Dynamic borrowing in the presence of treatment effect heterogeneity

Approximate Bayesian Analysis for Borrowing External Controls for Randomized Controlled Trials With Dynamic Borrowing and Covariate Balancing Adjustment

Borrowing controls from external sources has become popular for augmenting the control arm in small randomized controlled trials (RCTs). Due to the difference between the external and RCT populations, bias can be introduced that may lead to invalid statistical inference based on combined data. To mitigate this risk, dynamic borrowing which adaptively determines the amount of borrowing, can be used together with pre-adjustment for prognostic factors in the external data. To take into account the variability due to the estimation of the amount of borrowing and the pre-adjustment, we propose a Bayesian bootstrap (BB)-based integrated Bayesian approach together with covariate balancing (CB) for pre-adjustment. We show that the proposed BB based approach is a valid approximate Bayesian approach with CB using different distances, particularly Euclidean or entropy distance. This justification is not trivial because CB has a different nature from the probability-based approach. We also propose a BB-algorithm for generating an approximate posterior sample, which is easy to implement and computationally efficient. Statistical inference for estimand of interest using combined external and internal data can be based on the bootstrapped posterior sample or on an approximate normal distribution with parameters estimated by BB. To examine the properties of the proposed approach, we conduct an extensive simulation study. The approach is illustrated by borrowing controls for an acute myeloid leukemia trial from another study.

Comparison of Borrowing Methods for Incorporating Historical Data in Single-Arm Phase II Clinical Trials

BACKGROUND

Over the last few years, many efforts have been made to leverage historical information in clinical trials. Incorporating historical data into current trials allows for a more efficient design, smaller studies, or shorter duration and may potentially increase the relative amount of information on efficacy and safety. Despite these advantages, it is crucial to select external data sources appropriately to avoid introducing potential bias into the new study. This is where borrowing methods become useful. We illustrate and compare the latest methods of borrowing historical data in a single-arm phase II clinical trial setting, examining their impact on statistical power and type I error.

METHODS

We implemented static and dynamic versions of the power prior method, incorporating overlapping coefficient and loss functions and meta-analytic predictive priors. These methods were compared with standard and pooling approaches, in which none or all historical data are used.

RESULTS

Dynamic borrowing methods achieve lower type I error inflation than pooling. The power prior approach, integrated with overlapping coefficient, allowed for measuring the similarity of the subjects considering their baseline characteristics, thus the likelihood of the data contains information about both confounders and outcome. Using a discounting function to estimate the power parameter guarantees the similarity of historical information and current trial data.

CONCLUSION

We provided a comprehensive overview of borrowing methods, encompassing frequentist and Bayesian approaches as well as static and dynamic technique, to guide researchers in selecting the most appropriate strategy.

Pharmacometrics-Enhanced Bayesian Borrowing for Pediatric Extrapolation - A Case Study of the DINAMO Trial

Bayesian borrowing analyses have an important role in the design and analysis of pediatric trials. This paper describes use of a prespecified Pharmacometrics Enhanced Bayesian Borrowing (PEBB) analysis that was conducted to overcome an expectation for reduced statistical power in the pediatric DINAMO trial due to a greater than expected variability in the primary endpoint. The DINAMO trial assessed the efficacy and safety of an empagliflozin dosing regimen versus placebo and linagliptin versus placebo on glycemic control (change in HbA1c over 26 weeks) in young people with type 2 diabetes (T2D). Previously fitted pharmacokinetic and exposure-response models for empagliflozin and linagliptin based on available historical data in adult and pediatric patients with T2D were used to simulate participant data and derive the informative component of a Bayesian robust mixture prior distribution. External experts and representatives from the U.S. Food and Drug Administration provided recommendations to determine the effective sample size of the prior and the weight of the informative prior component. Separate exposure response-based Bayesian borrowing analyses for empagliflozin and linagliptin showed posterior mean and 95% credible intervals that were consistent with the trial results. Sensitivity analyses with a full range of alternative weights were also performed. The use of PEBB in this analysis combined advantages of mechanistic modeling of pharmacometric differences between adults and young people with T2D, with advantages of partial extrapolation through Bayesian dynamic borrowing. Our findings suggest that the described PEBB approach is a promising option to optimize the power for future pediatric trials.

Reinforced Borrowing Framework: Leveraging Auxiliary Data for Individualized Inference

Increasingly during the past decade, researchers have sought to leverage auxiliary data for enhancing individualized inference. Many existing methods, such as multisource exchangeability models (MEM), have been developed to borrow information from multiple supplemental sources to support parameter inference in a primary source. MEM and its alternatives decide how much information to borrow based on the exchangeability of the primary and supplemental sources, where exchangeability is defined as equality of the target parameter. Other information that may also help determine the exchangeability of sources is ignored. In this article, we propose a generalized reinforced borrowing framework (RBF) leveraging auxiliary data for enhancing individualized inference using a distance-embedded prior which uses data not only about the target parameter but also uses different types of auxiliary information sources to "reinforce" inference on the target parameter. RBF improves inference with minimal additional computational burden. We demonstrate the application of RBF to a study investigating the impact of the COVID-19 pandemic on individual activity and transportation behaviors, where RBF achieves 20%-40% lower MSE compared with existing methods.

