Communications in Statistics-Simulation and Computation最新文献

A New Estimation Algorithm for Destructive Cure Model: Illustration with Exponentially Weighted Poisson Competing Risks. 破坏性治愈模型的一种新的估计算法：指数加权泊松竞争风险的说明。

IF 0.8 4区数学

Communications in Statistics-Simulation and Computation Pub Date : 2026-04-08 DOI: 10.1080/03610918.2026.2637684

Suvra Pal, Souvik Roy

{"title":"A New Estimation Algorithm for Destructive Cure Model: Illustration with Exponentially Weighted Poisson Competing Risks.","authors":"Suvra Pal, Souvik Roy","doi":"10.1080/03610918.2026.2637684","DOIUrl":"10.1080/03610918.2026.2637684","url":null,"abstract":"We propose an improved estimation method for the destructive cure rate model by introducing a generic maximum likelihood algorithm, the sequential quadratic Hamiltonian (SQH) scheme, which employs a gradient-free optimization approach. The SQH algorithm is applied to the destructive cure model with exponentially weighted Poisson competing risks, and its performance is evaluated through a comprehensive simulation study. Specifically, we compare the model-fitting accuracy of SQH with the recently developed conjugate gradient line search (CGLS) algorithm. Given that the CGLS method has been shown to outperform the widely used expectation-maximization algorithm and other optimization routines available in R (e.g., optim, nlm, and Rcgmin), our focus is on assessing whether the SQH algorithm can offer further improvements. Simulation results show that the SQH algorithm yields parameter estimates with consistently lower bias and root mean square error, resulting in more accurate and precise cure rate estimation. Furthermore, due to its gradient-free nature, the SQH algorithm requires less CPU time than CGLS. These advantages position the SQH algorithm as a preferred estimation method over CGLS for the destructive cure rate model. To demonstrate its practical utility, we apply the SQH algorithm to a well-known melanoma dataset and present the analysis results.","PeriodicalId":55240,"journal":{"name":"Communications in Statistics-Simulation and Computation","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13089356/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147724678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian variable selection for logistic regression with a differentially misclassified binary covariate. 歧义错分类二元协变量逻辑回归的贝叶斯变量选择。

IF 0.8 4区数学

Communications in Statistics-Simulation and Computation Pub Date : 2025-05-05 DOI: 10.1080/03610918.2025.2496305

Daniel P Beavers, Yutong Li, James D Stamey, Stephanie Powers, Walter T Ambrosius

引用次数: 0

Statistical methods for assessing treatment effects on ordinal outcomes using observational data. 使用观察数据评估治疗效果对有序结果的统计方法。

IF 0.8 4区数学

Communications in Statistics-Simulation and Computation Pub Date : 2025-04-14 DOI: 10.1080/03610918.2025.2488945

Huirong Hu, Qi Zheng, Maiying Kong

{"title":"Statistical methods for assessing treatment effects on ordinal outcomes using observational data.","authors":"Huirong Hu, Qi Zheng, Maiying Kong","doi":"10.1080/03610918.2025.2488945","DOIUrl":"10.1080/03610918.2025.2488945","url":null,"abstract":"In this article, we propose a marginal structural ordinal logistic regression model (MS-OLRM) to assess treatment effects on ordinal outcomes. Many statistical methods have been developed to estimate average treatment effect (ATE) when the outcome is continuous or binary. The methodology for assessing the effect of treatment for an ordinal outcome is less studied. To address this, we propose utilizing a superiority score as a measure of treatment effect, assessing whether the outcome under treatment is stochastically larger than the outcome under control. Our approach involves employing MS-OLRM in conjunction with Inverse Probability of Treatment Weighting (IPTW) to estimate the superiority score under treatment compared to the control. This methodology adjusts for confounding factors between treatment and outcome by utilizing IPTW, ensuring that all covariates are balanced among different treatment groups in the weighted sample. To assess the performance of the proposed method, we conduct extensive simulation studies. Finally, we apply the developed method to assess the treatment effects of medications and behavioral therapies on patients' recovery from alcohol use disorders using the Kentucky Medicaid 2012-2019 database.","PeriodicalId":55240,"journal":{"name":"Communications in Statistics-Simulation and Computation","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12338247/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144979288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sampling Spiked Wishart Eigenvalues. 抽样尖刺Wishart特征值。

