Bayesian Design of Proof-of-Concept Trials

Fisch, Roland; Jones, Ieuan; Jones, Julie; Kerman, Jouni; Rosenkranz, G; Schmidli, Heinz

doi:10.1177/2168479014533970

Cited by 46 publications

(47 citation statements)

References 23 publications

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“…In a Bayesian framework, uncertainty about any differences observed is expressed via probability distributions, enabling quantitative comparisons between experimental treatment and control. In addition, it has been reported that Bayesian statistics are useful for “go/no‐go” decision‐making in proof‐of‐concept studies . The Bayesian posterior probability was therefore appropriate for making comparisons between drug groups in this study.…”

Section: Discussionmentioning

confidence: 95%

A randomized controlled phase II clinical trial comparing ONO‐4053, a novel DP1 antagonist, with a leukotriene receptor antagonist pranlukast in patients with seasonal allergic rhinitis

Okubo

Hashiguchi

Takeda

et al. 2017

Allergy

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BackgroundProstaglandin D2 (PGD 2) is primarily produced by mast cells and is contributing to the nasal symptoms including nasal obstruction and rhinorrhea.ObjectiveThis study aimed to evaluate the efficacy and safety of a novel PGD 2 receptor 1 (DP1) antagonist, ONO‐4053, in patients with seasonal allergic rhinitis (SAR).MethodsThis study was a multicenter, randomized, double‐blind, parallel‐group study of patients with SAR. Following a one‐week period of placebo run‐in, patients who met the study criteria were randomized to either the ONO‐4053, leukotriene receptor antagonist pranlukast, or placebo group for a two‐week treatment period. A total of 200 patients were planned to be randomly assigned to receive ONO‐4053, pranlukast, or placebo in a 2:2:1 ratio. Nasal and eye symptoms were evaluated.ResultsBoth ONO‐4053 and pranlukast had higher efficacy than placebo on all nasal and eye symptoms. ONO‐4053 outperformed pranlukast in a total of three nasal symptom scores (T3NSS) as well as in individual scores for sneezing, rhinorrhea, and nasal itching. For T3NSS, the Bayesian posterior probabilities that pranlukast was better than placebo and ONO‐4053 was better than pranlukast were 70.0% and 81.6%, respectively, suggesting that ONO‐4053 has a higher efficacy compared with pranlukast. There was no safety‐related issue in this study.ConclusionsWe demonstrated that the efficacy of ONO‐4053 was greater than that of pranlukast with a similar safety profile. This study indicates the potential of ONO‐4053 for use as a treatment for SAR (JapicCTI‐142706).

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Section: Discussionmentioning

confidence: 95%

A randomized controlled phase II clinical trial comparing ONO‐4053, a novel DP1 antagonist, with a leukotriene receptor antagonist pranlukast in patients with seasonal allergic rhinitis

Okubo

Hashiguchi

Takeda

et al. 2017

Allergy

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“…However, much of what is presented here might apply to, or be adapted to apply to, these types of pilot or feasibility studies or similar types of trial, such as “proof of concept” or phase II trials done in the development of drugs [16, 17]. Proof of concept or phase II trials are small RCTs the main objective of which are to inform the sponsor whether or not to continue the development of a drug with larger trials.…”

