arXiv subjects

Pavel Mozgunov

Publications and source records attributed to Pavel Mozgunov.

At least 19 recordsLinked to original sources

arXiv2026

A Bayesian Phase I/II basket design with robust information borrowing to identify subtrial-specific optimal biological doses

The objective of modern early oncology dose-finding is to identify an optimal biological dose (OBD), rather than simply the maximum tolerated dose. In basket trials, the dose-toxicity and dose-efficacy relationships may differ across biomarker or disease-defined subtrials, so a single common dose from pooled analysis may be suboptimal. We propose a flexible exchangeability-non-exchangeability (EXNEX) dose finding design (DF-EXNEX design) for subtrial-specific OBD selection in basket phase I/II trials with binary toxicity and continuous efficacy endpoints. Patient toxicity is modelled by a monotone logistic regression and efficacy by a quadratic dose-response curve. Robust borrowing is introduced through extended EXNEX mixture priors on the subtrial-specific curve parameters, allowing the strength of borrowing to adapt to the similarity of subtrials. Dose recommendation is based on an admissible set defined by posterior safety and futility rules, and an OBD-oriented utility function combining toxicity and efficacy on comparable scales. The operating characteristics were evaluated in a large-scale simulation study for the basket trial with four subtrials and five dose levels, and 70 scenarios covering all non-redundant combinations of true subtrial-specific OBD locations. Results showed that, compared with a no-borrowing NEX design, the DF-EXNEX design can increase the correct OBD selection for most scenarios while reducing overly toxic recommendation as final OBD. The improvement increased with subtrial similarity due to robust information borrowing, but a small number of mixed low/high OBD scenarios showed negative or near-zero gains, consistent with occasional over-borrowing towards intermediate doses. These results support robust borrowing for subtrial-specific OBD finding while highlighting the need to monitor borrowing behaviour when true OBDs are widely separated.

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arXiv2026

On the interplay between prior weight and variance of the robustification component in Robust Mixture Prior Bayesian Dynamic Borrowing approach

Robust Mixture Prior (RMP) is a popular Bayesian dynamic borrowing method, which combines an informative historical distribution with a less informative component (referred as robustification component) in a mixture prior to enhance the efficiency of hybrid-control randomized trials. Current practice typically focuses solely on the selection of the prior weight that governs the relative influence of these two components, often fixing the variance of the robustification component to that of a single observation. In this study we demonstrate that the performance of RMPs critically depends on the joint selection of both weight and variance of the robustification component. In particular, we show that a wide range of weight-variance pairs can yield practically identical posterior inferences (in particular regions of the parameter space) and that large variance robust components may be employed without incurring in the so called Lindley's paradox. We further show that the use of large variance robustification components leads to improved asymptotic Type I error control and enhanced robustness of the RMP to the specification of the location parameter of the robustification component. Finally, we leverage these theoretical results to propose a novel and practical hyper-parameter elicitation routine.

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arXiv2026

A response-adaptive multi-arm design for continuous endpoints based on a weighted information measure

Multi-arm trials are gaining interest in practice given the statistical and logistical advantages they can offer. The standard approach uses a fixed allocation ratio, but there is a call for making it adaptive and skewing the allocation of patients towards better-performing arms. However, it is well-known that these approaches might suffer from lower statistical power. We present a response-adaptive design for continuous endpoints which explicitly allows to control the trade-off between the number of patients allocated to the "optimal" arm and the statistical power. Such a balance is achieved through the calibration of a tuning parameter, and we explore robust procedures to select it. The proposed criterion is based on a context-dependent information measure which gives greater weight to treatment arms with characteristics close to a pre-specified clinical target. We establish conditions under which the procedure consistently selects the target arm and derive the corresponding limiting allocation ratios. We also introduce a simulation-based hypothesis testing procedure which focuses on selecting the target arm and discuss strategies to effectively control the type-I error rate. The practical implementation of the proposed criterion and its potential advantage over currently used alternatives are illustrated in the context of early Phase IIa proof-of-concept oncology trials.

