Bayesian clinical trials in action

Two-stage randomized clinical trials with a right-censored endpoint: Comparison of frequentist and Bayesian adaptive designs

Adaptive randomized clinical trials are of major interest when dealing with a time-to-event outcome in a prolonged observation window. No consensus exists either to define stopping boundaries or to combinep $$ p $$ values or test statistics in the terminal analysis in the case of a frequentist design and sample size adaptation. In a one-sided setting, we compared three frequentist approaches using stopping boundaries relying onα $$ \alpha $$ -spending functions and a Bayesian monitoring setting with boundaries based on the posterior distribution of the log-hazard ratio. All designs comprised a single interim analysis with an efficacy stopping rule and the possibility of sample size adaptation at this interim step. Three frequentist approaches were defined based on the terminal analysis: combination of stagewise statistics (Wassmer) or ofp $$ p $$ values (Desseaux), or on patientwise splitting (Jörgens), and we compared the results with those of the Bayesian monitoring approach (Freedman). These different approaches were evaluated in a simulation study and then illustrated on a real dataset from a randomized clinical trial conducted in elderly patients with chronic lymphocytic leukemia. All approaches controlled for the type I error rate, except for the Bayesian monitoring approach, and yielded satisfactory power. It appears that the frequentist approaches are the best in underpowered trials. The power of all the approaches was affected by the violation of the proportional hazards (PH) assumption. For adaptive designs with a survival endpoint and a one-sided alternative hypothesis, the Wassmer and Jörgens approaches after sample size adaptation should be preferred, unless violation of PH is suspected.

Developments in the Design, Conduct, and Reporting of Child Health Trials

To identify priority areas to improve the design, conduct, and reporting of pediatric clinical trials, the international expert network, Standards for Research (StaR) in Child Health, was assembled and published the first 6 Standards in Pediatrics in 2012. After a recent review summarizing the 247 publications by StaR Child Health authors that highlight research practices that add value and reduce research "waste," the current review assesses the progress in key child health trial methods areas: consent and recruitment, containing risk of bias, roles of data monitoring committees, appropriate sample size calculations, outcome selection and measurement, and age groups for pediatric trials. Although meaningful change has occurred within the child health research ecosystem, measurable progress is still disappointingly slow. In this context, we identify and review emerging trends that will advance the agenda of increased clinical usefulness of pediatric trials, including patient and public engagement, Bayesian statistical approaches, adaptive designs, and platform trials. We explore how implementation science approaches could be applied to effect measurable improvements in the design, conducted, and reporting of child health research.

Designing a Bayesian adaptive clinical trial to evaluate novel mechanical ventilation strategies in acute respiratory failure using integrated nested Laplace approximations

BACKGROUND

Adaptive trials usually require simulations to determine values for design parameters, demonstrate error rates, and establish the sample size. We designed a Bayesian adaptive trial comparing ventilation strategies for patients with acute hypoxemic respiratory failure using simulations. The complexity of the analysis would usually require computationally expensive Markov Chain Monte Carlo methods but this barrier to simulation was overcome using the Integrated Nested Laplace Approximations (INLA) algorithm to provide fast, approximate Bayesian inference.

METHODS

We simulated two-arm Bayesian adaptive trials with equal randomization that stratified participants into two disease severity states. The analysis used a proportional odds model, fit using INLA. Trials were stopped based on pre-specified posterior probability thresholds for superiority or futility, separately for each state. We calculated the type I error and power across 64 scenarios that varied the probability thresholds and the initial minimum sample size before commencing adaptive analyses. Two designs that maintained a type I error below 5%, a power above 80%, and a feasible mean sample size were evaluated further to determine the optimal design.

RESULTS

Power generally increased as the initial sample size and the futility threshold increased. The chosen design had an initial recruitment of 500 and a superiority threshold of 0.9925, and futility threshold of 0.95. It maintained high power and was likely to reach a conclusion before exceeding a feasible sample size.

CONCLUSIONS

We designed a Bayesian adaptive trial to evaluate novel strategies for ventilation using the INLA algorithm to efficiently evaluate a wide range of designs through simulation.

Partial Hepatectomy and Ablation for Survival of Early-Stage Hepatocellular Carcinoma Patients: A Bayesian Emulation Analysis

Partial hepatectomy and ablation therapy are two widely used surgical procedures for localized early-stage hepatocellular carcinoma (HCC) patients. This article aimed to evaluate their relative effectiveness in terms of overall survival. An emulation analysis approach was first developed based on the Bayesian technique. We estimated propensity scores via Bayesian logistic regression and adopted a weighted Bayesian Weibull accelerated failure time (AFT) model incorporating prior information contained in the published literature. With the Surveillance, Epidemiology, and End Results (SEER)-Medicare data, an emulated target trial with rigorously defined inclusion/exclusion criteria and treatment regimens for early-stage HCC patients over 66 years old was developed. For the main cohort with tumor size less than or equal to 5 cm, a total of 1146 patients were enrolled in the emulated trial, with 301 and 845 in the partial hepatectomy and ablation arms, respectively. The analysis suggested ablation to be significantly associated with inferior overall survival (hazard ratio [HR] = 1.35; 95% credible interval [CrI]: 1.14, 1.60). For the subgroup with tumor size less than or equal to 3 cm, there was no significant difference in overall survival between the two arms (HR = 1.15; 95% CrI: 0.88, 1.52). Overall, the comparative treatment effect of ablation and partial hepatectomy on survival remains inconclusive. This finding may provide further insight into HCC clinical treatment.

A comparison of computational algorithms for the Bayesian analysis of clinical trials

BACKGROUND

Clinical trials are increasingly using Bayesian methods for their design and analysis. Inference in Bayesian trials typically uses simulation-based approaches such as Markov Chain Monte Carlo methods. Markov Chain Monte Carlo has high computational cost and can be complex to implement. The Integrated Nested Laplace Approximations algorithm provides approximate Bayesian inference without the need for computationally complex simulations, making it more efficient than Markov Chain Monte Carlo. The practical properties of Integrated Nested Laplace Approximations compared to Markov Chain Monte Carlo have not been considered for clinical trials. Using data from a published clinical trial, we aim to investigate whether Integrated Nested Laplace Approximations is a feasible and accurate alternative to Markov Chain Monte Carlo and provide practical guidance for trialists interested in Bayesian trial design.

METHODS

Data from an international Bayesian multi-platform adaptive trial that compared therapeutic-dose anticoagulation with heparin to usual care in non-critically ill patients hospitalized for COVID-19 were used to fit Bayesian hierarchical generalized mixed models. Integrated Nested Laplace Approximations was compared to two Markov Chain Monte Carlo algorithms, implemented in the software JAGS and stan, using packages available in the statistical software R. Seven outcomes were analysed: organ-support free days (an ordinal outcome), five binary outcomes related to survival and length of hospital stay, and a time-to-event outcome. The posterior distributions for the treatment and sex effects and the variances for the hierarchical effects of age, site and time period were obtained. We summarized these posteriors by calculating the mean, standard deviations and the 95% equitailed credible intervals and presenting the results graphically. The computation time for each algorithm was recorded.

RESULTS

The average overlap of the 95% credible interval for the treatment and sex effects estimated using Integrated Nested Laplace Approximations was 96% and 97.6% compared with stan, respectively. The graphical posterior densities for these effects overlapped for all three algorithms. The posterior mean for the variance of the hierarchical effects of age, site and time estimated using Integrated Nested Laplace Approximations are within the 95% credible interval estimated using Markov Chain Monte Carlo but the average overlap of the credible interval is lower, 77%, 85.6% and 91.3%, respectively, for Integrated Nested Laplace Approximations compared to stan. Integrated Nested Laplace Approximations and stan were easily implemented in clear, well-established packages in R, while JAGS required the direct specification of the model. Integrated Nested Laplace Approximations was between 85 and 269 times faster than stan and 26 and 1852 times faster than JAGS.

