Establishment of diagnostic biomarkers of previous disease exposure is essential in precision medicine. One of the challenges in this application is associated with the certainty of the cases being either positive or negative in the training and test analysis sets used to establish a reliable cutoff. Practical situations like asymptomatic cases, non-reported records, absence of doctors’ visits, missing biomarker samples or lack of assay sensitivity at the time of testing may result in subjects being misclassified as negative cases. The uncertain response labels subsequently lead to a biased cutoff value determination since the main assumption of supervised classification methods is that the labels of training and test samples are true with random errors. This paper provides statistical solutions to address the unknown potential labeling issue focusing on two practical aspects: 1. Statistical visualization methods to explore sample responses and so identifying potential mislabeled cases; 2. Application of a semi-supervised learning method, Robust Mixture Discriminant Analysis (RMDA), to responses with uncertain labels for determination of cutoff value; These topics are illustrated using real pediatric serum IgA biomarker dataset for identification of RSV previous exposure status.

: Overall survival (OS) is considered a gold standard clinical endpoint for evaluating the effectiveness of a drug. In oncology studies, OS is relatively simple and straightforward to measure in a clinical trial. Recent advancements in medical sciences and cancer care have significantly prolonged the lifespan of cancer patients. As a result, it is becoming challenging to measure OS due to its long course in some cancer diseases. In addition, it may be challenging to interpret OS in some circumstances; for example, when patients switch from the control arm to the experiment arm to receive a novel therapy, a detrimental effect might be observed in OS in the experimental arm with uncertainty, or discrepancy between OS and other clinical endpoints can be observed. This manuscript will provide an overview of the challenges of using OS as a clinical endpoint in pivotal oncology trials. Discussions will focus on using OS as both a safety and efficacy endpoint for decision-making.

The recently released third draft version of ICH E6(R3) has a great emphasis on Risk-Based Quality Management (RBQM) principles and includes the concept of Quality Tolerance Limits (QTLs) that are regarded as an example of predefined acceptable ranges that, if exceeded, might potentially effect participants safety or the reliability of trial results. This change allows for greater flexibility and adaptability in managing quality and risks in clinical trials, leading to more effective and efficient trials. In this paper, we conduct simulations to evaluate statistical methods, including statistical process control and Bayesian methods, for implementing QTLs in clinical trials. We evaluate the operating characteristics such as average run length, alarm rate, false alarm rate, and other performance metrics. Generally, all methods performed better with larger sample sizes and higher expected probabilities. There was greater variability in performance across methods early in the review cycle when sample sizes were small. Statistical process control methods performed better in most scenarios, while Bayesian methods were more effective at detecting an out-of-control process earlier for lower expected probabilities. Not all scenarios could be investigated; thus, method selection depends on factors like assumptions, statistical complexity, and feasibility.

For handling intercurrent events in clinical trials, one of the strategies outlined in the ICH E9(R1) addendum targets a hypothetical scenario where an intercurrent event would not occur. While this strategy is often implemented by setting data after the intercurrent event to “missing” even if they have been collected, g-estimation allows for a more efficient estimation by using the information contained in post intercurrent event data. As the g-estimation methods have largely developed outside of randomized clinical trials, optimization for the application in clinical trials are possible. In this article, we describe and investigate the performance of modifications to the established g-estimation methods, leveraging the assumption that some intercurrent events are expected to have the same impact on the outcome regardless of the timing of their occurrence. In a simulation study in Alzheimer’s disease, the modifications show a substantial efficiency advantage for the estimation of an estimand that applies the hypothetical strategy to the use of symptomatic treatment while retaining approximate unbiasedness and adequate Type I error control.

Confirmatory cancer clinical trials in chronic or indolent diseases often use imaging endpoints as a primary measure of efficacy due to relatively long survival time. Progression-free survival (PFS) is often such an image-based endpoint. A substantial increase in PFS in the absence of severe toxicities may be considered a meaningful clinical benefit. In these trials, overall survival (OS) is often a secondary or exploratory endpoint with low or unknown statistical power. Due to relatively long OS, few OS events occur at the time of a trial’s primary completion (e.g., the primary PFS analysis). Additionally, unlike the primary endpoint, analyses of OS are often not well planned and described in the protocol. All these make it challenging to interpret OS results in order to determine OS benefit or detriment. However, OS is an ultimate measure of safety as well as efficacy. We present two methods for planning analyses of OS for safety evaluation: a three-outcome and a two-outcome procedure. They can be used to plan OS safety analyses so that sufficient data are available to provide at least a minimum level of information required to rule out a substantial detriment. They also provide guidelines for interpreting OS results.

