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 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.
Center for Research in Scientific Computation, North Carolina State University, Raleigh, NC 27695-8212.
H T
HT
Banks
Center for Research in Scientific Computation, North Carolina State University, Raleigh, NC 27695-8212; Certara, Inc., Cary, NC 27518.
Shuhua
S
Hu
Center for Research in Scientific Computation, North Carolina State University, Raleigh, NC 27695-8212; Departments of Pathology and Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114.
Eric
E
Rosenberg
HIV; hypothesis testing; inverse problems; ordinary differential equation