Submit to IJERPH Review for IJERPH Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Bayesian Design in Clinical Trials

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601).

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 43375

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Bayesian Design in Clinical Trials book cover image

Share This Special Issue

Special Issue Editors

Dr. Paola Berchialla

E-Mail Website1 Website2
Guest Editor

Center for Biostatistics, Epidemiology and Public Health, Department of Clinical and Biological Sciences, University of Torin, 10043 Turin, Italy
Interests: bayesian statistics; machine learning; clinical epidemiology; precision medicine
Special Issues, Collections and Topics in MDPI journals

Dr. Ileana Baldi

E-Mail Website
Guest Editor

Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, 35122 Padova, PD, Italy
Interests: clinical trials; adaptive design; population-based studies; machine Learning

Special Issue Information

Dear Colleagues,

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
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Bayesian design
Interim analyses
Bayesian hierarchical models
Treatment response adaptive randomization
Bayesian sequential design
Bayesian monitoring
Power priors
Dynamic treatment regimes
Historical controls

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (12 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

10 pages, 336 KiB

Open AccessArticle

Bayesian Design for Identifying Cohort-Specific Optimal Dose Combinations Based on Multiple Endpoints: Application to a Phase I Trial in Non-Small Cell Lung Cancer

by Bethany Jablonski Horton, Nolan A. Wages and Ryan D. Gentzler

Int. J. Environ. Res. Public Health 2021, 18(21), 11452; https://doi.org/10.3390/ijerph182111452 - 30 Oct 2021

Cited by 1 | Viewed by 2265

Abstract

Immunotherapy and chemotherapy combinations have proven to be a safe and efficacious treatment approach in multiple settings. However, it is not clear whether approved doses of chemotherapy developed to achieve a maximum tolerated dose are the ideal dose when combining cytotoxic chemotherapy with immunotherapy to induce immune responses. This trial of a modulated dose chemotherapy and Pembrolizumab, with or without a second immunomodulatory agent, uses a Bayesian design to select the optimal treatment combination by balancing both safety and efficacy of the chemotherapy and immunotherapy agents within each of two cohorts. The simulation study provides evidence that the proposed Bayesian design successfully addresses the primary study aim to identify the optimal dose combination for each of the two independent patient cohorts. This conclusion is supported by the high percentage of simulated trials which select a treatment combination that is both safe and highly efficacious. The proposed trial was funded and was being finalized when the sponsoring company decided not to proceed due to negative findings in another patient population. The proposed trial design will continue to be relevant as multiple chemotherapy and immunotherapy combinations become the standard of care and future research will require evaluating the appropriate doses of various components of multiple drug regimens. Full article

(This article belongs to the Special Issue Bayesian Design in Clinical Trials)

17 pages, 727 KiB

Open AccessArticle

Bayesian Sequential Monitoring of Single-Arm Trials: A Comparison of Futility Rules Based on Binary Data

by Valeria Sambucini

Int. J. Environ. Res. Public Health 2021, 18(16), 8816; https://doi.org/10.3390/ijerph18168816 - 20 Aug 2021

Cited by 2 | Viewed by 2602

Abstract

In clinical trials, futility rules are widely used to monitor the study while it is in progress, with the aim of ensuring early termination if the experimental treatment is unlikely to provide the desired level of efficacy. In this paper, we focus on Bayesian strategies to perform interim analyses in single-arm trials based on a binary response variable. Designs that exploit both posterior and predictive probabilities are described and a slight modification of the futility rules is introduced when a fixed historical response rate is used, in order to add uncertainty in the efficacy probability of the standard treatment through the use of prior distributions. The stopping boundaries of the designs are compared under the same trial settings and simulation studies are performed to evaluate the operating characteristics when analogous procedures are used to calibrate the probability cut-offs of the different decision rules. Full article

(This article belongs to the Special Issue Bayesian Design in Clinical Trials)

