Statistical Methods in Medical Research最新文献_第7页

A Bayesian latent class approach to causal inference with longitudinal data. 纵向数据因果推理的贝叶斯潜类方法。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-01-01 Epub Date: 2024-12-12 DOI: 10.1177/09622802241298704

Kuan Liu, Olli Saarela, George Tomlinson, Brian M Feldman, Eleanor Pullenayegum

{"title":"A Bayesian latent class approach to causal inference with longitudinal data.","authors":"Kuan Liu, Olli Saarela, George Tomlinson, Brian M Feldman, Eleanor Pullenayegum","doi":"10.1177/09622802241298704","DOIUrl":"10.1177/09622802241298704","url":null,"abstract":"Bayesian methods are becoming increasingly in demand in clinical and public health comparative effectiveness research. Limited literature has explored parametric Bayesian causal approaches to handle time-dependent treatment and time-dependent covariates. In this article, building on to the work on Bayesian g-computation, we propose a fully Bayesian causal approach, implemented using latent confounder classes which represent the patient's disease and health status. Our setting is suitable when the latent class represents a true disease state that the physician is able to infer without misclassification based on manifest variables. We consider a causal effect that is confounded by the visit-specific latent class in a longitudinal setting and formulate the joint likelihood of the treatment, outcome and latent class models conditionally on the class indicators. The proposed causal structure with latent classes features dimension reduction of time-dependent confounders. We examine the performance of the proposed method using simulation studies and compare the proposed method to other causal methods for longitudinal data with time-dependent treatment and time-dependent confounding. Our approach is illustrated through a study of the effectiveness of intravenous immunoglobulin in treating newly diagnosed juvenile dermatomyositis.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"55-68"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11800708/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819216","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

Quantile outcome adaptive lasso: Covariate selection for inverse probability weighting estimator of quantile treatment effects. 分位数结果自适应套索：分位数治疗效果逆概率加权估计的协变量选择。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-01-01 Epub Date: 2024-12-12 DOI: 10.1177/09622802241299410

Takehiro Shoji, Jun Tsuchida, Hiroshi Yadohisa

{"title":"Quantile outcome adaptive lasso: Covariate selection for inverse probability weighting estimator of quantile treatment effects.","authors":"Takehiro Shoji, Jun Tsuchida, Hiroshi Yadohisa","doi":"10.1177/09622802241299410","DOIUrl":"10.1177/09622802241299410","url":null,"abstract":"When using the propensity score method to estimate the treatment effects, it is important to select the covariates to be included in the propensity score model. The inclusion of covariates unrelated to the outcome in the propensity score model led to bias and large variance in the estimator of treatment effects. Many data-driven covariate selection methods have been proposed for selecting covariates related to outcomes. However, most of them assume an average treatment effect estimation and may not be designed to estimate quantile treatment effects (QTEs), which are the effects of treatment on the quantiles of outcome distribution. In QTE estimation, we consider two relation types with the outcome as the expected value and quantile point. To achieve this, we propose a data-driven covariate selection method for propensity score models that allows for the selection of covariates related to the expected value and quantile of the outcome for QTE estimation. Assuming the quantile regression model as an outcome regression model, covariate selection was performed using a regularization method with the partial regression coefficients of the quantile regression model as weights. The proposed method was applied to artificial data and a dataset of mothers and children born in King County, Washington, to compare the performance of existing methods and QTE estimators. As a result, the proposed method performs well in the presence of covariates related to both the expected value and quantile of the outcome.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"69-84"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11800702/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819221","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

Joint quantile regression of longitudinal continuous proportions and time-to-event data: Application in medication adherence and persistence. 纵向连续比例和事件时间数据的联合分位数回归：在药物依从性和持久性中的应用。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-01-01 Epub Date: 2024-12-12 DOI: 10.1177/09622802241300845

Divan Aristo Burger, Sean van der Merwe, Janet van Niekerk, Emmanuel Lesaffre, Antoine Pironet

