Assessing efficacy in important subgroups in confirmatory trials: An example using Bayesian dynamic borrowing

Biomarker-Driven Oncology Trial Design and Subgroup Characterization: Challenges and Potential Solutions

In oncology drug development, using biomarkers to select a study population more likely to benefit from a therapeutic effect is critical to increase the efficiency of a clinical trial in demonstrating effectiveness. This perspective delves into therapeutic product approvals that were tested in pivotal trials with all-comers populations, but ultimately received US Food and Drug Administration approval for use within specific patient subgroups identified by biomarkers. Despite initial designs for efficacy and safety assessments in overall populations, a favorable benefit-risk assessment was primarily established in biomarker-positive subgroups. Analyzing these cases, we summarize key considerations pivotal to totality of evidence for regulatory benefit-risk assessments for biomarker-defined subgroup versus all-comers approvals, including biological and clinical rationales, biomarker prevalence, safety data, overall trial design, and subgroup efficacy characterization. Furthermore, a decision tree is proposed to guide optimal clinical trial design, delineating between patient enrichment and stratification, accounting for key factors including biological and clinical rationale, marker type (discreate or continuous), prevalence, assay readiness, and turnaround times for marker assessment. Finally, a recommended approach for subgroup characterization involves prespecifying magnitude of improvement that would be considered clinically meaningful in the biomarker-negative subgroup, which can be supplemented with methodologies such as Bayesian to incorporate evidence from similar studies when available. In summary, this perspective underscores the importance of clinical trial innovations, statistical methodologies and regulatory considerations, to optimize biomarker-driven drug development for patients with cancer.

Augmenting external control arms using Bayesian borrowing: a case study in first-line non-small cell lung cancer

Aim: This study aimed to improve comparative effectiveness estimates and discuss challenges encountered through the application of Bayesian borrowing (BB) methods to augment an external control arm (ECA) constructed from real-world data (RWD) using historical clinical trial data in first-line non-small-cell lung cancer (NSCLC). Materials & methods: An ECA for a randomized controlled trial (RCT) in first-line NSCLC was constructed using ConcertAI Patient360™ to assess chemotherapy with or without cetuximab, in the bevacizumab-inappropriate subpopulation. Cardinality matching was used to match patient characteristics between the treatment arm (cetuximab + chemotherapy) and ECA. Overall survival (OS) was assessed as the primary outcome using Cox proportional hazards (PH). BB was conducted using a static power prior under a Weibull PH parameterization with borrowing weights from 0.0 to 1.0 and augmentation of the ECA from a historical control trial. Results: The constructed ECA yielded a higher overall survival (OS) hazard ratio (HR) (HR = 1.53; 95% CI: 1.21-1.93) than observed in the matched population of the RCT (HR = 0.91; 95% CI: 0.73-1.13). The OS HR decreased through the incorporation of BB (HR = 1.30; 95% CI: 1.08-1.54, borrowing weight = 1.0). BB was applied to augment the RCT control arm via a historical control which improved the precision of the observed HR estimate (1.03; 95% CI: 0.86-1.22, borrowing weight = 1.0), in comparison to the matched population of the RCT alone. Conclusion: In this study, the RWD ECA was unable to successfully replicate the OS estimates from the matched population of the selected RCT. The inability to replicate could be due to unmeasured confounding and variations in time-periods, follow-up and subsequent therapy. Despite these findings, we demonstrate how BB can improve precision of comparative effectiveness estimates, potentially aid as a bias assessment tool and mitigate challenges of traditional methods when appropriate external data sources are available.

Digital twins and Bayesian dynamic borrowing: Two recent approaches for incorporating historical control data

Recent years have seen an increasing interest in incorporating external control data for designing and evaluating randomized clinical trials (RCT). This may decrease costs and shorten inclusion times by reducing sample sizes. For small populations, with limited recruitment, this can be especially important. Bayesian dynamic borrowing (BDB) has been a popular choice as it claims to protect against potential prior data conflict. Digital twins (DT) has recently been proposed as another method to utilize historical data. DT, also known as PROCOVA™, is based on constructing a prognostic score from historical control data, typically using machine learning. This score is included in a pre-specified ANCOVA as the primary analysis of the RCT. The promise of this idea is power increase while guaranteeing strong type 1 error control. In this paper, we apply analytic derivations and simulations to analyze and discuss examples of these two approaches. We conclude that BDB and DT, although similar in scope, have fundamental differences which need be considered in the specific application. The inflation of the type 1 error is a serious issue for BDB, while more evidence is needed of a tangible value of DT for real RCTs.