► Show Figures

Figure 1

9 pages, 461 KiB

Open AccessArticle

Adjustment for Baseline Covariates to Increase Efficiency in RCTs with Binary Endpoint: A Comparison of Bayesian and Frequentist Approaches

by Paola Berchialla, Veronica Sciannameo, Sara Urru, Corrado Lanera, Danila Azzolina, Dario Gregori and Ileana Baldi

Int. J. Environ. Res. Public Health 2021, 18(15), 7758; https://doi.org/10.3390/ijerph18157758 - 22 Jul 2021

Viewed by 2787

Abstract

Background: In a randomized controlled trial (RCT) with binary outcome the estimate of the marginal treatment effect can be biased by prognostic baseline covariates adjustment. Methods that target the marginal odds ratio, allowing for improved precision and power, have been developed. Methods: The performance of different estimators for the treatment effect in the frequentist (targeted maximum likelihood estimator, inverse-probability-of-treatment weighting, parametric G-computation, and the semiparametric locally efficient estimator) and Bayesian (model averaging), adjustment for confounding, and generalized Bayesian causal effect estimation frameworks are assessed and compared in a simulation study under different scenarios. The use of these estimators is illustrated on an RCT in type II diabetes. Results: Model mis-specification does not increase the bias. The approaches that are not doubly robust have increased standard error (SE) under the scenario of mis-specification of the treatment model. The Bayesian estimators showed a higher type II error than frequentist estimators if noisy covariates are included in the treatment model. Conclusions: Adjusting for prognostic baseline covariates in the analysis of RCTs can have more power than intention-to-treat based tests. However, for some classes of model, when the regression model is mis-specified, inflated type I error and potential bias on treatment effect estimate may arise. Full article

(This article belongs to the Special Issue Bayesian Design in Clinical Trials)

► Show Figures

Figure 1

9 pages, 3250 KiB

Open AccessFeature PaperArticle

Study Protocol: Phase I Dose Escalation Study of Oxaliplatin, Cisplatin and Doxorubicin Applied as PIPAC in Patients with Peritoneal Metastases

by Manuela Robella, Paola Berchialla, Alice Borsano, Armando Cinquegrana, Alba Ilari Civit, Michele De Simone and Marco Vaira

Int. J. Environ. Res. Public Health 2021, 18(11), 5656; https://doi.org/10.3390/ijerph18115656 - 25 May 2021

Cited by 4 | Viewed by 3153

Abstract

Pressurized Intra-Peritoneal Aerosol Chemotherapy (PIPAC) is a novel laparoscopic intraperitoneal chemotherapy approach offered in selected patients affected by non-resectable peritoneal carcinomatosis. Drugs doses currently established for nebulization are very low: oxaliplatin (OXA) 120 mg/sm, cisplatin (CDDP) 10.5 mg/sm and doxorubicin (DXR) 2.1 mg/sm. A model-based approach for dose-escalation design in a single PIPAC procedure and subsequent dose escalation steps is planned. The starting dose of oxaliplatin is 100 mg/sm with a maximum estimated dose of 300 mg/sm; an escalation with overdose and under-dose control (for probability of toxicity less than 16% in case of under-dosing and probability of toxicity greater than 33% in case of overdosing) will be further applied. Cisplatin is used in association with doxorubicin: A two-dimensional dose-finding design is applied on the basis of the estimated dose limiting toxicity (DLT) at all combinations. The starting doses are 15 mg/sm for cisplatin and 3 mg/sm for doxorubicin. Safety is assessed according to Common Terminology Criteria for Adverse Events (CTCAE version 4.03). Secondary endpoints include radiological response according to Response Evaluation Criteria in Solid Tumor (version 1.1) and pharmacokinetic analyses. This phase I study can provide the scientific basis to maximize the optimal dose of cisplatin, doxorubicin and oxaliplatin applied as PIPAC. Full article

(This article belongs to the Special Issue Bayesian Design in Clinical Trials)