CONCLUSION

Integrated Nested Laplace Approximations could reduce the computational complexity of Bayesian analysis in clinical trials as it is easy to implement in R, substantially faster than Markov Chain Monte Carlo methods implemented in JAGS and stan, and provides near identical approximations to the posterior distributions for the treatment effect. Integrated Nested Laplace Approximations was less accurate when estimating the posterior distribution for the variance of hierarchical effects, particularly for the proportional odds model, and future work should determine if the Integrated Nested Laplace Approximations algorithm can be adjusted to improve this estimation.

Using Bayesian statistics in confirmatory clinical trials in the regulatory setting: a tutorial review

Bayesian statistics plays a pivotal role in advancing medical science by enabling healthcare companies, regulators, and stakeholders to assess the safety and efficacy of new treatments, interventions, and medical procedures. The Bayesian framework offers a unique advantage over the classical framework, especially when incorporating prior information into a new trial with quality external data, such as historical data or another source of co-data. In recent years, there has been a significant increase in regulatory submissions using Bayesian statistics due to its flexibility and ability to provide valuable insights for decision-making, addressing the modern complexity of clinical trials where frequentist trials are inadequate. For regulatory submissions, companies often need to consider the frequentist operating characteristics of the Bayesian analysis strategy, regardless of the design complexity. In particular, the focus is on the frequentist type I error rate and power for all realistic alternatives. This tutorial review aims to provide a comprehensive overview of the use of Bayesian statistics in sample size determination, control of type I error rate, multiplicity adjustments, external data borrowing, etc., in the regulatory environment of clinical trials. Fundamental concepts of Bayesian sample size determination and illustrative examples are provided to serve as a valuable resource for researchers, clinicians, and statisticians seeking to develop more complex and innovative designs.

Prophylaxis to Prevent Cardiotoxicity in Patients Receiving Anthracycline for Breast Cancer: A Combined Bayesian and Frequentist Network Meta-Analysis of Randomised Controlled Trials

BACKGROUND

The benefits in survivorship gained with anthracycline (ANT)-based chemotherapies for breast cancer are unfortunately mitigated for some patients by irreversible cardiotoxicity. Randomised controlled trials (RCTs) have explored multiple cardioprotection options, however, it remains unclear which drug is most effective in preserving left ventricular ejection fraction (LVEF). This study aimed to perform a systematic review and network meta-analysis, using Bayesian and frequentist approaches, of RCTs evaluating cardioprotective agents.

METHODS

Two authors searched four databases (CENTRAL, Cochrane Reviews, MEDLINE, SCOPUS), to find RCTs evaluating cardioprotective agents. Trial populations were limited to patients with breast cancer without prior ANT exposure. The primary outcome was mean LVEF change pre and post ANT dosing. Our primary analysis utilised a Bayesian approach, while our sensitivity analysis used frequentist methodology (Prospero registration number CRD42020199580).

RESULTS

From 4,007 search results, we identified 12 RCTs, with their various trial arms considered separately-nine beta-blocker (BB), two angiotensin-converting enzyme inhibitor /angiotensin receptor blockers [(AA)+BB=AABB], one AA, one spironolactone, one statin-evaluating 1,126 patients (age 50.5 years). Bayesian network meta-analysis showed no difference in LVEF preservation between AA (1.3%, 95% credible interval [-0.20, 2.9]), BB (0.77, [-0.21, 1.8]), AABB (0.84 [-1.1, 2.8]), spironolactone (0.72, [-2.3, 3.7]) or statin (0.60, [-2.4, 3.6]) when compared against placebo. However, the frequentist analysis showed benefits from using AA (mean difference, 1.32% [0.32, 2.33]) and BB (mean difference, 0.76% [0.12, 1.4]).

CONCLUSIONS

There is insufficient evidence to support prophylactic cardioprotection to prevent EF reduction. However, frequentist analysis suggested that AA or BBs provide cardioprotection. Thus, for those already on other anti-hypertensives, switching to AA or BBs could be considered.

Therapy-Induced Senescence Contributes to the Efficacy of Abemaciclib in Patients with Dedifferentiated Liposarcoma

PURPOSE

We conducted research on CDK4/6 inhibitors (CDK4/6i) simultaneously in the preclinical and clinical spaces to gain a deeper understanding of how senescence influences tumor growth in humans.

PATIENTS AND METHODS

We coordinated a first-in-kind phase II clinical trial of the CDK4/6i abemaciclib for patients with progressive dedifferentiated liposarcoma (DDLS) with cellular studies interrogating the molecular basis of geroconversion.

RESULTS

Thirty patients with progressing DDLS enrolled and were treated with 200 mg of abemaciclib twice daily. The median progression-free survival was 33 weeks at the time of the data lock, with 23 of 30 progression-free at 12 weeks (76.7%, two-sided 95% CI, 57.7%-90.1%). No new safety signals were identified. Concurrent preclinical work in liposarcoma cell lines identified ANGPTL4 as a necessary late regulator of geroconversion, the pathway from reversible cell-cycle exit to a stably arrested inflammation-provoking senescent cell. Using this insight, we were able to identify patients in which abemaciclib induced tumor cell senescence. Senescence correlated with increased leukocyte infiltration, primarily CD4-positive cells, within a month of therapy. However, those individuals with both senescence and increased TILs were also more likely to acquire resistance later in therapy. These suggest that combining senolytics with abemaciclib in a subset of patients may improve the duration of response.

CONCLUSIONS

Abemaciclib was well tolerated and showed promising activity in DDLS. The discovery of ANGPTL4 as a late regulator of geroconversion helped to define how CDK4/6i-induced cellular senescence modulates the immune tumor microenvironment and contributes to both positive and negative clinical outcomes. See related commentary by Weiss et al., p. 649.

Pragmatic randomized controlled trials: strengthening the concept through a robust international collaborative network: PRIME-9-Pragmatic Research and Innovation through Multinational Experimentation

In an era focused on value-based healthcare, the quality of healthcare and resource allocation should be underpinned by empirical evidence. Pragmatic clinical trials (pRCTs) are essential in this endeavor, providing randomized controlled trial (RCT) insights that encapsulate real-world effects of interventions. The rising popularity of pRCTs can be attributed to their ability to mirror real-world practices, accommodate larger sample sizes, and provide cost advantages over traditional RCTs. By harmonizing efficacy with effectiveness, pRCTs assist decision-makers in prioritizing interventions that have a substantial public health impact and align with the tenets of value-based health care. An international network for pRCT provides several advantages, including larger and diverse patient populations, access to a broader range of healthcare settings, sharing knowledge and expertise, and overcoming ethical and regulatory barriers. The hypothesis and study design of pRCT answers the decision-maker's questions. pRCT compares clinically relevant alternative interventions, recruits participants from diverse practice settings, and collects data on various health outcomes. They are scarce because the medical products industry typically does not fund pRCT. Prioritizing these studies by expanding the infrastructure to conduct clinical research within the healthcare delivery system and increasing public and private funding for these studies will be necessary to facilitate pRCTs. These changes require more clinical and health policy decision-makers in clinical research priority setting, infrastructure development, and funding. This paper presents a comprehensive overview of pRCTs, emphasizing their importance in evidence-based medicine and the advantages of an international collaborative network for their execution. It details the development of PRIME-9, an international initiative across nine countries to advance pRCTs, and explores various statistical approaches for these trials. The paper underscores the need to overcome current challenges, such as funding limitations and infrastructural constraints, to leverage the full potential of pRCTs in optimizing healthcare quality and resource utilization.