Dapagliflozin Utilization in Chronic Kidney Disease and Its Real-World Effectiveness Among Patients with Lower Levels of Albuminuria in the USA and Japan

INTRODUCTION

Sodium-glucose cotransporter 2 inhibitors such as dapagliflozin have been proven effective for slowing chronic kidney disease (CKD) progression in large outcomes trials that mainly included patients with higher levels of albuminuria. Understanding the real-world utilization and effectiveness of these drugs among patients with CKD with lower levels of albuminuria can inform clinical decision-making in this population.

METHODS

Claims data from the USA and Japan were used to describe patients with CKD and urinary albumin-to-creatinine ratio (UACR) < 200 mg/g who were eligible for dapagliflozin 10 mg treatment (initiators and untreated) following its approval for CKD. A quantile regression analysis was performed to evaluate the effect of dapagliflozin 10 mg initiation versus no initiation on estimated glomerular filtration rate (eGFR) slope in a propensity score-matched cohort, using a prevalent new-user design.

RESULTS

Dapagliflozin initiators (n = 20,407) mostly had stage 3-4 CKD (69-81% across databases). The most common comorbidities were type 2 diabetes, hypertension and cardiovascular disease. At baseline, a renin-angiotensin system inhibitor was prescribed in 53-81% of patients. Eligible but untreated patients were older and had a higher eGFR and lower comorbidity burden than initiators. Following dapagliflozin initiation, the differences in median eGFR slope between initiators and matched non-initiators were 1.07 mL/min/1.73 m2/year (95% confidence interval [CI] 0.40-1.74) in all patients with UACR < 200 mg/g and 1.28 mL/min/1.73 m2/year (95% CI - 1.56 to 4.12) in patients with UACR < 200 mg/g without type 2 diabetes.

CONCLUSIONS

Dapagliflozin 10 mg was prescribed to a broad range of patients with CKD. In patients with UACR < 200 mg/g, dapagliflozin initiation was associated with a clinically meaningful attenuation of eGFR slope compared with non-initiation. These findings supplement available clinical efficacy evidence and suggest that dapagliflozin effectiveness may extend to patients with CKD and UACR < 200 mg/g. Graphical Abstract and Video Abstract available for this article. (Video Abstract 245964 kb).

Using Bayesian Dynamic Borrowing to Maximize the Use of Existing Data: A Case-Study

Bayesian Dynamic Borrowing (BDB) designs are being increasingly used in clinical drug development. These methods offer a mathematically rigorous and robust approach to increase efficiency and strengthen evidence by integrating existing trial data into a new clinical trial. The regulatory acceptability of BDB is evolving and varies between and within regulatory agencies. This paper describes how BDB can be used to design a new randomised clinical trial including external data to supplement the planned sample size and discusses key considerations related to data re-use and BDB in drug development programs. A case-study illustrating the planning and evaluation of a BDB approach to support registration of a new medicine with the Center for Drug Evaluation in China will be presented. Key steps and considerations for the use of BDB will be discussed and evaluated, including how to decide whether it is appropriate to borrow external data, which external data can be re-used, the weight to put on the external data and how to decide if the new study has successfully demonstrated treatment benefit.

Iptacopan in Idiopathic Immune Complex-Mediated Membranoproliferative Glomerulonephritis: Protocol of the APPARENT Multicenter, Randomized Phase 3 Study

Introduction

Immune complex-mediated membranoproliferative glomerulonephritis (IC-MPGN) is an ultra-rare, fast-progressing kidney disease that may be idiopathic (primary) or secondary to chronic infection, autoimmune disorders, or monoclonal gammopathies. Dysregulation of the alternative complement pathway is implicated in the pathophysiology of IC-MPGN; and currently, there are no approved targeted treatments. Iptacopan is an oral, highly potent proximal complement inhibitor that specifically binds to factor B and inhibits the alternative pathway (AP).

