arXiv subjects

Haiyan Zheng

Publications and source records attributed to Haiyan Zheng.

14 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.

stat.AP↗

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.

stat.AP↗

arXiv2025

A Bayesian Treatment Selection Design for Phase II Randomised Cancer Clinical Trials

It is crucial to design Phase II cancer clinical trials that balance the efficiency of treatment selection with clinical practicality. Sargent and Goldberg proposed a frequentist design that allow decision-making even when the primary endpoint is ambiguous. However, frequentist approaches rely on fixed thresholds and long-run frequency properties, which can limit flexibility in practical applications. In contrast, the Bayesian decision rule, based on posterior probabilities, enables transparent decision-making by incorporating prior knowledge and updating beliefs with new data, addressing some of the inherent limitations of frequentist designs. In this study, we propose a novel Bayesian design, allowing selection of a best-performing treatment. Specifically, concerning phase II clinical trials with a binary outcome, our decision rule employs posterior interval probability by integrating the joint distribution over all values, for which the 'success rate' of the bester-performing treatment is greater than that of the other(s). This design can then determine which a treatment should proceed to the next phase, given predefined decision thresholds. Furthermore, we propose two sample size determination methods to empower such treatment selection designs implemented in a Bayesian framework. Through simulation studies and real-data applications, we demonstrate how this approach can overcome challenges related to sample size constraints in randomised trials. In addition, we present a user-friendly R Shiny application, enabling clinicians to Bayesian designs. Both our methodology and the software application can advance the design and analysis of clinical trials for evaluating cancer treatments.

stat.ME↗

arXiv2024

Bayesian sample size determination using robust commensurate priors with interpretable discrepancy weights

Randomized controlled clinical trials provide the gold standard for evidence generation in relation to the efficacy of a new treatment in medical research. Relevant information from previous studies may be desirable to incorporate in the design and analysis of a new trial, with the Bayesian paradigm providing a coherent framework to formally incorporate prior knowledge. Many established methods involve the use of a discounting factor, sometimes related to a measure of `similarity' between historical and the new trials. However, it is often the case that the sample size is highly nonlinear in those discounting factors. This hinders communication with subject-matter experts to elicit sensible values for borrowing strength at the trial design stage. Focusing on a commensurate predictive prior method that can incorporate historical data from multiple sources, we highlight a particular issue of nonmonotonicity and explain why this causes issues with interpretability of the discounting factors (hereafter referred to as `weights'). We propose a solution for this, from which an analytical sample size formula is derived. We then propose a linearization technique such that the sample size changes uniformly over the weights. Our approach leads to interpretable weights that represent the probability that historical data are (ir)relevant to the new trial, and could therefore facilitate easier elicitation of expert opinion on their values. Keywords: Bayesian sample size determination; Commensurate priors; Historical borrowing; Prior aggregation; Uniform shrinkage.

stat.ME↗

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.

stat.ME↗

arXiv2023

A Bayesian adaptive design for dual-agent phase I-II oncology trials integrating efficacy data across stages

Combination of several anti-cancer treatments has typically been presumed to have enhanced drug activity. Motivated by a real clinical trial, this paper considers phase I-II dose finding designs for dual-agent combinations, where one main objective is to characterize both the toxicity and efficacy profiles. We propose a two-stage Bayesian adaptive design that accommodates a change of patient population in-between. In stage I, we estimate a maximum tolerated dose combination using the escalation with overdose control (EWOC) principle. This is followed by a stage II, conducted in a new yet relevant patient population, to find the most efficacious dose combination. We implement a robust Bayesian hierarchical random-effects model to allow sharing of information on the efficacy across stages, assuming that the related parameters are either exchangeable or nonexchangeable. Under the assumption of exchangeability, a random-effects distribution is specified for the main effects parameters to capture uncertainty about the between-stage differences. The inclusion of nonexchangeability assumption further enables that the stage-specific efficacy parameters have their own priors. The proposed methodology is assessed with an extensive simulation study. Our results suggest a general improvement of the operating characteristics for the efficacy assessment, under a conservative assumption about the exchangeability of the parameters \textit{a priori}

stat.ME↗

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.

