Bayesian statistical method was underused despite its advantages in the assessment of implantable medical devices

Pibouleau, Leslie; Chevret, Sylvie

doi:10.1016/j.jclinepi.2010.03.018

Cited by 20 publications

(13 citation statements)

References 35 publications

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“…Different approaches exist for incorporating historical data, [20][21][22][23][24] but they are scarcely used in practice and, despite their advantages, authors seem to prefer non-informative priors. The small number of trials identified by this review is consistent with the findings of Pibouleau and Chevret 76 who reviewed the use of Bayesian methods in medical device trials and found 12 clinical studies. A review of trials conducted at the MD Anderson Cancer Center, known for a particular interest in Bayesian trials, found that Bayesian methods tended to be used more in early phase trials but were rarely used in seamless phase II/ III or phase III trials where time-to-event endpoints are more typically measured.…”

Section: Discussionsupporting

confidence: 86%

Bayesian survival analysis in clinical trials: What methods are used in practice?

et al. 2016

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Background: Bayesian statistics are an appealing alternative to the traditional frequentist approach to designing, analysing, and reporting of clinical trials, especially in rare diseases. Time-to-event endpoints are widely used in many medical fields. There are additional complexities to designing Bayesian survival trials which arise from the need to specify a model for the survival distribution. The objective of this article was to critically review the use and reporting of Bayesian methods in survival trials. Methods: A systematic review of clinical trials using Bayesian survival analyses was performed through PubMed and Web of Science databases. This was complemented by a full text search of the online repositories of pre-selected journals. Cost-effectiveness, dose-finding studies, meta-analyses, and methodological papers using clinical trials were excluded. Results: In total, 28 articles met the inclusion criteria, 25 were original reports of clinical trials and 3 were re-analyses of a clinical trial. Most trials were in oncology (n = 25), were randomised controlled (n = 21) phase III trials (n = 13), and half considered a rare disease (n = 13). Bayesian approaches were used for monitoring in 14 trials and for the final analysis only in 14 trials. In the latter case, Bayesian survival analyses were used for the primary analysis in four cases, for the secondary analysis in seven cases, and for the trial re-analysis in three cases. Overall, 12 articles reported fitting Bayesian regression models (semi-parametric, n = 3; parametric, n = 9). Prior distributions were often incompletely reported: 20 articles did not define the prior distribution used for the parameter of interest. Over half of the trials used only non-informative priors for monitoring and the final analysis (n = 12) when it was specified. Indeed, no articles fitting Bayesian regression models placed informative priors on the parameter of interest. The prior for the treatment effect was based on historical data in only four trials. Decision rules were pre-defined in eight cases when trials used Bayesian monitoring, and in only one case when trials adopted a Bayesian approach to the final analysis. Conclusion: Few trials implemented a Bayesian survival analysis and few incorporated external data into priors. There is scope to improve the quality of reporting of Bayesian methods in survival trials. Extension of the Consolidated Standards of Reporting Trials statement for reporting Bayesian clinical trials is recommended.

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Section: Discussionsupporting

confidence: 86%

Bayesian survival analysis in clinical trials: What methods are used in practice?

et al. 2016

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“…48 The use of Bayesian methods is recommended for the clinical evaluation of MDs. 48 – 51 The advantage of these methods lies in the reduction of the number of patients required, which is particularly interesting for small target populations. The main disadvantage is that arbitrary and erroneous prior information can be taken into account, which can substantially influence the final result.…”

Section: Methods Used To Overcome the Problems Identifiedmentioning

confidence: 99%

Methodological choices for the clinical development of medical devices

Bernard¹,

Vaneau²,

Fournel³

et al. 2014

MDER

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Clinical evidence available for the assessment of medical devices (MDs) is frequently insufficient. New MDs should be subjected to high quality clinical studies to demonstrate their benefit to patients. The randomized controlled trial (RCT) is the study design reaching the highest level of evidence in order to demonstrate the efficacy of a new MD. However, the clinical context of some MDs makes it difficult to carry out a conventional RCT. The objectives of this review are to present problems related to conducting conventional RCTs and to identify other experimental designs, their limitations, and their applications. A systematic literature search was conducted for the period January 2000 to July 2012 by searching medical bibliographic databases. Problems related to conducting conventional RCTs of MDs were identified: timing the assessment, eligible population and recruitment, acceptability, blinding, choice of comparator group, and learning curve. Other types of experimental designs have been described. Zelen’s design trials and randomized consent design trials facilitate the recruitment of patients, but can cause ethical problems to arise. Expertise-based RCTs involve randomization to a team that specializes in a given intervention. Sometimes, the feasibility of an expertise-based randomized trial may be greater than that of a conventional trial. Cross-over trials reduce the number of patients, but are not applicable when a learning curve is required. Sequential trials have the advantage of allowing a trial to be stopped early depending on the results of first inclusions, but they require an independent committee. Bayesian methods combine existing information with information from the ongoing trial. These methods are particularly useful in situations where the number of subjects is small. The disadvantage is the risk of including erroneous prior information. Other types of experimental designs exist when conventional trials cannot always be applied to the clinical development of MDs.

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“…For a complete list of publicly available submissions see Campbell (2011Campbell ( , 2013. A report on implantable medical devices is Pibouleau and Chevret (2011).…”

Section: Center For Devices and Radiological Health Review And Successesmentioning

confidence: 99%

Bayesian methods in clinical trials with applications to medical devices

Campbell

2017

CSAM

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Bayesian statistics can play a key role in the design and analysis of clinical trials and this has been demonstrated for medical device trials. By 1995 Bayesian statistics had been well developed and the revolution in computing powers and Markov chain Monte Carlo development made calculation of posterior distributions within computational reach. The Food and Drug Administration (FDA) initiative of Bayesian statistics in medical device clinical trials, which began almost 20 years ago, is reviewed in detail along with some of the key decisions that were made along the way. Both Bayesian hierarchical modeling using data from previous studies and Bayesian adaptive designs, usually with a non-informative prior, are discussed. The leveraging of prior study data has been accomplished through Bayesian hierarchical modeling. An enormous advantage of Bayesian adaptive designs is achieved when it is accompanied by modeling of the primary endpoint to produce the predictive posterior distribution. Simulations are crucial to providing the operating characteristics of the Bayesian design, especially for a complex adaptive design. The 2010 FDA Bayesian guidance for medical device trials addressed both approaches as well as exchangeability, Type I error, and sample size. Treatment response adaptive randomization using the famous extracorporeal membrane oxygenation example is discussed. An interesting real example of a Bayesian analysis using a failed trial with an interesting subgroup as prior information is presented. The implications of the likelihood principle are considered. A recent exciting area using Bayesian hierarchical modeling has been the pediatric extrapolation using adult data in clinical trials. Historical control information from previous trials is an underused area that lends itself easily to Bayesian methods. The future including recent trends, decision theoretic trials, Bayesian benefit-risk, virtual patients, and the appalling lack of penetration of Bayesian clinical trials in the medical literature are discussed.

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Bayesian statistical method was underused despite its advantages in the assessment of implantable medical devices

Cited by 20 publications

References 35 publications

Bayesian survival analysis in clinical trials: What methods are used in practice?

Bayesian survival analysis in clinical trials: What methods are used in practice?

Methodological choices for the clinical development of medical devices

Bayesian methods in clinical trials with applications to medical devices

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