Statistical Methods in Medical Research最新文献_第9页

Causal mediation analysis for time-to-event mediator and outcome in the presence of left truncation. 左截断存在的时间-事件中介和结果的因果中介分析。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-05-01 Epub Date: 2025-03-24 DOI: 10.1177/09622802241313291

Jih-Chang Yu, Yen-Tsung Huang

{"title":"Causal mediation analysis for time-to-event mediator and outcome in the presence of left truncation.","authors":"Jih-Chang Yu, Yen-Tsung Huang","doi":"10.1177/09622802241313291","DOIUrl":"10.1177/09622802241313291","url":null,"abstract":"We propose a causal mediation approach to semi-competing risks under left truncation sampling by considering an intermediate event as a mediator and a terminal event as an outcome. We focus on the causal relationship from exposure to the terminal outcome in relation to the intermediate event. In particular, we study the direct effect, the effect of exposure on the terminal event that is not through the intermediate event, and the indirect effect-the effect of exposure on the terminal event that is mediated through the intermediate event. We propose nonparametric and semiparametric methods, both accounting for left truncation. The nonparametric estimator can be viewed as a model-free time-varying Nelson-Aalen estimator that is robust to model misspecification. The semiparametric estimator calculated with the Cox proportional hazards model enjoys flexibility in adjusting for potential confounders as covariates. The asymptotic properties for both estimators, including uniform consistency and weak convergence, were established using the martingale theorem and functional delta method. The finite sample performance of the proposed estimators was evaluated through extensive numerical studies that investigated the influences of left truncation, confounding, and sample size. The utility of the proposed methods was illustrated using a hepatitis study.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1001-1017"},"PeriodicalIF":1.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143693419","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

Outcome adaptive propensity score methods for handling censoring and high-dimensionality: Application to insurance claims. 处理审查和高维的结果自适应倾向评分方法：在保险索赔中的应用。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-05-01 Epub Date: 2025-02-27 DOI: 10.1177/09622802241306856

Jiacong Du, Youfei Yu, Min Zhang, Zhenke Wu, Andrew M Ryan, Bhramar Mukherjee

{"title":"Outcome adaptive propensity score methods for handling censoring and high-dimensionality: Application to insurance claims.","authors":"Jiacong Du, Youfei Yu, Min Zhang, Zhenke Wu, Andrew M Ryan, Bhramar Mukherjee","doi":"10.1177/09622802241306856","DOIUrl":"10.1177/09622802241306856","url":null,"abstract":"Propensity scores are commonly used to reduce the confounding bias in non-randomized observational studies for estimating the average treatment effect. An important assumption underlying this approach is that all confounders that are associated with both the treatment and the outcome of interest are measured and included in the propensity score model. In the absence of strong prior knowledge about potential confounders, researchers may agnostically want to adjust for a high-dimensional set of pre-treatment variables. As such, variable selection procedure is needed for propensity score estimation. In addition, studies show that including variables related to treatment only in the propensity score model may inflate the variance of the treatment effect estimators, while including variables that are predictive of only the outcome can improve efficiency. In this article, we propose to incorporate outcome-covariate relationship in the propensity score model by including the predicted binary outcome probability as a covariate. Our approach can be easily adapted to an ensemble of variable selection methods, including regularization methods and modern machine-learning tools based on classification and regression trees. We evaluate our method to estimate the treatment effects on a binary outcome, which is possibly censored, across multiple treatment groups. Simulation studies indicate that incorporating outcome probability for estimating the propensity scores can improve statistical efficiency and protect against model misspecification. The proposed methods are applied to a cohort of advanced-stage prostate cancer patients identified from a private insurance claims database for comparing the adverse effects of four commonly used drugs for treating castration-resistant prostate cancer.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"847-866"},"PeriodicalIF":1.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143516792","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

On flexible inverse probability of treatment and intensity weighting: Informative censoring, variable selection, and weight trimming. 柔性逆概率处理和强度加权：信息筛选、变量选择和权重修剪。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-05-01 Epub Date: 2025-04-28 DOI: 10.1177/09622802241313289

Grace Tompkins, Joel A Dubin, Michael Wallace

{"title":"On flexible inverse probability of treatment and intensity weighting: Informative censoring, variable selection, and weight trimming.","authors":"Grace Tompkins, Joel A Dubin, Michael Wallace","doi":"10.1177/09622802241313289","DOIUrl":"10.1177/09622802241313289","url":null,"abstract":"Many observational studies feature irregular longitudinal data, where the observation times are not common across individuals in the study. Furthermore, the observation times may be related to the longitudinal outcome. In this setting, failing to account for the informative observation process may result in biased causal estimates. This can be coupled with other sources of bias, including nonrandomized treatment assignments and informative censoring. This paper provides an overview of a flexible weighting method used to adjust for informative observation processes and nonrandomized treatment assignments. We investigate the sensitivity of the flexible weighting method to violations of the noninformative censoring assumption, examine variable selection for the observation process weighting model, known as inverse intensity weighting, and look at the impacts of weight trimming for the flexible weighting model. We show that the flexible weighting method is sensitive to violations of the noninformative censoring assumption and that a previously proposed extension fails under such violations. We also show that variables confounding the observation and outcome processes should always be included in the observation intensity model. Finally, we show scenarios where weight trimming should and should not be used, and highlight sensitivities of the flexible inverse probability of treatment and intensity weighting method to extreme weights. We conclude with an application of the methodology to a real data set to examine the impacts of household water sources on malaria diagnoses.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"915-937"},"PeriodicalIF":1.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144015142","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

