Methods for Addressing Missing Patient-Reported Outcome Data in Longitudinal Clinical Trials: A Secondary Analysis of the Frequent Hemodialysis Network Trials
Victoria Zdunek, Guangyong Zou, Amit X. Garg, Pavel S. RoshanovBackground
Patient-reported outcomes from longitudinal clinical trials on hemodialysis typically have missing data, yet there is a lack of empirical evidence to guide appropriate analysis and interpretation.
Objective
To compare approaches for analysis and interpretation of patient-reported outcomes in the Frequent Hemodialysis Network (FHN) Trials.
Design
Secondary analysis of two multicentre randomized controlled trials.
Setting
Hemodialysis centres and home hemodialysis programs in North America.
Patients
Participants in the FHN Daily (n = 245) and Nocturnal (n = 87) Trials.
Measurements
RAND-36 physical health composite scores at baseline, months 4 and 12.
Methods
Complete case analysis (CCA), mixed model for repeated measures (MMRM), and multiple imputation (MI) were used to estimate mean differences in scores at Month 12. To relax the normality assumption and enhance interpretation of mean differences, rank-based analyses were performed to estimate the win probability (WinP) that a treated participant has a better score than (or wins over) a control participant at Month 12.
Results
Data complete at both post-randomization timepoints were available for 74% to 75% of participants. In the Daily Trial, estimates of Month 12 mean difference (95% CI) were 3.04 (0.66 to 5.42, p = 0.012) by MMRM, 2.97 (0.42 to 5.52, p = 0.023) by MI, and 2.76 (0.31 to 5.21, p = 0.027) by CCA. Estimates of WinP were 0.59 (0.52 to 0.65, p = 0.007) by MMRM and 0.59 (0.52 to 0.65, p = 0.011) by CCA. In the Nocturnal Trial, treatment effects were small, imprecise, and not statistically significant across all methods.
Limitations
This was a secondary analysis and rank-based analyses were exploratory.
Conclusions
Approaches that retained participants with partially observed data can be more efficient than complete-case analyses. Win probability analyses provided an alternative to mean differences in patient-reported outcome measure scores.