IF 0.8 4区数学

Communications in Statistics-Simulation and Computation Pub Date : 2025-04-10 DOI: 10.1080/03610918.2025.2490204

Thomas G Brooks

引用次数: 0

Automated Parameter Selection in Singular Spectrum Analysis for Time Series Analysis. 时间序列分析中奇异谱分析参数的自动选择。

IF 0.8 4区数学

Communications in Statistics-Simulation and Computation Pub Date : 2025-01-28 DOI: 10.1080/03610918.2025.2456575

James J Yang, Anne Buu

{"title":"Automated Parameter Selection in Singular Spectrum Analysis for Time Series Analysis.","authors":"James J Yang, Anne Buu","doi":"10.1080/03610918.2025.2456575","DOIUrl":"https://doi.org/10.1080/03610918.2025.2456575","url":null,"abstract":"In spite of wide applications of the singular spectrum analysis (SSA) method, understanding how SSA reconstructs time series and eliminates noise remains challenging due to its complex process. This study provided a novel geometric perspective to elucidate the underlying mechanism of SSA. To address the key issue of conventional SSA that requires a fixed window length and a given threshold for determining the number of groups, we proposed a sequential reconstruction approach that averages reconstructed series from various window lengths with a stopping rule based on a symmetric test. Three main advantages of the proposed method were demonstrated by the simulations and real data analysis of 7-day heart rate data from an e-cigarette user: 1) requiring no prior knowledge of the window length or group number; 2) yielding smaller values of root mean square error (RMSE) than the conventional SSA; and 3) revealing both local features and sudden changes related to events of interest. While conventional SSA excels in extracting stable signal structures, the proposed method is tailored for time series with varying structures such as heart rate data from smartwatches, and thus will have even wider applications.","PeriodicalId":55240,"journal":{"name":"Communications in Statistics-Simulation and Computation","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12352492/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144979298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adjusted curves for clustered survival and competing risks data. 聚类生存和竞争风险数据的调整曲线。

IF 0.8 4区数学

Communications in Statistics-Simulation and Computation Pub Date : 2025-01-01 Epub Date: 2023-08-16 DOI: 10.1080/03610918.2023.2245583

Manoj Khanal, Soyoung Kim, Kwang Woo Ahn

{"title":"Adjusted curves for clustered survival and competing risks data.","authors":"Manoj Khanal, Soyoung Kim, Kwang Woo Ahn","doi":"10.1080/03610918.2023.2245583","DOIUrl":"10.1080/03610918.2023.2245583","url":null,"abstract":"Observational studies with right-censored data often have clustered data due to matched pairs or a study center effect. In such data, there may be an imbalance in patient characteristics between treatment groups, where Kaplan-Meier curves or unadjusted cumulative incidence curves can be misleading and may not represent the average patient on a given treatment arm. Adjusted curves are desirable to appropriately display survival or cumulative incidence curves in this case. We propose methods for estimating the adjusted survival and cumulative incidence probabilities for clustered right-censored data. For the competing risks outcome, we allow both covariate-independent and covariate-dependent censoring. We develop an R package adjSURVCI to implement the proposed methods. It provides the estimates of adjusted survival and cumulative incidence probabilities along with their standard errors. Our simulation results show that the adjusted survival and cumulative incidence estimates of the proposed method are unbiased with approximate 95% coverage rates. We apply the proposed method to stem cell transplant data of leukemia patients.","PeriodicalId":55240,"journal":{"name":"Communications in Statistics-Simulation and Computation","volume":" 3","pages":"120-143"},"PeriodicalIF":0.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11708817/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72382911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BayCAR: A Bayesian based Covariate-Adaptive Randomization method for multi-arm trials. BayCAR：一种基于贝叶斯的多组试验协变量自适应随机化方法。

IF 0.8 4区数学

Communications in Statistics-Simulation and Computation Pub Date : 2024-12-23 DOI: 10.1080/03610918.2024.2443202

Shengping Yang, Jianrong Wu

引用次数: 0

Bayes factors for longitudinal model assessment via power posteriors 通过功率后验评估纵向模型的贝叶斯系数

IF 0.9 4区数学

Communications in Statistics-Simulation and Computation Pub Date : 2024-09-16 DOI: 10.1080/03610918.2024.2399159

Gabriel Calvo, Carmen Armero, Luigi Spezia, Maria Grazia Pennino