Randomized longitudinal clinical trials are the gold standard to evaluate the effectiveness of interventions among different patient treatment groups. However, analysis of such clinical trials becomes difficult in the presence of missing data, especially in the case where the study endpoints become difficult to measure because of subject dropout rates or/and the time to discontinue the assigned interventions are different among the patient groups. Here we report on using a validated mathematical model combined with an inverse problem approach to predict the values for the missing endpoints. A small randomized HIV clinical trial where endpoints for most of patients are missing is used to demonstrate this approach.

纵向随机临床试验是评估不同患者治疗组之间干预措施有效性的金标准。但是，在缺少数据的情况下，对此类临床试验的分析变得困难，尤其是在由于受试者的辍学率或/和中断指定干预措施的时间在患者组之间而导致难以衡量研究终点的情况下。在这里，我们报告了如何使用经过验证的数学模型与逆问题方法相结合来预测缺失端点的值。一项小型的随机HIV临床试验（其中缺少大多数患者的终点指标）用于证明这种方法。