A conservative approach to leveraging external evidence for effective clinical trial design

Prior probabilities of clinical hypotheses are not systematically used for clinical trial design yet, due to a concern that poor priors may lead to poor decisions. To address this concern, a conservative approach to Bayesian trial design is illustrated here, requiring that the operational characteristics of the primary trial outcome are stronger than the prior. This approach is complementary to current Bayesian design methods, in that it insures against prior-data conflict by defining a sample size commensurate to a discrete design prior. This approach is ethical, in that it requires designs appropriate to achieving pre-specified levels of clinical equipoise imbalance. Practical examples are discussed, illustrating design of trials with binary or time to event endpoints. Moderate increases in phase II study sample size are shown to deliver strong levels of overall evidence for go/no-go clinical development decisions. Levels of negative evidence provided by group sequential confirmatory designs are found negligible, highlighting the importance of complementing efficacy boundaries with non-binding futility criteria.

baymedr: an R package and web application for the calculation of Bayes factors for superiority, equivalence, and non-inferiority designs

BACKGROUND

Clinical trials often seek to determine the superiority, equivalence, or non-inferiority of an experimental condition (e.g., a new drug) compared to a control condition (e.g., a placebo or an already existing drug). The use of frequentist statistical methods to analyze data for these types of designs is ubiquitous even though they have several limitations. Bayesian inference remedies many of these shortcomings and allows for intuitive interpretations, but are currently difficult to implement for the applied researcher.

RESULTS

We outline the frequentist conceptualization of superiority, equivalence, and non-inferiority designs and discuss its disadvantages. Subsequently, we explain how Bayes factors can be used to compare the relative plausibility of competing hypotheses. We present baymedr, an R package and web application, that provides user-friendly tools for the computation of Bayes factors for superiority, equivalence, and non-inferiority designs. Instructions on how to use baymedr are provided and an example illustrates how existing results can be reanalyzed with baymedr.

CONCLUSIONS

Our baymedr R package and web application enable researchers to conduct Bayesian superiority, equivalence, and non-inferiority tests. baymedr is characterized by a user-friendly implementation, making it convenient for researchers who are not statistical experts. Using baymedr, it is possible to calculate Bayes factors based on raw data and summary statistics.

Bayesian methods: a potential path forward for sepsis trials

BACKGROUND

Given the success of recent platform trials for COVID-19, Bayesian statistical methods have become an option for complex, heterogenous syndromes like sepsis. However, study design will require careful consideration of how statistical power varies using Bayesian methods across different choices for how historical data are incorporated through a prior distribution and how the analysis is ultimately conducted. Our objective with the current analysis is to assess how different uses of historical data through a prior distribution, and type of analysis influence results of a proposed trial that will be analyzed using Bayesian statistical methods.

METHODS

We conducted a simulation study incorporating historical data from a published multicenter, randomized clinical trial in the US and Canada of polymyxin B hemadsorption for treatment of endotoxemic septic shock. Historical data come from a 179-patient subgroup of the previous trial of adult critically ill patients with septic shock, multiple organ failure and an endotoxin activity of 0.60-0.89. The trial intervention consisted of two polymyxin B hemoadsorption treatments (2 h each) completed within 24 h of enrollment.

RESULTS

In our simulations for a new trial of 150 patients, a range of hypothetical results were observed. Across a range of baseline risks and treatment effects and four ways of including historical data, we demonstrate an increase in power with the use of clinically defensible incorporation of historical data. In one possible trial result, for example, with an observed reduction in risk of mortality from 44 to 37%, the probability of benefit is 96% with a fixed weight of 75% on prior data and 90% with a commensurate (adaptive-weighting) prior; the same data give an 80% probability of benefit if historical data are ignored.

CONCLUSIONS

Using Bayesian methods and a biologically justifiable use of historical data in a prior distribution yields a study design with higher power than a conventional design that ignores relevant historical data. Bayesian methods may be a viable option for trials in critical care medicine where beneficial treatments have been elusive.

FeBRILe3: Safety Evaluation of Febrile Infant Guidelines Through Prospective Bayesian Monitoring

OBJECTIVES

Despite evidence supporting earlier discharge of well-appearing febrile infants at low risk of serious bacterial infection (SBI), admissions for ≥48 hours remain common. Prospective safety monitoring may support broader guideline implementation.

METHODS

A sequential Bayesian safety monitoring framework was used to evaluate a new hospital guideline recommending early discharge of low-risk infants. Hospital readmissions within 7 days of discharge were regularly assessed against safety thresholds, derived from historic rates and expert opinion, and specified a priori (8 per 100 infants). Infants aged under 3 months admitted to 2 Western Australian metropolitan hospitals for management of fever without source were enrolled (August 2019-December 2021), to a prespecified maximum 500 enrolments.

RESULTS

Readmission rates remained below the prespecified threshold at all scheduled analyses. Median corrected age was 34 days, and 14% met low-risk criteria (n = 71). SBI was diagnosed in 159 infants (32%), including urinary tract infection (n = 140) and bacteraemia (n = 18). Discharge occurred before 48 hours for 192 infants (38%), including 52% deemed low-risk. At study completion, 1 of 37 low-risk infants discharged before 48 hours had been readmitted (3%), for issues unrelated to SBI diagnosis. In total, 20 readmissions were identified (4 per 100 infants; 95% credible interval 3, 6), with >0.99 posterior probability of being below the prespecified noninferiority threshold, indicating acceptable safety.

CONCLUSIONS

A Bayesian monitoring approach supported safe early discharge for many infants, without increased risk of readmission. This framework may be used to embed safety evaluations within future guideline implementation programs to further reduce low-value care.

Clinical Trials Overview: From Explanatory to Pragmatic Clinical Trials

Clinical trials have been the bedrock of research to evaluate the safety and efficacy of new medical, surgical, or other interventions. Traditional "explanatory" clinical trials have aimed to explain a biological cause (new treatment) and effect (patient outcome) while controlling for many factors that might impact the evaluation, such as restricted eligibility criteria, frequent follow-up visits, and multiple clinical and laboratory measures. Despite the benefits of a well-controlled clinical trial, compromises have been made that can limit who might benefit from a new intervention, can increase complexity of the conduct of a trial, or that lead to excessively long durations of trials. An alternative approach to evaluate the effectiveness of an intervention is based on "pragmatic" clinical trials, which consider how an intervention affects a patient's condition in the real world, accounting for how to optimize an intervention within the operations of busy and diverse clinical practices. Although we describe explanatory and pragmatic trial designs as separate approaches, there is a continuum of approaches that intersect. Some key points are the need to maintain scientific rigor, increase efficiency of clinical trials operations, ensure that trial results can be generalized to a broad spectrum of patients, and balance the needs of real-world clinical care. Pragmatic trials can leverage technology and telecommunication strategies of decentralized trials to further reach underrepresented and underserved patients to close the health disparity gaps.

A Bayesian-adaptive decision-theoretic approach can reduce the sample sizes for multiarm exercise oncology trials

OBJECTIVES

Adaptive designs may reduce trial sample sizes and costs. This study illustrates a Bayesian-adaptive decision-theoretic design applied to a multiarm exercise oncology trial.

STUDY DESIGN AND SETTING

In the Physical exercise during Adjuvant Chemotherapy Effectiveness Study (PACES) trial, 230 breast cancer patients receiving chemotherapy were randomized to supervised resistance and aerobic exercise (OnTrack), home-based physical activity (OncoMove) or usual care (UC). Data were reanalyzed as an adaptive trial using both Bayesian decision-theoretic and a frequentist group-sequential approach incorporating interim analyses after every 36 patients. Endpoint was chemotherapy treatment modifications (any vs. none). Bayesian analyses were performed for different continuation thresholds and settings with and without arm dropping and both in a 'pick-the-winner' and a 'pick-all-treatments-superior-to-control' setting.

RESULTS

Treatment modifications occurred in 34% of patients in UC and OncoMove vs. 12% in OnTrack (P = 0.002). Using a Bayesian-adaptive decision-theoretic design, OnTrack was identified as most effective after 72 patients in the 'pick-the-winner' setting and after 72-180 patients in the 'pick-all-treatments-superior-to-control' setting. In a frequentist setting, the trial would have been stopped after 180 patients, and the proportion of patients with treatment modifications was significantly lower for OnTrack than UC.

