Open AccessAttributable fraction functions for censored event times
Author(s) -
Li Chen,
Dan Lin,
Donglin Zeng
Publication year - 2010
Publication title -
biometrika
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.307
H-Index - 122
eISSN - 1464-3510
pISSN - 0006-3444
DOI - 10.1093/biomet/asq023
Subject(s) - mathematics , estimator , fraction (chemistry) , nonparametric statistics , econometrics , event (particle physics) , semiparametric regression , statistics , inference , semiparametric model , population , causal inference , attributable risk , proportional hazards model , computer science , medicine , artificial intelligence , chemistry , physics , environmental health , organic chemistry , quantum mechanics
Attributable fractions are commonly used to measure the impact of risk factors on disease incidence in the population. These static measures can be extended to functions of time when the time to disease occurrence or event time is of interest. The present paper deals with nonparametric and semiparametric estimation of attributable fraction functions for cohort studies with potentially censored event time data. The semiparametric models include the familiar proportional hazards model and a broad class of transformation models. The proposed estimators are shown to be consistent, asymptotically normal and asymptotically efficient. Extensive simulation studies demonstrate that the proposed methods perform well in practical situations. A cardiovascular health study is provided. Connections to causal inference are discussed.