A phase 1 study to assess the absolute bioavailability, mass balance, pharmacokinetics, metabolism, and excretion of [14C]-mobocertinib, an oral inhibitor of EGFR exon 20 insertion mutations, in healthy participants.

Mobocertinib (TAK-788) is a first-in-class oral epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor that received accelerated approval for the treatment of patients with non-small cell lung cancer with EGFR exon 20 insertion mutations previously treated with platinum-based chemotherapy. This phase 1, 2-period, study was conducted to assess the absolute bioavailability of mobocertinib (Period 1), as well as mass balance, pharmacokinetics, metabolism, and excretion of [14C]-mobocertinib (Period 2) in healthy adult males. In Period 1, participants received a single oral capsule dose of 160 mg mobocertinib, followed by a 15-minute intravenous infusion of 50 µg (~ 2 µCi) [14C]-mobocertinib administered from 3.75 to 4 h after the capsule dose. In Period 2, a single oral dose of 160 mg (~ 100 µCi) [14C]-mobocertinib was administered as an oral solution. The geometric mean absolute bioavailability of mobocertinib was determined to be 36.7%. After oral administration of [14C]-mobocertinib, mobocertinib and its active metabolites, AP32960 and AP32914, were minor components in plasma, accounting for only 0.275% of total plasma radioactivity as the majority of mobocertinib-related material was covalently bound to plasma proteins. The geometric mean percentage of the administered radioactive dose recovered in the urine and feces was 3.57% and 76.0%, respectively. Only 0.39% of the oral dose of [14C]-mobocertinib was recovered in the urine as mobocertinib; thus, indicating that renal excretion of unchanged drug was a very minor pathway of elimination. In both treatment periods, mobocertinib was generally safe and well-tolerated as all adverse events were Grade 1 in severity. (Trial registration number ClinicalTrials.gov NCT03811834. Registration date January 22, 2019).

A Bayesian platform trial design with hybrid control based on multisource exchangeability modelling.

Enrolling patients to the standard of care (SOC) arm in randomized clinical trials, especially for rare diseases, can be very challenging due to the lack of resources, restricted patient population availability, and ethical considerations. As the therapeutic effect for the SOC is often well documented in historical trials, we propose a Bayesian platform trial design with hybrid control based on the multisource exchangeability modelling (MEM) framework to harness historical control data. The MEM approach provides a computationally efficient method to formally evaluate the exchangeability of study outcomes between different data sources and allows us to make better informed data borrowing decisions based on the exchangeability between historical and concurrent data. We conduct extensive simulation studies to evaluate the proposed hybrid design. We demonstrate the proposed design leads to significant sample size reduction for the internal control arm and borrows more information compared to competing Bayesian approaches when historical and internal data are compatible.

An adaptive seamless 2-in-1 design with biomarker-driven subgroup enrichment.

Adaptive seamless phase 2/3 subgroup enrichment design plays a pivotal role in streamlining efficient drug development within a competitive landscape, while also enhancing patient access to promising treatments. This design approach identifies biomarker subgroups with the highest potential to benefit from investigational regimens. The seamless integration of Phase 2 and Phase 3 ensures a timely confirmation of clinical benefits. One significant challenge in adaptive enrichment decisions is determining the optimal timing and maturity of the primary endpoint. In this paper, we propose an adaptive seamless 2-in-1 biomarker-driven subgroup enrichment design that addresses this challenge by allowing subgroup selection using an early intermediate endpoint that predicts clinical benefits (i.e. a surrogate endpoint). The proposed design initiates with a Phase 2 stage involving all participants and can potentially expand into a Phase 3 study focused on the subgroup demonstrating the most favorable clinical outcomes. We will show that, under certain correlation assumptions, the overall type I error may not be inflated at the end of the study. In scenarios where the assumptions may not hold, we present a general framework to control the multiplicity. The flexibility and efficacy of the proposed design are highlighted through an extensive simulation study and illustrated in a case study in multiple myeloma.