Ensuring diversity in clinical trials is critical for understanding treatment effects across different populations. This paper explores innovative statistical strategies to enhance the representation of underrepresented racial and ethnic groups in clinical research. We review Bayesian borrowing methods in single-arm trials, emphasizing their potential to leverage historical data or real-world data (RWD) while addressing risks of bias. In the context of randomized clinical trials (RCTs), we discuss adaptive enrichment and hybrid designs as approaches to mitigate demographic disparities while maintaining scientific rigor. Beyond trial design innovations, the integration of RWD offers opportunities to supplement evidence and improve inclusivity. However, challenges such as data quality, selection bias, and endpoint comparability must be carefully addressed. We present a hypothetical case study demonstrating Bayesian borrowing in a post-market setting to illustrate its practical implications.

ABSTRACT Hypothetical strategy allows to define an estimand for the pure treatment effect subjected to the original randomized treatments on overall survival (OS) without impact from effective subsequent therapies. Novel statistical methods including Inverse-Probability-of-Censoring Weights analysis (IPCW) and Two-Stage estimation (TSE), have been proposed for handling initiation of subsequent treatment. However, OS can vary across different types of subsequent therapies, which is ignored by these methods. Motivated by the GADOLIN trial, we propose modified methods (m-IPCW, m-TSE) to account for two different types of subsequent therapy. RCT data were simulated with various scenarios. The occurrence of subsequent therapies was simulated to vary with individual patient’s characteristics and randomized treatment. The performance of different methods was evaluated using bias and root-mean-square deviation compared to the true log hazard ratio in OS without subsequent therapies. Across simulated scenarios, both modified and standard TSE and IPCW methods demonstrated superior performance to naive censoring approach. The m-TSE outperformed its standard counterpart, as confirmed by the application in the GADOLIN trial. Our results suggested that in the presence of different effective subsequent therapies, statistical methods differentiating subsequent therapy types might yield more valid estimators for investigational treatment effect on OS under the hypothetical strategy.

Leveraging real-world data (RWD) as external controls in clinical trials can strengthen inference, reduce costs, and address ethical concerns. However, discrepancies between RWD and concurrent control data may introduce bias and inflate Type I error. Existing methods, such as Bayesian dynamic borrowing and propensity-score-based approaches, adjust for either outcomes or covariates but struggle under partial or non-exchangeability, leading to biased estimates and unreliable inference. We propose a novel power prior that employs weights derived by minimizing the weighted energy distance between external and concurrent control data. This model-free, computationally efficient approach dynamically adjusts borrowing based on both covariate and outcome similarity, ensuring robust external data integration. Through simulation, we show that the method outperforms existing approaches by reducing bias, minimizing mean squared error, controlling Type I error, and maintaining statistical power, particularly when unobserved outcome differences create substantial non-exchangeability. An applied case study further illustrates its practical utility, demonstrating unbiased estimates with narrower uncertainty intervals than existing strategies. The proposed method provides a principled framework for integrating RWD into clinical trials while preserving statistical rigor and regulatory acceptability. By calibrating external data adaptively, it enhances trial efficiency without compromising validity, making it a promising approach for modern drug development.

ABSTRACT Health Technology Assessment (HTA) evaluations play a crucial role in informing decisions related to the adoption, reimbursement, and utilization of healthcare technologies. To ensure robust and reliable outcomes, HTA requires a diverse range of evidence, which may vary depending on the specific technology under evaluation, the questions to be answered, and the available data sources. It is imperative to design and conduct studies that generate high-quality and pertinent evidence to facilitate effective HTA evaluations. Furthermore, sophisticated and appropriate statistical methodologies are often necessary to analyze and interpret the collected data in HTA assessments. Recognizing the lack of discussion and best practice recommendations to fulfill the HTA needs, the American Statistical Association (ASA) Biopharmaceutical Section (BIOP) Health Technology Assessment (HTA) Scientific Working Group (SWG) has undertaken an initiative to assess the HTA landscape in major global markets. We aim to offer strategic considerations for evidence planning related to HTA, alongside specific statistical methodologies commonly used in delivering clinical evidence and demonstrating value. Our targeted audience includes statisticians working in clinical development who may not be familiar with the intricacies and specific needs of HTA. This paper focuses specifically on study designs and statistical methods. This paper sheds light on the challenges that persist in study design and analytic approaches concerning HTA evidence requirements and discusses potential opportunities and mitigations. By bridging the knowledge gap in HTA needs and offering practical guidance on study designs and statistical methods, this research advances the field of statistics within HTA.

Event size re-estimation (ESR) is a natural extension of sample size re-estimation (SSR) to clinical trials with a time-to-event endpoint. Even though the same Type I error approaches are shared between ESR and SSR, the survival endpoint is more complicated than continuous and binary ones. We look into all the popular methods to control Type I error rate under ESR. Moreover, we propose the specification of incorporating stratification factors into the combination test for clinical trials with data from different stages. The properties of all the above methods are thoroughly studied and discussed.