Reporting of core items in hierarchical Bayesian analysis for aggregating N-of-1 trials to estimate population treatment effects is suboptimal

The Bayesian simulation study (BASIS) framework for simulation studies in statistical and methodological research

Statistical simulation studies are becoming increasingly popular to demonstrate the performance or superiority of new computational procedures and algorithms. Despite this status quo, previous surveys of the literature have shown that the reporting of statistical simulation studies often lacks relevant information and structure. The latter applies in particular to Bayesian simulation studies, and in this paper the Bayesian simulation study framework (BASIS) is presented as a step towards improving the situation. The BASIS framework provides a structured skeleton for planning, coding, executing, analyzing, and reporting Bayesian simulation studies in biometrical research and computational statistics. It encompasses various features of previous proposals and recommendations in the methodological literature and aims to promote neutral comparison studies in statistical research. Computational aspects covered in the BASIS include algorithmic choices, Markov-chain-Monte-Carlo convergence diagnostics, sensitivity analyses, and Monte Carlo standard error calculations for Bayesian simulation studies. Although the BASIS framework focuses primarily on methodological research, it also provides useful guidance for researchers who rely on the results of Bayesian simulation studies or analyses, as current state-of-the-art guidelines for Bayesian analyses are incorporated into the BASIS.

Baduanjin exercise for chronic non-specific low back pain: protocol for a series of N-of-1 trials

INTRODUCTION

Chronic non-specific low back pain (CNLBP) is one of the most common health problems worldwide. According to the clinical guideline released by the American College of Physicians, exercise has been recommended for the treatment of chronic LBP. In recent years, traditional Chinese medicine (TCM) is becoming increasingly popular for the management of chronic LBP. Baduanjin exercise is one of the exercise therapies in TCM. N-of-1 trial is a randomised cross-over self-controlled trial suitable for patients with this chronic disease. A series of similar N-of-1 trials can be pooled to estimate the overall and individual therapeutic effects synchronously by hierarchical Bayesian analysis. And N-of-1 trials are considered as a good tool for evaluating the therapeutic effect of TCM. Therefore, this study aims to conduct a series of N-of-1 trials with hierarchical Bayesian analysis for assessing whether Baduanjin exercise is effective and safe for CNLBP.

METHODS AND ANALYSIS

This study conducts a series of N-of-1 trials on Baduanjin exercise for the management of CNLBP. Fifty participants will receive 1-3 treatment cycles. They will be randomised into a Baduanjin exercise or waiting list group for a week during the two periods of each treatment cycle. The primary outcome is the 10-point Visual Analogue Scale. The secondary outcomes include the Oswestry Disability Index, the Japanese Orthopaedic Association Back Pain Evaluation Questionnaire and the Short Form Health Survey 12. Statistical analysis will be conducted with WinBUGS V.1.4.3 software. Overall and individual therapeutic effects will be estimated synchronously by hierarchical Bayesian analysis.

ETHICS AND DISSEMINATION

This study is approved by the Medical Ethics Committee of Tianjin University of TCM (reference number TJUTCM-EC20220005). Our findings will be published in a peer-reviewed journal or international conference.

TRIAL REGISTRATION NUMBER

ChiCTR2200063307.

The reporting quality and transparency of orthopaedic studies using Bayesian analysis requires improvement: A systematic review

Background

Bayesian methods are being used more frequently in orthopaedics. To advance the use and transparent reporting of Bayesian studies, reporting guidelines have been recommended. There is currently little known about the use or applications of Bayesian analysis in orthopedics including adherence to recommended reporting guidelines. The objective is to investigate the reporting of Bayesian analysis in orthopedic surgery studies; specifically, to evaluate if these papers adhere to reporting guidelines.

Methods

We searched PUBMED to December 2nd, 2020. Two reviewers independently identified studies and full-text screening. We included studies that focused on one or more orthopaedic surgical interventions and used Bayesian methods.

Results

After full-text review, 100 articles were included. The most frequent study designs were meta-analysis or network meta-analysis (56%, 95% CI 46-65) and cohort studies (25%, 95% CI 18-34). Joint replacement was the most common subspecialty (33%, 95% CI 25-43). We found that studies infrequently reported key concepts in Bayesian analysis including, specifying the prior distribution (37-39%), justifying the prior distribution (18%), the sensitivity to different priors (7-8%), and the statistical model used (22%). In contrast, general methodological items on the checklists were largely well reported.

Conclusions

There is an opportunity to improve reporting quality and transparency of orthopaedic studies using Bayesian analysis by encouraging adherence to reporting guidelines such as ROBUST, JASP, and BayesWatch. There is an opportunity to better report prior distributions, sensitivity analyses, and the statistical models used.

