A dynamical modeling approach for analysis of longitudinal clinical trials in the presence of missing endpoints

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• 作者：H.T. Banks^a ; ^{htbanks@ncsu.edu" class="auth_mail" title="E-mail the corresponding author} ; Shuhua Hu^a ; ^b ; Eric Rosenberg^a ; ^c
• 关键词：HIV ; Hypothesis testing ; Ordinary differential equation ; Inverse problems
• 刊名：Applied Mathematics Letters
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：63
• 期：Complete
• 页码：109-117
• 全文大小：683 K

文摘

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.