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

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...

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Bibliographic Details
Published inApplied mathematics letters Vol. 63; pp. 109 - 117
Main Authors Banks, H.T., Hu, Shuhua, Rosenberg, Eric
Format Journal Article
LanguageEnglish
Published United States Elsevier Ltd 01.01.2017
Subjects
HIV
Hypothesis testing
Inverse problems
Ordinary differential equation
Hypothesis testing
Inverse problems
HIV
Ordinary differential equation
hypothesis testing
ordinary differential equation
inverse problems
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ISSN0893-9659
1873-5452
1873-5452
DOI10.1016/j.aml.2016.07.002

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Summary: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.
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ISSN:0893-9659
1873-5452
1873-5452
DOI:10.1016/j.aml.2016.07.002