Leveraging information from secondary endpoints to enhance dynamic borrowing across subpopulations

Randomized trials seek efficient treatment effect estimation within target populations, yet scientific interest often also centers on subpopulations. Although there are typically too few subjects within each subpopulation to efficiently estimate these subpopulation treatment effects, one can gain precision by borrowing strength across subpopulations, as is the case in a basket trial. While dynamic borrowing has been proposed as an efficient approach to estimating subpopulation treatment effects on primary endpoints, additional efficiency could be gained by leveraging the information found in secondary endpoints. We propose a multisource exchangeability model (MEM) that incorporates secondary endpoints to more efficiently assess subpopulation exchangeability. Across simulation studies, our proposed model almost uniformly reduces the mean squared error when compared to the standard MEM that only considers data from the primary endpoint by gaining efficiency when subpopulations respond similarly to the treatment and reducing the magnitude of bias when the subpopulations are heterogeneous. We illustrate our model's feasibility using data from a recently completed trial of very low nicotine content cigarettes to estimate the effect on abstinence from smoking within three priority subpopulations. Our proposed model led to increases in the effective sample size two to four times greater than under the standard MEM.

Semi-supervised mixture multi-source exchangeability model for leveraging real-world data in clinical trials

The traditional trial paradigm is often criticized as being slow, inefficient, and costly. Statistical approaches that leverage external trial data have emerged to make trials more efficient by augmenting the sample size. However, these approaches assume that external data are from previously conducted trials, leaving a rich source of untapped real-world data (RWD) that cannot yet be effectively leveraged. We propose a semi-supervised mixture (SS-MIX) multisource exchangeability model (MEM); a flexible, two-step Bayesian approach for incorporating RWD into randomized controlled trial analyses. The first step is a SS-MIX model on a modified propensity score and the second step is a MEM. The first step targets a representative subgroup of individuals from the trial population and the second step avoids borrowing when there are substantial differences in outcomes among the trial sample and the representative observational sample. When comparing the proposed approach to competing borrowing approaches in a simulation study, we find that our approach borrows efficiently when the trial and RWD are consistent, while mitigating bias when the trial and external data differ on either measured or unmeasured covariates. We illustrate the proposed approach with an application to a randomized controlled trial investigating intravenous hyperimmune immunoglobulin in hospitalized patients with influenza, while leveraging data from an external observational study to supplement a subgroup analysis by influenza subtype.

Dynamic enrichment of Bayesian small-sample, sequential, multiple assignment randomized trial design using natural history data: a case study from Duchenne muscular dystrophy

In Duchenne muscular dystrophy (DMD) and other rare diseases, recruiting patients into clinical trials is challenging. Additionally, assigning patients to long-term, multi-year placebo arms raises ethical and trial retention concerns. This poses a significant challenge to the traditional sequential drug development paradigm. In this paper, we propose a small-sample, sequential, multiple assignment, randomized trial (snSMART) design that combines dose selection and confirmatory assessment into a single trial. This multi-stage design evaluates the effects of multiple doses of a promising drug and re-randomizes patients to appropriate dose levels based on their Stage 1 dose and response. Our proposed approach increases the efficiency of treatment effect estimates by (i) enriching the placebo arm with external control data, and (ii) using data from all stages. Data from external control and different stages are combined using a robust meta-analytic combined (MAC) approach to consider the various sources of heterogeneity and potential selection bias. We reanalyze data from a DMD trial using the proposed method and external control data from the Duchenne Natural History Study (DNHS). Our method's estimators show improved efficiency compared to the original trial. Also, the robust MAC-snSMART method most often provides more accurate estimators than the traditional analytic method. Overall, the proposed methodology provides a promising candidate for efficient drug development in DMD and other rare diseases.

Dynamic use of historical controls in clinical trials for rare disease research: A re-evaluation of the MILES trial

BACKGROUND

Randomized controlled trials offer the best design for eliminating bias in estimating treatment effects but can be slow and costly in rare disease research. Additionally, an equal randomization approach may not be optimal in studies in which prior evidence of superiority of one or more treatments exist. Supplementing prospectively enrolled, concurrent controls with historical controls can reduce recruitment requirements and provide patients a higher likelihood of enrolling in a new and possibly superior treatment arm. Appropriate methods need to be employed to ensure comparability of concurrent and historical controls to minimize bias and variability in the treatment effect estimates and reduce the chances of drawing incorrect conclusions regarding treatment benefit.

METHODS

MILES was a phase III placebo-controlled trial employing 1:1 randomization that led to US Food and Drug Administration approval of sirolimus for treating patients with lymphangioleiomyomatosis. We re-analyzed the MILES trial data to learn whether substituting concurrent controls with controls from a historical registry could have accelerated subject enrollment while leading to similar study conclusions. We used propensity score matching to identify exchangeable historical controls from a registry balancing the baseline characteristics of the two control groups. This allowed more new patients to be assigned to the sirolimus arm. We used trial data and simulations to estimate key outcomes under an array of alternative designs.

