Improved statistical analysis of moclobemide dose effects on panic disorder treatment.

Clinical trials with several measurement occasions are frequently analyzed using only the last available observation as the dependent variable [last observation carried forward (LOCF)]. This ignores intermediate observations. We reanalyze, with complete data methods, a clinical trial previously reported using LOCF, comparing placebo and five dosage levels of moclobemide in the treatment of outpatients with panic disorder to illustrate the superiority of methods using repeated observations. We initially analyzed unprovoked and situational, major and minor attacks as the four dependent variables, by repeated measures maximum likelihood methods. The model included parameters for linear and curvilinear time trends and regression of measures during treatment on baseline measures. Significance tests using this method take into account the structure of the error covariance matrix. This makes the sphericity assumption irrelevant. Missingness is assumed to be unrelated to eventual outcome and the residuals are assumed to have a multivariate normal distribution. No differential treatment effects for limited attacks were found. Since similar results were obtained for both types of major attack, data for the two types of major attack were combined. Overall downward linear and negatively accelerated downward curvilinear time trends were found. There were highly significant treatment differences in the regression slopes of scores during treatment on baseline observations. For major attacks, all treatment groups improved over time. The flatter regression slopes, obtained with higher doses, indicated that higher doses result in uniformly lower attack rates regardless of initial severity. Lower doses do not lower the attack rate of severely ill patients to those achieved in the less severely ill. The clinical implication is that more severe patients require higher doses to attain best benefit. Further, the significance levels obtained by LOCF analyses were only in the 0.05-0.01 range, while significance levels of <0.00001 were obtained by these repeated measures analyses indicating increased power. The greater sensitivity to treatment effect of this complete data method is illustrated. To increase power, it is often recommended to increase sample size. However, this is often impractical since a major proportion of the cost per subject is due to the initial evaluation. Increasing the number of repeated observations increases power economically and also allows detailed longitudinal trajectory analyses.