arXiv subjects

Lara Maleyeff

Publications and source records attributed to Lara Maleyeff.

6 recordsLinked to original sources

arXiv2026

A Bayesian adaptive enrichment design using aggregate historical data to inform individualized treatment recommendations

Adaptive enrichment trials aim to identify and recruit participants most likely to benefit from treatment based on evolving biomarker evidence, with the goal of informing individualized treatment recommendations. Bayesian methods are well suited to these designs because they allow external information to be incorporated in a principled manner. In practice, prior studies often provide only summary-level information, with subgroup-specific estimates unavailable due to design or privacy constraints. Existing dynamic borrowing approaches therefore rely on aggregate measures, such as the average treatment effect, and implicitly assume that historical information maps directly onto model parameters. In adaptive enrichment settings aimed at identifying individualized treatment effects, however, subgroup-specific treatment parameters are not identifiable when only marginal historical effects are available. To address this gap, we propose a Bayesian adaptive enrichment design that borrows information from external studies using a normalized power prior anchored on one or more summary measures, such as the average treatment effect. { To our knowledge, no existing method addresses this gap.} Interim analyses use posterior probabilities to guide early stopping for efficacy or futility, or to continue recruitment within promising biomarker-defined subgroups. Simulation studies evaluate operating characteristics across historical bias, sample size, and prior informativeness. Together with a motivating future trial in obstructive sleep apnea, the results show efficiency gains versus non-borrowing designs, including improved power, earlier stopping, and reduced expected sample size.

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arXiv2025

An Efficient Approach to Design Bayesian Platform Trials

Platform trials evaluate multiple experimental treatments against a common control group (and/or against each other), which often reduces the trial duration and sample size. Bayesian platform designs offer several practical advantages, including the flexible addition or removal of experimental arms using posterior probabilities and the incorporation of prior/external information. Regulatory agencies require that the operating characteristics of Bayesian designs are assessed by estimating the sampling distribution of posterior probabilities via Monte Carlo simulation. It is computationally intensive to repeat this simulation process for all design configurations considered, particularly for platform trials with complex interim decision procedures. In this paper, we propose an efficient method to assess operating characteristics and determine sample sizes as well as other design parameters for Bayesian platform trials. We prove theoretical results that allow us to model the joint sampling distribution of posterior probabilities across multiple endpoints and trial stages using simulations conducted at only two sample sizes. This work is motivated by design complexities in the SSTARLET trial, an ongoing Bayesian adaptive platform trial for tuberculosis preventive therapies (ClinicalTrials.gov ID: NCT06498414). Our proposed design method is not only computationally efficient but also capable of accommodating intricate, real-world trial constraints like those encountered in SSTARLET.

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arXiv2025

The efficiencies of pilot feasibility trials in rare diseases using Bayesian methods

Pilot feasibility studies play a pivotal role in the development of clinical trials for rare diseases, where small populations and slow recruitment often threaten trial viability. While such studies are commonly used to assess operational parameters, they also offer a valuable opportunity to inform the design and analysis of subsequent definitive trials-particularly through the use of Bayesian methods. In this paper, we demonstrate how data from a single, protocol-aligned pilot study can be incorporated into a definitive trial using robust meta-analytic-predictive priors. We focus on the case of a binary efficacy outcome, motivated by a feasibility trial of intravenous immunoglobulin tapering in autoimmune inflammatory myopathies. Through simulation studies, we evaluate the operating characteristics of trials informed by pilot data, including sample size, expected trial duration, and the probability of meeting recruitment targets. Our findings highlight the operational and ethical advantages of leveraging pilot data via robust Bayesian priors, and offer practical guidance for their application in rare disease settings.

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arXiv2025

A Precision Trial Case Study for Heterogeneous Treatment Effects in Obstructive Sleep Apnea

Precision medicine tailors treatments to individual patient characteristics, which is especially valuable for conditions like obstructive sleep apnea (OSA), where treatment responses vary widely. Traditional trials often overlook subgroup differences, leading to suboptimal recommendations. Current approaches rely on pre-specified thresholds with inherent uncertainty, assuming these thresholds are correct-a flawed assumption. This case study compares pre-specified thresholds to two advanced Bayesian methods: the established FK-BMA method and its novel variant, FK. The FK approach retains the flexibility of free-knot splines but omits variable selection, providing stable, interpretable models. Using biomarker data from large studies, this design identifies subgroups dynamically, allowing early trial termination or enrollment adjustments. Simulations in this specific context show FK improves precision, efficiency, and subgroup detection, offering practical benefits over FK-BMA and advancing precision medicine for OSA.

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arXiv2025

fkbma: An R Package for Detecting Tailoring Variables with Free-Knot B-Splines and Bayesian Model Averaging

Precision medicine aims to optimize treatment by identifying patient subgroups most likely to benefit from specific interventions. To support this goal, we introduce fkbma, an R package that implements a Bayesian model averaging approach with free-knot B-splines for identifying tailoring variables. The package employs a reversible jump Markov chain Monte Carlo algorithm to flexibly model treatment effect heterogeneity while accounting for uncertainty in both variable selection and non-linear relationships. fkbma provides a comprehensive framework for detecting predictive biomarkers, integrating Bayesian adaptive enrichment strategies, and enabling robust subgroup identification in clinical trials and observational studies. This paper details the statistical methodology underlying fkbma, outlines its computational implementation, and demonstrates its application through simulations and real-world examples. The package's flexibility makes it a valuable tool for precision medicine research, offering a principled approach to treatment personalization.

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arXiv2024

An adaptive enrichment design using Bayesian model averaging for selection and threshold-identification of tailoring variables

Precision medicine stands as a transformative approach in healthcare, offering tailored treatments that can enhance patient outcomes and reduce healthcare costs. As understanding of complex disease improves, clinical trials are being designed to detect subgroups of patients with enhanced treatment effects. Biomarker-driven adaptive enrichment designs, which enroll a general population initially and later restrict accrual to treatment-sensitive patients, are gaining popularity. Current practice often assumes either pre-trial knowledge of biomarkers defining treatment-sensitive subpopulations or a simple, linear relationship between continuous markers and treatment effectiveness. Motivated by a trial studying rheumatoid arthritis treatment, we propose a Bayesian adaptive enrichment design which identifies important tailoring variables out of a larger set of candidate biomarkers. Our proposed design is equipped with a flexible modelling framework where the effects of continuous biomarkers are introduced using free knot B-splines. The parameters of interest are then estimated by marginalizing over the space of all possible variable combinations using Bayesian model averaging. At interim analyses, we assess whether a biomarker-defined subgroup has enhanced or reduced treatment effects, allowing for early termination due to efficacy or futility and restricting future enrollment to treatment-sensitive patients. We consider pre-categorized and continuous biomarkers, the latter of which may have complex, nonlinear relationships to the outcome and treatment effect. Using simulations, we derive the operating characteristics of our design and compare its performance to two existing approaches.

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