Methods

This randomized, double-blind, placebo-controlled phase 3 study (APPARENT; NCT05755386) will evaluate the efficacy and safety of iptacopan in patients with idiopathic (primary) IC-MPGN, enrolling up to 68 patients (minimum of 10 adolescents) aged 12 to 60 years with biopsy-confirmed IC-MPGN, proteinuria ≥1 g/g, and estimated glomerular filtration rate (eGFR) ≥30 ml/min per 1.73 m2. All patients will receive maximally tolerated angiotensin-converting enzyme inhibitor/angiotensin receptor blocker and vaccination against encapsulated bacteria. Patients with any organ transplant, progressive crescentic glomerulonephritis, or kidney biopsy with >50% interstitial fibrosis/tubular atrophy, will be excluded. Patients will be randomized 1:1 to receive either iptacopan 200 mg twice daily (bid) or placebo for 6 months, followed by open-label treatment with iptacopan 200 mg bid for all patients for 6 months. The primary objective of the study is to evaluate the efficacy of iptacopan versus placebo in proteinuria reduction measured as urine protein-to-creatinine ratio (UPCR) (24-h urine) at 6 months. Key secondary end points will assess kidney function measured by eGFR, patients who achieve a proteinuria-eGFR composite end point, and patient-reported fatigue.

Conclusion

This study will provide evidence toward the efficacy and safety of iptacopan in idiopathic (primary) IC-MPGN.

Incorporation of healthy volunteers data on receptor occupancy into a phase II proof-of-concept trial using a Bayesian dynamic borrowing design

Receptor occupancy in targeted tissues measures the proportion of receptors occupied by a drug at equilibrium and is sometimes used as a surrogate of drug efficacy to inform dose selection in clinical trials. We propose to incorporate data on receptor occupancy from a phase I study in healthy volunteers into a phase II proof-of-concept study in patients, with the objective of using all the available evidence to make informed decisions. A minimal physiologically based pharmacokinetic modeling is used to model receptor occupancy in healthy volunteers and to predict it in the patients of a phase II proof-of-concept study, taking into account the variability of the population parameters and the specific differences arising from the pathological condition compared to healthy volunteers. Then, given an estimated relationship between receptor occupancy and the clinical endpoint, an informative prior distribution is derived for the clinical endpoint in both the treatment and control arms of the phase II study. These distributions are incorporated into a Bayesian dynamic borrowing design to supplement concurrent phase II trial data. A simulation study in immuno-inflammation demonstrates that the proposed design increases the power of the study while maintaining a type I error at acceptable levels for realistic values of the clinical endpoint.

Pharmacometrics enhanced Bayesian borrowing approach to improve clinical trial efficiency: Case of empagliflozin in type 2 diabetes

We report use of a pharmacometrics enhanced Bayesian borrowing (PEBB) approach to leverage historical clinical trial data on a drug product to build models, project the outcome of future clinical trials, and borrow information from these projections to improve the efficiency of future target trials. This design takes a two-stage approach. First, a design phase is performed before target trial data are available to determine the operating characteristics and an appropriate tuning parameter that will be used in the subsequent analysis phase of a chosen target trial. Second, once the target trial data are available, the analysis phase is performed with the determined tuning parameter. This step is where borrowing is applied from these projections to inform the results for the target trial. To illustrate how a PEBB could improve the efficiency of clinical trials, we apply our design to trials with empagliflozin for treating patients with type 2 diabetes. We performed a retrospective evaluation applying the method to a phase III target trial and a hypothetical smaller trial. The type I error could be kept below 10% while increasing the trial power and effective sample size. Our findings suggest that a PEBB has the potential to increase the power of clinical trials, while controlling for type I error, by leveraging the information from previous trials through population pharmacokinetic/pharmacodynamic modeling and simulation.

Power priors for replication studies

The ongoing replication crisis in science has increased interest in the methodology of replication studies. We propose a novel Bayesian analysis approach using power priors: The likelihood of the original study's data is raised to the power of α , and then used as the prior distribution in the analysis of the replication data. Posterior distribution and Bayes factor hypothesis tests related to the power parameter α quantify the degree of compatibility between the original and replication study. Inferences for other parameters, such as effect sizes, dynamically borrow information from the original study. The degree of borrowing depends on the conflict between the two studies. The practical value of the approach is illustrated on data from three replication studies, and the connection to hierarchical modeling approaches explored. We generalize the known connection between normal power priors and normal hierarchical models for fixed parameters and show that normal power prior inferences with a beta prior on the power parameter α align with normal hierarchical model inferences using a generalized beta prior on the relative heterogeneity variance I 2 . The connection illustrates that power prior modeling is unnatural from the perspective of hierarchical modeling since it corresponds to specifying priors on a relative rather than an absolute heterogeneity scale.

