Multiple Imputation Strategies for Handling Missing Data When Transporting Randomized Clinical Trial Findings Using Propensity Score-Based Methodologies

• Activity Number: 154 - Integrating Real World Data with Clinical Trials: Opportunities and Challenges
• Type: Topic Contributed
• Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 11:50 AM
• Sponsor: Caucus for Women in Statistics
• Author(s): Albee Ling* and Maria Montez-Rath and Kris Kapphahn and Maya Mathur and Manisha Desai
• Companies: Stanford University and Stanford University and Stanford University and Stanford University and Stanford University
• Keywords: transportability; propensity score methods; multiple imputation; missing data

Abstract

Randomized clinical trials (RCTs) are the gold standard for estimating treatment effects with limited external validity. Recent work has led to promising methods to translate trial findings to target populations using propensity score (PS) based methods, such as inverse probability of sampling weighting (IPSW). However, the validity of these methods is threatened by missing data, which has not been adequately addressed in existing studies. Multiple Imputation (MI) is a well-established and accessible method for handling missing data, but there is no consensus on best statistical practice for utilizing MI in this context. We conducted an extensive simulation study to evaluate properties of estimators under a variety of MI strategies that fall under two umbrellas (passive and active), coupled with two integration strategies (within and across) for applying IPSW. Additionally, we applied these methods to a real-world example of transporting the Frequent Hemodialysis Daily Network Trial findings to United States Renal Data System data.