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arXiv2025

Sample size reassessment in Bayesian hybrid clinical trials

The use of historical controls offers a valuable alternative when traditional randomized controlled trials are not feasible. However, such approaches may introduce bias due to temporal changes in patient populations, diagnostic criteria, and/or treatment standards. Hybrid designs, which combine a concurrent control arm with historical control data, can help mitigate the possible bias. We propose a novel Bayesian two-arm randomized clinical trial design incorporating an interim analysis. At the interim analysis, a new criterion derived from the Hellinger distance is used to quantify the similarity between historical and concurrent control data outcomes. This measure informs both (1) the variance function of the control prior distribution in the final analysis and (2) the sample size reassessment for the second stage of the trial. The proposed approach is designed to accommodate both continuous and binary endpoints and is assessed through extensive simulation studies. Results demonstrate the method flexibility and robustness in adapting to varying degrees of historical-control heterogeneity.

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arXiv2025

U-DESPE: a Bayesian Utility-based methodology for dosing regimen optimization in early-phase oncology trials based on Dose-Exposure, Safety, Pharmacodynamics, Efficacy

With the development of novel therapies such as molecularly targeted agents and immunotherapy, the maximum tolerated dose paradigm that "more is better" does not necessarily hold anymore. In this context, doses and schedules of novel therapies may be inadequately characterized and oncology drug dose-finding approaches should be revised. This is increasingly recognized by health authorities, notably through the Optimus project. We developed a Bayesian dose-finding design, called U-DESPE, which allows to either determine the optimal dosing regimen at the end of the dose-escalation phase, or use of dedicated cohorts for randomizing patients to candidate optimal dosing regimens after that safe dosing regimens have been found. U-DESPE design relies on a dose-exposure model built from pharmacokinetic data using non-linear mixed-effect modeling approaches. Then three models are built to assess the relationships between exposure and the probability of selected relevant endpoints on safety, efficacy, and pharmacodynamics. These models are then combined to predict the different endpoints for every candidate dosing regimens. Finally, a utility function is proposed to quantify the trade-off between these endpoints and to determine the optimal dosing regimen. We applied the proposed method on a clinical trial case study and performed an extensive simulation study to evaluate the operating characteristics of the method.

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arXiv2025

Using joint models in phase I dose-finding designs in oncology: considerations for frequentist approaches

Dose-finding trials for oncology studies are traditionally designed to assess safety in the early stages of drug development. With the rise of molecularly targeted therapies and immuno-oncology compounds, biomarker-driven approaches have gained significant importance. In this paper, we propose a novel approach that incorporates multiple values of a predictive biomarker to assist in evaluating binary toxicity outcomes using the factorization of a joint model in phase I dose-finding oncology trials. The proposed joint model framework, which utilizes additional repeated biomarker values as an early predictive marker for potential toxicity, is compared to the likelihood-based continual reassessment method (CRM) using only binary toxicity data, across various dose-toxicity relationship scenarios. Our findings highlight a critical limitation of likelihood-based approaches in early-phase dose-finding studies with small sample sizes: estimation challenges that have been previously overlooked in the phase I dose-escalation setting. We explore potential remedies to address these challenges and emphasize the appropriate use of likelihood-based methods. Simulation results demonstrate that the proposed joint model framework, by integrating biomarker information, can alleviate estimation problems in the the likelihood-based continual reassessment method (CRM) and improve the proportion of correct selection. However, we highlight that the inherent data limitations in early-phase dose-finding studies remain a significant challenge that cannot fully be overcomed in the frequentist framework.

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arXiv2025

Robust Bayesian hierarchical models for basket trials enabling joint evaluation of toxicity and efficacy

Basket trials have gained increasing attention for their efficiency, as multiple patient subgroups are evaluated simultaneously. Conducted basket trials focus primarily on establishing the early efficacy of a treatment, yet continued monitoring of toxicity is essential. In this paper, we propose two Bayesian hierarchical models that enable bivariate analyses of toxicity and efficacy, while accounting for heterogeneity present in the treatment effects across patient subgroups. Specifically, one assumes the subgroup-specific toxicity and efficacy treatment effects, as a parameter vector, can be exchangeable or non-exchangeable; the other allows either the toxicity or efficacy parameters specific to the subgroups, to be exchangeable or non-exchangeable. The bivariate exchangeability and non-exchangeability distributions introduce a correlation parameter between treatment effects, while we stipulate a zero correlation when only toxicity or efficacy parameters are exchangeable. Simulation results show that our models perform robustly under different scenarios compared to the standard Bayesian hierarchical model and the stand-alone analyses, especially in producing higher power when the subgroup-specific effects are exchangeable in toxicity or efficacy only. When considerable correlation between the toxicity and efficacy effects exists, our methodology gives small error rates and greater power than alternatives that analyse toxicity and efficacy by parts.