► Show Figures

Figure 1

16 pages, 1524 KiB

Open AccessArticle

Handling Poor Accrual in Pediatric Trials: A Simulation Study Using a Bayesian Approach

by Danila Azzolina, Giulia Lorenzoni, Silvia Bressan, Liviana Da Dalt, Ileana Baldi and Dario Gregori

Int. J. Environ. Res. Public Health 2021, 18(4), 2095; https://doi.org/10.3390/ijerph18042095 - 21 Feb 2021

Cited by 4 | Viewed by 2504

Abstract

In the conduction of trials, a common situation is related to potential difficulties in recruiting the planned sample size as provided by the study design. A Bayesian analysis of such trials might provide a framework to combine prior evidence with current evidence, and it is an accepted approach by regulatory agencies. However, especially for small trials, the Bayesian inference may be severely conditioned by the prior choices. The Renal Scarring Urinary Infection (RESCUE) trial, a pediatric trial that was a candidate for early termination due to underrecruitment, served as a motivating example to investigate the effects of the prior choices on small trial inference. The trial outcomes were simulated by assuming 50 scenarios combining different sample sizes and true absolute risk reduction (ARR). The simulated data were analyzed via the Bayesian approach using 0%, 50%, and 100% discounting factors on the beta power prior. An informative inference (0% discounting) on small samples could generate data-insensitive results. Instead, the 50% discounting factor ensured that the probability of confirming the trial outcome was higher than 80%, but only for an ARR higher than 0.17. A suitable option to maintain data relevant to the trial inference is to define a discounting factor based on the prior parameters. Nevertheless, a sensitivity analysis of the prior choices is highly recommended. Full article

(This article belongs to the Special Issue Bayesian Design in Clinical Trials)

► Show Figures

Figure 1

15 pages, 4554 KiB

Open AccessArticle

by Adrien Ollier, Sarah Zohar, Satoshi Morita and Moreno Ursino

Int. J. Environ. Res. Public Health 2021, 18(4), 1639; https://doi.org/10.3390/ijerph18041639 - 9 Feb 2021

Cited by 5 | Viewed by 3137

Abstract

Bridging studies are designed to fill the gap between two populations in terms of clinical trial data, such as toxicity, efficacy, comorbidities and doses. According to ICH-E5 guidelines, clinical data can be extrapolated from one region to another if dose–reponse curves are similar between two populations. For instance, in Japan, Phase I clinical trials are often repeated due to this physiological/metabolic paradigm: the maximum tolerated dose (MTD) for Japanese patients is assumed to be lower than that for Caucasian patients, but not necessarily for all molecules. Therefore, proposing a statistical tool evaluating the similarity between two populations dose–response curves is of most interest. The aim of our work is to propose several indicators to evaluate the distance and the similarity of dose–toxicity curves and MTD distributions at the end of some of the Phase I trials, conducted on two populations or regions. For this purpose, we extended and adapted the commensurability criterion, initially proposed by Ollier et al. (2019), in the setting of completed phase I clinical trials. We evaluated their performance using three synthetic sets, built as examples, and six case studies found in the literature. Visualization plots and guidelines on the way to interpret the results are proposed. Full article

(This article belongs to the Special Issue Bayesian Design in Clinical Trials)

► Show Figures

Figure 1

18 pages, 1071 KiB

Open AccessArticle

Bayesian Meta-Analysis for Binary Data and Prior Distribution on Models

by Miguel-Angel Negrín-Hernández, María Martel-Escobar and Francisco-José Vázquez-Polo

Int. J. Environ. Res. Public Health 2021, 18(2), 809; https://doi.org/10.3390/ijerph18020809 - 19 Jan 2021