CONCLUSION

A Bayesian-adaptive decision-theoretic approach substantially reduced the sample size required for this three-arm exercise trial, especially in the 'pick-the-winner' setting.

BayesESS: A tool for quantifying the impact of parametric priors in Bayesian analysis

Bayesian inference has become an attractive choice for scientists seeking to incorporate prior knowledge into their modeling framework. While the R community has been an important contributor in facilitating Bayesian statistical analyses, software to evaluate the impact of prior knowledge to such modeling framework has been lacking. In this article, we present BayesESS, a comprehensive, free, and open source R package for quantifying the impact of parametric priors in Bayesian analysis. We also introduce an accompanying web-based application for estimating and visualizing Bayesian effective sample size for purposes of conducting or planning Bayesian analyses.

Recent innovations in adaptive trial designs: A review of design opportunities in translational research

Clinical trials are constantly evolving in the context of increasingly complex research questions and potentially limited resources. In this review article, we discuss the emergence of "adaptive" clinical trials that allow for the preplanned modification of an ongoing clinical trial based on the accumulating evidence with application across translational research. These modifications may include terminating a trial before completion due to futility or efficacy, re-estimating the needed sample size to ensure adequate power, enriching the target population enrolled in the study, selecting across multiple treatment arms, revising allocation ratios used for randomization, or selecting the most appropriate endpoint. Emerging topics related to borrowing information from historic or supplemental data sources, sequential multiple assignment randomized trials (SMART), master protocol and seamless designs, and phase I dose-finding studies are also presented. Each design element includes a brief overview with an accompanying case study to illustrate the design method in practice. We close with brief discussions relating to the statistical considerations for these contemporary designs.

Developing a Bayesian hierarchical model for a prospective individual patient data meta-analysis with continuous monitoring

BACKGROUND

Numerous clinical trials have been initiated to find effective treatments for COVID-19. These trials have often been initiated in regions where the pandemic has already peaked. Consequently, achieving full enrollment in a single trial might require additional COVID-19 surges in the same location over several years. This has inspired us to pool individual patient data (IPD) from ongoing, paused, prematurely-terminated, or completed randomized controlled trials (RCTs) in real-time, to find an effective treatment as quickly as possible in light of the pandemic crisis. However, pooling across trials introduces enormous uncertainties in study design (e.g., the number of RCTs and sample sizes might be unknown in advance). We sought to develop a versatile treatment efficacy assessment model that accounts for these uncertainties while allowing for continuous monitoring throughout the study using Bayesian monitoring techniques.

METHODS

We provide a detailed look at the challenges and solutions for model development, describing the process that used extensive simulations to enable us to finalize the analysis plan. This includes establishing prior distribution assumptions, assessing and improving model convergence under different study composition scenarios, and assessing whether we can extend the model to accommodate multi-site RCTs and evaluate heterogeneous treatment effects. In addition, we recognized that we would need to assess our model for goodness-of-fit, so we explored an approach that used posterior predictive checking. Lastly, given the urgency of the research in the context of evolving pandemic, we were committed to frequent monitoring of the data to assess efficacy, and we set Bayesian monitoring rules calibrated for type 1 error rate and power.

RESULTS

The primary outcome is an 11-point ordinal scale. We present the operating characteristics of the proposed cumulative proportional odds model for estimating treatment effectiveness. The model can estimate the treatment's effect under enormous uncertainties in study design. We investigate to what degree the proportional odds assumption has to be violated to render the model inaccurate. We demonstrate the flexibility of a Bayesian monitoring approach by performing frequent interim analyses without increasing the probability of erroneous conclusions.

CONCLUSION

This paper describes a translatable framework using simulation to support the design of prospective IPD meta-analyses.

A Bayesian approach to correct the under-count of cancer registry statistics before population-based cancer registry program

Aim

This study aims to correct undercounts in cancer data before initiating a population-based cancer registry program, employing an innovative Bayesian methodology.

Background

Underestimation is a widespread issue in cancer registries within developing countries.

Methods

This secondary study utilized cancer registry data. We employed the Bayesian approach to correct undercounting in cancer data from 2005 to 2010, using the ratio of pathology to population-based data in the Golestan province as the initial value.

Results

The results of this study showed that the lowest percentage of undercounting belonged to Khorasan Razavi province with an average of 21% and the highest percentage belonged to Sistan and Baluchestan province with an average of 38%.The average age-standardized incidence rate (ASR) for all provinces of the country except Golestan province was equal to 105.72 (Confidence interval (CI) 95% 105.35-106.09) per 100,000 and after Bayesian correction was 137.17 (CI 95% 136.74-137.60) per 100,000. In 2010 the amount of ASR before Bayesian correction was 100.28 (CI 95% 124.39-127.09) per 100,000 for women and 136.49 (CI 95% 171.20-174.38) per 100,000 for men. Also, after implementing the Bayesian correction, ASR increased to 125.74 per 100,000 for women and 172.79 per 100,000 for men.

Conclusion

The study demonstrates the effectiveness of the Bayesian approach in correcting undercounting in cancer registries. By utilizing the Bayesian method, the average ASR after Bayesian correction with a 29.74 percent change was 137.17 per 100,000. These corrected estimates provide more accurate information on cancer burden and can contribute to improved public health programs and policy evaluation. Furthermore, this research emphasizes the suitability of the Bayesian method for addressing underestimation in cancer registries. It also underscores its pivotal role in shaping the trajectory of future investigations in this field.

Avoiding Under- and Overrecruitment in Behavioral Intervention Trials Using Bayesian Sequential Designs: Tutorial

Reducing research waste and protecting research participants from unnecessary harm should be top priorities for researchers studying interventions. However, the traditional use of fixed sample sizes exposes trials to risks of under- and overrecruitment by requiring that effect sizes be determined a priori. One mitigating approach is to adopt a Bayesian sequential design, which enables evaluation of the available evidence continuously over the trial period to decide when to stop recruitment. Target criteria are defined, which encode researchers' intentions for what is considered findings of interest, and the trial is stopped once the scientific question is sufficiently addressed. In this tutorial, we revisit a trial of a digital alcohol intervention that used a fixed sample size of 2129 participants. We show that had a Bayesian sequential design been used, the trial could have ended after collecting data from approximately 300 participants. This would have meant exposing far fewer individuals to trial procedures, including being allocated to the waiting list control condition, and the evidence from the trial could have been made public sooner.

Prevalence and Associated Factors of Cryptococcal Antigenemia in HIV-Infected Patients with CD4 < 200 Cells/µL in São Paulo, Brazil: A Bayesian Analysis

Cryptococcosis is a severe life-threatening disease and a major cause of mortality in people with advanced AIDS and CD4 ≤ 100 cells/µL. Considering the knowledge gap regarding the benefits of routine application of antigenemia tests in HIV-infected patients with 100−200 CD4 cells/µL for the prevention of cryptococcal meningitis (CM), we aimed to evaluate the prevalence of positive antigenemia through lateral flow assay (LFA) and associated factors in HIV-infected patients with CD4 < 200 cells/µL. Our findings of 3.49% of positive LFA (LFA+) patients with CD4 < 100 cells/µL and 2.24% with CD4 between 100−200 cells/µL have been included in a Bayesian analysis with 12 other studies containing similar samples worldwide. This analysis showed a proportion of 3.6% LFA+ patients (95% credible interval-Ci [2.5−5.7%]) with CD4 < 100 cells/µL and 1.1% (95%Ci [0.5−4.3%]) with CD4 between 100−200 cells/µL, without statistical difference between these groups. The difference between mortality rates in LFA+ and negative LFA groups was e = 0.05013. Cryptococcoma and CM were observed in the LFA+ group with 100−200 and <100 CD4 cells/µL, respectively. Considering the benefits of antifungal therapy for LFA+ patients, our data reinforced the recommendation to apply LFA as a routine test in patients with 100−200 CD4 cells/µL aiming to expand cost-effectiveness studies in this group.

