Generating Chest X-Ray Counterfactuals by Specialising Foundation Image Models
本文提出一种数据和参数高效的方法，通过专业化预训练的非因果生成模型来解决胸部X光片反事实生成问题，提高反事实推理的准确性。
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Gain-function optimisation of graphical multiple testing procedures for confirmatory clinical trials
论文提出通过最大化显式增益函数来优化图形多重检验过程，以解决临床试验中选择合适图形的问题，从而更好地反映试验目标。
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When Text and Numbers Disagree: Evidence Arbitration in Large Language Models
研究通过引入合成基准探讨大型语言模型如何在文本与数值冲突时进行仲裁，发现模型偏好使用特定策略而非随机选择。
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NP-LEAP: Nonparametric Latent Exchangeability Prior for Model-Lean Borrowing from Historical Data
This study addresses the vulnerability of existing Bayesian dynamic borrowing methods to parametric model misspecification by proposing a nonparametric latent exchangeable prior framework. Integrating Bayesian model averaging with kernel methods, this approach enables individual-level assessment for historical data borrowing without requiring outcome model assumptions, thereby effectively mitigating triple misspecification risks while ensuring posterior consistency. Simulation studies demonstrate that the proposed method outperforms conventional parametric and semiparametric alternatives. Furthermore, its efficacy is successfully validated in a lung cancer clinical trial. Collectively, this work provides a robust nonparametric solution for dynamic information borrowing, offering significant improvements in reliability over traditional approaches when model assumptions are uncertain or violated.
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A framework for classifying and visualising experimental designs when subjects are measured repeatedly
This study addresses the persistent ambiguity in classifying repeated measures experimental designs, which often arises from conceptual confusion. To resolve this issue, the authors systematically clarify the core characteristics of such designs and propose a novel classification framework grounded in experimental units and randomization strategies. For the first time in this context, Hasse diagrams are introduced to visually represent the hierarchical structure of these designs. This approach effectively distinguishes among various types of repeated measures designs, eliminates terminological ambiguities, and substantially enhances both the rigor and interpretability of experimental planning and reporting.
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Generating Chest X-Ray Counterfactuals by Specialising Foundation Image Models
本文提出一种数据和参数高效的方法，通过专业化预训练的非因果生成模型来解决胸部X光片反事实生成问题，提高反事实推理的准确性。
0 citationsRead paper
Gain-function optimisation of graphical multiple testing procedures for confirmatory clinical trials
论文提出通过最大化显式增益函数来优化图形多重检验过程，以解决临床试验中选择合适图形的问题，从而更好地反映试验目标。
0 citationsRead paper
When Text and Numbers Disagree: Evidence Arbitration in Large Language Models
研究通过引入合成基准探讨大型语言模型如何在文本与数值冲突时进行仲裁，发现模型偏好使用特定策略而非随机选择。
0 citationsRead paper
NP-LEAP: Nonparametric Latent Exchangeability Prior for Model-Lean Borrowing from Historical Data
This study addresses the vulnerability of existing Bayesian dynamic borrowing methods to parametric model misspecification by proposing a nonparametric latent exchangeable prior framework. Integrating Bayesian model averaging with kernel methods, this approach enables individual-level assessment for historical data borrowing without requiring outcome model assumptions, thereby effectively mitigating triple misspecification risks while ensuring posterior consistency. Simulation studies demonstrate that the proposed method outperforms conventional parametric and semiparametric alternatives. Furthermore, its efficacy is successfully validated in a lung cancer clinical trial. Collectively, this work provides a robust nonparametric solution for dynamic information borrowing, offering significant improvements in reliability over traditional approaches when model assumptions are uncertain or violated.
0 citationsRead paper
A framework for classifying and visualising experimental designs when subjects are measured repeatedly
This study addresses the persistent ambiguity in classifying repeated measures experimental designs, which often arises from conceptual confusion. To resolve this issue, the authors systematically clarify the core characteristics of such designs and propose a novel classification framework grounded in experimental units and randomization strategies. For the first time in this context, Hasse diagrams are introduced to visually represent the hierarchical structure of these designs. This approach effectively distinguishes among various types of repeated measures designs, eliminates terminological ambiguities, and substantially enhances both the rigor and interpretability of experimental planning and reporting.
0 citationsRead paper