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

A structured iterative division approach for non-sparse regression models and applications in biological data analysis. 非稀疏回归模型的结构化迭代分割方法及其在生物数据分析中的应用

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-07-01 Epub Date: 2024-05-23 DOI: 10.1177/09622802241254251

Shun Yu, Yuehan Yang

{"title":"A structured iterative division approach for non-sparse regression models and applications in biological data analysis.","authors":"Shun Yu, Yuehan Yang","doi":"10.1177/09622802241254251","DOIUrl":"10.1177/09622802241254251","url":null,"abstract":"In this paper, we focus on the modeling problem of estimating data with non-sparse structures, specifically focusing on biological data that exhibit a high degree of relevant features. Various fields, such as biology and finance, face the challenge of non-sparse estimation. We address the problems using the proposed method, called structured iterative division. Structured iterative division effectively divides data into non-sparse and sparse structures and eliminates numerous irrelevant variables, significantly reducing the error while maintaining computational efficiency. Numerical and theoretical results demonstrate the competitive advantage of the proposed method on a wide range of problems, and the proposed method exhibits excellent statistical performance in numerical comparisons with several existing methods. We apply the proposed algorithm to two biology problems, gene microarray datasets, and chimeric protein datasets, to the prognostic risk of distant metastasis in breast cancer and Alzheimer's disease, respectively. Structured iterative division provides insights into gene identification and selection, and we also provide meaningful results in anticipating cancer risk and identifying key factors.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1233-1248"},"PeriodicalIF":1.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141082254","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

Group sequential methods based on supremum logrank statistics under proportional and nonproportional hazards. 基于比例和非比例危害下的至高对数秩统计的分组序列方法。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-07-01 Epub Date: 2024-06-05 DOI: 10.1177/09622802241254211

Jean Marie Boher, Thomas Filleron, Patrick Sfumato, Pierre Bunouf, Richard J Cook

引用次数: 0

Robust integration of secondary outcomes information into primary outcome analysis in the presence of missing data. 在数据缺失的情况下，将次要结果信息可靠地纳入主要结果分析。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-07-01 Epub Date: 2024-05-20 DOI: 10.1177/09622802241254195

Daxuan Deng, Vernon M Chinchilli, Hao Feng, Chixiang Chen, Ming Wang

{"title":"Robust integration of secondary outcomes information into primary outcome analysis in the presence of missing data.","authors":"Daxuan Deng, Vernon M Chinchilli, Hao Feng, Chixiang Chen, Ming Wang","doi":"10.1177/09622802241254195","DOIUrl":"10.1177/09622802241254195","url":null,"abstract":"In clinical and observational studies, secondary outcomes are frequently collected alongside the primary outcome for each subject, yet their potential to improve the analysis efficiency remains underutilized. Moreover, missing data, commonly encountered in practice, can introduce bias to estimates if not appropriately addressed. This article presents an innovative approach that enhances the empirical likelihood-based information borrowing method by integrating missing-data techniques, ensuring robust data integration. We introduce a plug-in inverse probability weighting estimator to handle missingness in the primary analysis, demonstrating its equivalence to the standard joint estimator under mild conditions. To address potential bias from missing secondary outcomes, we propose a uniform mapping strategy, imputing incomplete secondary outcomes into a unified space. Extensive simulations highlight the effectiveness of our method, showing consistent, efficient, and robust estimators under various scenarios involving missing data and/or misspecified secondary models. Finally, we apply our proposal to the Uniform Data Set from the National Alzheimer's Coordinating Center, exemplifying its practical application.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1249-1263"},"PeriodicalIF":1.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141065604","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

Testing for marginal covariate effect when the subgroup size induced by the covariate is informative. 当协变量引起的亚组规模具有信息量时，测试协变量的边际效应。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-07-01 Epub Date: 2024-05-20 DOI: 10.1177/09622802241254196