{"title":"Joint quantile regression of longitudinal continuous proportions and time-to-event data: Application in medication adherence and persistence.","authors":"Divan Aristo Burger, Sean van der Merwe, Janet van Niekerk, Emmanuel Lesaffre, Antoine Pironet","doi":"10.1177/09622802241300845","DOIUrl":"10.1177/09622802241300845","url":null,"abstract":"This study introduces a novel joint modeling framework integrating quantile regression for longitudinal continuous proportions data with Cox regression for time-to-event analysis, employing integrated nested Laplace approximation for Bayesian inference. Our approach facilitates an examination across the entire distribution of patient health metrics over time, including the occurrence of key health events and their impact on patient outcomes, particularly in the context of medication adherence and persistence. Integrated nested Laplace approximation's fast computational speed significantly enhances the efficiency of this process, making the model particularly suitable for applications requiring rapid data analysis and updates. Applying this model to a dataset of patients who underwent treatment with atorvastatin, we demonstrate the significant impact of targeted interventions on improving medication adherence and persistence across various patient subgroups. Furthermore, we have developed a dynamic prediction method within this framework that rapidly estimates persistence probabilities based on the latest medication adherence data, demonstrating integrated nested Laplace approximation's quick updates and prediction capability. The simulation study validates the reliability of our modeling approach, evidenced by minimal bias and appropriate credible interval coverage probabilities across different quantile levels.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"111-130"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142819220","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

Hierarchical selection of genetic and gene by environment interaction effects in high-dimensional mixed models. 高维混合模型中遗传和基因在环境相互作用下的层次选择。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-01-01 Epub Date: 2024-12-10 DOI: 10.1177/09622802241293768

Julien St-Pierre, Karim Oualkacha, Sahir Rai Bhatnagar

{"title":"Hierarchical selection of genetic and gene by environment interaction effects in high-dimensional mixed models.","authors":"Julien St-Pierre, Karim Oualkacha, Sahir Rai Bhatnagar","doi":"10.1177/09622802241293768","DOIUrl":"10.1177/09622802241293768","url":null,"abstract":"Interactions between genes and environmental factors may play a key role in the etiology of many common disorders. Several regularized generalized linear models have been proposed for hierarchical selection of gene by environment interaction effects, where a gene-environment interaction effect is selected only if the corresponding genetic main effect is also selected in the model. However, none of these methods allow to include random effects to account for population structure, subject relatedness and shared environmental exposure. In this article, we develop a unified approach based on regularized penalized quasi-likelihood estimation to perform hierarchical selection of gene-environment interaction effects in sparse regularized mixed models. We compare the selection and prediction accuracy of our proposed model with existing methods through simulations under the presence of population structure and shared environmental exposure. We show that for all simulation scenarios, including and additional random effect to account for the shared environmental exposure reduces the false positive rate and false discovery rate of our proposed method for selection of both gene-environment interaction and main effects. Using the <math><msub><mi>F</mi><mn>1</mn></msub></math> score as a balanced measure of the false discovery rate and true positive rate, we further show that in the hierarchical simulation scenarios, our method outperforms other methods for retrieving important gene-environment interaction effects. Finally, we apply our method to a real data application using the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) study, and found that our method retrieves previously reported significant loci.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"180-198"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11800719/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808101","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

Testing the equality of response rate functions for paired binary data with multiple groups. 测试多组配对二进制数据的响应率函数的相等性。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-01-01 Epub Date: 2024-12-10 DOI: 10.1177/09622802241292672

