Dealing With Missing Outcome Data in Randomized Trials and Observational Studies

TLDR

Missing outcome data are a major problem in randomized trials and observational studies, yet practical methods to address them are often difficult to apply. The authors simulated continuous and dichotomous missing‑outcome scenarios typical of trials and observational studies, comparing complete case analysis, single imputation, and multiple imputation—each with and without covariate adjustment. When data were missing at random, single and multiple imputations (with covariate adjustment) and complete case analysis with covariate adjustment produced unbiased estimates with near‑95 % coverage; when data were missing not at random, all methods were biased but still less biased than unadjusted complete case, and complete case with covariate adjustment and multiple imputation gave comparable results when the same predictors were used, indicating that complete case with covariate adjustment should be the primary analysis and multiple imputation is especially useful for sensitivity analyses.

Abstract

Although missing outcome data are an important problem in randomized trials and observational studies, methods to address this issue can be difficult to apply. Using simulated data, the authors compared 3 methods to handle missing outcome data: 1) complete case analysis; 2) single imputation; and 3) multiple imputation (all 3 with and without covariate adjustment). Simulated scenarios focused on continuous or dichotomous missing outcome data from randomized trials or observational studies. When outcomes were missing at random, single and multiple imputations yielded unbiased estimates after covariate adjustment. Estimates obtained by complete case analysis with covariate adjustment were unbiased as well, with coverage close to 95%. When outcome data were missing not at random, all methods gave biased estimates, but handling missing outcome data by means of 1 of the 3 methods reduced bias compared with a complete case analysis without covariate adjustment. Complete case analysis with covariate adjustment and multiple imputation yield similar estimates in the event of missing outcome data, as long as the same predictors of missingness are included. Hence, complete case analysis with covariate adjustment can and should be used as the analysis of choice more often. Multiple imputation, in addition, can accommodate the missing-not-at-random scenario more flexibly, making it especially suited for sensitivity analyses.