Interpreting a Bayesian phase II futility clinical trial

Background

A resurgence of research into phase II trial design in the mid-2000s led to the use of futility designs in a wide variety of disease areas. Phase II futility studies differ from efficacy studies in that their null hypothesis is that treatment, relative to control, does not meet or exceed the level of benefit required to justify additional study. A rejection of the null hypothesis indicates that the treatment should not proceed to a larger confirmatory trial.

Methods

Bayesian approaches to the design of phase II futility clinical trials are presented and allow for the quantification of key probabilities, such as the predictive probability of current trial success or even the predictive probability of a future trial’s success.

Results

We provide an illustration of the design and interpretation of a phase II futility study constructed in a Bayesian framework. We focus on the operating characteristics of our motivating trial based on a simulation study, as well as the general interpretation of trial outcomes, type I, and type II errors in this framework.

Conclusions

Phase II futility clinical trials, when designed under in a Bayesian framework, offer an alternative approach to the design of mid-phase studies which provide unique benefits relative to trials designed in a frequentist framework and designs which focus on treatment efficacy.

Application of Bayesian methods to accelerate rare disease drug development: scopes and hurdles

Background

Design and analysis of clinical trials for rare and ultra-rare disease pose unique challenges to the practitioners. Meeting conventional power requirements is infeasible for diseases where sample sizes are inherently very small. Moreover, rare disease populations are generally heterogeneous and widely dispersed, which complicates study enrollment and design. Leveraging all available information in rare and ultra-rare disease trials can improve both drug development and informed decision-making processes.

Main text

Bayesian statistics provides a formal framework for combining all relevant information at all stages of the clinical trial, including trial design, execution, and analysis. This manuscript provides an overview of different Bayesian methods applicable to clinical trials in rare disease. We present real or hypothetical case studies that address the key needs of rare disease drug development highlighting several specific Bayesian examples of clinical trials. Advantages and hurdles of these approaches are discussed in detail. In addition, we emphasize the practical and regulatory aspects in the context of real-life applications.

Conclusion

The use of innovative trial designs such as master protocols and complex adaptive designs in conjunction with a Bayesian approach may help to reduce sample size, select the correct treatment and population, and accurately and reliably assess the treatment effect in the rare disease setting.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13023-022-02342-5.

Remote, real-time expert elicitation to determine the prior probability distribution for Bayesian sample size determination in international randomised controlled trials: Bronchiolitis in Infants Placebo Versus Epinephrine and Dexamethasone (BIPED) study

Background

Bayesian methods are increasing in popularity in clinical research. The design of Bayesian clinical trials requires a prior distribution, which can be elicited from experts. In diseases with international differences in management, the elicitation exercise should recruit internationally, making a face-to-face elicitation session expensive and more logistically challenging. Thus, we used a remote, real-time elicitation exercise to construct prior distributions. These elicited distributions were then used to determine the sample size of the Bronchiolitis in Infants with Placebo Versus Epinephrine and Dexamethasone (BIPED) study, an international randomised controlled trial in the Pediatric Emergency Research Network (PERN). The BIPED study aims to determine whether the combination of epinephrine and dexamethasone, compared to placebo, is effective in reducing hospital admission for infants presenting with bronchiolitis to the emergency department.

Methods

We developed a Web-based tool to support the elicitation of the probability of hospitalisation for infants with bronchiolitis. Experts participated in online workshops to specify their individual prior distributions, which were aggregated using the equal-weighted linear pooling method. Experts were then invited to provide their comments on the aggregated distribution. The average length criterion determined the BIPED sample size.

Results

Fifteen paediatric emergency medicine clinicians from Canada, the USA, Australia and New Zealand participated in three workshops to provide their elicited prior distributions. The mean elicited probability of admission for infants with bronchiolitis was slightly lower for those receiving epinephrine and dexamethasone compared to supportive care in the aggregate distribution. There were substantial differences in the individual beliefs but limited differences between North America and Australasia. From this aggregate distribution, a sample size of 410 patients per arm results in an average 95% credible interval length of less than 9% and a relative predictive power of 90%.

Conclusion

Remote, real-time expert elicitation is a feasible, useful and practical tool to determine a prior distribution for international randomised controlled trials. Bayesian methods can then determine the trial sample size using these elicited prior distributions. The ease and low cost of remote expert elicitation mean that this approach is suitable for future international randomised controlled trials.

Trial registration

ClinicalTrials.govNCT03567473

Supplementary Information

The online version contains supplementary material available at 10.1186/s13063-022-06240-w.

Application of machine learning methods in clinical trials for precision medicine

OBJECTIVE

A key component for precision medicine is a good prediction algorithm for patients' response to treatments. We aim to implement machine learning (ML) algorithms into the response-adaptive randomization (RAR) design and improve the treatment outcomes.

MATERIALS AND METHODS

We incorporated 9 ML algorithms to model the relationship of patient responses and biomarkers in clinical trial design. Such a model predicted the response rate of each treatment for each new patient and provide guidance for treatment assignment. Realizing that no single method may fit all trials well, we also built an ensemble of these 9 methods. We evaluated their performance through quantifying the benefits for trial participants, such as the overall response rate and the percentage of patients who receive their optimal treatments.

RESULTS

Simulation studies showed that the adoption of ML methods resulted in more personalized optimal treatment assignments and higher overall response rates among trial participants. Compared with each individual ML method, the ensemble approach achieved the highest response rate and assigned the largest percentage of patients to their optimal treatments. For the real-world study, we successfully showed the potential improvements if the proposed design had been implemented in the study.

CONCLUSION

In summary, the ML-based RAR design is a promising approach for assigning more patients to their personalized effective treatments, which makes the clinical trial more ethical and appealing. These features are especially desirable for late-stage cancer patients who have failed all the Food and Drug Administration (FDA)-approved treatment options and only can get new treatments through clinical trials.

Adaptive treatment allocation and selection in multi-arm clinical trials: a Bayesian perspective

Background

Adaptive designs offer added flexibility in the execution of clinical trials, including the possibilities of allocating more patients to the treatments that turned out more successful, and early stopping due to either declared success or futility. Commonly applied adaptive designs, such as group sequential methods, are based on the frequentist paradigm and on ideas from statistical significance testing. Interim checks during the trial will have the effect of inflating the Type 1 error rate, or, if this rate is controlled and kept fixed, lowering the power.

Results

The purpose of the paper is to demonstrate the usefulness of the Bayesian approach in the design and in the actual running of randomized clinical trials during phase II and III. This approach is based on comparing the performance of the different treatment arms in terms of the respective joint posterior probabilities evaluated sequentially from the accruing outcome data, and then taking a control action if such posterior probabilities fall below a pre-specified critical threshold value. Two types of actions are considered: treatment allocation, putting on hold at least temporarily further accrual of patients to a treatment arm, and treatment selection, removing an arm from the trial permanently. The main development in the paper is in terms of binary outcomes, but extensions for handling time-to-event data, including data from vaccine trials, are also discussed. The performance of the proposed methodology is tested in extensive simulation experiments, with numerical results and graphical illustrations documented in a Supplement to the main text. As a companion to this paper, an implementation of the methods is provided in the form of a freely available R package ’barts’.

Conclusion

The proposed methods for trial design provide an attractive alternative to their frequentist counterparts.

Bayesian statistics in anesthesia practice: a tutorial for anesthesiologists

This narrative review intends to provide the anesthesiologist with the basic knowledge of the Bayesian concepts and should be considered as a tutorial for anesthesiologists in the concept of Bayesian statistics. The Bayesian approach represents the mathematical formulation of the idea that we can update our initial belief about data with the evidence obtained from any kind of acquired data. It provides a theoretical framework and a statistical method to use pre-existing information within the context of new evidence. Several authors have described the Bayesian approach as capable of dealing with uncertainty in medical decision-making. This review describes the Bayes theorem and how it is used in clinical studies in anesthesia and critical care. It starts with a general introduction to the theorem and its related concepts of prior and posterior probabilities. Second, there is an explanation of the basic concepts of the Bayesian statistical inference. Last, a summary of the applicability of some of the Bayesian statistics in current literature is provided, such as Bayesian analysis of clinical trials and PKPD modeling.

