MULTIPLE-OUTCOME METAANALYSIS OF CLINICAL-TRIALS

When several clinical trials report multiple outcomes, meta-analyses o rdinarily analyse each outcome separately. Instead, by applying genera lized-least-squares (GLS) regression, Raudenbush et al. showed how to analyse the multiple outcomes jointly in a single model. A variant of their GLS approach, discussed here, can incorporate correlations among the outcomes within treatment groups and thus provide more accurate e stimates, Also, it facilitates adjustment for covariates. In our appro ach, each study need not report all outcomes nor evaluate all treatmen ts. For example, a meta-analysis may evaluate two or more treatments ( one 'treatment' may be a control) and include all randomized controlle d trials that report on any subset (of one or more) of the treatments of interest. The analysis omits other treatments that these trials eva luated but that are not of interest to the meta-analyst. In the propos ed fixed-effects GLS regression model, study-level and treatment-arm-l evel covariates may be predictors of one or more of the outcomes. An a nalysis of rheumatoid arthritis data from trials of second-line drug t reatments (used after initial standard therapies prove unsatisfactory for a patient) motivates and applies the method. Data from 44 randomiz ed controlled trials were used to evaluate the effectiveness of inject able gold and auranofin on the three outcomes tender joint count, grip strength, and erythrocyte sedimentation rate. The covariates in the r egression model were quality and duration of trial and baseline measur es of the patients' disease severity and disease activity in each tria l. The meta-analysis found that gold was significantly more effective than auranofin on all three treatment outcomes. For all estimated coef ficients, the multiple-outcomes model produced moderate changes in the ir values and slightly smaller standard errors, to the three separate outcomes models.