Reducing the total cost of a clinical trial: An optimization problem
Abstract
A well designed clinical trial can provide beneficial results with minimal costs and minimal numbers of patients. One of the greatest hurdles in conducting a clinical trial is defining the entry criteria. Entry criteria that are too broad allow for an increased number of potential non-responders in the trial population. Entry criteria that are too narrow can exclude many potential responders. Implementing a screening test that can identify patients for targeted therapies who are likely responders on the basis of genetics, anatomy or biology allows researchers to better optimize the trial population. Finding the right trial population becomes an optimization problem that can be solved by implementing a mathematical and quantitative approach to predict the effects of the strictness or looseness of entry criteria. This thesis tests such a model based approach using realistic clinical data reconstructed from a published clinical trial of targeted drug therapy for lung cancer. An optimal set of inclusion criteria is defined based upon patient characteristics that can be determined from a pre-trial screening test. A mathematical model of the responsiveness of the trial population, based on available preliminary data, together with a mathematical model of the statistical analysis of trial data are combined to estimate the number of patients that must be screened and the number of patients accepted into the trial for each of many possible sets of inclusion criteria. These predictions of trial size are combined with realistic cost factors to estimate the total cost of the trial. The test scenario for the lung cancer study demonstrates that differing inclusion criteria can have a dramatic effect on trial size, duration, and cost and even success or failure of the trial at any cost. Model based analysis can impact the design of a clinical trial by decreasing the number of patients needed to show a statistical significant result as well as reducing the total cost of the trial.
Degree
M.S.
Advisors
Babbs, Purdue University.
Subject Area
Health care management
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