When several clinical trials report multiple outcomes, meta-analyses ordinarily analyse each outcome separately. Instead, by applying generalized-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 estimates. Also, it facilitates adjustment for covariates. In our approach, each study need not report all outcomes nor evaluate all treatments. For example, a meta-analysis may evaluate two or more treatments (one 'treatment' may be a control) and include all randomized controlled trials that report on any subset (of one or more) of the treatments of interest. The analysis omits other treatments that these trials evaluated but that are not of interest to the meta-analyst. In the proposed fixed-effects GLS regression model, study-level and treatment-arm-level covariates may be predictors of one or more of the outcomes. An analysis of rheumatoid arthritis data from trials of second-line drug treatments (used after initial standard therapies prove unsatisfactory for a patient) motivates and applies the method. Data from 44 randomized controlled trials were used to evaluate the effectiveness of injectable gold and auranofin on the three outcomes tender joint count, grip strength, and erythrocyte sedimentation rate. The covariates in the regression model were quality and duration of trial and baseline measures of the patients' disease severity and disease activity in each trial. The meta-analysis found that gold was significantly more effective than auranofin on all three treatment outcomes. For all estimated coefficients, the multiple-outcomes model produced moderate changes in their values and slightly smaller standard errors, to the three separate outcome models.