ORCID Profile
0000-0002-2676-5912
Current Organisation
Uppsala University
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Publisher: Springer Science and Business Media LLC
Date: 27-08-2013
DOI: 10.1007/S10928-013-9331-3
Abstract: Investigate the possibility to directly optimize a clinical trial design for statistical power to detect a drug effect and compare to optimal designs that focus on parameter precision. An improved statistic derived from the general formulation of the Wald approximation was used to predict the statistical power for given trial designs of a disease progression study. The predicted value was compared, together with the classical Wald statistic, to a type I error-corrected model-based power determined via clinical trial simulations. In a second step, a study design for maximal power was determined by directly maximizing the new statistic. The resulting power-optimal designs and their corresponding performance based on empirical power calculations were compared to designs focusing on parameter precision. Comparisons of empirically determined power and the newly developed statistic, showed excellent agreement across all scenarios investigated. This was in contrast to the classical Wald statistic, which consistently over-predicted the reference power with deviations of up to 90 %. Designs maximized using the proposed metric differed from traditional optimal designs and showed equal or up to 20 % higher power in the subsequent clinical trial simulations. Furthermore, the proposed method was used to minimize the number of in iduals required to achieve 80 % power through a simultaneous optimization of study size and study design. The targeted power of 80 % was confirmed in subsequent simulation study. A new statistic was developed, allowing for the explicit optimization of a clinical trial design with respect to statistical power.
Publisher: Springer Science and Business Media LLC
Date: 24-10-2017
DOI: 10.1007/S10928-017-9552-Y
Abstract: Optimal designs for nonlinear models are dependent on the choice of parameter values. Various methods have been proposed to provide designs that are robust to uncertainty in the prior choice of parameter values. These methods are generally based on estimating the expectation of the determinant (or a transformation of the determinant) of the information matrix over the prior distribution of the parameter values. For high dimensional models this can be computationally challenging. For nonlinear mixed-effects models the question arises as to the importance of accounting for uncertainty in the prior value of the variances of the random effects parameters. In this work we explore the influence of the variance of the random effects parameters on the optimal design. We find that the method for approximating the expectation and variance of the likelihood is of potential importance for considering the influence of random effects. The most common approximation to the likelihood, based on a first-order Taylor series approximation, yields designs that are relatively insensitive to the prior value of the variance of the random effects parameters and under these conditions it appears to be sufficient to consider uncertainty on the fixed-effects parameters only.
Publisher: Wiley
Date: 19-12-2014
DOI: 10.1111/BCP.12352
Publisher: Wiley
Date: 2012
Abstract: A simulation-based comparison study on the relative merits of dose-control trials (DCTs) with exposure-response analysis versus concentration-control trials (CCTs) for drugs with narrow therapeutic index showed that DCT designs are more informative about the exposure-response relationship. The authors revisit the question employing optimal design methodology and propose strategies for designing ethically attractive trials for these drugs, balancing between in idual-collective risk and informativeness. An optimal study was performed considering a hypothetical immunosuppressant agent with 2 clinical end points. Different scenarios were optimized applying cost-based designs (unwanted events vs number of subjects/trial or maximal in idual risk). Dose/exposure targets and number of subjects per trial/arm were optimized. Prior information inclusion on baseline risks was evaluated. DCTs were more informative, needing smaller studies to provide the same information as CCTs. Using the number of unwanted events-rather than subjects-as cost resulted in ethically more attractive designs. Including prior baseline risk information reduced the number of subject/events and allowed the use of targets closer to the optimal. Designing dose-finding trials for some narrow therapeutic index drugs may be improved by using DCTs with exposure-response analysis, cost-based designs, prior information, and optimal design analysis providing information on the ethical trade-off between in idual risk and information gain.
Publisher: Springer Science and Business Media LLC
Date: 15-07-2011
Publisher: Wiley
Date: 03-2009
Abstract: Optimal design has been used in the past mainly to optimize s ling schedules for clinical trials. Optimization on design variables other than s ling times has been published in the literature only once before. This study shows, as an ex le, optimization on the length of treatment periods to obtain reliable estimates of drug effects on longterm disease progression studies. Disease progression studies are high in cost, effort, and time therefore, optimization of treatment length is highly recommended to avoid failure or loss of information. Results are provided for different drug effects (eg, protective and symptomatic) and for different lengths of studies and s ling schedules. The merits of extending the total study length versus inclusion of more s les per participants are investigated. The authors demonstrate that if no observations are taken during the washout period, a trial can lose up to 40% of its efficiency. Furthermore, when optimization of treatment length is performed using multiple possible drug effect models simultaneously, these studies show high power in discriminating between different drug effect models.
Publisher: Elsevier BV
Date: 11-2012
DOI: 10.1016/J.CMPB.2012.05.005
Abstract: Several developments have facilitated the practical application and increased the general use of optimal design for nonlinear mixed effects models. These developments include new methodology for utilizing advanced pharmacometric models, faster optimization algorithms and user friendly software tools. In this paper we present the extension of the optimal design software PopED, which incorporates many of these recent advances into an easily useable enhanced GUI. Furthermore, we present new solutions to problems related to the design of experiments such as: faster and more robust FIM calculations and optimizations, optimizing over cost/utility functions and diagnostic tools and plots to evaluate design performance. Ex les for (i) Group size optimization and efficiency translation, (ii) Cost/constraint optimization, (iii) Optimizations with different FIM approximations and (iv) optimization with parallel computing demonstrate the new features in PopED and underline the potential use of this tool when designing experiments.
Publisher: Wiley
Date: 03-2012
Abstract: A time and s ling intensive pretransplant test dose design was to be reduced, but at the same time optimized so that there was no loss in the precision of predicting the in idual pharmacokinetic (PK) estimates of posttransplant dosing. The following variables were optimized simultaneously: s ling times, ciclosporin dose, time of second dose, infusion duration, and administration order, using a published ciclosporin population PK model as prior information. The original design was reduced from 22 s les to 6 s les atient and both doses (intravenous oral) were administered within 8 hours. Compared with the prior information given by the published ciclosporin population PK model, the expected standard deviations (SDs) of the in idual parameters for clearance and bioavailability could be reduced by, on average, 40% under the optimized sparse designs. The gain of performing the original rich design compared with the optimal reduced design, considering the standard errors of the parameter estimates, was found to be minimal. This application demonstrates, in a practical clinical scenario, how optimal design techniques may be used to improve diagnostic procedures given available software and methods.
No related grants have been discovered for Andrew Hooker.