Journal ArticleDOI
Adaptive designs for dose-finding studies based on sigmoid Emax model.
Reads0
Chats0
TLDR
An adaptive procedure for dose-finding in clinical trials when the primary efficacy endpoint is continuous is proposed, which model the mean of the efficacy endpoint, given the dose, as a four-parameter logistic function.Abstract:
We propose an adaptive procedure for dose-finding in clinical trials when the primary efficacy endpoint is continuous. We model the mean of the efficacy endpoint, given the dose, as a four-parameter logistic function. The efficacy endpoint at each dose is distributed according to either a normal or a gamma distribution. We consider the cases of fixed variance and fixed coefficient of variation assuming them to be both known and unknown. The analytic formulae for the Fisher information matrix are obtained, which are used to build the locally and adaptive D-optimal designs.read more
Citations
More filters
Journal ArticleDOI
The Hill equation: a review of its capabilities in pharmacological modelling
Sylvain Goutelle,Michel Maurin,Florent Rougier,X. Barbaut,Laurent Bourguignon,Michel Ducher,Pascal Maire +6 more
TL;DR: The Hill equation has many different properties which can be of great interest for those interested in mathematical modelling in pharmacology and biosciences, and is introduced as a probabilistic view of the Hill equation.
Journal ArticleDOI
Adaptive Designs for Randomized Trials in Public Health
C. Hendricks Brown,Thomas R. Ten Have,Booil Jo,Getachew A. Dagne,Peter A. Wyman,Bengt Muthén,Robert D. Gibbons +6 more
TL;DR: Whether public health evaluations can or should be analyzed as if they were formal randomized trials is discussed, and practical as well as ethical issues arising in the conduct of these new-generation trials are discussed.
Journal ArticleDOI
Model‐based dose finding under model uncertainty using general parametric models
TL;DR: In this article, the authors developed an approach to perform efficient multiple comparisons and modeling for dose finding, under uncertainty about the dose-response shape, using general parametric models, including generalized nonlinear models, linear and nonlinear mixed effects models, Cox proportional hazards models, with the main restriction being that a univariate doseresponse relationship is modeled, that is, both dose and response correspond to univariate measurements.
Journal ArticleDOI
On Optimal Designs for Nonlinear Models: A General and Efficient Algorithm
TL;DR: This work presents a new algorithm that can be used to find optimal designs with respect to a broad class of optimality criteria, when the model parameters or functions thereof are of interest, and for both locally optimal and multistage design strategies.
Journal ArticleDOI
Practical considerations for optimal designs in clinical dose finding studies
TL;DR: Practical considerations for establishing efficient study designs to estimate relevant target doses are considered and optimal designs for estimating both the minimum effective dose and the dose achieving a certain percentage of the maximum treatment effect are considered.
References
More filters
Book
Mixed-Effects Models in S and S-PLUS
TL;DR: Linear Mixed-Effects and Nonlinear Mixed-effects (NLME) models have been studied in the literature as mentioned in this paper, where the structure of grouped data has been used for fitting LME models.
Book
Aspects of multivariate statistical theory
TL;DR: In this paper, the authors present a set of standard tests on Covariance Matrices and Mean Vectors, and test independence between k Sets of Variables and Canonical Correlation Analysis.
Book
Optimum experimental designs
Anthony C. Atkinson,A. N. Donev +1 more
TL;DR: In this article, the authors present an analysis of experiments with both qualitative and quantitative factors: Blocking response surface designs, restricted region designs, failure of the experiment and design augmentation, and discrimination between models.
Journal ArticleDOI
Understanding the dose-effect relationship: clinical application of pharmacokinetic-pharmacodynamic models.
TL;DR: The design of rational dosing regimens for clinical therapeutics cannot be performed with a knowledge of pharmacokinelics alone, and the linking of pharmacokinetics and pharmacodynamics to predict firstly the dose-concentration, and then the concentration-effect relationship is required.