stat.ME↗

arXiv2020

Bayesian sample size determination using commensurate priors to leverage pre-experimental data

This paper develops Bayesian sample size formulae for experiments comparing two groups. We assume the experimental data will be analysed in the Bayesian framework, where pre-experimental information from multiple sources can be represented into robust priors. In particular, such robust priors account for preliminary belief about the pairwise commensurability between parameters that underpin the historical and new experiments, to permit flexible borrowing of information. Averaged over the probability space of the new experimental data, appropriate sample sizes are found according to criteria that control certain aspects of the posterior distribution, such as the coverage probability or length of a defined density region. Our Bayesian methodology can be applied to circumstances where the common variance in the new experiment is known or unknown. Exact solutions are available based on most of the criteria considered for Bayesian sample size determination, while a search procedure is described in cases for which there are no closed-form expressions. We illustrate the application of our Bayesian sample size formulae in the setting of designing a clinical trial. Hypothetical data examples, motivated by a rare-disease trial with elicitation of expert prior opinion, and a comprehensive performance evaluation of the proposed methodology are presented.

stat.ME↗

arXiv2020

Borrowing of information across patient subgroups in a basket trial based on distributional discrepancy

Basket trials have emerged as a new class of efficient approaches in oncology to evaluate a new treatment in several patient subgroups simultaneously. In this paper, we extend the key ideas to disease areas outside of oncology, developing a robust Bayesian methodology for randomised, placebo-controlled basket trials with a continuous endpoint to enable borrowing of information across subtrials with similar treatment effects. After adjusting for covariates, information from a complementary subtrial can be represented into a commensurate prior for the parameter that underpins the subtrial under consideration. We propose using distributional discrepancy to characterise the commensurability between subtrials for appropriate borrowing of information through a spike-and-slab prior, which is placed on the prior precision factor. When the basket trial has at least three subtrials, commensurate priors for point-to-point borrowing are combined into a marginal predictive prior, according to the weights transformed from the pairwise discrepancy measures. In this way, only information from subtrial(s) with the most commensurate treatment effect is leveraged. The marginal predictive prior is updated to a robust posterior by the contemporary subtrial data to inform decision making. Operating characteristics of the proposed methodology are evaluated through simulations motivated by a real basket trial in chronic diseases. The proposed methodology has advantages compared to other selected Bayesian analysis models, for (i) identifying the most commensurate source of information, and (ii) gauging the degree of borrowing from specific subtrials. Numerical results also suggest that our methodology can improve the precision of estimates and, potentially, the statistical power for hypothesis testing.

stat.ME↗

arXiv2020

A Bayesian decision-theoretic approach to incorporate preclinical information into phase I oncology trials

Leveraging preclinical animal data for a phase I first-in-man trial is appealing yet challenging. A prior based on animal data may place large probability mass on values of the dose-toxicity model parameter(s), which appear infeasible in light of data accrued from the ongoing phase I clinical trial. In this paper, we seek to use animal data to improve decision making in a model-based dose-escalation procedure for phase I oncology trials. Specifically, animal data are incorporated via a robust mixture prior for the parameters of the dose-toxicity relationship. This prior changes dynamically as the trial progresses. After completion of treatment for each cohort, the weight allocated to the informative component, obtained based on animal data alone, is updated using a decision-theoretic approach to assess the commensurability of the animal data with the human toxicity data observed thus far. In particular, we measure commensurability as a function of the utility of optimal prior predictions for the human responses (toxicity or no toxicity) on each administered dose. The proposed methodology is illustrated through several examples and an extensive simulation study. Results show that our proposal can address difficulties in coping with prior-data conflict commencing in sequential trials with a small sample size.

stat.AP↗

arXiv2019

A Bayesian hierarchical model for bridging across patient subgroups in phase I clinical trials with animal data