Incorporating external real-world data (RWD) in confirmatory adaptive design.

Adaptive designs, such as group sequential designs (and the ones with additional adaptive features) or adaptive platform trials, have been quintessential efficient design strategies in trials of unmet medical needs, especially for generating evidence from global regions. Such designs allow interim decision making and making adjustment to study design when necessary, meanwhile maintaining study integrity and operating characteristics. However, driven by the heightened competitive landscape and the desire to bring effective treatment to patients faster, innovation in the already functional designs is still germane to further propel drug development to a more efficient path. One way to achieve this is by leveraging external real-world data (RWD) in the adaptive designs to support interim or final decision making. In this paper, we propose a novel framework of incorporating external RWD in adaptive design to improve interim and/or final analysis decision making. Within this framework, researchers can prespecify the decision process and choose the timing and amount of borrowing while maintaining objectivity and controlling of type I error. Simulation studies in various scenarios are provided to describe power, type I error, and other performance metrics for interim/final decision making. A case study in non-small cell lung cancer is used for illustration on proposed design framework.

Flexible seamless 2-in-1 design with sample size adaptation.

The 2-in-1 design is becoming popular in oncology drug development, with the flexibility in using different endpoints at different decision time. Based on the observed interim data, sponsors can choose to seamlessly advance a small phase 2 trial to a full-scale confirmatory phase 3 trial with a pre-determined maximum sample size or remain in a phase 2 trial. While this approach may increase efficiency in drug development, it is rigid and requires a pre-specified fixed sample size. In this paper, we propose a flexible 2-in-1 design with sample size adaptation, while retaining the advantage of allowing an intermediate endpoint for interim decision-making. The proposed design reflects the needs of the recent FDA's Project FrontRunner initiative, which encourages the use of an earlier surrogate endpoint to potentially support accelerated approval with conversion to standard approval with long-term endpoints from the same randomized study. Additionally, we identify the interim decision cut-off to allow a conventional test procedure at the final analysis. Extensive simulation studies showed that the proposed design requires much a smaller sample size and shorter timeline than the simple 2-in-1 design, while achieving similar power. We present a case study in multiple myeloma to demonstrate the benefits of the proposed design.

Covariate adjusted meta-analytic predictive (CA-MAP) prior for historical borrowing using patient-level data.

Utilization of historical data is increasingly common for gaining efficiency in the drug development and decision-making processes. The underlying issue of between-trial heterogeneity in clinical trials is a barrier in making these methods standard practice in the pharmaceutical industry. Common methods for historical borrowing discount the borrowed information based on the similarity between outcomes in the historical and current data. However, individual clinical trials and their outcomes are intrinsically heterogenous due to differences in study design, patient characteristics, and changes in standard of care. Additionally, differences in covariate distributions can produce inconsistencies in clinical outcome data between historical and current data when there may be a consistent covariate effect. In such scenario, borrowing historical data is still advantageous even though the population level outcome summaries are different. In this paper, we propose a covariate adjusted meta-analytic-predictive (CA-MAP) prior for historical control borrowing. A MAP prior is assigned to each covariate effect, allowing the amount of borrowing to be determined by the consistency of the covariate effects across the current and historical data. This approach integrates between-trial heterogeneity with covariate level heterogeneity to tune the amount of information borrowed. Our method is unique as it directly models the covariate effects instead of using the covariates to select a similar population to borrow from. In summary, our proposed patient-level extension of the MAP prior allows for the amount of historical control borrowing to depend on the similarity of covariate effects rather than similarity in clinical outcomes.

DOD-Combo: Bayesian dose finding design in combination trials with meta-analytic-predictive prior.