Samuel Anyaso-Samuel, Somnath Datta

{"title":"Testing for marginal covariate effect when the subgroup size induced by the covariate is informative.","authors":"Samuel Anyaso-Samuel, Somnath Datta","doi":"10.1177/09622802241254196","DOIUrl":"10.1177/09622802241254196","url":null,"abstract":"In many cluster-correlated data analyses, informative cluster size poses a challenge that can potentially introduce bias in statistical analyses. Different methodologies have been introduced in statistical literature to address this bias. In this study, we consider a complex form of informativeness where the number of observations corresponding to latent levels of a unit-level continuous covariate within a cluster is associated with the response variable. This type of informativeness has not been explored in prior research. We present a novel test statistic designed to evaluate the effect of the continuous covariate while accounting for the presence of informativeness. The covariate induces a continuum of latent subgroups within the clusters, and our test statistic is formulated by aggregating values from an established statistic that accounts for informative subgroup sizes when comparing group-specific marginal distributions. Through carefully designed simulations, we compare our test with four traditional methods commonly employed in the analysis of cluster-correlated data. Only our test maintains the size across all data-generating scenarios with informativeness. We illustrate the proposed method to test for marginal associations in periodontal data with this distinctive form of informativeness.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1264-1277"},"PeriodicalIF":1.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141065634","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 capture-recapture modeling framework emphasizing expert opinion in disease surveillance. 在疾病监测中强调专家意见的捕获-再捕获建模框架。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-07-01 Epub Date: 2024-05-20 DOI: 10.1177/09622802241254217

Yuzi Zhang, Lin Ge, Lance A Waller, Sarita Shah, Robert H Lyles

{"title":"A capture-recapture modeling framework emphasizing expert opinion in disease surveillance.","authors":"Yuzi Zhang, Lin Ge, Lance A Waller, Sarita Shah, Robert H Lyles","doi":"10.1177/09622802241254217","DOIUrl":"10.1177/09622802241254217","url":null,"abstract":"In disease surveillance, capture-recapture methods are commonly used to estimate the number of diseased cases in a defined target population. Since the number of cases never identified by any surveillance system cannot be observed, estimation of the case count typically requires at least one crucial assumption about the dependency between surveillance systems. However, such assumptions are generally unverifiable based on the observed data alone. In this paper, we advocate a modeling framework hinging on the choice of a key population-level parameter that reflects dependencies among surveillance streams. With the key dependency parameter as the focus, the proposed method offers the benefits of (a) incorporating expert opinion in the spirit of prior information to guide estimation; (b) providing accessible bias corrections, and (c) leveraging an adapted credible interval approach to facilitate inference. We apply the proposed framework to two real human immunodeficiency virus surveillance datasets exhibiting three-stream and four-stream capture-recapture-based case count estimation. Our approach enables estimation of the number of human immunodeficiency virus positive cases for both examples, under realistic assumptions that are under the investigator's control and can be readily interpreted. The proposed framework also permits principled uncertainty analyses through which a user can acknowledge their level of confidence in assumptions made about the key non-identifiable dependency parameter.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1197-1210"},"PeriodicalIF":1.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11347122/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141065594","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

Goodness-of-fit tests for modified Poisson regression possibly producing fitted values exceeding one in binary outcome analysis. 对二元结果分析中可能产生拟合值超过 1 的修正泊松回归进行拟合优度测试。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-07-01 Epub Date: 2024-05-23 DOI: 10.1177/09622802241254220

Yasuhiro Hagiwara, Yutaka Matsuyama

{"title":"Goodness-of-fit tests for modified Poisson regression possibly producing fitted values exceeding one in binary outcome analysis.","authors":"Yasuhiro Hagiwara, Yutaka Matsuyama","doi":"10.1177/09622802241254220","DOIUrl":"10.1177/09622802241254220","url":null,"abstract":"Modified Poisson regression, which estimates the regression parameters in the log-binomial regression model using the Poisson quasi-likelihood estimating equation and robust variance, is a useful tool for estimating the adjusted risk and prevalence ratio in binary outcome analysis. Although several goodness-of-fit tests have been developed for other binary regressions, few goodness-of-fit tests are available for modified Poisson regression. In this study, we proposed several goodness-of-fit tests for modified Poisson regression, including the modified Hosmer-Lemeshow test with empirical variance, Tsiatis test, normalized Pearson chi-square tests with binomial variance and Poisson variance, and normalized residual sum of squares test. The original Hosmer-Lemeshow test and normalized Pearson chi-square test with binomial variance are inappropriate for the modified Poisson regression, which can produce a fitted value exceeding 1 owing to the unconstrained parameter space. A simulation study revealed that the normalized residual sum of squares test performed well regarding the type I error probability and the power for a wrong link function. We applied the proposed goodness-of-fit tests to the analysis of cross-sectional data of patients with cancer. We recommend the normalized residual sum of squares test as a goodness-of-fit test in the modified Poisson regression.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1185-1196"},"PeriodicalIF":1.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141082304","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

