Statistical Methods in Clinical Trials

Derniers papiers

• Augmented Hypothesis Testing with Persona-Based LLM Simulations

  Ziyad Benomar, Aymen Al Marjani, Paul Missault, Saab Mansour · 22 septembre 2026

  A/B testing requires large sample sizes, long timelines, and significant costs. When auxiliary predictions of experimental outcomes are available from machine learning models, uncertain prediction quality precludes replacing human experiments entirely, yet these predictions may still contain useful …

• Learning a Size-Weight Frontier for Synthetic-Augmented Inference

  Chengpiao Huang, Kaizheng Wang · 31 août 2026

  Synthetic data can improve statistical inference when real data are scarce, but naively treating synthetic samples as real data can introduce bias and lead to unreliable inference. We develop a general framework for synthetic-augmented inference across a population of related tasks. It characterizes…

• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation

  Jiachun Li, David Simchi-Levi · 1 juillet 2026

  Adaptive experiments for average treatment effects (ATE) require randomized allocations balancing valid inference with statistical efficiency. The oracle design is a covariate-dependent Neyman rule governed by unknown arm-conditional outcome variances. We investigate whether this sequential variance…

• Brownian Motion with a Pulse: A Biostatistician's Guide to Diffusions, Bridges, Functional PCA, and First-Passage Models

  Eliuvish Han Cui · 23 juin 2026

  Brownian motion is a compact mathematical language for continuous-time uncertainty in biostatistics. This tutorial develops the process from construction and path properties to tools that recur in applied biomedical work: the Markov and strong Markov properties, the Karhunen-Loeve expansion, functio…

• Finite Resources False Discovery Rate Control in Structured Hypothesis Spaces

  Binyamin Perets, Shie Mannor · 16 juin 2026

  Scientific discovery relies on large-scale hypothesis testing. However, the capacity to identify true discoveries while controlling false discovery faces major challenges: obtaining relevant reference data (the null distribution) is resource-intensive, leaving finite-data uncertainty, and the proced…

• Everywhere Valid Bounds on False Discovery Proportions in Conformal Inference

  Ziang Song, Ying Jin, Emmanuel J. Cand\`es · 21 mai 2026

  Modern applications of conformal inference to multiple testing problems, such as outlier detection and candidate selection, often involve selecting test samples whose conformal p-values fall below a threshold. The quality of such methods is often measured by the false discovery proportion (FDP), def…

• General Frameworks for Conditional Two-Sample Testing

  Seongchan Lee, Suman Cha, Ilmun Kim · 5 mai 2026

  We study the problem of conditional two-sample testing, which aims to determine whether two populations have the same distribution after accounting for confounding factors. This problem commonly arises in various applications, such as domain adaptation and algorithmic fairness, where comparing two g…

• ClinicalReTrial: Clinical Trial Redesign with Self-Evolving Agents

  Sixue Xing, Kerui Wu, Xuanye Xia, Meng Jiang, Jintai Chen, Tianfan Fu · 6 avril 2026

  Clinical trials constitute a critical yet exceptionally challenging and costly stage of drug development (\$2.6B per drug), where protocols are encoded as complex natural language documents, motivating the use of AI systems beyond manual analysis. Existing AI methods accurately predict trial failure…

• Early Risk Stratification of Dosing Errors in Clinical Trials Using Machine Learning

  F\'elicien H\^eche, Sohrab Ferdowsi, Anthony Yazdani, Sara Sansaloni-Pastor, Douglas Teodoro · 27 février 2026

  Objective: The objective of this study is to develop a machine learning (ML)-based framework for early risk stratification of clinical trials (CTs) according to their likelihood of exhibiting a high rate of dosing errors, using information available prior to trial initiation. Materials and Methods: …

• Statistical Inference Leveraging Synthetic Data with Distribution-Free Guarantees

  Meshi Bashari, Yonghoon Lee, Roy Maor Lotan, Edgar Dobriban, Yaniv Romano · 19 février 2026

  The rapid proliferation of high-quality synthetic data -- generated by advanced AI models or collected as auxiliary data from related tasks -- presents both opportunities and challenges for statistical inference. This paper introduces a GEneral Synthetic-Powered Inference (GESPI) framework that wrap…