Combination therapy, a treatment modality that involves multiple treatment agents, has become imperative for improving treatment effectiveness and addressing resistance in the field of oncology. However, determining the most effective dose for these combinations, particularly when dealing with intricate drug interactions and diverse toxicity patterns, presents a substantial challenge. This paper introduces a novel Bayesian dose-finding design for combination therapies with information borrowing, named the DOD-Combo design. Leveraging historical single-agent trials and the meta-analytic-predictive (MAP) power prior, our approach utilizes a copula-type model to connect individual drug priors with joint toxicity probabilities in combination treatments. The MAP power prior allows the integration of information from multiple historical trials, constructing informative priors for each agent. Extensive simulations confirm our method's superior performance compared to combination designs with no information borrowing. By adaptively incorporating historical data, our approach reduces sample sizes and enhances efficiency in selecting the maximum tolerated dose (MTD), effectively addressing the intricate challenges presented by combination trials.

Considerations for master protocols using external controls.

There has been an increasing use of master protocols in oncology clinical trials because of its efficiency to accelerate cancer drug development and flexibility to accommodate multiple substudies. Depending on the study objective and design, a master protocol trial can be a basket trial, an umbrella trial, a platform trial, or any other form of trials in which multiple investigational products and/or subpopulations are studied under a single protocol. Master protocols can use external data and evidence (e.g. external controls) for treatment effect estimation, which can further improve efficiency of master protocol trials. This paper provides an overview of different types of external controls and their unique features when used in master protocols. Some key considerations in master protocols with external controls are discussed including construction of estimands, assessment of fit-for-use real-world data, and considerations for different types of master protocols. Similarities and differences between regular randomized controlled trials and master protocols when using external controls are discussed. A targeted learning-based causal roadmap is presented which constitutes three key steps: (1) define a target statistical estimand that aligns with the causal estimand for the study objective, (2) use an efficient estimator to estimate the target statistical estimand and its uncertainty, and (3) evaluate the impact of causal assumptions on the study conclusion by performing sensitivity analyses. Two illustrative examples for master protocols using external controls are discussed for their merits and possible improvement in causal effect estimation.

Preparation and efficacy verification of three-dimensional printed partitioned multi-effect precision-care gel facial mask.

OBJECTIVE: A partition multi-effect precision-care gel facial mask conforming to facial skin characteristics was prepared using three-dimensional (3D) printing technology. METHODS: First, the hydrogel matrix and humectant of a 3D-printed gel for facial masks were screened, and three 3D-printed gels of arbutin, hexapeptide, and salicylic acid were prepared with whitening, wrinkle removal, and oil control functions, respectively. Skin irritation tests were performed on the gels. Physicochemical properties such as pH, heat and cold tolerance were evaluated. The efficacy of three 3D-printed gels was assessed by measuring melanin value, wrinkle depression score, and oil secretion. Finally, the facial mask model design and printing parameters were studied, and a partition multi-effect precision-care gel facial mask was printed in line with facial skin characteristics. RESULTS: For the 3D-printed facial mask, the gel prescription with 2% hydroxyethyl cellulose gel as matrix and 7% glycerol as humectant was the best. The prepared 3D-printed gel did not irritate the human skin, and its physicochemical properties met the Chinese facial mask industry standard (QB/T2872-2017). We showed that three types of 3D-printed gels containing arbutin, hexapeptide, and salicylic acid could be applied to the corresponding parts of the face to solve different problems, such as facial skin dullness, wrinkles, and oil secretion. Therefore, according to facial physiological characteristics, the facial mask model was designed for the forehead and nasolabial fold, which needs to be anti-wrinkled; the cheek, which needs to be whitened; and the nose and chin, which need oil control. The optimal printing parameters were 0.26 mm nozzle diameter, 90 mm/s printing speed, 30% filling density, 140% wire extrusion ratio, and 0.25 mm layer height. Different skin care effects can be achieved using a three-nozzle printer to print arbutin, hexapeptide, or salicylic acid gel on the mask's forehead and nasolabial fold, cheek, and nose and chin, respectively. CONCLUSION: The 3D-printed partition multi-effect care gel facial mask prepared according to the skin features of different parts of the face can overcome the problem of the single skincare effect of the mass-produced facial masks.

