In many clinical trials, outcomes of interest include binary-valued endpoints. It is not uncommon that a binary-valued outcome is dichotomized from a continuous outcome at a threshold of clinical interest. To reach the objective, common approaches include (a) fitting the generalized linear mixed model (GLMM) to the dichotomized longitudinal binary outcome and (b) imputation method (MI): imputing the missing values in the continuous outcome, dichotomizing it into a binary outcome, and then fitting the generalized linear model for the "complete" data. We conducted comprehensive simulation studies to compare the performance of GLMM with MI for estimating risk difference and logarithm of odds ratio between two treatment arms at the end of study. In those simulation studies, we considered a range of multivariate distribution options for the continuous outcome (including a multivariate normal distribution, a multivariate t-distribution, a multivariate log-normal distribution, and the empirical distribution from a real clinical trial data) to evaluate the robustness of the estimators to various data-generating models. Simulation results demonstrate that both methods work well under those considered distribution options, but MI is more efficient with smaller mean squared errors compared to GLMM. We further applied both the GLMM and MI to 29 phase 3 diabetes clinical trials, and found that the MI method generally led to smaller variance estimates compared to GLMM.
翻译:在许多临床试验中,人们感兴趣的结果包括二进制结果,包括二进制结果;二进制结果与临床兴趣临界点持续结果的二进制分解;为了达到目标,共同的方法包括:(a) 将一般线性混合模型(GLMM)与二进制纵向二进制结果相匹配;(b) 估算方法(MI):将连续结果中缺失的值归为二进制结果,将其分解为二进制结果,然后将“完全”数据与通用线性模型相匹配;我们进行了综合模拟研究,将GLMM和MI的性能表现与估计值作对比,以估算两种治疗武器在研究结束时的风险差异和对差比率。 在这些模拟研究中,我们考虑了一系列关于连续结果的多变分布选项(包括多变正常分布、多变的图象 t分布、多变逻辑正常分布,以及从真正的临床试验数据中的经验分布),以评价测算器与各种数据生成模型的稳健健性,我们比了研究结束时,将GMMMMM的测算结果比为G阶段。