, withdrawal may only affect observation of some components of the composite endpoint, for example when another component is death, information on which may be available from a national registry. In this paper, we use the theory of augmented inverse probability weighted estimating equations to show how such partial information on the composite endpoint for subjects who withdraw from the study can be incorporated in a principled way into the estimation of the distribution of time to composite endpoint, typically leading to increased efficiency without relying on additional assumptions above those that would be made by standard approaches. We describe our proposed approach theoretically, and demonstrate its properties in a simulation study.

DOI10.1007/s10985-013-9261-9
Alternate JournalLifetime Data Anal
Original PublicationEfficient estimation of the distribution of time to composite endpoint when some endpoints are only partially observed.
PubMed ID23722304
PubMed Central IDPMC3982403
Grant ListG1002283 / MRC_ / Medical Research Council / United Kingdom
R37-AI031789 / AI / NIAID NIH HHS / United States
P01- CA142538 / CA / NCI NIH HHS / United States
R37 AI031789 / AI / NIAID NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
R01 HL118336 / HL / NHLBI NIH HHS / United States
Project: