Background.Having missing data complicates the statistical analysis of health-related quality-of-life (HRQOL) data and, depending on the extent and nature of missing data, can introduce significant bias in treatment comparisons.Objective.We evaluated the bias associated with 4 different imputation methods for estimating physical health status (PHS) scores missing as a result of mortality.Methods.A simulation study was conducted in which we systematically varied mortality rates from 0% to 30% and change in PHS scores from −20 to 20 on a 100-point scale for a 2-group clinical trial with follow-up over 18 months. The 4 imputation methods were last value carried forward (LVCF), arbitrary substitution (ARBSUB), empirical Bayes (BAYES), and within-subject modeling (WSMOD). Pseudo–root mean square residuals (RMSRs) and differences between true and estimated slopes were used to evaluate how well the imputation methods reproduced the true characteristics of the simulated population data.Results.ARBSUB and BAYES methods have the smallest RMSRs compared with LVCF and WSMOD across all mortality rates. As the rate of missing data resulting from mortality increased, all imputation techniques deviated more from population data. The BAYES technique was best at reproducing group slopes in cases with differential mortality rates or when mortality rates exceeded 15%. WSMOD and LVCF significantly underestimated changes in PHS.Conclusions.The different imputation methods produced comparable results when there were few missing data. The BAYES approach most closely estimated true population differences and change in PHS regardless of missing data rates. These findings are limited to physical health and functioning measures.