Consequences of handling missing data for treatment response in osteoarthritis: a simulation study

Abstract

Objective: To understand how handling of missing data influences the statistical power and bias of treatment effects in randomised controlled trials of painful knee osteoarthritis (OA).

Methods: We simulated trials with missing data (withdrawals) due to lack-of-efficacy. Outcome measures were response/non-response according to the Outcome Measures in Rheumatology-Osteoarthritis Research Society International (OMERACT-OARSI) set of responder criteria, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain and physical function from the WOMAC questionnaire, and patient global assessment. We used five methods for managing missing data: ignoring the missing data, last and baseline observation carried forward (LOCF and BOCF), and multiple imputation with two different strategies. The treatment effect was then analysed by appropriate univariate and longitudinal statistical methods, and power, bias and mean squared error (MSE) was assessed by comparing the estimated treatment effect in the trials with missing data with the estimated treatment effect on the trials without missing data.

Results: The best imputation method in terms of high power and low bias/MSE was our implementation of regression multiple imputation. The most conservative method was the data augmentation Markov chain Monte Carlo (MCMC) multiple imputation. The LOCF, BOCF and the complete-case methods were not particularly conservative and gave relatively low power and high bias. The analysis on the WOMAC pain scale gave less bias and higher power than the OMERACT-OARSI responder outcome measure.

Conclusions: Multiple imputation of missing data may be used to decrease bias/MSE and increase power in OA trials. These results can guide investigators in the choice of outcome measures and especially how missing data can be handled.