model validation study conduct

Binary endpoints are frequently employed as surrogate markers for time-to-event outcomes, yet the validity of their association with true endpoints—both at the individual and trial levels—across varying trial designs remains insufficiently evaluated. This study presents the first comprehensive assessment of the performance of these two types of surrogacy measures under diverse design configurations, leveraging a meta-analytic framework informed by large-scale simulations and real-world clinical trial data. The findings elucidate how key design parameters influence the accuracy of surrogacy estimation, thereby offering empirical evidence and methodological guidance to inform regulatory decisions regarding the appropriate use of binary surrogate endpoints in clinical trials.

In two-stage sampling under multi-model competition, conventional second-stage validation sample selection suffers from low efficiency and susceptibility to bias induced by any single candidate model. Method: This paper proposes a cross-model variability integration method based on Principal Component Analysis (PCA), which projects prediction uncertainties from multiple candidate models onto the principal component space and performs targeted sampling in the extreme tails to maximize information gain. Contribution/Results: To our knowledge, this is the first application of PCA to balance sampling priorities across competing models, thereby mitigating validation bias arising from model dominance. Implemented in the R package *auditDesignR*, the method is validated on NHANES data and extensive simulation studies. Compared with traditional single-model sampling strategies, it achieves statistically significant improvements in estimation efficiency across all target models while reducing overall validation cost.

This work addresses the inefficiencies in oncology clinical trial statistical workflows—often fragmented, leading to redundant efforts, poor collaboration, and inconsistent analyses—by developing grstat, an open-source R package that integrates standardized analytical tools within a governance framework featuring requirement traceability, peer review, automated testing, and phased validation. By unifying technical implementation with a structured, reproducible process, grstat establishes a shared, auditable, and maintainable analytical toolkit. Empirical application demonstrates that this approach substantially enhances analytical efficiency, consistency, and long-term maintainability, offering academic biostatistics teams a scalable and transferable collaborative paradigm.

This study addresses the challenge that data-driven protocol selection in target trial emulation often invalidates statistical inference. To resolve this, the authors propose a two-stage strategy based on sample splitting: the first subsample is used to explore and finalize the target trial protocol, while the second, independent subsample is employed to implement the selected protocol and conduct causal inference. Inspired by the exploratory-to-confirmatory paradigm in clinical trials, this approach decouples protocol specification from inference, thereby preserving the flexibility of scientific exploration while rigorously maintaining nominal coverage guarantees for statistical inference. By integrating sample splitting, target trial emulation, and causal inference, the work provides both theoretical assurance and a practical framework for valid statistical inference in observational studies.

This study addresses the need for more reliable regulatory decision-making in Bayesian clinical trials by systematically calibrating Bayesian success criteria to control decision errors. It establishes the first theoretical correspondence between Bayesian decision error metrics and frequentist operating characteristics—specifically Type I and Type II error rates—and proposes a practical calibration strategy grounded in this relationship. The approach is illustrated through a case study on a revascularization trial in cardiogenic shock. To facilitate adoption under the FDA’s emerging Bayesian framework, the authors also developed an interactive Shiny web application that enables sponsors and regulators to efficiently and reliably formulate decisions while maintaining rigorous error control.

This study addresses the challenge of estimation bias arising from multivariate measurement error in routinely collected data, such as electronic health records. The authors propose a novel approach that integrates error-contaminated full-cohort data with a validation subsample, embedding generalized raking calibration weights within the cumulative probability model (CPM) framework. This is the first method to enable efficient and robust modeling of continuous, ordinal, or mixed-type outcomes under CPM while accounting for measurement error. By combining semiparametric rank-based regression with validation subsampling, the proposed technique substantially improves estimation accuracy, as demonstrated in an application to gestational weight gain research. Empirical results show clear advantages over existing methods, confirming its effectiveness and practical utility in real-world biomedical settings.

This study addresses the challenge of safely and effectively reducing sample sizes in registered clinical trials while adhering to regulatory requirements. Building upon the FDA’s seven-step risk assessment framework, the work presents the first systematic application of AI model trustworthiness evaluation guidelines to the context of sample size reduction. By constructing prognostic covariates, conducting risk-informed model development and validation, and integrating these with statistical re-estimation methods, the approach recalculates the required trial sample size. Demonstrated in a randomized controlled trial for Alzheimer’s disease, the methodology enabled prospective sample size reduction, substantially shortening trial duration, lowering costs, and accelerating the availability of effective therapies. This provides a generalizable, AI-driven framework for enhancing the efficiency of drug development.

This study introduces the novel concept of “clinical trial engineering”—the systematic manipulation of statistical analyses to generate misleading clinical trial evidence in support of drug approval, distinct from conventional paper mills. Focusing on 23 studies linked to Iran’s CinnaGen and its subsidiary Orchid Pharmed, the authors applied the INSPECT-SR credibility framework, integrating PubMed literature screening, raw data verification, and co-authorship network analysis to systematically evaluate evidentiary reliability. The investigation uncovered 180 issues spanning nine categories of systemic bias, including incomplete reporting, arithmetic errors, and design flaws. These findings reveal a structural pattern of research manipulation driven by commercial pressures, publication incentives, and permissive regulatory pathways, prompting regulatory agencies to reassess the credibility of the associated clinical evidence.