Demystifying estimands in cluster-randomised trials. 解密分组随机试验中的估计值。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-07-01 Epub Date: 2024-05-23 DOI: 10.1177/09622802241254197

Brennan C Kahan, Bryan S Blette, Michael O Harhay, Scott D Halpern, Vipul Jairath, Andrew Copas, Fan Li

{"title":"Demystifying estimands in cluster-randomised trials.","authors":"Brennan C Kahan, Bryan S Blette, Michael O Harhay, Scott D Halpern, Vipul Jairath, Andrew Copas, Fan Li","doi":"10.1177/09622802241254197","DOIUrl":"10.1177/09622802241254197","url":null,"abstract":"Estimands can help clarify the interpretation of treatment effects and ensure that estimators are aligned with the study's objectives. Cluster-randomised trials require additional attributes to be defined within the estimand compared to individually randomised trials, including whether treatment effects are marginal or cluster-specific, and whether they are participant- or cluster-average. In this paper, we provide formal definitions of estimands encompassing both these attributes using potential outcomes notation and describe differences between them. We then provide an overview of estimators for each estimand, describe their assumptions, and show consistency (i.e. asymptotically unbiased estimation) for a series of analyses based on cluster-level summaries. Then, through a re-analysis of a published cluster-randomised trial, we demonstrate that the choice of both estimand and estimator can affect interpretation. For instance, the estimated odds ratio ranged from 1.38 (p = 0.17) to 1.83 (p = 0.03) depending on the target estimand, and for some estimands, the choice of estimator affected the conclusions by leading to smaller treatment effect estimates. We conclude that careful specification of the estimand, along with an appropriate choice of estimator, is essential to ensuring that cluster-randomised trials address the right question.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1211-1232"},"PeriodicalIF":1.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11348634/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141082300","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

Retraction notice. 撤稿通知。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2024-06-01 Epub Date: 2015-05-17 DOI: 10.1177/0962280215586011

引用次数: 0

Sample size estimation for stratified cluster randomization trial with survival endpoint. 以生存为终点的分层分组随机试验的样本量估算。

IF 2.3 3区医学

Statistical Methods in Medical Research Pub Date : 2024-05-01 Epub Date: 2024-03-29 DOI: 10.1177/09622802241236953

Senmiao Ni, Zihang Zhong, Yang Zhao, Feng Chen, Jingwei Wu, Hao Yu, Jianling Bai

{"title":"Sample size estimation for stratified cluster randomization trial with survival endpoint.","authors":"Senmiao Ni, Zihang Zhong, Yang Zhao, Feng Chen, Jingwei Wu, Hao Yu, Jianling Bai","doi":"10.1177/09622802241236953","DOIUrl":"10.1177/09622802241236953","url":null,"abstract":"Cluster randomization trials with survival endpoint are predominantly used in drug development and clinical care research when drug treatments or interventions are delivered at a group level. Unlike conventional cluster randomization design, stratified cluster randomization design is generally considered more effective in reducing the impacts of imbalanced baseline prognostic factors and varying cluster sizes between groups when these stratification factors are adopted in the design. Failure to account for stratification and cluster size variability may lead to underpowered analysis and inaccurate sample size estimation. Apart from the sample size estimation in unstratified cluster randomization trials, there are no development of an explicit sample size formula for survival endpoint when a stratified cluster randomization design is employed. In this article, we present a closed-form sample size formula based on the stratified cluster log-rank statistics for stratified cluster randomization trials with survival endpoint. It provides an integrated solution for sample size estimation that account for cluster size variation, baseline hazard heterogeneity, and the estimated intracluster correlation coefficient based on the preliminary data. Simulation studies show that the proposed formula provides the appropriate sample size for achieving the desired statistical power under various parameter configurations. A real example of a stratified cluster randomization trial in the population with stable coronary heart disease is presented to illustrate our method.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"838-857"},"PeriodicalIF":2.3,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140319184","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

Contrast-specific propensity scores for causal inference with multiple interventions. 针对多重干预因果推断的对比度倾向分数。

IF 2.3 3区医学

Statistical Methods in Medical Research Pub Date : 2024-05-01 Epub Date: 2024-03-18 DOI: 10.1177/09622802241236952

Shasha Han, Joel Goh, Fanwen Meng, Melvin Khee-Shing Leow, Donald B Rubin

引用次数: 0