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arXiv2025

Design specification of Partial Ordering Continual Reassessment Method based on consistency conditions

The study of combinations of drugs/drug-schedules gained increasing attention in various therapeutic areas recently. In oncology, the aim of phase I combination clinical trial is to find the maximum tolerated combination (MTC). Many innovative designs were proposed, among which the Partial Ordering Continual Reassessment Method (POCRM) is increasingly applied due to its simplicity and versatility. The POCRM requires specification of plausible monotonic orderings of combinations. However, the choice remains a major difficulty, especially in trials with many compounds or/and combinations. Practical recommendations are given to select six orderings based on statistical considerations, while simulation studies found the design performs poorly when the MTC is in the middle of the combination grid. We prove that the POCRM under currently recommended orderings can be inconsistent (i.e., cannot achieve 100\% correct selection even under infinite samples) which translates into poor performance under small sample size. Based on the derived consistency conditions, we provide two practical recommendations on how to select orderings for real studies (i) based on plausible combination-toxicity scenarios, (ii) regardless of the possible scenarios. We also provide guidance on how to choose other design parameters based on the asymptotic properties and demonstrate how it improves small sample behaviours.

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arXiv2025

A multi-arm multi-stage design for trials with all pairwise testing

Multi-arm multi-stage (MAMS) trials have gained popularity to enhance the efficiency of clinical trials, potentially reducing both duration and costs. This paper focuses on designing MAMS trials where no control treatment exists. This can arise when multiple standard treatments are already established or no treatment is available for a severe disease, making it unethical to withhold a potentially helpful option. The proposed design incorporates interim analyses to allow early termination of notably worst treatments and stops the trial entirely if all remaining treatments are performing similarly. The proposed design controls the familywise error rate (FWER) for all pairwise comparisons and provides the conditions guaranteeing FWER control in the strong sense. The FWER and power are used to calculate both the stopping boundaries and the sample size required. Analytic solutions to compute the expected sample size are also derived. A trial motivated by a study conducted in sepsis, where there was no control treatment, is shown. The multi-arm multi-stage all pairwise (MAMSAP) design proposed here is compared to multiple different approaches. For the trial studied, the proposed method yields the lowest required maximum and expected sample size when controlling the FWER and power at the desired levels.

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arXiv2024

Partial Ordering Bayesian Logistic Regression Model for Phase I Combination Trials and Computationally Efficient Approach to Operational Prior Specification

Recent years have seen increased interest in combining drug agents and/or schedules. Several methods for Phase I combination-escalation trials are proposed, among which, the partial ordering continual reassessment method (POCRM) gained great attention for its simplicity and good operational characteristics. However, the one-parameter nature of the POCRM makes it restrictive in more complicated settings such as the inclusion of a control group. This paper proposes a Bayesian partial ordering logistic model (POBLRM), which combines partial ordering and the more flexible (than CRM) two-parameter logistic model. Simulation studies show that the POBLRM performs similarly as the POCRM in non-randomised settings. When patients are randomised between the experimental dose-combinations and a control, performance is drastically improved. Most designs require specifying hyper-parameters, often chosen from statistical considerations (operational prior). The conventional "grid search'' calibration approach requires large simulations, which are computationally costly. A novel "cyclic calibration" has been proposed to reduce the computation from multiplicative to additive. Furthermore, calibration processes should consider wide ranges of scenarios of true toxicity probabilities to avoid bias. A method to reduce scenarios based on scenario-complexities is suggested. This can reduce the computation by more than 500 folds while remaining operational characteristics similar to the grid search.

