Optimizing Trial Designs for Targeted Therapies

Ondra, Thomas; Jobjörnsson, Sebastian; Beckman, Robert A.; Burman, Carl‐Fredrik; König, Franz; Stallard, Nigel; Posch, Martin

doi:10.1371/journal.pone.0163726

Cited by 26 publications

(42 citation statements)

References 30 publications

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“…Another more practical aspect when choosing a selection rule is trial optimization, both from a sponsor's and a public health perspective. Such optimized trial designs are presented by Graf et al and Ondra et al Based on a decision‐theoretic framework, they optimized adaptive designs with subgroup selection by maximizing utility functions that account for costs and risks of the clinical trial as well as benefit when detecting efficacy in a particular subgroup.…”

Section: Discussionmentioning

confidence: 99%

A conditional error function approach for adaptive enrichment designs with continuous endpoints

Placzek

Friede

2019

Statistics in Medicine

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Adaptive enrichment designs offer an efficient and flexible way to demonstrate the efficacy of a treatment in a clinically defined full population or in, eg, biomarker-defined subpopulations while controlling the family-wise Type I error rate in the strong sense. Frequently used testing strategies in designs with two or more stages include the combination test and the conditional error function approach. Here, we focus on the latter and present some extensions.In contrast to previous work, we allow for multiple subgroups rather than one subgroup only. For nested as well as nonoverlapping subgroups with normally distributed endpoints, we explore the effect of estimating the variances in the subpopulations. Instead of using a normal approximation, we derive new t-distribution-based methods for two different scenarios. First, in the case of equal variances across the subpopulations, we present exact results using a multivariate t-distribution. Second, in the case of potentially varying variances across subgroups, we provide some improved approximations compared to the normal approximation. The performance of the proposed conditional error function approaches is assessed and compared to the combination test in a simulation study. The proposed methods are motivated by an example in pulmonary arterial hypertension. KEYWORDSadaptive design, enrichment, interim analysis, multiple testing, subgroup analysis Statistics in Medicine. 2019;38:3105-3122. wileyonlinelibrary.com/journal/sim

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Section: Discussionmentioning

confidence: 99%

A conditional error function approach for adaptive enrichment designs with continuous endpoints

Placzek

Friede

2019

Statistics in Medicine

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“…Several criteria to ensure limited length of posterior credibility intervals can be defined to determine a Bayesian sample size; see Joseph and Bélisle for normal means and their differences and M'Lan, Joseph, and Wolfson for the binomial case. Moreover, aspects from different approaches might be combined; eg, a significance test to decide upon treatment recommendation could be incorporated into the decision‐theoretic frame . Uncertainties about cost parameter or recruitment parameter might be handled using prior distributions for these parameters as well.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, aspects from different approaches might be combined; eg, a significance test to decide upon treatment recommendation could be incorporated into the decision-theoretic frame. 26,31,32 Uncertainties about cost parameter or recruitment parameter might be handled using prior distributions for these parameters as well.…”

Section: Discussionmentioning

confidence: 99%

Approaches to sample size calculation for clinical trials in rare diseases

Miller

Zohar

Stallard

et al. 2018

Pharmaceutical Statistics

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We discuss 3 alternative approaches to sample size calculation: traditional sample size calculation based on power to show a statistically significant effect, sample size calculation based on assurance, and sample size based on a decision-theoretic approach. These approaches are compared head-to-head for clinical trial situations in rare diseases. Specifically, we consider 3 case studies of rare diseases (Lyell disease, adult-onset Still disease, and cystic fibrosis) with the aim to plan the sample size for an upcoming clinical trial. We outline in detail the reasonable choice of parameters for these approaches for each of the 3 case studies and calculate sample sizes. We stress that the influence of the input parameters needs to be investigated in all approaches and recommend investigating different sample size approaches before deciding finally on the trial size. Highly influencing for the sample size are choice of treatment effect parameter in all approaches and the parameter for the additional cost of the new treatment in the decision-theoretic approach. These should therefore be discussed extensively.

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“…Optimal designs for confirmatory studies using decision-theoretic and value-of-information (VOI) approaches Design of confirmatory studies with stratified populations for personalized medicines Key publications: [12][13][14][15] Key publications: [17,[20][21][22][23]…”

Section: Efficient Study Designmentioning

confidence: 99%

Recent advances in methodology for clinical trials in small populations: the InSPiRe project

Friede

Posch

Zohar

et al. 2018

Orphanet J Rare Dis

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Where there are a limited number of patients, such as in a rare disease, clinical trials in these small populations present several challenges, including statistical issues. This led to an EU FP7 call for proposals in 2013. One of the three projects funded was the Innovative Methodology for Small Populations Research (InSPiRe) project. This paper summarizes the main results of the project, which was completed in 2017.The InSPiRe project has led to development of novel statistical methodology for clinical trials in small populations in four areas. We have explored new decision-making methods for small population clinical trials using a Bayesian decision-theoretic framework to compare costs with potential benefits, developed approaches for targeted treatment trials, enabling simultaneous identification of subgroups and confirmation of treatment effect for these patients, worked on early phase clinical trial design and on extrapolation from adult to pediatric studies, developing methods to enable use of pharmacokinetics and pharmacodynamics data, and also developed improved robust meta-analysis methods for a small number of trials to support the planning, analysis and interpretation of a trial as well as enabling extrapolation between patient groups. In addition to scientific publications, we have contributed to regulatory guidance and produced free software in order to facilitate implementation of the novel methods.

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Optimizing Trial Designs for Targeted Therapies

Cited by 26 publications

References 30 publications

A conditional error function approach for adaptive enrichment designs with continuous endpoints

A conditional error function approach for adaptive enrichment designs with continuous endpoints

Approaches to sample size calculation for clinical trials in rare diseases

Recent advances in methodology for clinical trials in small populations: the InSPiRe project

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