Cited by 5 | Viewed by 2658

Abstract

In meta-analysis, the structure of the between-sample heterogeneity plays a crucial role in estimating the meta-parameter. A Bayesian meta-analysis for binary data has recently been proposed that measures this heterogeneity by clustering the samples and then determining the posterior probability of the cluster models through model selection. The meta-parameter is then estimated using Bayesian model averaging techniques. Although an objective Bayesian meta-analysis is proposed for each type of heterogeneity, we concentrate the attention of this paper on priors over the models. We consider four alternative priors which are motivated by reasonable but different assumptions. A frequentist validation with simulated data has been carried out to analyze the properties of each prior distribution for a set of different number of studies and sample sizes. The results show the importance of choosing an adequate model prior as the posterior probabilities for the models are very sensitive to it. The hierarchical Poisson prior and the hierarchical uniform prior show a good performance when the real model is the homogeneity, or when the sample sizes are high enough. However, the uniform prior can detect the true model when it is an intermediate model (neither homogeneity nor heterogeneity) even for small sample sizes and few studies. An illustrative example with real data is also given, showing the sensitivity of the estimation of the meta-parameter to the model prior. Full article

(This article belongs to the Special Issue Bayesian Design in Clinical Trials)

► Show Figures

Figure 1

11 pages, 450 KiB

Open AccessArticle

Sample Size Requirements for Calibrated Approximate Credible Intervals for Proportions in Clinical Trials

by Fulvio De Santis and Stefania Gubbiotti

Int. J. Environ. Res. Public Health 2021, 18(2), 595; https://doi.org/10.3390/ijerph18020595 - 12 Jan 2021

Cited by 3 | Viewed by 3474

Abstract

In Bayesian analysis of clinical trials data, credible intervals are widely used for inference on unknown parameters of interest, such as treatment effects or differences in treatments effects. Highest Posterior Density (HPD) sets are often used because they guarantee the shortest length. In most of standard problems, closed-form expressions for exact HPD intervals do not exist, but they are available for intervals based on the normal approximation of the posterior distribution. For small sample sizes, approximate intervals may be not calibrated in terms of posterior probability, but for increasing sample sizes their posterior probability tends to the correct credible level and they become closer and closer to exact sets. The article proposes a predictive analysis to select appropriate sample sizes needed to have approximate intervals calibrated at a pre-specified level. Examples are given for interval estimation of proportions and log-odds. Full article

(This article belongs to the Special Issue Bayesian Design in Clinical Trials)

► Show Figures

Figure 1

19 pages, 426 KiB

Open AccessArticle

Using an Interaction Parameter in Model-Based Phase I Trials for Combination Treatments? A Simulation Study

by Pavel Mozgunov, Rochelle Knight, Helen Barnett and Thomas Jaki

Int. J. Environ. Res. Public Health 2021, 18(1), 345; https://doi.org/10.3390/ijerph18010345 - 5 Jan 2021

Cited by 11 | Viewed by 3264

Abstract

There is growing interest in Phase I dose-finding studies studying several doses of more than one agent simultaneously. A number of combination dose-finding designs were recently proposed to guide escalation/de-escalation decisions during the trials. The majority of these proposals are model-based: a parametric combination-toxicity relationship is fitted as data accumulates. Various parameter shapes were considered but the unifying theme for many of these is that typically between 4 and 6 parameters are to be estimated. While more parameters allow for more flexible modelling of the combination-toxicity relationship, this is a challenging estimation problem given the typically small sample size in Phase I trials of between 20 and 60 patients. These concerns gave raise to an ongoing debate whether including more parameters into combination-toxicity model leads to more accurate combination selection. In this work, we extensively study two variants of a 4-parameter logistic model with reduced number of parameters to investigate the effect of modelling assumptions. A framework to calibrate the prior distributions for a given parametric model is proposed to allow for fair comparisons. Via a comprehensive simulation study, we have found that the inclusion of the interaction parameter between two compounds does not provide any benefit in terms of the accuracy of selection, on average, but is found to result in fewer patients allocated to the target combination during the trial. Full article

(This article belongs to the Special Issue Bayesian Design in Clinical Trials)

