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Additive hazards regression with censoring indicators missing at random
Author(s) -
Song Xinyuan,
Sun Liuquan,
Mu Xiaoyun,
Dinse Gregg E.
Publication year - 2010
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.10072
Subject(s) - inverse probability , censoring (clinical trials) , estimator , missing data , statistics , econometrics , nonparametric regression , nonparametric statistics , regression analysis , mathematics , inverse probability weighting , regression , smoothing , estimating equations , bayesian probability , posterior probability
In this article, the authors consider a semiparametric additive hazards regression model for right‐censored data that allows some censoring indicators to be missing at random. They develop a class of estimating equations and use an inverse probability weighted approach to estimate the regression parameters. Nonparametric smoothing techniques are employed to estimate the probability of non‐missingness and the conditional probability of an uncensored observation. The asymptotic properties of the resulting estimators are derived. Simulation studies show that the proposed estimators perform well. They motivate and illustrate their methods with data from a brain cancer clinical trial. The Canadian Journal of Statistics 38: 333–351; 2010 © 2010 Statistical Society of Canada