Statistical Methods in Medical Research最新文献_第4页

Boosting distributional copula regression for bivariate binary, discrete and mixed responses. 促进二元、离散和混合响应的分布联结回归。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-05-01 Epub Date: 2025-03-21 DOI: 10.1177/09622802241313294

Guillermo Briseño Sanchez, Nadja Klein, Hannah Klinkhammer, Andreas Mayr

{"title":"Boosting distributional copula regression for bivariate binary, discrete and mixed responses.","authors":"Guillermo Briseño Sanchez, Nadja Klein, Hannah Klinkhammer, Andreas Mayr","doi":"10.1177/09622802241313294","DOIUrl":"10.1177/09622802241313294","url":null,"abstract":"Motivated by challenges in the analysis of biomedical data and observational studies, we develop statistical boosting for the general class of bivariate distributional copula regression with arbitrary marginal distributions, which is suited for binary, count, continuous or mixed outcomes. To arrive at a flexible model for the entire conditional distribution, not only the marginal distribution parameters but also the copula parameters are related to covariates through additive predictors. We suggest estimation by means of an adapted component-wise gradient boosting algorithm. A key benefit of boosting as opposed to classical likelihood or Bayesian estimation is the implicit data-driven variable selection mechanism as well as shrinkage. To the best of our knowledge, our implementation is the only one that combines a wide range of covariate effects, marginal distributions, copula functions, and implicit data-driven variable selection. We showcase the versatility of our approach to data from genetic epidemiology, healthcare utilization and childhood undernutrition. Our developments are implemented in the R package gamboostLSS, fostering transparent and reproducible research.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"887-902"},"PeriodicalIF":1.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12177205/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143674451","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

Additive hazard causal model with a binary instrumental variable. 具有二元工具变量的加性危险因果模型。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-05-01 Epub Date: 2025-03-20 DOI: 10.1177/09622802251314288

Zhisong Zhao, Huijuan Ma, Yong Zhou

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

Methodology for supervised optimization of the construction of physician shared-patient networks. 医生-患者共享网络构建的监督优化方法学。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-05-01 Epub Date: 2025-03-31 DOI: 10.1177/09622802241313281

A James O'Malley, Yifan Zhao, Carly Bobak, Chuanling Qin, Erika L Moen, Daniel N Rockmore

{"title":"Methodology for supervised optimization of the construction of physician shared-patient networks.","authors":"A James O'Malley, Yifan Zhao, Carly Bobak, Chuanling Qin, Erika L Moen, Daniel N Rockmore","doi":"10.1177/09622802241313281","DOIUrl":"10.1177/09622802241313281","url":null,"abstract":"There is growing use of shared-patient physician networks in health services research and practice, but minimal study of the consequences of decisions made in constructing them. To address this gap, we surveyed physician employees of a National Physician Organization (NPO) on their peer physician relationships. Using the physicians' survey nominations as ground truths, we evaluated the diagnostic accuracy of shared-patient edge-weights and the optimal construction of physician networks from sequences of patient-physician encounters. To further improve diagnostic accuracy, we optimized network construction with respect to the within-dyad difference and summation of edge-strength (two orthogonal measures), optimally combining them to form a final edge-weight. To achieve these goals, we develop statistical procedures to quantify the extent that directionality and other features of referral paths yield edge-weights with improved diagnostic properties. We also develop network models of the survey nominations incorporating directed (edge) and undirected (dyadic) shared-patient network measures as edge and dyad attributes to demonstrate that the measurement of the network as a whole is improved. Finally, we estimate the association of the physicians' centrality in the NPO shared-patient network (a sociocentric feature that cannot be evaluated for the partially-measured survey-based network) with their beliefs regarding physician peer-influence.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"938-955"},"PeriodicalIF":1.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143753847","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

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

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

Partial areas under the curve of the cumulative distribution function as a new composite estimand for randomized clinical trials. 累积分布函数曲线下的部分面积作为随机临床试验的一种新的复合估计。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-04-28 DOI: 10.1177/09622802251314195

Masataka Taguri, Kenichi Hayashi

{"title":"Partial areas under the curve of the cumulative distribution function as a new composite estimand for randomized clinical trials.","authors":"Masataka Taguri, Kenichi Hayashi","doi":"10.1177/09622802251314195","DOIUrl":"10.1177/09622802251314195","url":null,"abstract":"Clinical trials often face the challenge of post-randomization events, such as the initiation of rescue therapy or the premature discontinuation of randomized treatment. Such events, called \"intercurrent events\" (ICEs) in ICH E9(R1), may influence the estimation and interpretation of treatment effects. According to ICH E9(R1), there are five strategies for handling ICEs. This study focuses on the composite strategy, which incorporates ICEs in the outcome of interest and defines the treatment effects using composite endpoints that combine the measured continuous variables and ICEs. An advantage of this strategy is that it avoids the occurrence of missing data because they are defined as part of the outcome of interest. In this study, we propose a new composite estimand: the difference in the partial areas under the curves (pAUCs) of the cumulative distribution function. While the pAUC is closely related to the trimmed mean approach proposed by Permutt and Li, it offers the advantage of allowing pre-specification of the cutoff value for a \"good\" response based on clinical considerations. This ensures that the pAUC can be calculated irrespective of the proportion of ICEs. We describe the causal interpretation of our method and its relationship with two other strategies (treatment policy and hypothetical strategies) using a potential outcome framework. We present simulation results in which our method performs reasonably well compared to several existing approaches in terms of type I error, power, and the proportion of undefined test statistics.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251314195"},"PeriodicalIF":1.6,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144062239","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

Covariate selection for optimizing balance with an innovative adaptive randomization approach. 用一种创新的自适应随机化方法优化平衡的协变量选择。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-04-13 DOI: 10.1177/09622802241313283

Ziqing Guo, Yang Liu, Lucy Xia

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