A dynamical modeling approach for analysis of longitudinal clinical trials in the presence of missing endpoints. (January 2017)

• Record Type: Journal Article
• Authors: Banks, H.T.; Hu, Shuhua; Rosenberg, Eric
• Is Part Of: Applied mathematics letters. Volume 63(2017:Jan.)
• Page Start: 109
• Page End: 117
• Publication Date: 2017-01
• Subjects: HIV -- Hypothesis testing -- Ordinary differential equation -- Inverse problems; Applied mathematics -- Periodicals; 519.05
• DOI: 10.1016/j.aml.2016.07.002
• Languages: English
• ISSNs: 0893-9659

Abstract

Abstract: 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.