A stochastically curtailed single‐arm phase II trial design for binary outcomes

Phase II clinical trials are a critical aspect of the drug development process. With drug development costs ever increasing, novel designs that can improve the efficiency of phase II trials are extremely valuable.Phase II clinical trials for cancer treatments often measure a binary outcome. The final trial decision is generally to continue or cease development. When this decision is based solely on the result of a hypothesis test, the result may be known with certainty before the planned end of the trial. Unfortunately, there is often no opportunity for early stopping when this occurs.Some existing designs do permit early stopping in this case, accordingly reducing the required sample size and potentially speeding up drug development. However, more improvements can be achieved by stopping early when the final trial decision is very likely, rather than certain, known as stochastic curtailment. While some authors have proposed approaches of this form, these approaches have various limitations.In this work we address these limitations by proposing new design approaches for single-arm phase II binary outcome trials that use stochastic curtailment. We use exact distributions, avoid simulation, consider a wider range of possible designs and permit early stopping for promising treatments. As a result, we are able to obtain trial designs that have considerably reduced sample sizes on average.

Evaluating Medical Therapy for Calcific Aortic Stenosis: JACC State-of-the-Art Review

Despite numerous promising therapeutic targets, there are no proven medical treatments for calcific aortic stenosis (AS). Multiple stakeholders need to come together and several scientific, operational, and trial design challenges must be addressed to capitalize on the recent and emerging mechanistic insights into this prevalent heart valve disease. This review briefly discusses the pathobiology and most promising pharmacologic targets, screening, diagnosis and progression of AS, identification of subgroups that should be targeted in clinical trials, and the need to elicit the patient voice earlier rather than later in clinical trial design and implementation. Potential trial end points and tools for assessment and approaches to implementation and design of clinical trials are reviewed. The efficiencies and advantages offered by a clinical trial network and platform trial approach are highlighted. The objective is to provide practical guidance that will facilitate a series of trials to identify effective medical therapies for AS resulting in expansion of therapeutic options to complement mechanical solutions for late-stage disease.

Lessons Learned From Implementing a Novel Bayesian Adaptive Dose-Finding Design in Advanced Pancreatic Cancer

PURPOSE

Novel Bayesian adaptive designs provide an effective way to improve clinical trial efficiency. These designs are superior to conventional methods, but implementing them can be challenging. The aim of this article was to describe what we learned while applying a novel Bayesian phase I-II design in a recent trial.

METHODS

The primary goal of the trial was to optimize radiation therapy (RT) dose among three levels (low, standard, and high), given either with placebo (P) or an investigational agent (A), for treating locally advanced, radiation-naive pancreatic cancer, deemed appropriate for RT rather than surgery. Up to 48 patients were randomly assigned fairly between RT plus P and RT plus A, with RT dose-finding done within each arm using the late-onset efficacy-toxicity design on the basis of two coprimary end points, tumor response and dose-limiting toxicity, both evaluated at up to 90 days. The random assignment was blinded, but within each arm, unblinded RT doses were chosen adaptively using software developed within the institution.

RESULTS

Implementing the design involved double-blind balance-restricted random assignment, real-time assessment of patient outcomes to evaluate the efficacy-toxicity trade-off for each RT dose in each arm to optimize each patient's RT dose adaptively, and transition from a single-center trial to a multicenter trial. We present lessons learned and illustrative documentation.

CONCLUSION

Implementing novel Bayesian adaptive trial designs requires close collaborations between physicians, pharmacists, statisticians, data managers, and sponsors. The process is difficult but manageable and essential for efficient trial conduct. Close collaboration during trial conduct is a key component of any trial that includes real-time adaptive decision rules.

Telemedicine for Children With Medical Complexity: A Randomized Clinical Trial

BACKGROUND

Telemedicine is widely used but has uncertain value. We assessed telemedicine to further improve outcomes and reduce costs of comprehensive care (CC) for medically complex children.

METHODS

We conducted a single-center randomized clinical trial comparing telemedicine with CC relative to CC alone for medically complex children in reducing care days outside the home (clinic, emergency department, or hospital; primary outcome), rate of children developing serious illnesses (causing death, ICU admission, or hospital stay >7 days), and health system costs. We used intent-to-treat Bayesian analyses with neutral prior assuming no benefit. All participants received CC, which included 24/7 phone access to primary care providers (PCPs), low patient-to-PCP ratio, and hospital consultation from PCPs. The telemedicine group also received remote audiovisual communication with the PCPs.

RESULTS

Between August 22, 2018, and March 23, 2020, we randomly assigned 422 medically complex children (209 to CC with telemedicine and 213 to CC alone) before meeting predefined stopping rules. The probability of a reduction with CC with telemedicine versus CC alone was 99% for care days outside the home (12.94 vs 16.94 per child-year; Bayesian rate ratio, 0.80 [95% credible interval, 0.66-0.98]), 95% for rate of children with a serious illness (0.29 vs 0.62 per child-year; rate ratio, 0.68 [0.43-1.07]) and 91% for mean total health system costs (US$33 718 vs US$41 281 per child-year; Bayesian cost ratio, 0.85 [0.67-1.08]).

CONCLUSION

The addition of telemedicine to CC likely reduced care days outside the home, serious illnesses, other adverse outcomes, and health care costs for medically complex children.

Pharmacometrics meets statistics-A synergy for modern drug development

Modern drug development problems are very complex and require integration of various scientific fields. Traditionally, statistical methods have been the primary tool for design and analysis of clinical trials. Increasingly, pharmacometric approaches using physiology-based drug and disease models are applied in this context. In this paper, we show that statistics and pharmacometrics have more in common than what keeps them apart, and collectively, the synergy from these two quantitative disciplines can provide greater advances in clinical research and development, resulting in novel and more effective medicines to patients with medical need.

The Frontiers of Uncertainty Estimation in Interdisciplinary Disaster Research and Practice

Conceptualizing, assessing, and managing disaster risks involve collecting and synthesizing pluralistic information-from natural, built, and human systems-to characterize disaster impacts and guide policy on effective resilience investments. Disaster research and practice, therefore, are highly complex and inherently interdisciplinary endeavors. Characterizing the uncertainties involved in interdisciplinary disaster research is imperative, since misrepresenting uncertainty can lead to myopic decisions and suboptimal societal outcomes. Efficacious disaster mitigation should, therefore, explicitly address the uncertainties associated with all stages of hazard modeling, preparation, and response. However, uncertainty assessment and communication in the context of interdisciplinary disaster research remain understudied. In this "Perspective" article, we argue that in harnessing interdisciplinary methods and diverse data types in disaster research, careful deliberations on assessing Type III and Type IV errors are imperative. Additionally, we discuss the pathologies in frequentist approaches, calling for an increasing role for Bayesian methods in uncertainty estimations. Moreover, we discuss the potential tradeoffs associated with information and uncertainty, calling for deliberate consideration of the role of diversity of information prior to setting the scope in interdisciplinary modeling. Future research guided by further reflections on the ideas raised in this article could help push the frontiers of uncertainty estimation in interdisciplinary hazard research and practice.

Prospective individual patient data meta-analysis: Evaluating convalescent plasma for COVID-19

As the world faced the devastation of the COVID‐19 pandemic in late 2019 and early 2020, numerous clinical trials were initiated in many locations in an effort to establish the efficacy (or lack thereof) of potential treatments. As the pandemic has been shifting locations rapidly, individual studies have been at risk of failing to meet recruitment targets because of declining numbers of eligible patients with COVID‐19 encountered at participating sites. It has become clear that it might take several more COVID‐19 surges at the same location to achieve full enrollment and to find answers about what treatments are effective for this disease. This paper proposes an innovative approach for pooling patient‐level data from multiple ongoing randomized clinical trials (RCTs) that have not been configured as a network of sites. We present the statistical analysis plan of a prospective individual patient data (IPD) meta‐analysis (MA) from ongoing RCTs of convalescent plasma (CP). We employ an adaptive Bayesian approach for continuously monitoring the accumulating pooled data via posterior probabilities for safety, efficacy, and harm. Although we focus on RCTs for CP and address specific challenges related to CP treatment for COVID‐19, the proposed framework is generally applicable to pooling data from RCTs for other therapies and disease settings in order to find answers in weeks or months, rather than years.

