A comparison of statistical approaches for analysing missing longitudinal patient reported outcome data in randomised controlled trials

Missing data are a potential source of bias in the results of randomised controlled trials (RCTs), but are generally unavoidable in clinical research, particularly in patient reported outcome measures (PROMs). For longitudinally collected outcomes, often only a small subset of participants will have complete data for all relevant time points. Approaches to handling missing longitudinal data include maximum likelihood (ML), multiple imputation (MI) and inverse probability weighting (IPW)