RESULTS

Borrowing information from historical controls would have allowed the trial to enroll fewer concurrent controls while leading to the same conclusion reached in the trial. Simulations showed similar statistical performance for borrowing as for the actual trial design without producing type I error inflation and preserving power for the same study size when concurrent and historical controls are comparable.

CONCLUSION

Substituting concurrent controls with propensity score-matched historical controls can allow more prospectively enrolled patients to be assigned to the active treatment and enable the trial to be conducted with smaller overall sample size, while maintaining covariate balance and study power and minimizing bias in response estimation. This approach does not fully eliminate the concern that introducing non-randomized historical controls in a trial may lead to bias in estimating treatment effects, and should be carefully considered on a case-by-case basis. Borrowing historical controls is best suited when conducting randomized controlled trials with conventional designs is challenging, as in rare disease research. High-quality data on covariates and outcomes must be available for candidate historical controls to ensure the validity of these designs. Additional precautions are needed to maintain blinding of the treatment assignment and to ensure comparability in the assessment of treatment safety.MILES ClinicalTrials.gov Number: NCT00414648.

Bayesian Statistics for Medical Devices: Progress Since 2010

The use of Bayesian statistics to support regulatory evaluation of medical devices began in the late 1990s. We review the literature, focusing on recent developments of Bayesian methods, including hierarchical modeling of studies and subgroups, borrowing strength from prior data, effective sample size, Bayesian adaptive designs, pediatric extrapolation, benefit-risk decision analysis, use of real-world evidence, and diagnostic device evaluation. We illustrate how these developments were utilized in recent medical device evaluations. In Supplementary Material, we provide a list of medical devices for which Bayesian statistics were used to support approval by the US Food and Drug Administration (FDA), including those since 2010, the year the FDA published their guidance on Bayesian statistics for medical devices. We conclude with a discussion of current and future challenges and opportunities for Bayesian statistics, including artificial intelligence/machine learning (AI/ML) Bayesian modeling, uncertainty quantification, Bayesian approaches using propensity scores, and computational challenges for high dimensional data and models.

Comparative Study of Bayesian Information Borrowing Methods in Oncology Clinical Trials

With deeper insight into precision medicine, more innovative oncology trial designs have been proposed to contribute to the characteristics of novel antitumor drugs. Bayesian information borrowing is an indispensable part of these designs, which shows great advantages in improving the efficiency of clinical trials. Bayesian methods provide an effective framework when incorporating information. However, the key point lies in how to choose an appropriate method for complex oncology clinical trials.

A group-sequential randomized trial design utilizing supplemental trial data

Definitive clinical trials are resource intensive, often requiring a large number of participants over several years. One approach to improve the efficiency of clinical trials is to incorporate historical information into the primary trial analysis. This approach has tremendous potential in the areas of pediatric or rare disease trials, where achieving reasonable power is difficult. In this article, we introduce a novel Bayesian group-sequential trial design based on Multisource Exchangeability Models, which allows for dynamic borrowing of historical information at the interim analyses. Our approach achieves synergy between group sequential and adaptive borrowing methodology to attain improved power and reduced sample size. We explore the frequentist operating characteristics of our design through simulation and compare our method to a traditional group-sequential design. Our method achieves earlier stopping of the primary study while increasing power under the alternative hypothesis but has a potential for type I error inflation under some null scenarios. We discuss the issues of decision boundary determination, power and sample size calculations, and the issue of information accrual. We present our method for a continuous and binary outcome, as well as in a linear regression setting.

A flexible Bayesian framework for individualized inference via adaptive borrowing

The explosion in high-resolution data capture technologies in health has increased interest in making inferences about individual-level parameters. While technology may provide substantial data on a single individual, how best to use multisource population data to improve individualized inference remains an open research question. One possible approach, the multisource exchangeability model (MEM), is a Bayesian method for integrating data from supplementary sources into the analysis of a primary source. MEM was originally developed to improve inference for a single study by asymmetrically borrowing information from a set of similar previous studies and was further developed to apply a more computationally intensive symmetric borrowing in the context of basket trial; however, even for asymmetric borrowing, its computational burden grows exponentially with the number of supplementary sources, making it unsuitable for applications where hundreds or thousands of supplementary sources (i.e., individuals) could contribute to inference on a given individual. In this article, we propose the data-driven MEM (dMEM), a two-stage approach that includes both source selection and clustering to enable the inclusion of an arbitrary number of sources to contribute to individualized inference in a computationally tractable and data-efficient way. We illustrate the application of dMEM to individual-level human behavior and mental well-being data collected via smartphones, where our approach increases individual-level estimation precision by 84% compared with a standard no-borrowing method and outperforms recently proposed competing methods in 80% of individuals.