OBJECTIF: Un masque facial de soin de précision en gel à effets multiples, adapté aux caractéristiques de la peau du visage, a été préparé à l'aide de la technologie d'impression tridimensionnelle (3D). MÉTHODES: Tout d'abord, la matrice d'hydrogel et l'humectant d'un gel imprimé en 3D pour les masques faciaux ont été sélectionnés, et trois gels imprimés en 3D d'arbutine, d'hexapeptide et d'acide salicylique ont été préparés avec des fonctions de blanchiment, d'élimination des rides et de contrôle du sébum, respectivement. Des tests d'irritation cutanée ont été réalisés sur les gels. Les propriétés physicochimiques telles que le pH et la tolérance à la chaleur et au froid ont été évaluées. L'efficacité des trois gels imprimés en 3D a été évaluée en mesurant la valeur de la mélanine, le score de dépression des rides et la sécrétion de sébum. Enfin, la conception du modèle de masque facial et les paramètres d'impression ont été étudiés, et un masque facial de gel de soin de précision à effets multiples a été imprimé en fonction des caractéristiques de la peau du visage. RÉSULTATS: Pour le masque facial imprimé en 3D, la prescription de gel avec 2 % de gel d'hydroxyéthylcellulose comme matrice et 7 % de glycérol comme humectant était la meilleure. Le gel imprimé en 3D n'a pas irrité la peau humaine et ses propriétés physicochimiques sont conformes à la norme industrielle chinoise relative aux masques faciaux (QB/T2872­2017). Nous avons montré que trois types de gels imprimés en 3D contenant de l'arbutine, de l'hexapeptide et de l'acide salicylique pouvaient être appliqués aux parties correspondantes du visage pour résoudre différents problèmes, tels que l'aspect terne de la peau du visage, les rides et la sécrétion de sébum. Par conséquent, en fonction des caractéristiques physiologiques du visage, le modèle de masque facial a été conçu pour le front et le sillon nasogénien, qui doivent être antirides, la joue, qui doit être blanchie, et le nez et le menton, qui ont besoin d'un contrôle du sébum. Les paramètres d'impression optimaux étaient les suivants : diamètre de buse de 0,26 mm, vitesse d'impression de 90 mm/s, densité de remplissage de 30 %, rapport d'extrusion du fil de 140 % et hauteur de couche de 0,25 mm. Différents effets de soin de la peau peuvent être obtenus en utilisant une imprimante à trois buses pour imprimer de l'arbutine, de l'hexapeptide ou du gel d'acide salicylique sur le front et le sillon nasogénien, la joue, le nez et le menton du masque, respectivement. CONCLUSION: Le masque facial en gel de soin à effets multiples imprimé en 3D et préparé en fonction des caractéristiques de la peau des différentes parties du visage peut résoudre le problème de l'effet de soin unique des masques faciaux produits en masse.

BEATS: Bayesian hybrid design with flexible sample size adaptation for time-to-event endpoints.

As the roles of historical trials and real-world evidence in drug development have substantially increased, several approaches have been proposed to leverage external data and improve the design of clinical trials. While most of these approaches focus on methodology development for borrowing information during the analysis stage, there is a risk of inadequate or absent enrollment of concurrent control due to misspecification of heterogeneity from external data, which can result in unreliable estimates of treatment effect. In this study, we introduce a Bayesian hybrid design with flexible sample size adaptation (BEATS) that allows for adaptive borrowing of external data based on the level of heterogeneity to augment the control arm during both the design and interim analysis stages. Moreover, BEATS extends the Bayesian semiparametric meta-analytic predictive prior (BaSe-MAP) to incorporate time-to-event endpoints, enabling optimal borrowing performance. Initially, BEATS calibrates the expected sample size and initial randomization ratio based on heterogeneity among the external data. During the interim analysis, flexible sample size adaptation is performed to address conflicts between the concurrent and historical control, while also conducting futility analysis. At the final analysis, estimation is provided by incorporating the calibrated amount of external data. Therefore, our proposed design allows for an approximation of an ideal randomized controlled trial with an equal randomization ratio while controlling the size of the concurrent control to benefit patients and accelerate drug development. BEATS also offers optimal power and robust estimation through flexible sample size adaptation when conflicts arise between the concurrent control and external data.