Incorporating preclinical animal data, which can be regarded as a special kind of historical data, into phase I clinical trials can improve decision making when very little about human toxicity is known. In this paper, we develop a robust hierarchical modelling approach to leverage animal data into new phase I clinical trials, where we bridge across non-overlapping, potentially heterogeneous patient subgroups. Translation parameters are used to bring both historical and contemporary data onto a common dosing scale. This leads to feasible exchangeability assumptions that the parameter vectors, which underpin the dose-toxicity relationship per study, are assumed to be drawn from a common distribution. Moreover, human dose-toxicity parameter vectors are assumed to be exchangeable either with the standardised, animal study-specific parameter vectors, or between themselves. Possibility of non-exchangeability for each parameter vector is considered to avoid inferences for extreme subgroups being overly influenced by the other. We illustrate the proposed approach with several trial data examples, and evaluate the operating characteristics of our model compared with several alternatives in a simulation study. Numerical results show that our approach yields robust inferences in circumstances, where data from multiple sources are inconsistent and/or the bridging assumptions are incorrect.

stat.ME↗

arXiv2017

Dehydrogenation through the pressure-induced polymerization processes of phosphine

PH3 is studied to understand the superconducting transition and responsible stoichiometry under high pressure by means of Raman, IR, and x-ray diffraction (XRD) measurements, and theoretical calculations. It is found PH3 is stable up to about 8 GPa and then starts to dehydrogenate through two dimerization processes at room temperature as pressure up to 25 GPa. Two resulting phosphorus hydrides, P2H4 and P4H6, are verified experimentally and can be recovered to ambient pressure. On further compression above 35 GPa, P4H6 directly decomposes into elemental phosphorus. The superconductivity transition temperatures of P4H6 at 100 and 200 GPa have been predicted to be 13 and 67 K in agreement with reported results, suggesting it might responsible for the superconductivity at higher pressures. Our results clearly show that P2H4 and P4H6 are only stable P-H compounds between PH3 and elemental phosphorus, shedding light on the superconducting mechanism.

cond-mat.mtrl-sci↗

arXiv2016

Novel chemistry of lithium oxides and superconducting low-pressure LiO4

We study the stability of Li-O compounds as a function of pressure, with Li ion battery applications and fundamental chemical interest in mind. Using the ab initio evolutionary algorithm, we predict stability of novel compounds LiO4, Li5O3 and Li6O under pressure. LiO4, formed at the pressure of just 6 GPa, can be seen as ε-O8 accepting two electrons from two Li atoms. This phase is superconducting, with Tc up to 12.2 K at 10 GPa. This is remarkable, because elemental oxygen becomes superconducting at much higher pressure (96 GPa) and has much lower Tc (<0.6 K), and suggests that chemical alloying with other elements has the potential of not only decreasing metallization pressure, but also of increasing Tc. Since ε-O8 is called red oxygen, LiO4 can be identified "lithium red-oxid", and is distinct from superoxide. Additionally, Li5O3 is stable at pressures above 70 GPa and can be represented as a hybrid structure 4Li2O {\cdot} Li2O2, and electride suboxide Li6O is stable above 62 GPa.

cond-mat.mtrl-sci↗

arXiv2011

Coherent manipulation of spin wave vector for polarization of photons in an atomic ensemble

We experimentally demonstrate the manipulation of two-orthogonal components of a spin wave in an atomic ensemble. Based on Raman two-photon transition and Larmor spin precession induced by magnetic field pulses, the coherent rotations between the two components of the spin wave is controllably achieved. Successively, the two manipulated spin-wave components are mapped into two orthogonal polarized optical emissions, respectively. By measuring Ramsey fringes of the retrieved optical signals, the π/2-pulse fidelity of ~96% is obtained. The presented manipulation scheme can be used to build an arbitrary rotation for qubit operations in quantum information processing based on atomic ensembles.

physics.atom-ph↗