Evaluating treatments in rare indications warrants a Bayesian approach

Evaluating efficacy and real-world effectiveness for novel therapies targeting rare mutations or patient subpopulations with unmet needs is a growing challenge in health economics and outcomes research (HEOR). In these settings it may be difficult to recruit enough patients to run adequately powered randomized clinical trials, resulting in greater reliance on single-arm trials or basket trial designs. Additionally, evidence networks for performing network meta-analysis may be sparse or disconnected when comparing available treatments in narrower patient populations. These challenges create an increased need for use of appropriate methods for handling small sample sizes, structural modelling assumptions and more nuanced decision rules to arrive at "best-available evidence" on comparative and non-comparative efficacy/effectiveness. We advocate for greater use of Bayesian methods to address these challenges as they can facilitate efficient and transparent borrowing of information across varied data sources under flexible modelling assumptions, probabilistic sensitivity analysis to assess model assumptions, and more nuanced decision-making where limited power reduces the utility of classical frequentist hypothesis testing. We illustrate how Bayesian methods have been recently used to overcome several challenges of rare indications in HEOR, including approaches to borrowing information from external data sources, evaluation of efficacy in basket trials, and incorporating non-randomized studies into network meta-analysis. Lastly, we provide several recommendations for HEOR practitioners on appropriate use of Bayesian methods to address challenges in the rare disease setting.

Statistical Considerations on the Use of RWD/RWE for Oncology Drug Approvals: Overview and Lessons Learned

Despite increasing utilization of real-world data (RWD)/real-world evidence (RWE) in regulatory submissions, their application to oncology drug approvals has seen limited success. Real-world data is most commonly summarized as a benchmark control for a single arm study or used to augment the concurrent control in a randomized clinical trial (RCT). While there has been substantial research on usage of RWD/RWE, our goal is to provide a comprehensive overview of their use in oncology drug approval submissions to inform future RWD/RWE study design. We will review examples of applications and summarize the strengths and weaknesses of each example identified by regulatory agencies. A few noteworthy case studies will be reviewed in detail. Operational aspects of RWD/RWE study design/analysis will be also discussed.

Comparison of Bayesian methods for incorporating adult clinical trial data to improve certainty of treatment effect estimates in children

There are challenges associated with recruiting children to take part in randomised clinical trials and as a result, compared to adults, in many disease areas we are less certain about which treatments are most safe and effective. This can lead to weaker recommendations about which treatments to prescribe in practice. However, it may be possible to 'borrow strength' from adult evidence to improve our understanding of which treatments work best in children, and many different statistical methods are available to conduct these analyses. In this paper we discuss four Bayesian methods for extrapolating adult clinical trial evidence to children. Using an exemplar dataset, we compare the effect of their modelling assumptions on the estimated treatment effect and associated heterogeneity. These modelling assumptions range from adult evidence being completely generalisable to being completely unrelated to the children's evidence. We finally discuss the appropriateness of these modelling assumptions in the context of estimating treatment effect in children.

Simulation study for evaluating an adaptive-randomisation Bayesian hybrid trial design with enrichment

Background

As we enter the era of precision medicine, the role of adaptive designs, such as response-adaptive randomisation or enrichment designs in drug discovery and development, has become increasingly important to identify the treatment given to a patient based on one or more biomarkers. Tailoring the ventilation supply technique according to the responsiveness of patients to positive end-expiratory pressure is a suitable setting for such a design.

Methods

In the setting of marker-strategy design, we propose a Bayesian response-adaptive randomisation with enrichment design based on group sequential analyses. This design combines the elements of enrichment design and response-adaptive randomisation. Concerning the enrichment strategy, Bayesian treatment-by-subset interaction measures were used to adaptively enrich the patients most likely to benefit from an experimental treatment while controlling the false-positive rate.The operating characteristics of the design were assessed by simulation and compared to those of alternate designs.