► Show Figures

Figure 1

Review

Jump to: Research

21 pages, 1103 KiB

Open AccessReview

Prior Elicitation for Use in Clinical Trial Design and Analysis: A Literature Review

by Danila Azzolina, Paola Berchialla, Dario Gregori and Ileana Baldi

Int. J. Environ. Res. Public Health 2021, 18(4), 1833; https://doi.org/10.3390/ijerph18041833 - 13 Feb 2021

Cited by 17 | Viewed by 4391

Abstract

Bayesian inference is increasingly popular in clinical trial design and analysis. The subjective knowledge derived from an expert elicitation procedure may be useful to define a prior probability distribution when no or limited data is available. This work aims to investigate the state-of-the-art Bayesian prior elicitation methods with a focus on clinical trial research. A literature search on the Current Index to Statistics (CIS), PubMed, and Web of Science (WOS) databases, considering “prior elicitation” as a search string, was run on 1 November 2020. Summary statistics and trend of publications over time were reported. Finally, a Latent Dirichlet Allocation (LDA) model was developed to recognise latent topics in the pertinent papers retrieved. A total of 460 documents pertinent to the Bayesian prior elicitation were identified. Of these, 213 (45.4%) were published in the “Probability and Statistics” area. A total of 42 articles pertain to clinical trial and the majority of them (81%) reports parametric techniques as elicitation method. The last decade has seen an increased interest in prior elicitation and the gap between theory and application getting narrower and narrower. Given the promising flexibility of non-parametric approaches to the experts’ elicitation, more efforts are needed to ensure their diffusion also in applied settings. Full article

(This article belongs to the Special Issue Bayesian Design in Clinical Trials)

► Show Figures

Figure 1

9 pages, 324 KiB

Open AccessReview

Bayesian Approaches for Confirmatory Trials in Rare Diseases: Opportunities and Challenges

by Moreno Ursino and Nigel Stallard

Int. J. Environ. Res. Public Health 2021, 18(3), 1022; https://doi.org/10.3390/ijerph18031022 - 24 Jan 2021

Cited by 13 | Viewed by 3449

Abstract

The aim of this narrative review is to introduce the reader to Bayesian methods that, in our opinion, appear to be the most important in the context of rare diseases. A disease is defined as rare depending on the prevalence of the affected patients in the considered population, for example, about 1 in 1500 people in U.S.; about 1 in 2500 people in Japan; and fewer than 1 in 2000 people in Europe. There are between 6000 and 8000 rare diseases and the main issue in drug development is linked to the challenge of achieving robust evidence from clinical trials in small populations. A better use of all available information can help the development process and Bayesian statistics can provide a solid framework at the design stage, during the conduct of the trial, and at the analysis stage. The focus of this manuscript is to provide a review of Bayesian methods for sample size computation or reassessment during phase II or phase III trial, for response adaptive randomization and of for meta-analysis in rare disease. Challenges regarding prior distribution choice, computational burden and dissemination are also discussed. Full article

(This article belongs to the Special Issue Bayesian Design in Clinical Trials)

15 pages, 370 KiB

Open AccessReview

The Bayesian Design of Adaptive Clinical Trials

by Alessandra Giovagnoli

Int. J. Environ. Res. Public Health 2021, 18(2), 530; https://doi.org/10.3390/ijerph18020530 - 10 Jan 2021

Cited by 42 | Viewed by 7325

Abstract

This paper presents a brief overview of the recent literature on adaptive design of clinical trials from a Bayesian perspective for statistically not so sophisticated readers. Adaptive designs are attracting a keen interest in several disciplines, from a theoretical viewpoint and also—potentially—from a practical one, and Bayesian adaptive designs, in particular, have raised high expectations in clinical trials. The main conceptual tools are highlighted here, with a mention of several trial designs proposed in the literature that use these methods, including some of the registered Bayesian adaptive trials to this date. This review aims at complementing the existing ones on this topic, pointing at further interesting reading material. Full article

Journal Menu

Journal Browser

Bayesian Design in Clinical Trials

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (12 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI