Application of random effects models and other methods to the analysis of multidimensional quality of life data in an AIDS clinical trial.

Wu, A. W. and Gray, S. M. and Brookmeyer, R. (1999) Application of random effects models and other methods to the analysis of multidimensional quality of life data in an AIDS clinical trial. Medical Care, 37 (3). pp. 249-258. ISSN 1537-1948

Abstract

Background. Current analytic methods applied to multidimensional health-related quality of life (HRQOL) data do not borrow strength across analyses and do not produce summary estimates of effect. Objectives. To compare a random effects modelling approach for the analysis of multidimensional HRQOL data to the following: (1) separate analyses for each dimension; (2) O'Brien's global test procedure; and (3) multivariate analysis of variance (MANOVA). Research Design. Randomized clinical trial comparing 3 treatments (Trimethoprim-Sulfamethoxazole [TS], Dapsone-Trimethoprim [DT], and Clindamycin-Primaquine [CP] for Pneumocystis carinii pneumonia [PCP]). Subjects. Patients with PCP enrolled in AIDS Clinical Trials Group Protocol 108. Measures. A 33-item battery assessing 7 dimensions of HRQOL: physical functioning, pain, energy, general health perceptions, disability, pulmonary symptoms, and constitutional symptoms. Results. Analyses focused on changes in score from baseline to Day 7 (n = 145). Separate analyses for each dimension suggested a trend favoring CP versus TS, but using a Bonferroni correction no differences were statistically significant. O'Brien's global procedure for a test of no-treatment effect versus superiority of one treatment yielded P = 0.07. MANOVA did not reveal significant differences among treatment groups. A random effects model using fixed treatment and dimension effects and separate random effects for each person showed a significant overall treatment effect (P = 0.02); changes in scores for CP averaged 10 points greater than for TS. Conclusions. Random-effects models provide a flexible class of models for analyzing multidimensional quality of life data and estimating treatment effects because they borrow strength across dimensions.