Individualized therapy trials: navigating patient care, research goals and ethics

'Individualized therapy' trials (sometimes called n-of-1 trials) use patients as their own controls to evaluate treatments. Here we divide such trials into three categories: multi-crossover trials aimed at individual patient management, multi-crossover trial series and pre-post trials. These trials all customize interventions for patients; however, the latter two categories also aim to inform medical practice and thus embody tensions between the goals of care and research that are typical of other types of clinical trials. In this Perspective, we discuss four domains where such tensions play out-clinical equipoise, informed consent, reporting and funding, and we provide recommendations for addressing each.

Bayesian Analysis Reporting Guidelines

Previous surveys of the literature have shown that reports of statistical analyses often lack important information, causing lack of transparency and failure of reproducibility. Editors and authors agree that guidelines for reporting should be encouraged. This Review presents a set of Bayesian analysis reporting guidelines (BARG). The BARG encompass the features of previous guidelines, while including many additional details for contemporary Bayesian analyses, with explanations. An extensive example of applying the BARG is presented. The BARG should be useful to researchers, authors, reviewers, editors, educators and students. Utilization, endorsement and promotion of the BARG may improve the quality, transparency and reproducibility of Bayesian analyses.

A Useful and Sustainable Role for N-of-1 Trials in the Healthcare Ecosystem

Clinicians and patients often try a treatment for an initial period to inform longer‐term therapeutic decisions. A more rigorous approach involves N‐of‐1 trials. In these single‐patient crossover trials, typically conducted in patients with chronic conditions, individual patients are given candidate treatments in a double‐blinded, random sequence of alternating periods to determine the most effective treatment for that patient. However, to date, these trials are rarely done outside of research settings and have not been integrated into general care where they could offer substantial benefit. Designating this classical, N‐of‐1 trial design as type 1, there also are new and evolving uses of N‐of‐1 trials that we designate as type 2. In these, rather than focusing on optimizing treatment for chronic diseases when multiple approved choices are available, as is typical of type 1, a type 2 N‐of‐1 trial tests treatments designed specifically for a patient with a rare disease, to facilitate personalized medicine. While the aims differ, both types face the challenge of collecting individual‐patient evidence using standard, trusted, widely accepted methods. To fulfill their potential for producing both clinical and research benefits, and to be available for wide use, N‐of‐1 trials will have to fit into the current healthcare ecosystem. This will require generalizable and accepted processes, platforms, methods, and standards. This also will require sustainable value‐based arrangements among key stakeholders. In this article, we review opportunities, stakeholders, issues, and possible approaches that could support general use of N‐of‐1 trials and deliver benefit to patients and the healthcare enterprise. To assess and expand the benefits of N‐of‐1 trials, we propose multistakeholder meetings, workshops, and the generation of methods, standards, and platforms that would support wider availability and the value of N‐of‐1 trials.

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

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.

N-of-1 trials in the clinical care of patients in developing countries: a systematic review

BACKGROUND

N-of-1 trials have a potential role in promoting patient-centered medicine in developing countries. However, there is limited academic literature regarding the use of N-of-1 trials in the clinical care of patients in resource-poor settings.

OBJECTIVE

To assess the extent of use, purpose and treatment outcome of N-of-1 trials in developing countries.

METHOD

A systematic review of clinical N-of-1 trials was conducted between 1985 and September 2015 using PubMed, Embase, CINAHL, Web of Science and the Cochrane Central Register of Controlled Trials. Grey literature databases and clinical trial registers were also searched. This review included randomized, multi-cycle, crossover within individual patient trials involving drug intervention. Quality assessment and data extraction were conducted by two independent reviewers.

RESULT

Out of 131 N-of-1 trials identified, only 6 (4.5%) were conducted in developing countries. The major reason that N-of-1 trials were used was to provide evidence on feasibility, effectiveness and safety of therapies. A total of 72 participants were involved in these trials. Five of the studies were conducted in China and all evaluated Chinese traditional medicine. The remaining study was conducted in Brazil. The completion rate was 93%. More than half, 46 (69%) of subjects made medication changes consistent with trial results after trial completion. A number of threats to the validity of the included evidence limited the validity of the evidence. In particular, the estimated overall effect in four of the included studies could have been affected by the "carry over" of the previous treatment effect as no adequate pharmacokinetic evidence regarding traditional medicines was presented.

CONCLUSION

The prevalence and scope of N-of-1 trials in developing countries is low. A coordinated effort among government, clinicians, researchers and sponsor organizations is needed to increase their uptake and quality in developing countries.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42015026841 .