Results

The results obtained allowed the detection of the superiority of one treatment over another and the presence of a treatment-by-subgroup interaction while keeping the false-positive rate at approximately 5\% and reducing the average number of included patients. In addition, simulation studies identified that the number of interim analyses and the burn-in period may have an impact on the performance of the scheme.

Conclusion

The proposed design highlights important objectives of precision medicine, such as determining whether the experimental treatment is superior to another and identifying wheter such an efficacy could depend on patient profile.

Reverse-Bayes methods for evidence assessment and research synthesis

It is now widely accepted that the standard inferential toolkit used by the scientific research community—null‐hypothesis significance testing (NHST)—is not fit for purpose. Yet despite the threat posed to the scientific enterprise, there is no agreement concerning alternative approaches for evidence assessment. This lack of consensus reflects long‐standing issues concerning Bayesian methods, the principal alternative to NHST. We report on recent work that builds on an approach to inference put forward over 70 years ago to address the well‐known “Problem of Priors” in Bayesian analysis, by reversing the conventional prior‐likelihood‐posterior (“forward”) use of Bayes' theorem. Such Reverse‐Bayes analysis allows priors to be deduced from the likelihood by requiring that the posterior achieve a specified level of credibility. We summarise the technical underpinning of this approach, and show how it opens up new approaches to common inferential challenges, such as assessing the credibility of scientific findings, setting them in appropriate context, estimating the probability of successful replications, and extracting more insight from NHST while reducing the risk of misinterpretation. We argue that Reverse‐Bayes methods have a key role to play in making Bayesian methods more accessible and attractive for evidence assessment and research synthesis. As a running example we consider a recently published meta‐analysis from several randomised controlled trials (RCTs) investigating the association between corticosteroids and mortality in hospitalised patients with COVID‐19.

Frequentist rules for regulatory approval of subgroups in phase III trials: A fresh look at an old problem

Background

The number of Phase III trials that include a biomarker in design and analysis has increased due to interest in personalised medicine. For genetic mutations and other predictive biomarkers, the trial sample comprises two subgroups, one of which, say B+ is known or suspected to achieve a larger treatment effect than the other B−. Despite treatment effect heterogeneity, trials often draw patients from both subgroups, since the lower responding B− subgroup may also gain benefit from the intervention. In this case, regulators/commissioners must decide what constitutes sufficient evidence to approve the drug in the B− population.

Methods and Results

Assuming trial analysis can be completed using generalised linear models, we define and evaluate three frequentist decision rules for approval. For rule one, the significance of the average treatment effect in B− should exceed a pre-defined minimum value, say ZB−>L. For rule two, the data from the low-responding group B− should increase statistical significance. For rule three, the subgroup-treatment interaction should be non-significant, using type I error chosen to ensure that estimated difference between the two subgroup effects is acceptable. Rules are evaluated based on conditional power, given that there is an overall significant treatment effect. We show how different rules perform according to the distribution of patients across the two subgroups and when analyses include additional (stratification) covariates in the analysis, thereby conferring correlation between subgroup effects.

Conclusions

When additional conditions are required for approval of a new treatment in a lower response subgroup, easily applied rules based on minimum effect sizes and relaxed interaction tests are available. Choice of rule is influenced by the proportion of patients sampled from the two subgroups but less so by the correlation between subgroup effects.

Assessing efficacy in important subgroups in confirmatory trials: An example using Bayesian dynamic borrowing

Assessment of efficacy in important subgroups - such as those defined by sex, age, race and region - in confirmatory trials is typically performed using separate analysis of the specific subgroup. This ignores relevant information from the complementary subgroup. Bayesian dynamic borrowing uses an informative prior based on analysis of the complementary subgroup and a weak prior distribution centred on a mean of zero to construct a robust mixture prior. This combination of priors allows for dynamic borrowing of prior information; the analysis learns how much of the complementary subgroup prior information to borrow based on the consistency between the subgroup of interest and the complementary subgroup. A tipping point analysis can be carried out to identify how much prior weight needs to be placed on the complementary subgroup component of the robust mixture prior to establish efficacy in the subgroup of interest. An attractive feature of the tipping point analysis is that it enables the evidence from the source subgroup, the evidence from the target subgroup, and the combined evidence to be displayed alongside each other. This method is illustrated with an example trial in severe asthma where efficacy in the adolescent subgroup was assessed using a mixture prior combining an informative prior from the adult data in the same trial with a non-informative prior.