Bayesian penalized log-likelihood ratio approach for dose response clinical trial studies.
In literature, there are a few unified approaches to test proof of concept and estimate a target dose, including the multiple comparison procedure using modeling approach, and the permutation approach proposed by Klingenberg. We discuss and compare the operating characteristics of these unified approaches and further develop an alternative approach in a Bayesian framework based on the posterior distribution of a penalized log-likelihood ratio test statistic. Our Bayesian approach is much more flexible to handle linear or nonlinear dose-response relationships and is more efficient than the permutation approach. The operating characteristics of our Bayesian approach are comparable to and sometimes better than both approaches in a wide range of dose-response relationships. It yields credible intervals as well as predictive distribution for the response rate at a specific dose level for the target dose estimation. Our Bayesian approach can be easily extended to continuous, categorical, and time-to-event responses. We illustrate the performance of our proposed method with extensive simulations and Phase II clinical trial data examples.
a Cardiovascular Research, Saint Luke's Mid America Heart Institute , Saint Luke's Hospital of Kansas City, Saint Luke's Health System , Kansas City , Missouri , USA.
Yuanyuan
Y
Tang
b Department of Internal Medicine , The University of Texas Health Science Center at Houston , Houston , Texas , USA.
Chunyan
C
Cai
c Department of Statistics , University of Nebraska-Lincoln , Lincoln , Nebraska, USA.
Liangrui
L
Sun
d Department of Biostatistics , University of Kansas Medical Center , Kansas City , Kansas , USA.
Jianghua
J
He
D001499:Bayes Theorem; D017322:Clinical Trials, Phase II as Topic / Q000379:methods / Q000706:statistics & numerical data*; D004305:Dose-Response Relationship, Drug; D006801:Humans; D016013:Likelihood Functions; D015337:Multicenter Studies as Topic / Q000379:methods / Q000706:statistics & numerical data*; D000075082:Proof of Concept Study
Bayesian approach; dose–response modeling; likelihood ratio statistic; model uncertainty; proof of concept; target dose estimation