Special Issue "Bayesian Design in Clinical Trials"
A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601).
Deadline for manuscript submissions: 31 May 2021.
Interests: biostatistics; bayesian statistics; epidemiology; risk analysis; latent class analysis
Special Issues and Collections in MDPI journals
Interests: clinical trials; adaptive design; population-based studies; machine Learning
In the last decade, the number of clinical trials using Bayesian methods has grown dramatically. Nowadays, regulatory authorities appear to be more receptive to Bayesian methods than ever.
The Bayesian methodology is well-suited to address the issues arising in the planning, the analysis, and the conduct of clinical trials. Due of their flexibility, Bayesian design methods based on the accrued data of on-going trials have been recommended by both the US Food and Drug Administration (FDA) and the European Medicines Agency for dose–response trials in early clinical development. More generally, since the inherent adaptive nature of Phase I and Phase II designs, the Bayesian approach tends to be more efficient.
A recent development for oncology clinical trials is represented by the basket studies or multi-disease trials, which enroll patients that have a common genetic mutation but include different tumor types. A Bayesian approach through the Bayesian hierarchical model has the appeal of being able to improve the efficiency of such trials by properly borrowing information.
Another distinctive feature of the Bayesian approach is that it naturally allows for dealing with external information, such as historical data, findings from previous studies, and expert opinions through prior elicitation. In fact, it provides a framework for embedding and handling the variability of such auxiliary information within the planning and analysis of the study. A growing body of literature examines the use of historical data to augment newly collected data, especially in clinical trials where patients are difficult to recruit, which is the case for rare disease, for example. Many works describe the importance of using the available data in clinical trials and how this can be done properly. Using historical data has been recognized as less controversial than eliciting prior information from experts’ opinion also by the FDA in its guidance on the use of Bayesian Statistics in Medical Device Clinical Trials.
Papers addressing these topics are invited for submission to this Special Issue. Novel applications of Bayesian modeling to data from clinical trials, Bayesian designs for early phase trials, seamless phase II/III and phase III clinical trials, the Bayesian approach for monitoring, and hybrid Bayesian–frequentist designs are welcome, as well as papers addressing the advantages and limitations of the Bayesian approach carrying out virtual re-executions of published trials.
Dr. Paola Berchialla
Dr. Ileana Baldi
Manuscript Submission Information
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- Bayesian design
- Interim analyses
- Bayesian hierarchical models
- Treatment response adaptive randomization
- Bayesian sequential design
- Bayesian monitoring
- Power priors
- Dynamic treatment regimes
- Historical controls
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Bayesian cost-effectiveness meta-analysis
Authors: Miguel A. Negrín et al
Affiliation: Department of Quantitative Methods, University of Las Palmas de Gran Canaria, Las Palmas, Spain
Title: Prior Elicitation for use in clinical trial design and analysis: a literature review
Authors: Danila Azzolina et al
Affiliation: Department of Translational Medicine, University of Eastern Piedmont, Novara, Italy
Title: Adjustment for baseline covariates to increase efficiency in RCTs: a comparison of Bayesian and frequentist approaches
Authors: Paola Berchialla et al
Affiliation: Department of Clinical and Biological Sciences, University of Torino, Torino, Italy
Title: Bayesian design in rare disease: opportunities and challenges
Authors: Moreno Ursino et al
Affiliation: Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, USPC, Université de Paris, Paris, France
Title: Bayesian sample size determination, between consensus and evidence
Authors: Fulvio De Santis and Stefania Gubbiotti
Affiliation: Department of Statistical Sciences, Sapienza University of Rome, Roma, Italy
Title: Bayesian Clinical Trial Adaptive Designs: some recent developments
Authors: Alessandra Giovagnoli et al
Affiliation: Department of Statistical Sciences, University of Bologna, Bologna, Italy