2014/10/10 by Kaifeng Lu
Mathematics · #Advanced Causal Inference Techniques #Statistical Methods and Bayesian Inference #Statistical Methods in Clinical Trials
paper · doi:10.1177/0962280214554439
openalex publication_date 2014/10/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
Multiple imputation procedures replace each missing value with a set of plausible values based on the posterior predictive distribution of missing data given observed data. In many applications, as few as five imputations are adequate to achieve high efficiency relative to an infinite number of imputations. However, substantially more imputations are often needed to stabilize imputation-based inference at the analysis stage. Imputation-based inference at the analysis stage is considered stable if the conditional variability of the multiple imputation estimator, half-width of 95% confidence interval, test statistic, and estimated fraction of missing information given observed data is within specified thresholds for simulation error. For the estimation of treatment difference at study end for normally distributed responses in longitudinal trials, we calculate the multiple imputation quantities for an infinite number of imputations analytically and use simulations to assess the variability of the number of imputations needed at the analysis stage in repeated sampling.