Additive hazards regression with censoring indicators missing at random
Copyright policy )Additive hazards regression with censoring indicators missing at random
AbstractIn 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 Statistics38: 333–351; 2010 © 2010 Statistical Society of Canada
- Chinese Academy of Sciences China (People's Republic of)
- Research Triangle Park Foundation United States
- National Institute of Health Pakistan
- National Institute of Environmental Health Sciences United States
- Academy of Mathematics and Systems Science China (People's Republic of)
brain cancer, missing at random, Censored data models, Asymptotic properties of nonparametric inference, kernel smoother, Computational problems in statistics, Nonparametric regression and quantile regression, Nonparametric estimation, weighted estimating equation
brain cancer, missing at random, Censored data models, Asymptotic properties of nonparametric inference, kernel smoother, Computational problems in statistics, Nonparametric regression and quantile regression, Nonparametric estimation, weighted estimating equation
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