Journal of Biopharmaceutical Statistics最新文献

Clinical trials with continuous endpoints evaluate efficacy by comparing the difference in mean changes from baseline between groups. However, clinicians often interpret results in terms of a proportional reduction rather than an absolute difference. An alternative approach is to reparametrize this difference as a proportional treatment effect (PTE), calculated by dividing the difference by the placebo mean change. PTE is not a new metric per se, but a specific reparameterization gaining traction in certain clinical contexts. We demonstrate that, in theory, PTE can be more powerful than the simple difference in means while still controlling the type I error rate. This is achieved using the delta method, as implemented in well-established computational tools like the R package 'msm' and the SAS procedure 'NLMIXED'. By analyzing data from phase III trials, we illustrate how a PTE connects treatment outcomes across various endpoints and different presentation formats. The availability of these well-established statistical tools for estimating proportional treatment effects, combined with this theoretical demonstration, suggests an alternative test statistic for clinical trials with continuous endpoints.

Bivariate data arise in several research fields, such as clinical trials and reliability studies. In clinical trials, patients are distributed into treatment groups using randomization designs, which prevent selection bias and incidental bias. Generalized randomized block design is among clinical trials' most famous and widely used randomization designs. This article investigates the use of the saddlepoint method to approximate the underlying permutation distributions of bivariate two-sample tests under a generalized randomized block design. Additionally, the saddlepoint method is utilized to approximate the tail probability of these tests. Through comprehensive simulation studies, the accuracy of this approximation method is thoroughly evaluated, revealing a significant improvement in precision compared to the asymptotic normal approximation.

Oncology dose-finding trials are shifting from identifying the maximum-tolerated dose (MTD) to determining the optimal biological dose (OBD), driven by the need for efficient methods that consider both toxicity and efficacy. This is particularly important for novel therapies, such as immunotherapies and molecularly targeted therapies, which often exhibit non-monotonic dose-efficacy curves. The Simple Toxicity and Efficacy Interval (STEIN) design has demonstrated strong performance in accommodating diverse dose-efficacy patterns and incorporating both toxicity and efficacy outcomes to select the OBD. However, the rapid accrual of patients and the often-delayed onset of toxicity and/or efficacy pose challenges to timely adaptive-dose decisions. To address these challenges, we propose TITE-STEIN, a model-assisted design that incorporates time-to-event (TITE) outcomes for toxicity and/or efficacy, by extending STEIN. In this article, we demonstrate that TITE-STEIN significantly shortens the trial duration compared to STEIN. Furthermore, by integrating an OBD verification procedure during OBD selection, TITE-STEIN effectively mitigates the risk of exposing patients to inadmissible doses when the OBD does not exist. Extensive simulations demonstrate that TITE-STEIN outperforms existing TITE designs, including TITE-BONI12, TITE-BOIN-ET, LO-TC, and Joint TITE-CRM, by selecting the OBD more accurately, allocating more patients to it, and improving overdose control.

Gene and genetically modified cell therapies offer incredible potential but may come with certain risks and unknowns of delayed adverse events due to unique characteristics of these products. Major regulatory agencies all require long-term follow-up (LTFU) trials, which can be as long as 15 years, to monitor potential delayed adverse events and the durability of effectiveness. Various innovative approaches have been proposed to reduce the operational burden of gene and genetically modified cell therapy LTFU trials. In this article, the authors aim to apply the ICH E9(R1) estimand framework in the context of such LTFU trials, which can be beneficial during the protocol development stage. A hypothetical LTFU study is used to illustrate the estimand considerations discussed in the current manuscript. Proper estimand specifications for a LTFU study of a gene or genetically modified cell therapy can help add clarity to the study design, data collection, and statistical analysis plan, and help ensure the study is robust, transparent, and capable of addressing the important research questions posed by these advanced therapies.

The landscape of oncology drug development is transitioning from traditional cytotoxic chemotherapy drugs to novel agents, such as molecularly targeted therapies (MTA) or immunotherapies. Conventional dose optimization methods based on chemotherapy that assume a monotone dose-response relationship might not be ideal for the development of these novel therapies. Recognizing these limitations, the US FDA has introduced Project Optimus, an initiative aimed to reform the current paradigm of dose optimization. In addition to dose optimization, another critical objective for early phase proof-of-concept clinical trials is indication selection. However, there are limited methodologies that can address dose optimization and indication selection simultaneously. In this paper, we propose a Bayesian Dose Optimization Design for Randomized Phase II trials with Multiple Indications (M-DODII) that integrates Bayesian continuous monitoring and Bayesian pick-the-winner approach, utilizing efficacy and toxicity endpoints to inform dose selection for multiple indications simultaneously. Through simulation studies, we demonstrate that M-DODII has favorable operating characteristics with controlled selection error. Compared to other adaptive designs, M-DODII shows a lower probability of choosing a suboptimal dose, a higher probability of selecting the optimal dose, and reduced total sample size.