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arXiv2024

How to Add Baskets to an Ongoing Basket Trial with Information Borrowing

Basket trials test a single therapeutic treatment on several patient populations under one master protocol. A desirable adaptive design feature in these studies may be the incorporation of new baskets to an ongoing study. Limited basket sample sizes can cause issues in power and precision of treatment effect estimates which could be amplified in added baskets due to the shortened recruitment time. While various Bayesian information borrowing techniques have been introduced to tackle the issue of small sample sizes, the impact of including new baskets in the trial and into the borrowing model has yet to be investigated. We explore approaches for adding baskets to an ongoing trial under information borrowing and highlight when it is beneficial to add a basket compared to running a separate investigation for new baskets. We also propose a novel calibration approach for the decision criteria that is more robust to false decision making. Simulation studies are conducted to assess the performance of approaches which is monitored primarily through type I error control and precision of estimates. Results display a substantial improvement in power for a new basket when information borrowing is utilized, however, this comes with potential inflation of error rates which can be shown to be reduced under the proposed calibration procedure.

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arXiv2024

Next generation clinical trials: Seamless designs and master protocols

Background: Drug development is often inefficient, costly and lengthy, yet it is essential for evaluating the safety and efficacy of new interventions. Compared with other disease areas, this is particularly true for Phase II / III cancer clinical trials where high attrition rates and reduced regulatory approvals are being seen. In response to these challenges, seamless clinical trials and master protocols have emerged to streamline the drug development process. Methods: Seamless clinical trials, characterized by their ability to transition seamlessly from one phase to another, can lead to accelerating the development of promising therapies while Master protocols provide a framework for investigating multiple treatment options and patient subgroups within a single trial. Results: We discuss the advantages of these methods through real trial examples and the principals that lead to their success while also acknowledging the associated regulatory considerations and challenges. Conclusion: Seamless designs and Master protocols have the potential to improve confirmatory clinical trials. In the disease area of cancer, this ultimately means that patients can receive life-saving treatments sooner.

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arXiv2024

Bayesian Model Averaging for Partial Ordering Continual Reassessment Methods

Phase I clinical trials are essential to bringing novel therapies from chemical development to widespread use. Traditional approaches to dose-finding in Phase I trials, such as the '3+3' method and the Continual Reassessment Method (CRM), provide a principled approach for escalating across dose levels. However, these methods lack the ability to incorporate uncertainty regarding the dose-toxicity ordering as found in combination drug trials. Under this setting, dose-levels vary across multiple drugs simultaneously, leading to multiple possible dose-toxicity orderings. The Partial Ordering CRM (POCRM) extends to these settings by allowing for multiple dose-toxicity orderings. In this work, it is shown that the POCRM is vulnerable to 'estimation incoherency' whereby toxicity estimates shift in an illogical way, threatening patient safety and undermining clinician trust in dose-finding models. To this end, the Bayesian model averaged POCRM (BMA-POCRM) is proposed. BMA-POCRM uses Bayesian model averaging to take into account all possible orderings simultaneously, reducing the frequency of estimation incoherencies. The effectiveness of BMA-POCRM in drug combination settings is demonstrated through a specific instance of estimate incoherency of POCRM and simulation studies. The results highlight the improved safety, accuracy and reduced occurrence of estimate incoherency in trials applying the BMA-POCRM relative to the POCRM model.

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arXiv2023

Design of platform trials with a change in the control treatment arm

Platform trials are a more efficient way of testing multiple treatments compared to running separate trials. In this paper we consider platform trials where, if a treatment is found to be superior to the control, it will become the new standard of care (and the control in the platform). The remaining treatments are then tested against this new control. In such a setting, one can either keep the information on both the new standard of care and the other active treatments before the control is changed or one could discard this information when testing for benefit of the remaining treatments. We will show analytically and numerically that retaining the information collected before the change in control can be detrimental to the power of the study. Specifically, we consider the overall power, the probability that the active treatment with the greatest treatment effect is found during the trial. We also consider the conditional power of the active treatments, the probability a given treatment can be found superior against the current control. We prove when, in a multi-arm multi-stage trial where no arms are added, retaining the information is detrimental to both overall and conditional power of the remaining treatments. This loss of power is studied for a motivating example. We then discuss the effect on platform trials in which arms are added later. On the basis of these observations we discuss different aspects to consider when deciding whether to run a continuous platform trial or whether one may be better running a new trial.

