HIV 2-LTR experiment design optimization.
LaMont CannonCesar Augusto Vargas-GarciaAditya JagarapuMichael J PiovosoRyan ZurakowskiPublished in: PloS one (2018)
Clinical trials are necessary in order to develop treatments for diseases; however, they can often be costly, time consuming, and demanding to the patients. This paper summarizes several common methods used for optimal design that can be used to address these issues. In addition, we introduce a novel method for optimizing experiment designs applied to HIV 2-LTR clinical trials. Our method employs Bayesian techniques to optimize the experiment outcome by maximizing the Expected Kullback-Leibler Divergence (EKLD) between the a priori knowledge of system parameters before the experiment and the a posteriori knowledge of the system parameters after the experiment. We show that our method is robust and performs equally well if not better than traditional optimal experiment design techniques.
Keyphrases
- clinical trial
- antiretroviral therapy
- hiv positive
- hiv infected
- end stage renal disease
- hiv testing
- human immunodeficiency virus
- healthcare
- hepatitis c virus
- hiv aids
- ejection fraction
- newly diagnosed
- men who have sex with men
- chronic kidney disease
- peritoneal dialysis
- randomized controlled trial
- patient reported outcomes
- open label
- patient reported
- study protocol
- double blind