Modeling the ratio of correlated biomarkers using copula regression. 利用copula回归对相关生物标志物的比例进行建模。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-05-01 Epub Date: 2025-02-11 DOI: 10.1177/09622802241313293

Moritz Berger, Nadja Klein, Michael Wagner, Matthias Schmid

引用次数: 0

A new cure model accounting for longitudinal data and flexible patterns of hazard ratios over time. 一个新的治愈模型，考虑纵向数据和随时间变化的风险比的灵活模式。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-04-01 Epub Date: 2025-02-28 DOI: 10.1177/09622802251320793

Can Xie, Xuelin Huang, Ruosha Li, Yu Shen, Nicholas J Short, Kapil N Bhalla

{"title":"A new cure model accounting for longitudinal data and flexible patterns of hazard ratios over time.","authors":"Can Xie, Xuelin Huang, Ruosha Li, Yu Shen, Nicholas J Short, Kapil N Bhalla","doi":"10.1177/09622802251320793","DOIUrl":"10.1177/09622802251320793","url":null,"abstract":"With the advancement of medical treatments, many historically incurable diseases have become curable. An accurate estimation of the cure rates is of great interest. When there are no clear biomarker indicators for cure, the estimation of cure rate is intertwined with and influenced by the specification of hazard functions for uncured patients. Consequently, the commonly used proportional hazards (PH) assumption, when violated, may lead to biased cure rate estimation. Meanwhile, longitudinal biomarker measurements for individual patients are usually available. To accommodate non-PH functions and incorporate individual longitudinal biomarker trajectories, we propose a new joint model for cure, survival, and longitudinal data, with hazard ratios between different covariate subgroups flexibly varying over time. The proposed joint model has individual random effects shared between its longitudinal and cure-survival submodels. The regression parameters are estimated by maximization of the non-parametric likelihood via the Monte Carlo expectation-maximization algorithm. The standard error estimation applies a jackknife resampling method. In simulation studies, we consider crossing and non-crossing survival curves, and the proposed model provides unbiased estimates for the cure rates. Our proposed joint cure model is illustrated via a study of chronic myeloid leukemia.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"683-700"},"PeriodicalIF":1.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12075895/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143524483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The relative efficiency of staircase and stepped wedge cluster randomised trial designs. 阶梯型和阶梯型楔形聚类随机试验设计的相对效率。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-04-01 Epub Date: 2025-02-16 DOI: 10.1177/09622802251317613

Kelsey L Grantham, Andrew B Forbes, Richard Hooper, Jessica Kasza

{"title":"The relative efficiency of staircase and stepped wedge cluster randomised trial designs.","authors":"Kelsey L Grantham, Andrew B Forbes, Richard Hooper, Jessica Kasza","doi":"10.1177/09622802251317613","DOIUrl":"10.1177/09622802251317613","url":null,"abstract":"The stepped wedge design is an appealing longitudinal cluster randomised trial design. However, it places a large burden on participating clusters by requiring all clusters to collect data in all periods of the trial. The staircase design may be a desirable alternative: treatment sequences consist of a limited number of measurement periods before and after the implementation of the intervention. In this article, we explore the relative efficiency of the stepped wedge design to several variants of the 'basic staircase' design, which has one control followed by one intervention period in each sequence. We model outcomes using linear mixed models and consider a sampling scheme where each participant is measured once. We first consider a basic staircase design embedded within the stepped wedge design, then basic staircase designs with either more clusters or larger cluster-period sizes, with the same total number of participants and with fewer total participants than the stepped wedge design. The relative efficiency of the designs depends on the intracluster correlation structure, correlation parameters and the trial configuration, including the number of sequences and cluster-period size. For a wide range of realistic trial settings, a basic staircase design will deliver greater statistical power than a stepped wedge design with the same number of participants, and in some cases, with even fewer total participants.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"701-716"},"PeriodicalIF":1.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12075890/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143433771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Simultaneous variable selection and estimation for a partially linear Cox model. 部分线性Cox模型的同时变量选择与估计。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-04-01 Epub Date: 2025-03-20 DOI: 10.1177/09622802251322988

Tingting Cai, Mengqi Xie, Tao Hu, Jianguo Sun

引用次数: 0

Generalized framework for identifying meaningful heterogenous treatment effects in observational studies: A parametric data-adaptive G-computation approach. 在观察性研究中识别有意义的异质性治疗效果的广义框架：参数数据自适应g计算方法。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-04-01 Epub Date: 2025-02-24 DOI: 10.1177/09622802251316969

