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Propensity score estimation in the presence of length‐biased sampling: a non‐parametric adjustment approach
Author(s) -
Ertefaie Ashkan,
Asgharian Masoud,
Stephens David
Publication year - 2014
Publication title -
stat
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.61
H-Index - 18
ISSN - 2049-1573
DOI - 10.1002/sta4.46
Subject(s) - covariate , propensity score matching , estimator , statistics , econometrics , hazard ratio , parametric statistics , survival analysis , proportional hazards model , mathematics , estimation , sample size determination , survival function , parametric model , confidence interval , economics , management
The pervasive use of prevalent cohort studies on disease duration increasingly calls for an appropriate methodology to account for the biases that invariably accompany samples formed by such data. It is well known, for example, that subjects with shorter lifetime are less likely to be present in such studies. Moreover, certain covariate values could be preferentially selected into the sample, being linked to the long‐term survivors. The existing methodology for estimating the propensity score using data collected on prevalent cases requires the correct conditional survival/hazard function given the treatment and covariates. This requirement can be alleviated if the disease under study has stationary incidence, the so‐called stationarity assumption. We propose a non‐parametric adjustment technique based on a weighted estimating equation for estimating the propensity score, which does not require modeling the conditional survival/hazard function when the stationarity assumption holds. The estimator's large‐sample properties are established, and its small‐sample behavior is studied via simulation. The estimated propensity score is utilized to estimate the survival curves. Copyright © 2014 John Wiley & Sons, Ltd