Missing Data Handling in the Application of Matching-Adjusted Indirect Comparison

Music City Center 

In the support of Health Teachnology Assessment (HTA) submission, we often need to conduct indirect treatment comparisons (ITC). One common type of ITC is population-adjusted indirect comparisons (PATC), in which the individual patient data (IPD) in one trial and the aggregate data (AgD) in the other trial are used to adjust for between-trial differences in the distribution of variables that influence outcome \cite{phillippo2016nice}. The most popular PATC method is the Matching-Adjusted Indirect Comparison (MAIC). However, the literature is lacking on how to handle missing data in either the outcome variable or the variables that influence the outcome in the application of MAIC. In this talk, we propose some methods for handling missing data in either the outcome variable or the variables that influence the outcome when applying MAIC.

Matching-adjusted indirect comparison (MAIC)

Aggregate data (AgD)

Patient-level data (IPD)

HTA

Individual patient data