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arXiv2023

A preplanned multi-stage platform trial for discovering multiple superior treatments with control of FWER and power

There is a growing interest in the implementation of platform trials, which provide the flexibility to incorporate new treatment arms during the trial and the ability to halt treatments early based on lack of benefit or observed superiority. In such trials, it can be important to ensure that error rates are controlled. This paper introduces a multi-stage design that enables the addition of new treatment arms, at any point, in a pre-planned manner within a platform trial, while still maintaining control over the family-wise error rate. This paper focuses on finding the required sample size to achieve a desired level of statistical power when treatments are continued to be tested even after a superior treatment has already been found. This may be of interest if there are other sponsors treatments which are also superior to the current control or multiple doses being tested. The calculations to determine the expected sample size is given. A motivating trial is presented in which the sample size of different configurations is studied. Additionally the approach is compared to running multiple separate trials and it is shown that in many scenarios if family wise error rate control is needed there may not be benefit in using a platform trial when comparing the sample size of the trial.

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arXiv2022

Bayesian sample size determination in basket trials borrowing information between subsets

Basket trials are increasingly used for the simultaneous evaluation of a new treatment in various patient subgroups under one overarching protocol. We propose a Bayesian approach to sample size determination in basket trials that permit borrowing of information between commensurate subsets. Specifically, we consider a randomised basket trial design where patients are randomly assigned to the new treatment or a control within each trial subset (`subtrial' for short). Closed-form sample size formulae are derived to ensure each subtrial has a specified chance of correctly deciding whether the new treatment is superior to or not better than the control by some clinically relevant difference. Given pre-specified levels of pairwise (in)commensurability, the subtrial sample sizes are solved simultaneously. The proposed Bayesian approach resembles the frequentist formulation of the problem in yielding comparable sample sizes for circumstances of no borrowing. When borrowing is enabled between commensurate subtrials, a considerably smaller trial sample size is required compared to the widely implemented approach of no borrowing. We illustrate the use of our sample size formulae with two examples based on real basket trials. A comprehensive simulation study further shows that the proposed methodology can maintain the true positive and false positive rates at desired levels.

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arXiv2021

A multi-arm multi-stage platform design that allows pre-planned addition of arms while still controlling the family-wise error

There is growing interest in platform trials that allow for adding of new treatment arms as the trial progresses as well as being able to stop treatments part way through the trial for either lack of benefit/futility or for superiority. In some situations, platform trials need to guarantee that error rates are controlled. This paper presents a multi-stage design that allows additional arms to be added in a platform trial in a pre-planned fashion, while still controlling the family wise error rate. A method is given to compute the sample size required to achieve a desired level of power and we show how the distribution of the sample size and the expected sample size can be found. A motivating trial is presented which focuses on two settings, with the first being a set number of stages per active treatment arm and the second being a set total number of stages, with treatments that are added later getting fewer stages. Through this example we show that the proposed method results in a smaller sample size while still controlling the errors compared to running multiple separate trials.

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arXiv2021

A Comparison of Various Aggregation Functions in Multi-Criteria Decision Analysis for Drug Benefit-Risk Assessment

Multi-criteria decision analysis (MCDA) is a quantitative approach to the drug benefit-risk assessment (BRA) which allows for consistent comparisons by summarising all benefits and risks in a single score. The MCDA consists of several components, one of which is the utility (or loss) score function that defines how benefits and risks are aggregated into a single quantity. While a linear utility score is one of the most widely used approach in BRA, it is recognised that it can result in counter-intuitive decisions, for example, recommending a treatment with extremely low benefits or high risks. To overcome this problem, alternative approaches to the scores construction, namely, product, multi-linear and Scale Loss Score models, were suggested. However, to date, the majority of arguments concerning the differences implied by these models are heuristic. In this work, we consider four models to calculate the aggregated utility/loss scores and compared their performance in an extensive simulation study over many different scenarios, and in a case study. It is found that the product and Scale Loss Score models provide more intuitive treatment recommendation decisions in the majority of scenarios compared to the linear and multi-linear models, and are more robust to the correlation in the criteria.

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