A practical guide to Bayesian group sequential designs

Gsponer, Thomas; Gerber, Florian; Bornkamp, Björn; Ohlssen, David; Vandemeulebroecke, Marc; Schmidli, Heinz

doi:10.1002/pst.1593

Cited by 63 publications

(64 citation statements)

References 37 publications

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“…Others have proposed to adjust the sample size of a clinical trial based on treatment effect estimates . Additionally, the use of Bayesian approaches has already be advocated for monitoring group sequential designs . In future research, incorporating prior information about the effect into the sample size re‐estimation should be studied.…”

Section: Discussionmentioning

confidence: 99%

Sample size re‐estimation incorporating prior information on a nuisance parameter

Mütze

Schmidli

Friede

2017

Pharmaceutical Statistics

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Prior information is often incorporated informally when planning a clinical trial. Here, we present an approach on how to incorporate prior information, such as data from historical clinical trials, into the nuisance parameter-based sample size re-estimation in a design with an internal pilot study. We focus on trials with continuous endpoints in which the outcome variance is the nuisance parameter. For planning and analyzing the trial, frequentist methods are considered. Moreover, the external information on the variance is summarized by the Bayesian meta-analytic-predictive approach. To incorporate external information into the sample size re-estimation, we propose to update the meta-analytic-predictive prior based on the results of the internal pilot study and to re-estimate the sample size using an estimator from the posterior. By means of a simulation study, we compare the operating characteristics such as power and sample size distribution of the proposed procedure with the traditional sample size re-estimation approach that uses the pooled variance estimator. The simulation study shows that, if no prior-data conflict is present, incorporating external information into the sample size re-estimation improves the operating characteristics compared to the traditional approach. In the case of a prior-data conflict, that is, when the variance of the ongoing clinical trial is unequal to the prior location, the performance of the traditional sample size re-estimation procedure is in general superior, even when the prior information is robustified. When considering to include prior information in sample size re-estimation, the potential gains should be balanced against the risks.

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Section: Discussionmentioning

confidence: 99%

Sample size re‐estimation incorporating prior information on a nuisance parameter

Mütze

Schmidli

Friede

2017

Pharmaceutical Statistics

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“…There should be a cautious reflection on the availability of timely data and their characteristics (including discussion with the data providers), the vaccine(s) and outcome(s) to be studied and the infra‐structure needed in case a signal is detected. Future directions for research might include further development and application of strategies for adjustment for confounding and data‐accrual lag, as well as consideration of other methods not yet applied to observational settings but in use in clinical trials, for example, Bayesian approaches to group sequential tests . Bayesian methods can incorporate previous information (such as the data generated by pre‐licensure studies) and potentially provide a more flexible approach.…”

Section: Discussionmentioning

confidence: 99%

Near real‐time vaccine safety surveillance using electronic health records—a systematic review of the application of statistical methods

Leite

Andrews

Thomas

2016

Pharmacoepidemiology and Drug

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PurposePre‐licensure studies have limited ability to detect rare adverse events (AEs) to vaccines, requiring timely post‐licensure studies. With the increasing availability of electronic health records (EHR) near real‐time vaccine safety surveillance using these data has emerged as an option. We reviewed methods currently used to inform development of similar systems for countries considering their introduction.MethodsMedline, EMBASE and Web of Science were searched, with additional searches of conference abstract books. Questionnaires were sent to organizations worldwide to ascertain unpublished studies. Eligible studies used EHR and regularly assessed pre‐specified AE to vaccine(s). Key features of studies were compared descriptively.ResultsFrom 2779 studies, 31 were included from the USA (23), UK (6), and Taiwan and New Zealand (1 each). These were published/conducted between May 2005 and April 2015. Thirty‐eight different vaccines were studied, focusing mainly on influenza (47.4%), especially 2009 H1N1 vaccines. Forty‐six analytic approaches were used, reflecting frequency of EHR updates and the AE studied. Poisson‐based maximized sequential probability ratio test was the most common (43.5%), followed by its binomial (23.9%) and conditional versions (10.9%). Thirty‐seven of 49 analyses (75.5%) mentioned control for confounding, using an adjusted expected rate (51.4% of those adjusting), stratification (16.2%) or a combination of a self‐controlled design and stratification (13.5%). Guillain‐Barré syndrome (11.9%), meningitis/encephalitis/myelitis (11.9%) and seizures (10.8%) were studied most often.ConclusionsNear real‐time vaccine safety surveillance using EHR has developed over the past decade but is not yet widely used. As more countries have access to EHR, it will be important that appropriate methods are selected, considering the data available and AE of interest. © 2016 The Authors. Pharmacoepidemiology and Drug Safety Published by John Wiley & Sons Ltd.

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“…In our companion article, we considered two alternative implementations of frequentist sequential designs. In the present work, we consider two Bayesian implementations of sequential designs adapted from Gsponer et al …”

Section: Methodsmentioning

confidence: 99%

Model‐Based Assessment of Alternative Study Designs in Pediatric Trials. Part II: Bayesian Approaches

Smania

Baiardi

Ceci

et al. 2016

CPT Pharmacom & Syst Pharma

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This study presents a pharmacokinetic‐pharmacodynamic based clinical trial simulation framework for evaluating the performance of a fixed‐sample Bayesian design (BD) and two alternative Bayesian sequential designs (BSDs) (i.e., a non‐hierarchical (NON‐H) and a semi‐hierarchical (SEMI‐H) one). Prior information was elicited from adult trials and weighted based on the expected similarity of response to treatment between the pediatric and adult populations. Study designs were evaluated in terms of: type I and II errors, sample size per arm (SS), trial duration (TD), and estimate precision. No substantial differences were observed between NON‐H and SEMI‐H. BSDs require, on average, smaller SS and TD compared to the BD, which, on the other hand, guarantees higher estimate precision. When large differences between children and adults are expected, BSDs can return very large SS. Bayesian approaches appear to outperform their frequentist counterparts in the design of pediatric trials even when little weight is given to prior information from adults.

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A practical guide to Bayesian group sequential designs

Cited by 63 publications

References 37 publications

Sample size re‐estimation incorporating prior information on a nuisance parameter

Sample size re‐estimation incorporating prior information on a nuisance parameter

Near real‐time vaccine safety surveillance using electronic health records—a systematic review of the application of statistical methods

Model‐Based Assessment of Alternative Study Designs in Pediatric Trials. Part II: Bayesian Approaches

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