A systematic review of reporting and handling of missing data in observational studies using the UNOS database.

Background: Missing data decreasing study power and introducing bias, thereby undermining a registry's ability to draw valid inferences. We evaluated how missing data are reported and addressed in heart transplantation (HT) studies using the United Network for Organ Sharing (UNOS) database.

Methods: We conducted a systematic literature search of Medline from January 1, 2018 through August 22, 2023 and included studies that used the UNOS database to evaluate adult (≥18 years) de novo HT recipients. We collected details on the study population, timeframe, primary end-point, use of missing data, and whether and what methods were used to handle missing data. Approaches were classified as variable selection, complete case analysis (CCA), missing indicator method, single imputation, or multiple imputation.

Results: Of the 229 included studies, 67 (29.3%) limited their cohorts to those without missing data for the outcome or key variables and 93 (40.6%) reported missing data in their final cohort. 78 (34.1%) studies reported how they handled missing data in their statistical modeling. Of these, CCA was most used (n = 41, 52.6%) followed by multiple imputation (n = 22, 28.2%), and other methods (n = 15, 19.2%). Thirty-one (13.5%) studies reported removing covariates from their analysis because of missingness.

Conclusions: Merely a third of the identified UNOS database studies reported how they handled missing data in their analysis, with strategies varying. Although no singular approach to handling missing data exists, methods are available that can improve upon the most used approaches. Future best practices should include explicit reporting of missingness, detailed methods, and sensitivity checks.