Statistical Methods in Medical Research最新文献_第2页

Beyond weighting: Propensity score modeling for causal inference. 超越加权：因果推理的倾向得分模型。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-04-08 DOI: 10.1177/09622802261436960

Rong J B Zhu

{"title":"Beyond weighting: Propensity score modeling for causal inference.","authors":"Rong J B Zhu","doi":"10.1177/09622802261436960","DOIUrl":"https://doi.org/10.1177/09622802261436960","url":null,"abstract":"Propensity score weighting is a common method in causal inference methods. However, this approach faces two well-known challenges: (i) high variance due to small probability values in the denominator, and (ii) sensitivity to model specification errors when estimating propensity scores in observational studies. In this article, we establish that the expected potential outcomes, conditional on the propensity score, can be identified as a function of the propensity score. Based on this identification result, we utilize spline regression to estimate the function. Treatment effect estimation and inference are derived from the asymptotic normality of the spline regression. Our approach preserves the low bias of inverse probability weighting (IPW), benefiting from the flexibility of nonparametric models, while achieving significantly lower variance due to its model-based stability. Furthermore, we extend this method to regression-based adjustment for improved efficiency in causal inference. Extensive simulations show that our approach achieves lower variance than IPW-based methods while maintaining low bias and robustness to propensity score misspecification. A real-data application further demonstrates its reliability in inference.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802261436960"},"PeriodicalIF":1.9,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147634260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Bayesian likely responder approach for the analysis of randomized controlled trials. 随机对照试验分析的贝叶斯可能应答者方法。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-04-08 DOI: 10.1177/09622802261427026

Annan Deng, Carole Siegel, Hyung G Park

{"title":"A Bayesian likely responder approach for the analysis of randomized controlled trials.","authors":"Annan Deng, Carole Siegel, Hyung G Park","doi":"10.1177/09622802261427026","DOIUrl":"https://doi.org/10.1177/09622802261427026","url":null,"abstract":"An important goal of precision medicine is to personalize medical treatment by identifying individuals who are most likely to benefit from a specific treatment. The likely responder (LR) framework, which identifies a subpopulation where treatment response is expected to exceed a certain clinical threshold, plays a role in this effort. However, the LR framework, and more generally, data-driven subgroup analyses, often fail to account for uncertainty in the estimation of model-based data-driven subgrouping. We propose a simple two-stage approach that integrates subgroup identification with subsequent subgroup-specific inference on treatment effects. We incorporate model estimation uncertainty from the first stage into subgroup-specific treatment effect estimation in the second stage, by utilizing Bayesian posterior distributions from the first stage. We evaluate our method through simulations, demonstrating that the proposed Bayesian two-stage model produces better calibrated confidence intervals than naïve approaches. We apply our method to an international COVID-19 treatment trial, which shows substantial variation in treatment effects across data-driven subgroups.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802261427026"},"PeriodicalIF":1.9,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147634151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Model-based clustering of multiple images incorporating covariates. 包含协变量的多幅图像的基于模型的聚类。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-04-08 DOI: 10.1177/09622802251393631

Ying Cui, Jeong Hoon Jang, Robert G Mannino, Amita K Manatunga

{"title":"Model-based clustering of multiple images incorporating covariates.","authors":"Ying Cui, Jeong Hoon Jang, Robert G Mannino, Amita K Manatunga","doi":"10.1177/09622802251393631","DOIUrl":"https://doi.org/10.1177/09622802251393631","url":null,"abstract":"In this paper, we develop a novel method for clustering multiple images while adjusting for the effect of available covariates on cluster membership. The key strategy is to represent each image as two-dimensional functional data and formulate a functional latent class mixed model, which fully leverages the structural information of images while effectively addressing their high dimensionality and accounting for covariate effects. We apply the proposed method to color intensity matrices extracted from patient-sourced smartphone fingernail photos to identify distinct subgroups, while adjusting for the effect of image metadata, which may act as an effect modifier on cluster membership. Information on these subgroups can assist public health officials in low-resource settings by enabling rapid and non-invasive identification of high-risk subpopulations for anemia, thereby facilitating the timely delivery of targeted interventions. The results suggest that the three clusters identified by the proposed method correspond to varying levels of anemia risk, with 0%, 79%, and 86% of subjects in each cluster classified as anemic. These findings demonstrate the utility of the proposed method and highlight the potential of a smartphone application leveraging fingernail images for non-invasive and cost-effective anemia screening.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251393631"},"PeriodicalIF":1.9,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147634231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Implementing empirical likelihood within the causal inference framework to study causal effects of air pollution on reproductive development. 在因果推理框架内实施经验似然，研究空气污染对生殖发育的因果影响。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-04-08 DOI: 10.1177/09622802261435966

