title: Exact two-sample inference with missing data.
abstract: 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.
authors: Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, New York 10032, USA. cheung@biostat.columbia.edu
authors: Ying Kuen
authors: YK
authors: Cheung
mesh_terms: D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D041981:Gastrointestinal Tract / Q000528:radiation effects; D006801:Humans; D007511:Ischemia / Q000473:pathology; D016013:Likelihood Functions; D016014:Linear Models; D016015:Logistic Models; D008279:Magnetic Resonance Imaging; D008297:Male; D015233:Models, Statistical; D009010:Monte Carlo Method; D016012:Poisson Distribution; D011336:Probability; D011471:Prostatic Neoplasms / Q000150:complications / Q000532:radiotherapy; D020266:Radiotherapy, Conformal / Q000009:adverse effects; D012044:Regression Analysis; D018709:Statistics, Nonparametric; D020521:Stroke / Q000473:pathology; D013997:Time Factors; D016896:Treatment Outcome