• Synthetic-Powered Multiple Testing with FDR Control

  Yonghoon Lee, Meshi Bashari, Edgar Dobriban, Yaniv Romano · 19 février 2026

  Multiple hypothesis testing with false discovery rate (FDR) control is a fundamental problem in statistical inference, with broad applications in genomics, drug screening, and outlier detection. In many such settings, researchers may have access not only to real experimental observations but also to…

• Distribution-free two-sample testing with blurred total variation distance

  Rohan Hore, Rina Foygel Barber · 6 février 2026

  Two-sample testing, where we aim to determine whether two distributions are equal or not equal based on samples from each one, is challenging if we cannot place assumptions on the properties of the two distributions. In particular, certifying equality of distributions, or even providing a tight uppe…

• Conformal Prediction for Causal Effects of Continuous Treatments

  Maresa Schr\"oder, Dennis Frauen, Jonas Schweisthal, Konstantin He{\ss}, Valentyn Melnychuk, Stefan Feuerriegel · 4 février 2026

  Uncertainty quantification of causal effects is crucial for safety-critical applications such as personalized medicine. A powerful approach for this is conformal prediction, which has several practical benefits due to model-agnostic finite-sample guarantees. Yet, existing methods for conformal predi…

• Hybrid Meta-learners for Estimating Heterogeneous Treatment Effects

  Zhongyuan Liang, Lars van der Laan, Ahmed Alaa · 3 février 2026

  Estimating conditional average treatment effects (CATE) from observational data involves modeling decisions that differ from supervised learning, particularly concerning how to regularize model complexity. Previous approaches can be grouped into two primary "meta-learner" paradigms that impose disti…

• Enhancing Study-Level Inference from Clinical Trial Papers via Reinforcement Learning-Based Numeric Reasoning

  Massimiliano Pronesti, Michela Lorandi, Paul Flanagan, Oisin Redmond, Anya Belz, Yufang Hou · 26 janvier 2026

  Systematic reviews in medicine play a critical role in evidence-based decision-making by aggregating findings from multiple studies. A central bottleneck in automating this process is extracting numeric evidence and determining study-level conclusions for specific outcomes and comparisons. Prior wor…

• ClinicalReTrial: A Self-Evolving AI Agent for Clinical Trial Protocol Optimization

  Sixue Xing, Xuanye Xia, Kerui Wu, Meng Jiang, Jintai Chen, Tianfan Fu · 5 janvier 2026

  Clinical trial failure remains a central bottleneck in drug development, where minor protocol design flaws can irreversibly compromise outcomes despite promising therapeutics. Although cutting-edge AI methods achieve strong performance in predicting trial success, they are inherently reactive for me…

• A Survey on LLM-Assisted Clinical Trial Recruitment

  Shrestha Ghosh, Moritz Schneider, Carina Reinicke, Carsten Eickhoff · 1 janvier 2026

  Recent advances in LLMs have greatly improved general-domain NLP tasks. Yet, their adoption in critical domains, such as clinical trial recruitment, remains limited. As trials are designed in natural language and patient data is represented as both structured and unstructured text, the task of match…

• How many patients could we save with LLM priors?

  Shota Arai, David Selby, Andrew Vargo, Sebastian Vollmer · 21 novembre 2025

  Imagine a world where clinical trials need far fewer patients to achieve the same statistical power, thanks to the knowledge encoded in large language models (LLMs). We present a novel framework for hierarchical Bayesian modeling of adverse events in multi-center clinical trials, leveraging LLM-info…

• Selective Risk Certification for LLM Outputs via Information-Lift Statistics: PAC-Bayes, Robustness, and Skeleton Design

  Sanjeda Akter, Ibne Farabi Shihab, Anuj Sharma · 20 novembre 2025

  Large language models often produce confident but incorrect outputs, creating a critical need for reliable uncertainty quantification with formal abstention guarantees. We introduce information-lift certificates that compare model probabilities to a skeleton baseline, accumulating evidence through s…

• Cohort Discovery: A Survey on LLM-Assisted Clinical Trial Recruitment

  Shrestha Ghosh, Moritz Schneider, Carina Reinicke, Carsten Eickhoff · 28 octobre 2025

  Recent advances in LLMs have greatly improved general-domain NLP tasks. Yet, their adoption in critical domains, such as clinical trial recruitment, remains limited. As trials are designed in natural language and patient data is represented as both structured and unstructured text, the task of match…