Title | Efficient estimation of the distribution of time to composite endpoint when some endpoints are only partially observed. |
Publication Type | Journal Article |
Year of Publication | 2013 |
Authors | Daniel, Rhian M., and Anastasios A. Tsiatis |
Journal | Lifetime Data Anal |
Volume | 19 |
Issue | 4 |
Pagination | 513-46 |
Date Published | 2013 Oct |
ISSN | 1572-9249 |
Keywords | Biostatistics, 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. |
DOI | 10.1007/s10985-013-9261-9 |
Alternate Journal | Lifetime Data Anal |
Original Publication | Efficient estimation of the distribution of time to composite endpoint when some endpoints are only partially observed. |
PubMed ID | 23722304 |
PubMed Central ID | PMC3982403 |
Grant List | G1002283 / 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 |
Efficient estimation of the distribution of time to composite endpoint when some endpoints are only partially observed.
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