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Exact Two‐Sample Inference with Missing Data
Author(s) -
Cheung Ying Kuen
Publication year - 2005
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2005.00332.x
Subject(s) - covariate , missing data , censoring (clinical trials) , inference , statistics , nonparametric statistics , mathematics , monte carlo method , resampling , econometrics , computer science , artificial intelligence
Summary When comparing follow‐up measurements from two independent populations, missing records may arise due to censoring by events whose occurrence is associated with baseline covariates. In these situations, inferences based only on the completely followed observations may be biased if the follow‐up measurements and the covariates are correlated. This article describes exact inference for a class of modified U ‐statistics under covariate‐dependent dropouts. The method involves weighing each permutation according to the retention probabilities, and thus requires estimation of the missing data mechanism. The proposed procedure is nonparametric in that no distributional assumption is necessary for the outcome variables and the missingness patterns. Monte Carlo approximation by the Gibbs sampler is proposed, and is shown to be fast and accurate via simulation. The method is illustrated in two small data sets for which asymptotic inferential procedures may not be appropriate.