Multiple Imputation Confidence Intervals for a Risk Difference With Missing Observations

Confidence interval estimation for a risk difference is commonly used in various applications. The method of variance estimates recovery (MOVER) is a useful method for constructing the confidence interval of the risk difference. The confidence interval estimation with incomplete data has been widely studied in recent years, as missing values can occur during data collection. In this study, for the Poisson and binomial distributions, we propose proper multiple imputation procedures for the MOVER to estimate the confidence intervals for the risk difference, not only for missing at random but also for missing not at random. A simulation study shows that the coverage probabilities of the proposed intervals are closer to the nominal level than those of existing intervals, particularly when the true parameters are near the boundaries. These multiple imputation confidence intervals are illustrated with real data examples.