Estimating Pathway Treatment Effects in the Presence of Intermediate Events with Multi-State Data
本文通过假设干预和半参数有效估计方法解决临床试验中因中介事件导致的治疗效果评估问题，以理解药物对生存终点的影响路径。
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A functional joint model with baseline functional covariates: linking sitting accumulation patterns to physical function and mortality among older women
研究通过提出一种新的功能联合模型，将加速度计数据转换为每日静坐累积模式，并结合基线功能协变量，探讨了静坐行为累积模式与老年女性身体机能和死亡率之间的关系。
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Conditional Distribution Estimation for Functional Responses with Random Forests
Existing approaches often reduce functional responses to scalars or conditional mean curves, thereby failing to capture the full influence of covariates on the entire response distribution—including its shape, temporal dynamics, and variability. This work proposes a functional distributional random forest that uniquely integrates random forests with kernel methods in function spaces. By employing maximum mean discrepancy based on Sobolev kernels or operator-induced kernels at leaf nodes, the method estimates covariate-dependent full conditional distributions nonparametrically. It enables inference on arbitrary distributional functionals while preserving the realism of predicted samples. Simulations demonstrate the model’s ability to recover distributional dynamics missed by baseline methods, and an analysis of NHANES accelerometer data reveals significant covariate effects on both the median activity profiles and predictive dispersion.
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Improving the efficiency of infectious disease prevention trials using negative control outcome event times
This study addresses the limited efficiency of conventional covariate adjustment in infectious disease prevention trials due to unobserved baseline pathogen exposure. The authors introduce, for the first time, a negative control outcome (NCO)—an event time that shares the same exposure mechanism as the primary endpoint but is unaffected by the intervention—and develop an efficient estimation method tailored for doubly right-censored data. Leveraging semiparametric inference, the efficient influence function, and cross-fitting, they propose a one-step estimator that achieves multiple robustness and asymptotic efficiency, while rigorously formalizing its identification assumptions. Applied to the HVTN 704/HPTN 085 antibody-mediated prevention trial, using time to bacterial sexually transmitted infection as the NCO reduces the variance of the HIV prevention efficacy estimate by approximately 27% compared to standard approaches.
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Learning from Literature: Integrating LLMs and Bayesian Hierarchical Modeling for Oncology Trial Design
This study addresses the limitations of modern oncology trials, which often suffer from hypothesis bias and underpowered sample sizes due to reliance on incomplete literature abstracts, leading to false-positive or false-negative conclusions. To overcome this, we propose the LEAD-ONC framework, which uniquely integrates large language models (LLMs) with Bayesian hierarchical modeling to automatically extract baseline characteristics from unstructured clinical trial reports, reconstruct individual patient data, and generate survival prediction distributions for target populations. Applied to five phase III trials of first-line treatment in non-small cell lung cancer, our approach identified three clinically interpretable subgroups and predicted a 2.8-month difference in median overall survival (95% credible interval: –2.0 to 7.6) between immunotherapy monotherapy and combination regimens in a mixed-histology population, with a 45% probability of achieving more than three months of benefit—substantially enhancing the precision and prospectiveness of trial design.
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Estimating Pathway Treatment Effects in the Presence of Intermediate Events with Multi-State Data
本文通过假设干预和半参数有效估计方法解决临床试验中因中介事件导致的治疗效果评估问题，以理解药物对生存终点的影响路径。
0 citationsRead paper
A functional joint model with baseline functional covariates: linking sitting accumulation patterns to physical function and mortality among older women
研究通过提出一种新的功能联合模型，将加速度计数据转换为每日静坐累积模式，并结合基线功能协变量，探讨了静坐行为累积模式与老年女性身体机能和死亡率之间的关系。
0 citationsRead paper
Conditional Distribution Estimation for Functional Responses with Random Forests
Existing approaches often reduce functional responses to scalars or conditional mean curves, thereby failing to capture the full influence of covariates on the entire response distribution—including its shape, temporal dynamics, and variability. This work proposes a functional distributional random forest that uniquely integrates random forests with kernel methods in function spaces. By employing maximum mean discrepancy based on Sobolev kernels or operator-induced kernels at leaf nodes, the method estimates covariate-dependent full conditional distributions nonparametrically. It enables inference on arbitrary distributional functionals while preserving the realism of predicted samples. Simulations demonstrate the model’s ability to recover distributional dynamics missed by baseline methods, and an analysis of NHANES accelerometer data reveals significant covariate effects on both the median activity profiles and predictive dispersion.
0 citationsRead paper
Improving the efficiency of infectious disease prevention trials using negative control outcome event times
This study addresses the limited efficiency of conventional covariate adjustment in infectious disease prevention trials due to unobserved baseline pathogen exposure. The authors introduce, for the first time, a negative control outcome (NCO)—an event time that shares the same exposure mechanism as the primary endpoint but is unaffected by the intervention—and develop an efficient estimation method tailored for doubly right-censored data. Leveraging semiparametric inference, the efficient influence function, and cross-fitting, they propose a one-step estimator that achieves multiple robustness and asymptotic efficiency, while rigorously formalizing its identification assumptions. Applied to the HVTN 704/HPTN 085 antibody-mediated prevention trial, using time to bacterial sexually transmitted infection as the NCO reduces the variance of the HIV prevention efficacy estimate by approximately 27% compared to standard approaches.
0 citationsRead paper
Learning from Literature: Integrating LLMs and Bayesian Hierarchical Modeling for Oncology Trial Design
This study addresses the limitations of modern oncology trials, which often suffer from hypothesis bias and underpowered sample sizes due to reliance on incomplete literature abstracts, leading to false-positive or false-negative conclusions. To overcome this, we propose the LEAD-ONC framework, which uniquely integrates large language models (LLMs) with Bayesian hierarchical modeling to automatically extract baseline characteristics from unstructured clinical trial reports, reconstruct individual patient data, and generate survival prediction distributions for target populations. Applied to five phase III trials of first-line treatment in non-small cell lung cancer, our approach identified three clinically interpretable subgroups and predicted a 2.8-month difference in median overall survival (95% credible interval: –2.0 to 7.6) between immunotherapy monotherapy and combination regimens in a mixed-histology population, with a 45% probability of achieving more than three months of benefit—substantially enhancing the precision and prospectiveness of trial design.
0 citationsRead paper