Information optimal experiment design of HIV 2-LTR clinical trials by Expected Kullback-Leibler Divergence
| Author(s) | Cannon, LaMont C., II | |
| Date Accessioned | 2018-06-27T11:52:40Z | |
| Date Available | 2018-06-27T11:52:40Z | |
| Publication Date | 2018 | |
| SWORD Update | 2018-02-23T17:27:33Z | |
| Abstract | Finding a cure for individuals infected with the Human Immunodeficiency Virus (HIV) has proved to be a challenging task. This is primarily due to the fact that conventional treatment has not been able to adequately disrupt the replication process in order to eradicate the virus. One of the possible explanations for this lack of treatment efficacy is that there are low levels of ongoing replication occurring in locations of reduced drug concentration called sanctuary sites. In order to effectively treat the disease, it would be advantageous to clinicians to know how much on-going replication is occurring. This knowledge would then help to guide patient specific treatment for the disease. A novel method to quantify the level on going replication has been suggested. This method entails taking blood samples and measuring biomarkers of on-going. In order to be identified as a valid method clinical trials must be carried out; however, they can often be costly, time consuming and demanding to the patients. For these reasons, meticulous effort should be applied to make sure that these trials are as efficient and informative as possible. ☐ This thesis summarizes several common methods used for optimal design that can be used to address these issues. A mathematical model is first employed to demonstrate the dynamics of the HIV and on-going replication biomarker system. Using this model in conjunction with preliminary laboratory data, Bayesian Markov Chain Monte Carlo Methods are applied to estimate model parameter distributions under a variety of different experiment assumptions. We then calculate the Expected Kullback-Leibler Divergence (EKLD) between the a priori parameter distributions and the a posteriori distributions for each experiment regimen. This value is taken to indicate the amount of information we can expect to gain from performing the experiment under each particular design. Through the use of genetic algorithms we then locate the experiment design that optimizes the expected gain in information. In doing so, this thesis shows that the EKLD optimization method is robust and performs equally well if not better than traditional optimal experiment design techniques under multiple experiment design criteria. Due to the increased capability provided by the EKLD optimization method in the design of experiments, it should be used in on-going replication quantification experiments in order to maximize information gain and to minimize costs. | en_US |
| Advisor | Zurakowski, Ryan M. | |
| Degree | Ph.D. | |
| Department | University of Delaware, Department of Biomedical Engineering | |
| DOI | https://doi.org/10.58088/71s2-7x98 | |
| Unique Identifier | 1042073688 | |
| URL | http://udspace.udel.edu/handle/19716/23595 | |
| Language | en | |
| Publisher | University of Delaware | en_US |
| URI | https://search.proquest.com/docview/2021741787?accountid=10457 | |
| Keywords | Biological sciences | en_US |
| Keywords | Applied sciences | en_US |
| Keywords | Design | en_US |
| Keywords | Experiment | en_US |
| Keywords | HIV | en_US |
| Keywords | Optimization | en_US |
| Keywords | Replication | en_US |
| Title | Information optimal experiment design of HIV 2-LTR clinical trials by Expected Kullback-Leibler Divergence | en_US |
| Type | Thesis | en_US |