title: Probability imputation revisited for prognostic factor studies.
abstract: The analysis of prognostic factor studies by Cox or logistic regression models is often impeded by missing covariate values. In 1990 Schemper and Smith recommended a conditional probability imputation technique (PIT) for the analysis of treatment studies which can be easily applied using standard software and which has been demonstrated to outperform the complete case and omission of covariates strategies. Recent research, however, showed that PIT cannot universally be recommended and it was concluded that model-based methods should be preferred. We agree with these conclusions but also think that there is enough empirical evidence to judge the performance of PIT to be satisfactory in typical prognostic factor studies. Furthermore, comparisons of PIT with multiple imputation in the same context did not indicate an advantage of the latter more involved technique. By means of an analysis of a prostate cancer data set various aspects of application of PIT are discussed, in particular that PIT permits direct comparability of marginal and partial effects analyses. We conclude that PIT continues to be an appropriate and attractive choice for analyses of prognostic factor studies.
authors.affiliation: Department of Medical Computer Sciences, Vienna University, Austria.
authors.forename: M
authors.initials: M
authors.lastname: Schemper
authors.forename: G
authors.initials: G
authors.lastname: Heinze
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D008297:Male; D009010:Monte Carlo Method; D011336:Probability; D011379:Prognosis; D011446:Prospective Studies; D011471:Prostatic Neoplasms / Q000401:mortality / Q000473:pathology; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D016019:Survival Analysis