This work introduces the Burdened Bayesian Logistic Regression Model (BBLRM), an enhancement of the Bayesian Logistic Regression Model (BLRM) for dose-finding in phase I oncology trials. The BLRM determines the maximum tolerated dose (MTD) based on dose-limiting toxicities (DLTs). However, clinicians often perceive model-based designs like BLRM as complex and less conservative than rule-based designs, such as the widely used 3 + 3 method. To address these concerns, BBLRM incorporates non-DLT adverse events (nDLTAEs), which, although not severe enough to be DLTs, indicate potential toxicity risks at higher doses. BBLRM introduces an additional parameter δ to account for nDLTAEs, adjusting toxicity probability estimates to make dose escalation more conservative while maintaining accurate MTD allocation. This parameter, generated basing on the proportion of patients experiencing nDLTAEs, is tuned to balance conservatism with model performance, reducing the risk of selecting overly toxic doses. Additionally, involving clinicians in identifying nDLTAEs enhances their engagement in the dose-finding process. A simulation study compares BBLRM with two other BLRM methods and a two-stage Continual Reassessment Method (CRM) incorporating nDLTAEs. Results show that BBLRM reduces the proportion of toxic doses selected as MTD without compromising the accuracy in MTD identification. These findings suggest that integrating nDLTAEs can improve the safety and acceptance of model-based designs in phase I oncology trials.

In oncology trials, patients in both the control and experimental arms can receive different subsequent anti-cancer therapies (SATs) after discontinuing their randomized study drugs, a phenomenon commonly referred to as treatment switching. SATs may have the potential to extend overall survival (OS) in patients treated with the control and experimental drugs. Without recovering the information from the SATs, the statistical power of the clinical trials could be drastically reduced, thus making it difficult or impossible to meet the efficacy objective. This article presents a novel statistical method for imputing the post-switching survival time multiple times to derive the point estimate of the true hazard ratio (HR) of OS between the experimental and control drugs and the associated 95% confidence interval (CI). The proposed method provides an effective solution for recovering lost information in the OS caused by SATs. It also offers an efficient way to evaluate the true causal treatment effect, potentially increasing the statistical power. Additionally, this method can be used for patients with a crossover from a placebo to an experimental treatment in placebo-controlled trials. Simulation studies demonstrated that the proposed method performed well and reliably, and applications to oncology trials using the simulated data are provided.

The assessment of human ether-a-go-go-related gene (hERG) safety assay is essential for estimating the risk that a drug will cause delayed repolarization and QT interval prolongation prior to human administration. Quantitative assessment of hERG safety assay similarity presents significant challenges due to the absence of consensus methodology and substantial inter-laboratory variability in hERG assay performance. We developed a statistical framework to conduct quantitative assessment of hERG safety assay similarity for drug products between sponsor's laboratories and laboratories that follow the ICH E14 S7b Q&A Best Practice recommended protocol. Our approach employs fixed margin equivalence testing methodology. Using real-world and/or simulated data, we demonstrate that the proposed equivalence testing methods successfully identify similar hERG assays between laboratories for 28 Comprehensive In Vitro Proarrhythmia Assay (CiPA) drugs. The testing results align with the domain experts' assessments, validating the framework's utility for regulatory decision-making.

Bioequivalence studies play a pivotal role in drug development by establishing the clinical equivalence of two drug formulations. These studies often utilize crossover designs to facilitate within-subject treatment comparisons, optimizing statistical power with fewer subjects. However, uncertainty regarding the variance of a new drug or formulation during planning presents a challenge for sample size determination. While adaptive designs offer a potential solution, their application in crossover studies is less explored compared to group sequential designs, and many existing adaptive methods require data unblinding during the trial. Only two blinded sample size re-estimation approaches have been developed in crossover settings to date. In this paper, we propose a novel method for blinded within-subject variance estimation at interim analysis and re-estimate the sample size to achieve the desired power. We thoroughly investigate its analytical properties and introduce a refined, unbiased estimator. Through extensive simulation studies, our method shows comparable performance to existing blinded approaches and offers a distinct advantage in scenarios with small treatment differences and large subject variances.

In this article, we propose a phase I-II design in two stages for the combination of molecularly targeted therapies. The design is motivated by a published case study that combines MEK and PIK3CA inhibitors; a setting in which higher dose levels do not necessarily translate into higher efficacy responses. The goal is therefore to identify dose combination(s) with a prespecified desirable risk-benefit trade-off. We propose a flexible cubic spline to model the marginal distribution of the efficacy response. In stage I, patients are allocated following the escalation with overdose control (EWOC) principle whereas, in stage II, we adaptively randomize patients to the available experimental dose combinations based on the continuously updated model parameters. A simulation study is presented to assess the design's performance under different scenarios, as well as to evaluate its sensitivity to the sample size and to model misspecification. Compared to a recently published dose finding algorithm for biologic drugs, our design is safer and more efficient at identifying optimal dose combinations.