The use of local and nonlocal priors in Bayesian test-based monitoring for single-arm phase II clinical trials

Bayesian sequential monitoring is widely used in adaptive phase II studies where the objective is to examine whether an experimental drug is efficacious. Common approaches for Bayesian sequential monitoring are based on posterior or predictive probabilities and Bayesian hypothesis testing procedures using Bayes factors. In the first part of the paper, we briefly show the connections between test-based (TB) and posterior probability-based (PB) sequential monitoring approaches. Next, we extensively investigate the choice of local and nonlocal priors for the TB monitoring procedure. We describe the pros and cons of different priors in terms of operating characteristics. We also develop a user-friendly Shiny application to implement the TB design.

Tobacco Use and Risk Factors for Hypertensive Individuals in Kenya

This study aimed to examine the association between hypertension and tobacco use as well as other known hypertensive risk factors (BMI, waist–hip ratio, alcohol consumption, physical activity, and socio-economic factors among adults) in Kenya. The study utilized the 2015 Kenya STEPs survey (adults aged 18–69) and investigated the association between tobacco use and hypertension. Descriptive statistics, correlation, frequencies, and regression (linear and logistic) analyses were used to execute the statistical analysis. The study results indicate a high prevalence of hypertension in association with certain risk factors—body mass index (BMI), alcohol, waist–hip ratio (WHR), and tobacco use—that were higher in males than females among the hypertensive group. Moreover, the findings noted an exceptionally low awareness level of hypertension in the general population. BMI, age, WHR, and alcohol use were prevalent risks of all three outcomes: hypertension, systolic blood pressure, and diastolic blood pressure. Healthcare authorities and policymakers can employ these findings to lower the burden of hypertension by developing health promotion and intervention policies.

How to use likelihood ratios to interpret evidence from randomized trials

OBJECTIVE

The likelihood ratio is a method for assessing evidence regarding two simple statistical hypotheses. Its interpretation is simple - for example, a value of 10 means that the first hypothesis is 10 times as strongly supported by the data as the second. A method is shown for deriving likelihood ratios from published trial reports.

STUDY DESIGN

The likelihood ratio compares two hypotheses in light of data: that a new treatment is effective, at a specified level (alternate hypothesis: for instance, the hazard ratio equals 0.7), and that it is not (null hypothesis: the hazard ratio equals 1). The result of the trial is summarised by the test statistic z (ie, the estimated treatment effect divided by its standard error). The expected value of z is 0 under the null hypothesis, and A under the alternate hypothesis. The logarithm of the likelihood ratio is given by z·A - A²/2. The values of A and z can be derived from the alternate hypothesis used for sample size computation, and from the observed treatment effect and its standard error or confidence interval.

RESULTS

Examples are given of trials that yielded strong or moderate evidence in favor of the alternate hypothesis, and of a trial that favored the null hypothesis. The resulting likelihood ratios are applied to initial beliefs about the hypotheses to obtain posterior beliefs.

CONCLUSIONS

The likelihood ratio is a simple and easily understandable method for assessing evidence in data about two competing a priori hypotheses.

P value and Bayesian analysis in randomized-controlled trials in child health research published over 10 years, 2007 to 2017: a methodological review protocol

BACKGROUND

There is an unresolved debate about the reliability of the interpretation of P value. Some investigators have suggested that an alternative Bayesian method is preferred in conducting health research. As randomized-controlled trials (RCTs) are important in generating research evidence, we decided to investigate the extent, if any, the inferential statistical framework in published RCTs in child health research have changed over 10 years. We aim to examine the change in P value and Bayesian analysis in RCTs in child health research papers published from 2007 to 2017.

METHODS

We will search the Cochrane Central Register of Controlled Trials (Wiley) to identify relevant citations. We will leverage a pre-existing sample of child health RCTs published in 2007 (n=300) used in our previous study of reporting quality of pediatric RCTs. Using the same strategy and study selection methods, we will identify a comparable random sample of child health RCTs published in 2017 (n=300). Eligible studies will include RCTs in health research among individuals aged 21 years and below. One reviewer will select studies for inclusion and extract the data and another reviewer will verify these. Disagreements will be resolved by a discussion between reviewers or by involving another reviewer. We will perform a descriptive analysis of 2007 and 2017 samples and analyze the results using both the frequentist and Bayesian methods. We will present specific characteristics of the clinical trials from 2007 and 2017 in tabular and graphical forms. We will report the difference in the proportion of P value and Bayesian analysis between 2007 and 2017 to assess the 10-year change. Clustering around P values of significance, if observed, will be reported.

DISCUSSION

This review will present the difference in the proportion of trials that reported on P value and Bayesian analysis between 2007 and 2017 to assess the 10-year change. The implications for future clinical research will be discussed and this research work will be published in a peer-reviewed journal. This review has the potential to help inform the need for a change in the methodological approach from the null hypothesis significance test to Bayesian methods.

SYSTEMATIC REVIEW REGISTRATION

Open Science Framework https://osf.io/aj2df.

Value of Adaptive Trials and Surrogate Endpoints for Clinical Decision-Making in Rare Cancers

Despite high-level endorsement, the number of adaptive Phase II/III trials in rare cancers needs to be improved, with better understanding of their value for clinical decisions in daily practice. This paper describes approaches to trial design in rare cancers, which has been supplemented by a search of ClinicalTrials.gov for adaptive trial designs in rare cancer. In addition, an online survey of 3,200 oncologists was conducted. Practicing physicians were questioned on the importance of different evidence levels, types of adaptive trial design, and categories of surrogate endpoints for clinical decision making. The results of the online survey revealed that evidence from Phase II/III trials with an adaptive design and relatively small sample size was considered high value in rare cancer by 97% of responders, similar to the randomized controlled trial rating (82%). Surrogate clinical endpoints were considered valuable alternatives to overall survival by 80% of oncologists. Preferred adaptive designs were futility analysis, interim analysis, adaptive sample size, and adaptive randomization. In conclusion, rare cancer oncologists rate evidence from adaptive clinical trials with as high a value and importance for clinical decision making processes as conventional randomized controlled trials. All stakeholders have a vested interest in advances in clinical trial designs to ensure efficient and timely development of innovative medicinal products to allow more patients faster access to the pivotal treatment.

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

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.

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

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.

The Bayesian Design of Adaptive Clinical Trials

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.

BOIN Suite: A Software Platform to Design and Implement Novel Early-Phase Clinical Trials

PURPOSE

Using novel Bayesian adaptive designs has great potential to improve the efficiency of early-phase clinical trials. A major barrier for clinical researchers to adopt novel designs is the lack of easy-to-use software. Our purpose is to develop a user-friendly software platform to implement novel clinical trial designs that address various challenges in early-phase dose-finding trials.

METHODS

We used R Shiny to develop a web-based software platform to facilitate the use of recent novel adaptive designs.

RESULTS

We developed a web-based software suite, called Bayesian optimal interval (BOIN) suite, which includes R Shiny applications to handle various clinical settings, including single-agent phase I trials with and without prior information, trials with late-onset toxicity, trials to find the optimal biological dose based on risk-benefit trade-off, and drug combination trials to find a single maximum tolerated dose (MTD) or the MTD contour. The applications are built using the same software architecture to ensure the best and a uniform user experience, and they are developed using a proven software development standard operating procedure to ensure accuracy, robustness, and reproducibility. The suite is freely available with internet access and a web browser without the need of installing any other software.