Sima Sharghi, Kevin E Stoll, Sally W Thurston, Emily Barrett, Brent Johnson

引用次数: 0

Sensitivity bounds for bias in hazard ratios: A causal hazard perspective. 风险比偏倚的敏感性界限：因果风险视角。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-04-02 DOI: 10.1177/09622802261436811

Yan-Lin Chen, Hsiang-Hsi Hung, Sheng-Hsuan Lin

{"title":"Sensitivity bounds for bias in hazard ratios: A causal hazard perspective.","authors":"Yan-Lin Chen, Hsiang-Hsi Hung, Sheng-Hsuan Lin","doi":"10.1177/09622802261436811","DOIUrl":"https://doi.org/10.1177/09622802261436811","url":null,"abstract":"The Cox proportional hazards model has popularized the conventional hazard ratio as a standard measure for assessing the effect of exposure on time-to-event outcomes. However, as noted in Hernán's influential critique, interpreting the hazard ratio as a causal effect can introduce substantial selection bias. While Hernán's work discusses this issue qualitatively, it lacks a method to quantify the extent of bias or the strength an unmeasured confounder would need to account for it. Our study fills this gap by proposing a nonparametric sensitivity analysis framework, inspired by Ding and VanderWeele, which quantifies \"how hazardous the hazard ratio is.\" Using the causal hazard ratio proposed by Aalen as the \"true hazard ratio,\" we derive an upper bound for the bias in the conventional hazard ratio (termed HR bias) in both discrete and continuous time settings, demonstrating that this bias accumulates over time. Additionally, we derive the E-value for HR bias, allowing researchers to evaluate whether unmeasured confounding alone could fully explain the conventional hazard ratio under a certain level of the causal hazard ratio. This approach provides researchers with a robust sensitivity analysis framework, reducing reliance on restrictive assumptions and enhancing the reliability of causal interpretations in survival analysis.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802261436811"},"PeriodicalIF":1.9,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147610052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Linearized maximum rank correlation estimation of doubly truncated data. 双截断数据的线性化最大秩相关估计。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-04-02 DOI: 10.1177/09622802261432834

Peijie Wang, Qihao Wang, Jianguo Sun

引用次数: 0

Vaccine efficacy estimands and power considerations. 疫苗效力估计和功率考虑。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-04-02 DOI: 10.1177/09622802251412833

Andrea Callegaro, Nathan W Bean

引用次数: 0

Improving finite sample performance of causal discovery by exploiting temporal structure. 利用时间结构改进因果发现的有限样本性能。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-03-31 DOI: 10.1177/09622802261422162

Christine Bang, Janine Witte, Ronja Foraita, Vanessa Didelez

引用次数: 0

Eliminating residual confounding in the stratified estimator via smoothing along with the propensity score. 通过平滑和倾向分数消除分层估计器中的残留混淆。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-03-31 DOI: 10.1177/09622802261432998

Naoto Tsujimoto, Satoshi Hattori

{"title":"Eliminating residual confounding in the stratified estimator via smoothing along with the propensity score.","authors":"Naoto Tsujimoto, Satoshi Hattori","doi":"10.1177/09622802261432998","DOIUrl":"https://doi.org/10.1177/09622802261432998","url":null,"abstract":"The stratified estimator by the propensity score is one of the most popular estimator for the average causal effect in the presence of confounding. Despite of its advantages of robustness and simplicity, it has a serious shortcoming of residual confounding even with the correctly specified propensity score. On the other hand, the inverse probability weighting estimator by the propensity score is free from residual confounding and many corresponding variants have been proposed. Wang et al. (2021, Robust estimation of propensity score weights via subclassification) pointed it out that the stratified estimator can be regarded as a special case of the inverse probability weighting estimator with piecewise constant propensity score and proposed a method eliminating residual confounding in the stratified estimator. In this paper, we provide an alternative interpretation of the stratified estimator as the outcome regression with a piecewise constant function over the propensity score. By considering kernel smoothing, an estimator free from residual confounding is proposed based on the stratified estimator, which preserves the robustness of the stratified estimator. We also propose a doubly robust estimator, which does not rely on the inverse probability weighting.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802261432998"},"PeriodicalIF":1.9,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147582389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Penalized estimation of linear transformation models for interval-censored data with time-dependent covariates. 具有时变协变量的区间截尾数据线性变换模型的惩罚估计。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-03-31 DOI: 10.1177/09622802261433000

Minggen Lu, Yahui Zhang, Chin-Shang Li, Guogen Shan

引用次数: 0