Section: Scope Of This Papermentioning

confidence: 99%

CONSORT 2010 statement: extension to randomised pilot and feasibility trials

Eldridge

Chan

Campbell

et al. 2016

Pilot Feasibility Stud

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1,227

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The Consolidated Standards of Reporting Trials (CONSORT) statement is a guideline designed to improve the transparency and quality of the reporting of randomised controlled trials (RCTs). In this article we present an extension to that statement for randomised pilot and feasibility trials conducted in advance of a future definitive RCT. The checklist applies to any randomised study in which a future definitive RCT, or part of it, is conducted on a smaller scale, regardless of its design (eg, cluster, factorial, crossover) or the terms used by authors to describe the study (eg, pilot, feasibility, trial, study). The extension does not directly apply to internal pilot studies built into the design of a main trial, non-randomised pilot and feasibility studies, or phase II studies, but these studies all have some similarities to randomised pilot and feasibility studies and so many of the principles might also apply.The development of the extension was motivated by the growing number of studies described as feasibility or pilot studies and by research that has identified weaknesses in their reporting and conduct. We followed recommended good practice to develop the extension, including carrying out a Delphi survey, holding a consensus meeting and research team meetings, and piloting the checklist.The aims and objectives of pilot and feasibility randomised studies differ from those of other randomised trials. Consequently, although much of the information to be reported in these trials is similar to those in randomised controlled trials (RCTs) assessing effectiveness and efficacy, there are some key differences in the type of information and in the appropriate interpretation of standard CONSORT reporting items. We have retained some of the original CONSORT statement items, but most have been adapted, some removed, and new items added. The new items cover how participants were identified and consent obtained; if applicable, the prespecified criteria used to judge whether or how to proceed with a future definitive RCT; if relevant, other important unintended consequences; implications for progression from pilot to future definitive RCT, including any proposed amendments; and ethical approval or approval by a research review committee confirmed with a reference number.This article includes the 26 item checklist, a separate checklist for the abstract, a template for a CONSORT flowchart for these studies, and an explanation of the changes made and supporting examples. We believe that routine use of this proposed extension to the CONSORT statement will result in improvements in the reporting of pilot trials.Editor’s note: In order to encourage its wide dissemination this article is freely accessible on the BMJ and Pilot and Feasibility Studies journal websites.Electronic supplementary materialThe online version of this article (doi:10.1186/s40814-016-0105-8) contains supplementary material, which is available to authorized users.

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“…In early phase trials, often double criteria for stopping for success are used, which are based on both statistical significance and clinical relevance. [47,48] Group sequential designs using such double criteria are described by Gsponer et al, which also discuss a multiple sclerosis example with negative binomially distributed lesion count data. [49] The negative binomial model is a common choice to model MRI lesions in relapsingremitting multiple sclerosis as discussed in Section 2.…”

Section: Discussion and Outlookmentioning

confidence: 99%

Group sequential designs for negative binomial outcomes

Mütze

Glimm

Schmidli

et al. 2018

Stat Methods Med Res

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Count data and recurrent events in clinical trials, such as the number of lesions in magnetic resonance imaging in multiple sclerosis, the number of relapses in multiple sclerosis, the number of hospitalizations in heart failure, and the number of exacerbations in asthma or in chronic obstructive pulmonary disease (COPD) are often modeled by negative binomial distributions. In this manuscript, we study planning and analyzing clinical trials with group sequential designs for negative binomial outcomes. We propose a group sequential testing procedure for negative binomial outcomes based on Wald statistics using maximum likelihood estimators. The asymptotic distribution of the proposed group sequential test statistics is derived. The finite sample size properties of the proposed group sequential test for negative binomial outcomes and the methods for planning the respective clinical trials are assessed in a simulation study. The simulation scenarios are motivated by clinical trials in chronic heart failure and relapsing multiple sclerosis, which cover a wide range of practically relevant settings. Our research assures that the asymptotic normal theory of group sequential designs can be applied to negative binomial outcomes when the hypotheses are tested using Wald statistics and maximum likelihood estimators. We also propose two methods, one based on Student's t-distribution and one based on resampling, to improve type I error rate control in small samples. The statistical methods studied in this manuscript are implemented in the R package gscounts, which is available for download on the Comprehensive R Archive Network (CRAN).

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Bayesian Design of Proof-of-Concept Trials

Cited by 46 publications

References 23 publications

A randomized controlled phase II clinical trial comparing ONO‐4053, a novel DP1 antagonist, with a leukotriene receptor antagonist pranlukast in patients with seasonal allergic rhinitis

A randomized controlled phase II clinical trial comparing ONO‐4053, a novel DP1 antagonist, with a leukotriene receptor antagonist pranlukast in patients with seasonal allergic rhinitis

CONSORT 2010 statement: extension to randomised pilot and feasibility trials

Group sequential designs for negative binomial outcomes

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