Roch A Nianogo, Stephen O'Neill, Kosuke Inoue

{"title":"Generalized framework for identifying meaningful heterogenous treatment effects in observational studies: A parametric data-adaptive G-computation approach.","authors":"Roch A Nianogo, Stephen O'Neill, Kosuke Inoue","doi":"10.1177/09622802251316969","DOIUrl":"10.1177/09622802251316969","url":null,"abstract":"There has been a renewed interest in identifying heterogenous treatment effects (HTEs) to guide personalized medicine. The objective was to illustrate the use of a step-by-step transparent parametric data-adaptive approach (the generalized HTE approach) based on the G-computation algorithm to detect heterogenous subgroups and estimate meaningful conditional average treatment effects (CATE). The following seven steps implement the generalized HTE approach: Step 1: Select variables that satisfy the backdoor criterion and potential effect modifiers; Step 2: Specify a flexible saturated model including potential confounders and effect modifiers; Step 3: Apply a selection method to reduce overfitting; Step 4: Predict potential outcomes under treatment and no treatment; Step 5: Contrast the potential outcomes for each individual; Step 6: Fit cluster modeling to identify potential effect modifiers; Step 7: Estimate subgroup CATEs. We illustrated the use of this approach using simulated and real data. Our generalized HTE approach successfully identified HTEs and subgroups defined by all effect modifiers using simulated and real data. Our study illustrates that it is feasible to use a step-by-step parametric and transparent data-adaptive approach to detect effect modifiers and identify meaningful HTEs in an observational setting. This approach should be more appealing to epidemiologists interested in explanation.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"648-662"},"PeriodicalIF":1.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12075891/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143493492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interval estimation for the Youden index of a continuous diagnostic test with verification biased data. 带有验证偏差数据的连续诊断测试尤登指数的区间估计。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-04-01 Epub Date: 2025-03-20 DOI: 10.1177/09622802251322989

Shirui Wang, Shuangfei Shi, Gengsheng Qin

{"title":"Interval estimation for the Youden index of a continuous diagnostic test with verification biased data.","authors":"Shirui Wang, Shuangfei Shi, Gengsheng Qin","doi":"10.1177/09622802251322989","DOIUrl":"10.1177/09622802251322989","url":null,"abstract":"In medical diagnostic studies, the Youden index plays a crucial role as a comprehensive measurement of the diagnostic test effectiveness, aiding in determining the optimal threshold values by maximizing the sum of sensitivity and specificity. However, in clinical practice, verification of true disease status might be partially missing and estimators based on partially validated subjects are usually biased. While verification bias-corrected estimation methods for the receiver operating characteristic curve have been widely studied, no such results have been specifically developed for the Youden index. In this paper, we propose bias-corrected interval estimation methods for the Youden index of a continuous test under the missing-at-random assumption. Based on four estimators (full imputation (FI), mean score imputation, inverse probability weighting, and the semiparametric efficient (SPE)) introduced by Alonzo and Pepe for handling verification bias, we develop multiple confidence intervals for the Youden index by applying bootstrap resampling and the method of variance estimates recovery (MOVER). Extensive simulation and real data studies show that when the disease model is correctly specified, MOVER-FI intervals yield better coverage probability. We also observe a tradeoff between methods when the verification proportion is low: Bootstrap approaches achieve higher accuracy, while MOVER approaches deliver greater precision. Remarkably, bootstrap-SPE interval exhibit appealing doubly robustness to model misspecification and perform adequately across almost all scenarios considered. Based on our findings, we recommend using the bootstrap-SPE intervals when the true disease model is unknown, and the MOVERws-FI interval if the true disease model can be well approximated.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"796-811"},"PeriodicalIF":1.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143670981","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

Distribution-free control charts for mixed-type data based on rank of interpoint distances. 基于点间距离秩的混合型数据无分布控制图。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-04-01 Epub Date: 2025-02-10 DOI: 10.1177/09622802251316964

Guojun Liu, Jyun-You Chiang, Yajie Bai, Zhengcheng Mou

{"title":"Distribution-free control charts for mixed-type data based on rank of interpoint distances.","authors":"Guojun Liu, Jyun-You Chiang, Yajie Bai, Zhengcheng Mou","doi":"10.1177/09622802251316964","DOIUrl":"10.1177/09622802251316964","url":null,"abstract":"Multivariate control charts have found wide application in healthcare, yet they primarily cater to continuous or categorical variables. However, the emergence of mixed-type data has sparked interest in adapting traditional control charts to handle such complexity. Unfortunately, existing methods often struggle to effectively manage this complexity, particularly in scenarios with limited historical in-control data. In response, this article introduces three distribution-free control charts specifically designed for monitoring mixed-type processes. The proposed approach revolves around computing distances between observations and a specified point, thereby reducing the data to a single dimension. Subsequently, the ranks of these one-dimensional distances are leveraged to develop monitoring statistics. Furthermore, to facilitate dimensionality reduction, a novel distance measure tailored for mixed-type data is introduced. Extensive validation of our proposed method is conducted through comprehensive simulation experiments. Moreover, we demonstrate the practical applicability of the proposed method using an example related to heart disease.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"633-647"},"PeriodicalIF":1.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143391936","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