Yufei Liu, Zhiming Li, Keyi Mou, Junhong Du

{"title":"Testing the equality of response rate functions for paired binary data with multiple groups.","authors":"Yufei Liu, Zhiming Li, Keyi Mou, Junhong Du","doi":"10.1177/09622802241292672","DOIUrl":"10.1177/09622802241292672","url":null,"abstract":"In clinical trials, we often encounter observations from patients' paired organs. In paired correlated data, there exist various measures to evaluate the therapeutic responses, such as risk difference, relative risk ratio, and odds ratio. These measures are essentially some forms of response rate functions. Based on this point, this article aims to test the equality of response rate functions such that the homogeneity tests of the above measures are special cases. Under an interclass correlation model, the global and constrained maximum likelihood estimations are obtained through algorithms. Furthermore, we construct likelihood ratio, score, and Wald-type statistics and provide the explicit expressions of the corresponding tests based on the risk difference, relative risk ratio, and odds ratio. Monte Carlo simulations are conducted to compare the performance of the proposed methods in terms of the empirical type I error rates and powers. The results show that the score tests perform satisfactorily as their type I error rates are close to the specified nominal level, followed by the likelihood ratio test. The Wald-type tests exhibit poor performance, especially for small sample sizes. A real example is given to illustrate the three proposed test statistics.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"131-149"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808105","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

Youden index estimation based on group-tested data. 基于组检验数据的约登指数估计。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-01-01 Epub Date: 2024-12-10 DOI: 10.1177/09622802241295319

Jin Yang, Aiyi Liu, Neil Perkins, Zhen Chen

引用次数: 0

Estimating an adjusted risk difference in a cluster randomized trial with individual-level analyses. 在分组随机试验中利用个体水平分析估算调整后的风险差异。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-01-01 Epub Date: 2024-11-05 DOI: 10.1177/09622802241293783

Jules Antoine Pereira Macedo, Bruno Giraudeau, Escient Collaborators

{"title":"Estimating an adjusted risk difference in a cluster randomized trial with individual-level analyses.","authors":"Jules Antoine Pereira Macedo, Bruno Giraudeau, Escient Collaborators","doi":"10.1177/09622802241293783","DOIUrl":"10.1177/09622802241293783","url":null,"abstract":"In cluster randomized trials (CRTs) with a binary outcome, intervention effects are usually reported as odds ratios, but the CONSORT statement advocates reporting both a relative and an absolute intervention effect. With a simulation study, we assessed several methods to estimate a risk difference (RD) in the framework of a CRT with adjustment on both individual- and cluster-level covariates. We considered both a conditional approach (with the generalized linear mixed model [GLMM]) and a marginal approach (with the generalized estimating equation [GEE]). For both approaches, we considered the Gaussian, binomial, and Poisson distributions. When considering the binomial or Poisson distribution, we used the g-computation method to estimate the RD. Convergence problems were observed with the GEE approach, especially with low intra-cluster coefficient correlation values, small number of clusters, small mean cluster size, high number of covariates, and prevalences close to 0. All methods reported no bias. The Gaussian distribution with both approaches and binomial and Poisson distributions with the GEE approach had satisfactory results in estimating the standard error. Results for type I error and coverage rates were better with the GEE than GLMM approach. We recommend using the Gaussian distribution because of its ease of use (the RD is estimated in one step only). The GEE approach should be preferred and replaced with the GLMM approach in cases of convergence problems.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"26-44"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142584356","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

An optimal exact confidence interval for the difference of two independent binomial proportions. 两个独立二项式比例之差的最佳精确置信区间。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-01-01 Epub Date: 2024-11-26 DOI: 10.1177/09622802241298706