Estimation of conditional power in the presence of auxiliary data.

Conditional power (CP) is a commonly used tool to inform interim decision-making in clinical trials, but the conventional approach using only primary endpoint data to calculate CP may not perform well when the primary endpoint requires a long follow-up period, or the treatment effect size changes during the trial. Several methods have been proposed to use additional short term auxiliary data observed at the interim analysis to improve the CP estimation in these situations, however, they may rely on strong assumptions, have limited applications, or use ad hoc choices of information fraction. In this paper we propose a general framework where the true CP formula is first derived in the presence of auxiliary data, and CP estimation is obtained by substituting the unknown parameters with consistent estimators. We conducted extensive simulations to examine the performance of both proposed and conventional approaches using the true CP as the benchmark. As the proposed approach is based on the true underlying CP, the simulations confirmed its superiority over the conventional approach in terms of efficiency and accuracy, especially if observed auxiliary data reflect the change of treatment effect size. The simulations also indicate that the magnitude of improvement in CP estimation is associated with the correlation between auxiliary and primary endpoints and/or the magnitude of the effect size change during the trial.

Semiparametric pseudo-score and pseudo-likelihood for evaluating correlate of protection in vaccine trials.

In vaccine clinical trials, vaccine efficacy endpoint analysis is usually associated with in high cost or extended study duration, due to the generally low infection rate. Correlate of protection (CoP), which refers to surrogate endpoint, usually immunological response, that can reliably predict the treatment effect, provides a more efficient and less costly approach to evaluate the vaccine. To handle the challenge of the missingness in the unobserved surrogate immune biomarker, the pseudo-score (PS) method, semiparametric method and pseudo-likelihood (PL) method demonstrated their advantages on different aspects. In this article, we propose new methodologies to combine the advantages of PS and PL with semiparametric methods respectively, to achieve higher estimate efficiency, allow continuous baseline predictor variable, and handle multiple surrogate markers. The advantage of our methodologies are demonstrated by a simulation study in different settings and applied to a case study, which eventually can improve the chance of a successful trial.

Ketamine exerts dual effects on the apoptosis of primary cultured hippocampal neurons from fetal rats in vitro.

Ketamine, a noncompetitive N-methyl D-aspartate (NMDA) receptor antagonist, is widely used in pediatric clinical practice. The neuroprotective and neurotoxic effects of ketamine on brain neurons during development remain controversial. The reason may be related to the different concentrations of ketamine used in practice and the small range of concentrations used in previous studies. In this study, cultured hippocampal neurons were treated with ketamine in a wide range of concentrations to comprehensively observe the effects of different concentrations of ketamine on neurons. We demonstrated that low concentrations of ketamine (10 µM, 100 µM and 1000 µM) promoted neuronal survival (p < 0.05) and reduced neuronal apoptosis (p < 0.05) compared with those of the control group. High concentrations of ketamine (2000 µM, 2500 µM and 3000 µM) reduced neuronal survival (p < 0.05) and promoted neuronal apoptosis (p < 0.05). The p38 MAPK inhibitor SB203580 reduced neuronal apoptosis induced by high concentrations of ketamine (2500 µM) (p < 0.05). Our findings indicate that ketamine exerts a dual effect on the apoptosis of primary cultured fetal rat hippocampal neurons in vitro and that the neurotoxic effects of ketamine are related to activation of the p38 MAPK signaling pathway.

Incorporating propensity scores for evidence synthesis under bayesian framework: review and recommendations for clinical studies.

