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Practical considerations for optimal designs in clinical dose finding studies

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TLDR
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.
Abstract
A key objective in the clinical development of a medicinal drug is the determination of an adequate dose level and, more broadly, the characterization of its dose response relationship. If the dose is set too high, safety and tolerability problems are likely to result, while selecting too low a dose makes it difficult to establish adequate efficacy in the confirmatory phase, possibly leading to a failed program. Hence, dose finding studies are of critical importance in drug development and need to be planned carefully. In this paper, we focus on practical considerations for establishing efficient study designs to estimate relevant target doses. We consider optimal designs for estimating both the minimum effective dose and the dose achieving a certain percentage of the maximum treatment effect. These designs are compared with D-optimal designs for a given dose response model. Extensions to robust designs accounting for model uncertainty are also discussed. A case study is used to motivate and illustrate the methods from this paper.

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Journal ArticleDOI

Lessons Learned from Alzheimer Disease: Clinical Trials with Negative Outcomes.

TL;DR: Drug development decision making can be improved based on lessons learned from past trials, improved interpretation of animal models, better pharmacologic characterization in phase I and phase II trials, appropriate sample size, diagnosis of AD with biomarker support, optimization of global recruitment, and avoiding inappropriate subgroup analyses can improve drug development success rates.
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A Simulation Study to Compare New Adaptive Dose–Ranging Designs

TL;DR: Results of a comprehensive simulation study are presented that compares and contrasts five new adaptive dose-ranging designs for a variety of different scenarios.
Journal ArticleDOI

Optimal designs for the emax, log-linear and exponential models

TL;DR: In this article, locally D- and EDp-optimal designs for the exponential, log-linear and three-parameter emax models are derived at the same set of points, while the corresponding weights are different.
Posted Content

Optimal designs for estimating the interesting part of a dose-effect curve

TL;DR: Compared with a traditional balanced design for this trial, it is shown that the optimal design is substantially more efficient, which implies either a gain in information, or essential savings in sample size.
References
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Book

Optimal Design of Experiments

TL;DR: Experimental designs in linear models Optimal designs for Scalar Parameter Systems Information Matrices Loewner Optimality Real Optimality Criteria Matrix Means The General Equivalence Theorem Optimal Moment Matrices and Optimal Designs D-, A-, E-, T-Optimality Admissibility of moment and information matrices Bayes Designs and Discrimination Designs Efficient Designs for Finite Sample Sizes Invariant Design Problems Kiefer Optimality Rotatability and Response Surface Designs Comments and References Biographies Bibliography Index as discussed by the authors
Journal ArticleDOI

General Equivalence Theory for Optimum Designs (Approximate Theory)

J. Kiefer
- 01 Sep 1974 - 
TL;DR: For general optimality criteria, this article obtained criteria equivalent to $\Phi$-optimality under various conditions on ''Phi'' and showed that such equivalent criteria are useful for analytic or machine computation of ''phi''-optimum designs.
Journal ArticleDOI

Locally Optimal Designs for Estimating Parameters

TL;DR: In this article, it was shown that locally optimal designs for large numbers of experiments can be approximated by selecting a certain set of randomized experiments and by repeating each of these randomized experiments in certain specified proportions.
Journal ArticleDOI

Optimum Allocation in Linear Regression Theory

TL;DR: For the estimation of a single quantity of form, the optimum allocation comprises two or three sources as discussed by the authors, and the corresponding number is 2 or 3 for estimation of both parameters, the best proportions are indicated in Sections 2 and 4 below.
Journal ArticleDOI

Combining multiple comparisons and modeling techniques in dose-response studies.

TL;DR: A unified strategy to the analysis of data from dose- response studies is described which combines multiple comparison and modeling techniques and assumes the existence of several candidate parametric models and uses multiple comparison techniques to choose the one most likely to represent the true underlying dose-response curve, while preserving the family-wise error rate.
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