Xingyun Cao, Weizhen Wang, Tianfa Xie

{"title":"An optimal exact confidence interval for the difference of two independent binomial proportions.","authors":"Xingyun Cao, Weizhen Wang, Tianfa Xie","doi":"10.1177/09622802241298706","DOIUrl":"10.1177/09622802241298706","url":null,"abstract":"The difference between two proportions is the most important parameter in comparing two treatments based on independent two binomials and has garnered widespread application across various fields, particularly in clinical trials. There exists significant interest in devising optimal confidence intervals for the difference. Approximate intervals relying on asymptotic normality may lack reliability, thus calling for enhancements in exact confidence interval construction to bolster reliability and precision. In this paper, we present a novel approach that leverages the most probable test statistic and employs the <math><mi>h</mi></math>-function method to construct an optimal exact interval for the difference. We juxtapose the proposed interval against other exact intervals established through methodologies such as the Agresti-Min exact unconditional method, the Wang method, the fiducial method, and the hybrid score method. Our comparative analysis, employing the infimum coverage probability and total interval length as evaluation metrics, underscores the uniformly superior performance of the proposed interval. Additionally, we elucidate the application of these exact intervals using two real datasets.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"98-110"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142717322","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

Weighted reverse counting process (WRCP): A novel approach to quantify the overall treatment effect with multiple time-to-event outcomes by adaptive weighting. 加权反向计数过程（WRCP）：一种新的方法，通过自适应加权来量化多个时间到事件结果的总体治疗效果。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-01-01 Epub Date: 2024-12-04 DOI: 10.1177/09622802241298702

Qianmiao Gao, Wei Zhong

{"title":"Weighted reverse counting process (WRCP): A novel approach to quantify the overall treatment effect with multiple time-to-event outcomes by adaptive weighting.","authors":"Qianmiao Gao, Wei Zhong","doi":"10.1177/09622802241298702","DOIUrl":"10.1177/09622802241298702","url":null,"abstract":"In a longitudinal randomized study where multiple time-to-event outcomes are collected, the overall treatment effect may be quantified by a composite endpoint defined as the time to the first occurrence of any of the selected events including death. The reverse counting process (RCP) was recently proposed to extend the restricted mean survival time (RMST) approach with an advantage of utilizing observations of events beyond the \"first-occurrence\" endpoint. However, the interpretation may be questionable because RCP treats all events equally without considering their different associations with the overall survival. In this work, we propose a novel approach, the weighted reverse counting process (WRCP), to construct a weighted composite endpoint to evaluate the overall treatment effect. A multi-state transition model is used to model the association between events, and an adaptive weighting algorithm is developed to determine the weight for individual endpoints based on the association between the nonfatal endpoints and death using the trial data. Simulation studies are presented to compare the performance of WRCP with RCP, log-rank test and RMST approach. The results show that WRCP is a powerful and robust method to detect the overall treatment effect while controlling the clinically false positive rate well across different simulation scenarios.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"85-97"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142781110","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

Analysing matched continuous longitudinal data: A review. 分析匹配的连续纵向数据：综述。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-01-01 Epub Date: 2024-12-10 DOI: 10.1177/09622802241300823

Margaux Delporte, Marc Aerts, Geert Verbeke, Geert Molenberghs

{"title":"Analysing matched continuous longitudinal data: A review.","authors":"Margaux Delporte, Marc Aerts, Geert Verbeke, Geert Molenberghs","doi":"10.1177/09622802241300823","DOIUrl":"10.1177/09622802241300823","url":null,"abstract":"Longitudinal data are frequently encountered in medical research, where participants are followed throughout time. Additional structure and hence complexity occurs when there is pairing between the participants (e.g. matched case-control studies) or within the participants (e.g. analysis of participants' both eyes). Various modelling approaches, identified through a systematic review, are discussed, including (un)paired <math><mi>t</mi></math>-tests, multivariate analysis of variance, difference scores, linear mixed models (LMMs), and new or more recent statistical methods. Next, highlighting the importance of selecting appropriate models based on the data's characteristics, the methods are applied to both a real-life case study in ophthalmology and a simulated case-control study. Key findings include the superiority of the conditional LMM and multilevel models in handling paired longitudinal data in terms of precision. Moreover, the article underscores the impact of accounting for intra-pair correlations and missing data mechanisms. Focus will be on discussing the advantages and disadvantages of the approaches, rather than on the mathematical or computational details.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"170-179"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808099","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