Efficient evaluation of treatment effects in the presence of missing covariate values

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Abstract
In clinical trials, treatment comparisons are often performed by models that incorporate important prognostic factors. Since these models require complete covariate information on all patients, statisticians frequently resort to complete case analysis or to omission of an important covariate. A probability imputation technique (PIT) is proposed that involves substituting conditional probabilities for missing covariate values when the covariate is qualitative. Simulation results are presented which demonstrate that the method neither violates the size of the treatment test nor introduces additional bias for the estimation of the treatment effect. It allows use of standard software. A clinical trial of breast cancer treatment, in which an important covariate was partly missing, was analysed by Cox's model. The use of PIT resulted in smaller observed error probability compared with case deletion, and sensitivity analysis supported these results.

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