%0 Journal Article
%T Comparison of statistical approaches for analyzing incomplete longitudinal patient-reported outcome data in randomized controlled trials
%A Alastair M Gray
%A Crispin Jenkinson
%A David W Murray
%A Ines Rombach
%A Oliver Rivero-Arias
%J Archive of "Patient Related Outcome Measures".
%D 2018
%R 10.2147/PROM.S147790
%X Missing data are a potential source of bias in the results of RCTs, but are often unavoidable in clinical research, particularly in patient-reported outcome measures (PROMs). Maximum likelihood (ML), multiple imputation (MI), and inverse probability weighting (IPW) can be used to handle incomplete longitudinal data. This paper compares their performance when analyzing PROMs, using a simulation study based on an RCT data set
%K missing data
%K repeated measures
%K patient-reported outcome measures
%K PROMS
%K multilevel mixed-effects models
%K multiple imputation
%K inverse probability weighting
%U https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6016604/