Sarcouncil Journal of Multidisciplinary

Abstract: Missing data is a severe problem in clinical trial studies that jeopardizes the validity of the statistical inference and undermines the integrity of the study. Complete case analysis and last observation carried forward are the most commonly used traditional methods, both of which are widely criticized as sources of bias and low statistical power, leading to the creation of more advanced types of imputation. Multiple imputation has become the new gold standard of addressing missing data, producing multiple plausible values per missing data and appropriately accounting for the imputation uncertainty with the combining rules by Rubin. Hot-deck imputation is a non-parametric method that does not alter empirical distributions, instead substituting any missing values with observed values of similar donors. The choice of the right imputation approaches is based on the knowledge about the mechanism of missing data, such as Missing Completely at Random, Missing at Random, and Missing Not at Random, each of which has to be analyzed with the help of different methods. Major imputation algorithms such as the MCMC algorithm, predictive mean matching, and propensity score imputation are effectively implemented with the help of SAS programming using PROC MI and PROC MIANALYZE. Simulation studies establish that multiple imputation retains unbiased estimates and correct coverage probabilities in the case of Missing at Random conditions, whereas traditional methods have enormous bias. The use of imputation methods has been shown to greatly influence estimates of treatment effects and clinical inferences based on real-world case studies of antidepressant and diabetes prevention trials. The regulatory agencies are now requiring prespecified approaches to missing data that are scientifically justified with sensitivity analyses that are exhaustive in nature. The introduction of modern imputation methods into the mainstream process improves the validity and reliability of clinical trial data, which facilitates evidence-based medicine and better patient outcomes.