%0 Journal Article
%T Joint Modelling of Efficacy and Toxicity in the Dose Escalation Phase I Studies
%A Mounir Aout
%A Abdelkader Seroutou
%J Open Journal of Statistics
%P 603-613
%@ 2161-7198
%D 2018
%I Scientific Research Publishing
%R 10.4236/ojs.2018.83039
%X Most Phase I oncology trials are primarily concerned
with establishing the safety profile of a new treatment and focu</span></span><span style=\"font-family:Verdana;\"><span style=\"font-family:Verdana;\"><span style=\"font-family:Verdana;\">s</span></span></span><span style=\"font-family:Verdana;\"><span style=\"font-family:Verdana;\"><span style=\"font-family:Verdana;\"> on toxicity
alone to determine the maximum tolerated dose (MTD) defined as the highest dose
with the probability of toxicity less than a pre-specified target toxicity rate.
When additional data are available, there is an interest in selecting a recommended
dose based on PK, PD, efficacy data, etc. We propose a method that uses modeling
of both toxicity and efficacy to further guide the estimation of the recommended
dose(s) by finding an optimal dose or range of doses that maximizes the efficacy
while safety is controlled. The toxicity model is a Bayesian Logistic Regression
Model (BLRM) assessing the dose-toxicity relationships. The efficacy model is a
polynomial logistic regression model describing the dose-response relationships.
This model generalizes the monotonic dose-response relationship and allows for different
dose-response shapes. In addition, the association between toxicity and efficacy
is included in the modelling using global cross-ratio method. All analyses are performed
in the Bayesian framework. The proposed method is evaluated by intensive simulation
analyses and operating characteristics are provided. The design identifies adequately
the range of the recommended doses while safety is controlled and potentially shortens
the duration of a trial by enrolling fewer patients.
%K Dose Escalation
%K Bayesian Logistic Regression Model
%K Bayesian Dose Toxicity Model
%K Bayesian Dose Response Model
%K Global Cross-Ratio
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=85476