The amount of real-world data (RWD) available from sources other than randomized-controlled trials (RCTs) has grown ultra-rapidly in recent years. It provides the impetus for generating substantial evidence of effectiveness and safety from both RCTs and RWD to accelerate medical product development. Especially in the areas of unmet needs, the conduct of fully powered RCTs is generally infeasible because of their sizes, duration, cost, or ethical constraints. The unique challenges in such areas include a small patient population, heterogeneity in disease presentation, and a lack of established endpoints. However, merging information from disparate sources is an intricate task. The value of the Bayesian framework has gained more recognition due to its flexibility in calibrating uncertainty and handling data heterogeneity, and its inherent updating process ideal for synthesizing information. Meanwhile, propensity score, as a powerful tool in causal inference, can be used in various ways to adjust for confounders. As a newly emerging data borrowing strategy in a regulatory setting, integrating propensity scores in a Bayesian setting not only utilizes the strengths from Bayesian models but also minimizes bias from external data borrowing. These methods potentially allow information sharing among data sources, provide more reliable estimates when the sample size is small, and improve the efficiency of treatment effect estimation. In this paper, we will review the recent development of methods incorporating propensity score for evidence synthesis under the Bayesian framework, and discuss different examples of incorporating external data with or without RCTs, as well as the recommendations for reporting in clinical studies.

PMED: Optimal Bayesian Platform Trial Design with Multiple Endpoints.

In oncology drug development, indication selection and optimal dose identification are the primary objectives for the early phase of clinical trials and could significantly impact the probability of success. Master protocols, e.g., basket trial, umbrella trial, and platform trial, have become popular in practice considering the connection of trial designs with multiple indications and treatment candidates. They also enable the optimization of operational resources and maximize the capability of data-driven decision-making. However, most of the available designs are developed with the efficacy endpoint only for treatment effect estimation and testing, without consideration of the safety end point. Thus, it often lacks a comprehensive quantitative framework to allow optimal treatment selection, which could put future development at risk. We propose an optimal Bayesian platform trial design with multiple end points (PMED) to characterize the overall benefit-risk profile. The design is further extended to allow treatment and indication selection within and across arms, with continuous monitoring on multiple interim analyses for futility. In addition, we propose dynamic borrowing across arms to increase the efficiency and accuracy of estimation given the level of similarity across arms. A hierarchical hypothesis structure is utilized to achieve optimal indication and treatment combination selection by controlling family-wise error. Through simulation studies, we show that PMED is a robust design under the studied scenarios with superb power and controlled family-wise error rate.

Bayesian Additive Regression Trees (BART) with covariate adjusted borrowing in subgroup analyses.

It is crucial in clinical trials to investigate treatment effect consistency across subgroups defined by patient baseline characteristics. However, there may be treatment effect variability across subgroups due to small subgroup sample size. Various Bayesian models have been proposed to incorporate this variability when borrowing information across subgroups. These models rely on the underlying assumption that patients with similar characteristics will have similar outcomes to the same treatment. Patient populations within each subgroup must subjectively be deemed similar enough Pocock (1976) to borrow response information across subgroups. We propose utilizing the machine learning method of Bayesian Additive Regression Trees (BART) to provide a method for subgroup borrowing that does not rely on an underlying assumption of homogeneity between subgroups. BART is a data-driven approach that utilizes patient-level observations. The amount of borrowing between subgroups automatically adjusts as BART learns the covariate-response relationships. Modeling patient-level data rather than treating the subgroup as a single unit minimizes assumptions regarding homogeneity across subgroups. We illustrate the use of BART in this context by comparing performance from existing subgroup borrowing methods in a simulation study and a case study in non-small cell lung cancer. The application of BART in the context of subgroup analyses alleviates the need to subjectively choose how much information to borrow based on subgroup similarity. Having the amount of borrowing be analytically determined and controlled for based on the similarity of individual patient-level characteristics allows for more objective decision making in the drug development process with many other applications including basket trials.

Using surrogate information to improve confirmatory platform trial with sample size re-estimation.