CONCLUSION

The BOIN suite allows clinical researchers to design various types of early-phase clinical trials under a unified framework. This work is extremely important because it not only advances the clinical research and drug development by facilitating the use of novel trial designs with optimal performance but also enhances collaborations between biostatisticians and clinicians by disseminating novel statistical methodology to broader scientific communities through user-friendly software. The BOIN suite establishes a KISS principle: keep it simple, but smart.

Hospital Consultation From Outpatient Clinicians for Medically Complex Children: A Randomized Clinical Trial

IMPORTANCE

Children with medical complexity (CMC) frequently experience fragmented care. We have demonstrated that outpatient comprehensive care (CC) reduces serious illnesses, hospitalizations, and costs for high-risk CMC. Yet continuity of care for CMC is often disrupted with emergency department (ED) visits and hospitalizations.

OBJECTIVE

To evaluate a hospital consultation (HC) service for CMC from their outpatient CC clinicians.

DESIGN, SETTING, AND PARTICIPANTS

Randomized quality improvement trial at the University of Texas Health Science Center at Houston with an outpatient CC clinic and tertiary pediatric hospital (Children's Memorial Hermann Hospital). Participants included high-risk CMC (≥2 hospitalizations or ≥1 pediatric intensive care unit [PICU] admission in the year before enrolling in our clinic) receiving CC. Data were analyzed between January 11, 2018, and December 20, 2019.

INTERVENTIONS

The HC included serial discussions between CC clinicians, ED physicians, and hospitalists addressing need for admission, inpatient treatment, and transition back to outpatient care. Usual hospital care (UHC) involved routine pediatric hospitalist care.

MAIN OUTCOMES AND MEASURES

Total hospital days (primary outcome), PICU days, hospitalizations, and health system costs in skeptical bayesian analyses (using a prior probability assuming no benefit).

RESULTS

From October 3, 2016, through October 2, 2017, 342 CMC were randomized to either HC (n = 167) or UHC (n = 175) before meeting the predefined bayesian stopping guideline (>80% probability of reduced hospital days). In intention-to-treat analyses, the probability that HC reduced total hospital days was 91% (2.72 vs 6.01 per child-year; bayesian rate ratio [RR], 0.61; 95% credible interval [CrI], 0.30-1.26). The probability of a reduction with HC vs UHC was 98% for hospitalizations (0.60 vs 0.93 per child-year; RR, 0.68; 95% CrI, 0.48-0.97), 89% for PICU days (0.77 vs 1.89 per child-year; RR, 0.59; 95% CrI, 0.26-1.38), and 94% for mean total health system costs ($24 928 vs $42 276 per child-year; cost ratio, 0.67; 95% CrI, 0.41-1.10). In secondary analysis using a bayesian prior centered at RR of 0.78, reflecting the opinion of 7 experts knowledgeable about CMC, the probability that HC reduced hospital days was 96%.

CONCLUSIONS AND RELEVANCE

Among CMC receiving comprehensive outpatient care, an HC service from outpatient clinicians likely reduced total hospital days, hospitalizations, PICU days, other outcomes, and health system costs. Additional trials of an HC service from outpatient CC clinicians are needed for CMC in other centers.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02870387.

Potential harm caused by physicians' a-priori beliefs in the clinical effectiveness of hydroxychloroquine and its impact on clinical and economic outcome - A simulation approach

Despite growing controversies around Hydroxychloroquine's effectiveness, the drug is still widely prescribed by clinicians to treat COVID19 patients. Therapeutic judgment under uncertainty and imperfect information may be influenced by personal preference, whereby individuals, to confirm a-priori beliefs, may propose drugs without knowing the clinical benefit. To estimate this disconnect between available evidence and prescribing behavior, we created a Bayesian model analyzing a-priori optimistic belief of physicians in Hydroxychloroquine's effectiveness. Methodology: We created a Bayesian model to simulate the impact of different a-priori beliefs related to Hydroxychloroquine's effectiveness on clinical and economic outcome. Results: Our hypothetical results indicate no significant difference in treatment effect (combined survival benefit and harm) up to a presumed drug's effectiveness level of 20%, with younger individuals being negatively affected by the treatment (RR 0.82, 0.55–1.2; (0.95 (1.1) % expected adverse events versus 0.05 (0.98) % expected death prevented). Simulated cost data indicate overall hospital cost (medicine, hospital stay, complication) of 18.361,41€ per hospitalized patient receiving Hydroxychloroquine treatment. Conclusion: Off-label use of Hydroxychloroquine needs a rational, objective and datadriven evaluation, as personal preferences may be flawed and cause harm to patients and to society.

Clinical trials in critical care: can a Bayesian approach enhance clinical and scientific decision making?

Recent Bayesian reanalyses of prominent trials in critical illness have generated controversy by contradicting the initial conclusions based on conventional frequentist analyses. Many clinicians might be sceptical that Bayesian analysis, a philosophical and statistical approach that combines prior beliefs with data to generate probabilities, provides more useful information about clinical trials than the frequentist approach. In this Personal View, we introduce clinicians to the rationale, process, and interpretation of Bayesian analysis through a systematic review and reanalysis of interventional trials in critical illness. In the majority of cases, Bayesian and frequentist analyses agreed. In the remainder, Bayesian analysis identified interventions where benefit was probable despite the absence of statistical significance, where interpretation depended substantially on choice of prior distribution, and where benefit was improbable despite statistical significance. Bayesian analysis in critical care medicine can help to distinguish harm from uncertainty and establish the probability of clinically important benefit for clinicians, policy makers, and patients.

Current status of Bayesian clinical trials for oncology, 2020

Bayesian methods had established a foothold in developing therapies in oncology trials. Methods: We identified clinical trials posted on the ClinicalTrials.gov database focused on Oncology trials with a Bayesian approach in their design. Differences in study characteristics such as design, study phase, randomization, masking, purpose of study, main outcomes, gender, age and funding involvement according to Bayesian approach were assessed using Chi-squared or Fisher's exact tests. Results: We identified 225 studies with Bayesian components in their design addressing oncological diseases. The most common designs were Bayesian Toxicity Monitoring (26.4%), Model-based designs (36%) Model-assisted designs (8%). Statistical methods such as Bayesian logistic regression model (59.4%), Bayesian piecewise exponential survival regression (10.9%) and the Continual reassessment method (9.4%) were the most used. Conclusions: Bayesian trials are more common in the early phases of drug development specifically in phase II trials (43.6%). Cancer institutes or Hospitals funded most of the studies retrieved. This type of design has increased over time and represent an innovative means of increasing trial efficiency.

IDeA States Pediatric Clinical Trials Network for Underserved and Rural Communities

The National Institutes of Health's Environmental Influences on Child Health Outcomes (ECHO) program aims to study high-priority and high-impact pediatric conditions. This broad-based health initiative is unique in the National Institutes of Health research portfolio and involves 2 research components: (1) a large group of established centers with pediatric cohorts combining data to support longitudinal studies (ECHO cohorts) and (2) pediatric trials program for institutions within Institutional Development Awards states, known as the ECHO Institutional Development Awards States Pediatric Clinical Trials Network (ISPCTN). In the current presentation, we provide a broad overview of the ISPCTN and, particularly, its importance in enhancing clinical trials capabilities of pediatrician scientists through the support of research infrastructure, while at the same time implementing clinical trials that inform future health care for children. The ISPCTN research mission is aligned with the health priority conditions emphasized in the ECHO program, with a commitment to bringing state-of-the-science trials to children residing in underserved and rural communities. ISPCTN site infrastructure is critical to successful trial implementation and includes research training for pediatric faculty and coordinators. Network sites exist in settings that have historically had limited National Institutes of Health funding success and lacked pediatric research infrastructure, with the initial funding directed to considerable efforts in professional development, implementation of regulatory procedures, and engagement of communities and families. The Network has made considerable headway with these objectives, opening two large research studies during its initial 18 months as well as producing findings that serve as markers of success that will optimize sustainability.