A Dose Finding Design for Seizure Reduction in Neonates

Ursino, Moreno; Yuan, Ying; Alberti, Corinne; Comets, Emmanuelle; Favrais, Géraldine; Friede, Tim; Lentz, Frederike; Stallard, Nigel; Zohar, Sarah

doi:10.1111/rssc.12289

Cited by 4 publications

(4 citation statements)

References 28 publications

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“…Hence, the MTD is defined as the dose with estimated overall mean response closest to the targeted one. Alternatively, the posterior median may also be used instead of the mean in (3.11) (Ursino et al (2019)).…”

Section: Mtd Estimationmentioning

confidence: 99%

Random-effects meta-analysis of Phase I dose-finding studies using stochastic process priors

et al. 2021

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Phase I dose-finding studies aim at identifying the maximum tolerated dose (MTD). Often, several dose-finding studies are conducted with some variation in the administration mode or dose panel. For instance, sorafenib (BAY 43-900) was used as monotherapy in 36 phase I trials, according to a recent clinicaltrials.gov search. Since the toxicity may not be directly related to the specific indication, synthesizing the information from several studies might be worthwhile. However, this is rarely done in practice and only a fixed-effect meta-analysis framework was proposed to date. We developed a Bayesian random-effects meta-analysis methodology to pool several phase I trials and suggest the MTD. A curve free hierarchical model on the logistic scale with random effects, accounting for between-trial heterogeneity, is used to model the probability of toxicity across the investigated doses. An Ornstein-Uhlenbeck Gaussian process is adopted for the random effects structure. Prior distributions for the curve-free model are based on a latent Gamma process. An extensive simulation study showed good performance of the proposed method also under model deviations. Sharing information between phase I studies can improve the precision of MTD selection, at least when the number of trials is reasonably large.

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Section: Mtd Estimationmentioning

confidence: 99%

Random-effects meta-analysis of Phase I dose-finding studies using stochastic process priors

et al. 2021

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“…34 As the trial was stopped early for serious adverse effects without apparent evidence of efficacy, a novel design was developed for a subsequent early phase trial of levetiracetam (NCT02229123). 35 Data from these ongoing trials (e.g., Boston bumetanide trial and levetiracetam trials) will need to be incorporated into the design of future neonatal ASD trials.…”

Section: Protocol Designmentioning

confidence: 99%

Recommendations for the design of therapeutic trials for neonatal seizures

et al. 2018

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Although seizures have a higher incidence in neonates than any other age group and are associated with significant mortality and neurodevelopmental disability, treatment is largely guided by physician preference and tradition, due to a lack of data from well-designed clinical trials. There is increasing interest in conducting trials of novel drugs to treat neonatal seizures, but the unique characteristics of this disorder and patient population require special consideration with regard to trial design. The Critical Path Institute formed a global working group of experts and key stakeholders from academia, the pharmaceutical industry, regulatory agencies, neonatal nurse associations, and patient advocacy groups to develop consensus recommendations for design of clinical trials to treat neonatal seizures. The broad expertise and perspectives of this group were invaluable in developing recommendations addressing: (1) use of neonate-specific adaptive trial designs, (2) inclusion/exclusion criteria, (3) stratification and randomization, (4) statistical analysis, (5) safety monitoring, and (6) definitions of important outcomes. The guidelines are based on available literature and expert consensus, pharmacokinetic analyses, ethical considerations, and parental concerns. These recommendations will ultimately facilitate development of a Master Protocol and design of efficient and successful drug trials to improve the treatment and outcome for this highly vulnerable population.

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“…For example, of the 59 articles published in the journal Bayesian Analysis in 2019 and 2020 until today (March, 2020), seven articles discuss in detail how results of prior elicitation could be used in Bayesian modeling and nine additional articles at least mention prior elicitation from experts. Prior elicitation is gaining popularity in many different fields such as economics, medicine, or engineering (for an overview, see O’Hagan et al, 2006) and is already performed routinely in some areas; for example, in pharmaceutical research (e.g., Guo, Park, & Liu, 2019; Thall & Cook, 2004; Thall, Nguyen, & Zinner, 2017; Ursino et al, 2019). In psychology, there have been relatively few prior elicitation efforts (for exceptions see Bolsinova, Hoijtink, Vermeulen, & Béguin, 2017; Gronau et al, in press; Sarma & Kay, 2020; Tessler & Goodman, 2019), but given the increasing use of Bayesian methods (Andrews & Baguley, 2013; Tendeiro & Kiers, 2019) and the broad support for informed Bayesian methods by Bayesian experts (Aczel et al, 2018), it seems plausible that prior elicitation may also become widely adopted in psychology in the future.…”

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confidence: 99%

Practical challenges and methodological flexibility in prior elicitation.

Stefan¹,

Evans²,

Wagenmakers³

2022

Psychological Methods

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The Bayesian statistical framework requires the specification of prior distributions, which reflect predata knowledge about the relative plausibility of different parameter values. As prior distributions influence the results of Bayesian analyses, it is important to specify them with care. Prior elicitation has frequently been proposed as a principled method for deriving prior distributions based on expert knowledge. Although prior elicitation provides a theoretically satisfactory method of specifying prior distributions, there are several implicit decisions that researchers need to make at different stages of the elicitation process, each of them constituting important researcher degrees of freedom. Here, we discuss some of these decisions and group them into 3 categories: decisions about (a) the setup of the prior elicitation; (b) the core elicitation process; and (c) combination of elicited prior distributions from different experts. Importantly, different decision paths could result in greatly varying priors elicited from the same experts. Hence, researchers who wish to perform prior elicitation are advised to carefully consider each of the practical decisions before, during, and after the elicitation process. By explicitly outlining the consequences of these practical decisions, we hope to raise awareness for methodological flexibility in prior elicitation and provide researchers with a more structured approach to navigate the decision paths in prior elicitation. Making the decisions explicit also provides the foundation for further research that can identify evidence-based best practices that may eventually reduce the methodologically flexibility in prior elicitation.

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A Dose Finding Design for Seizure Reduction in Neonates

Cited by 4 publications

References 28 publications

Random-effects meta-analysis of Phase I dose-finding studies using stochastic process priors

Random-effects meta-analysis of Phase I dose-finding studies using stochastic process priors

Recommendations for the design of therapeutic trials for neonatal seizures

Practical challenges and methodological flexibility in prior elicitation.

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