Platform design which allows exploring multiple arms with a common control simultaneously is becoming essential for efficient drug development. However, one of the critical challenges for confirmatory platform trials is immature data for interim decisions, particularly for the treatment arm selection and sample size determination with limited data available. We use a modified conditional power (CP) for both treatment arm selection and sample size determination at interim analysis for the proposed platform trial. The modified CP uses the available data from both primary and surrogate endpoints. We also demonstrated the application in a case study of a lung cancer trial.

Optimal sample size determination for single-arm trials in pediatric and rare populations with Bayesian borrowing.

In many therapeutic areas with unmet medical needs, such as pediatric oncology and rare diseases, one of the deterrent factors for clinical trial interpretability is the limited sample size with less-than-ideal operating characteristics. Single arm is usually the only viable design due to feasibility and ethical concerns. For the trial results to be more interpretable and conclusive, the evaluation of operating characteristics, such as type I error rate and power, and the appropriate utilization of prior information for study design, shall be prespecified and fully investigated during the trial planning phase. So far, very few existing literature addressed optimal sample size determination issues for the planning of pediatric and rare population trials, with majority of research focusing on analysis perspective with focus on Bayesian borrowing. In practice, when a single-arm trial is designed for rare population, it is not uncommon that the only information available is from an earlier trial and/or a few clinical publications based on observational studies, often constituting mixed or uncertain conclusions. In light of this, an optimal Bayesian sample size determination method for single-arm trial with binary or continuous endpoint is proposed, where conflicting prior beliefs can be readily incorporated. Prior effective sample size can be calculated to assess the robustness as well as the prior information borrowed. Moreover, due to the lack of closed-form posterior distributions in general, an alternative approach for calculating Bayesian power is described. Simulation studies are provided to demonstrate the utility of the proposed methods. In addition, a case study in pediatric patients with leukemia is included to illustrate the proposed method with the existing approaches.

Statistical methods of indirect comparison with real-world data for survival endpoint under non-proportional hazards.

In clinical studies that utilize real-world data, time-to-event outcomes are often germane to scientific questions of interest. Two main obstacles are the presence of non-proportional hazards and confounding bias. Existing methods that could adjust for NPH or confounding bias, but no previous work delineated the complexity of simultaneous adjustments for both. In this paper, a propensity score stratified MaxCombo and weighted Cox model is proposed. This model can adjust for confounding bias and NPH and can be pre-specified when NPH pattern is unknown in advance. The method has robust performance as demonstrated in simulation studies and in a case study.

Bayesian semiparametric meta-analytic-predictive prior for historical control borrowing in clinical trials.

When designing a clinical trial, borrowing historical control information can provide a more efficient approach by reducing the necessary control arm sample size while still yielding increased power. Several Bayesian methods for incorporating historical information via a prior distribution have been proposed, for example, (modified) power prior, (robust) meta-analytic predictive prior. When utilizing historical control borrowing, the prior parameter(s) must be specified to determine the magnitude of borrowing before the current data are observed. Thus, a flexible prior is needed in case of heterogeneity between historic trials or prior data conflict with the current trial. To incorporate the ability to selectively borrow historic information, we propose a Bayesian semiparametric meta-analytic-predictive prior. Using a Dirichlet process mixture prior allows for relaxation of parametric assumptions, and lets the model adaptively learn the relationship between the historic and current control data. Additionally, we generalize a method for estimating the prior effective sample size (ESS) for the proposed prior. This gives an intuitive quantification of the amount of information borrowed from historical trials, and aids in tuning the prior to the specific task at hand. We illustrate the effectiveness of the proposed methodology by comparing performance between existing methods in an extensive simulation study and a phase II proof-of-concept trial in ankylosing spondylitis. In summary, our proposed robustification of the meta-analytic-predictive prior alleviates the need for prespecifying the amount of borrowing, providing a more flexible and robust method to integrate historical data from multiple study sources in the design and analysis of clinical trials.