International Journal of Biostatistics最新文献_第5页

Tree Based Method for Aggregate Survival Data Modeling 基于树的总体生存数据建模方法

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2016-11-01 DOI: 10.1515/ijb-2015-0071

Asanao Shimokawa, Y. Narita, S. Shibui, E. Miyaoka

{"title":"Tree Based Method for Aggregate Survival Data Modeling","authors":"Asanao Shimokawa, Y. Narita, S. Shibui, E. Miyaoka","doi":"10.1515/ijb-2015-0071","DOIUrl":"https://doi.org/10.1515/ijb-2015-0071","url":null,"abstract":"Abstract In many scenarios, a patient in medical research is treated as a statistical unit. However, in some scenarios, we are interested in treating aggregate data as a statistical unit. In such situations, each set of aggregated data is considered to be a concept in a symbolic representation, and each concept has a hyperrectangle or multiple points in the variable space. To construct a tree-structured model from these aggregate survival data, we propose a new approach, where a datum can be included in several terminal nodes in a tree. By constructing a model under this condition, we expect to obtain a more flexible model while retaining the interpretive ease of a hierarchical structure. In this approach, the survival function of concepts that are partially included in a node is constructed using the Kaplan-Meier method, where the number of events and risks at each time point is replaced by the expectation value of the number of individual descriptions of concepts. We present an application of this proposed model using primary brain tumor patient data. As a result, we obtained a new interpretation of the data in comparison to the classical survival tree modeling methods.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":"39 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2015-0071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66987991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploration of Heterogeneous Treatment Effects via Concave Fusion 凹形融合异质治疗效果探讨

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2016-07-13 DOI: 10.1515/ijb-2018-0026

Shujie Ma, Jian Huang, Zhiwei Zhang, Mingming Liu

{"title":"Exploration of Heterogeneous Treatment Effects via Concave Fusion","authors":"Shujie Ma, Jian Huang, Zhiwei Zhang, Mingming Liu","doi":"10.1515/ijb-2018-0026","DOIUrl":"https://doi.org/10.1515/ijb-2018-0026","url":null,"abstract":"Abstract Understanding treatment heterogeneity is essential to the development of precision medicine, which seeks to tailor medical treatments to subgroups of patients with similar characteristics. One of the challenges of achieving this goal is that we usually do not have a priori knowledge of the grouping information of patients with respect to treatment effect. To address this problem, we consider a heterogeneous regression model which allows the coefficients for treatment variables to be subject-dependent with unknown grouping information. We develop a concave fusion penalized method for estimating the grouping structure and the subgroup-specific treatment effects, and derive an alternating direction method of multipliers algorithm for its implementation. We also study the theoretical properties of the proposed method and show that under suitable conditions there exists a local minimizer that equals the oracle least squares estimator based on a priori knowledge of the true grouping information with high probability. This provides theoretical support for making statistical inference about the subgroup-specific treatment effects using the proposed method. The proposed method is illustrated in simulation studies and illustrated with real data from an AIDS Clinical Trials Group Study.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":"16 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2018-0026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66988175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 33

Addressing Confounding in Predictive Models with an Application to Neuroimaging 解决预测模型中的混淆与神经影像学的应用

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2016-05-01 DOI: 10.1515/ijb-2015-0030

K. Linn, Bilwaj Gaonkar, J. Doshi, C. Davatzikos, R. Shinohara

引用次数: 38

Testing the Relative Performance of Data Adaptive Prediction Algorithms: A Generalized Test of Conditional Risk Differences 测试数据自适应预测算法的相对性能:条件风险差异的广义测试

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2016-05-01 DOI: 10.1515/ijb-2015-0014

B. Goldstein, E. Polley, F. Briggs, M. J. van der Laan, A. Hubbard

{"title":"Testing the Relative Performance of Data Adaptive Prediction Algorithms: A Generalized Test of Conditional Risk Differences","authors":"B. Goldstein, E. Polley, F. Briggs, M. J. van der Laan, A. Hubbard","doi":"10.1515/ijb-2015-0014","DOIUrl":"https://doi.org/10.1515/ijb-2015-0014","url":null,"abstract":"Abstract Comparing the relative fit of competing models can be used to address many different scientific questions. In classical statistics one can, if appropriate, use likelihood ratio tests and information based criterion, whereas clinical medicine has tended to rely on comparisons of fit metrics like C-statistics. However, for many data adaptive modelling procedures such approaches are not suitable. In these cases, statisticians have used cross-validation, which can make inference challenging. In this paper we propose a general approach that focuses on the “conditional” risk difference (conditional on the model fits being fixed) for the improvement in prediction risk. Specifically, we derive a Wald-type test statistic and associated confidence intervals for cross-validated test sets utilizing the independent validation within cross-validation in conjunction with a test for multiple comparisons. We show that this test maintains proper Type I Error under the null fit, and can be used as a general test of relative fit for any semi-parametric model alternative. We apply the test to a candidate gene study to test for the association of a set of genes in a genetic pathway.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":"12 1","pages":"117 - 129"},"PeriodicalIF":1.2,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2015-0014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66987670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A Semiparametric Bayesian Approach for Analyzing Longitudinal Data from Multiple Related Groups 多相关组纵向数据分析的半参数贝叶斯方法

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2015-11-01 DOI: 10.1515/ijb-2015-0002

Kiranmoy Das, Prince Afriyie, Lauren Spirko

{"title":"A Semiparametric Bayesian Approach for Analyzing Longitudinal Data from Multiple Related Groups","authors":"Kiranmoy Das, Prince Afriyie, Lauren Spirko","doi":"10.1515/ijb-2015-0002","DOIUrl":"https://doi.org/10.1515/ijb-2015-0002","url":null,"abstract":"Abstract Often the biological and/or clinical experiments result in longitudinal data from multiple related groups. The analysis of such data is quite challenging due to the fact that groups might have shared information on the mean and/or covariance functions. In this article, we consider a Bayesian semiparametric approach of modeling the mean trajectories for longitudinal response coming from multiple related groups. We consider matrix stick-breaking process priors on the group mean parameters which allows information sharing on the mean trajectories across the groups. Simulation studies are performed to demonstrate the effectiveness of the proposed approach compared to the more traditional approaches. We analyze data from a one-year follow-up of nutrition education for hypercholesterolemic children with three different treatments where the children are from different age-groups. Our analysis provides more clinically useful information than the previous analysis of the same dataset. The proposed approach will be a very powerful tool for analyzing data from clinical trials and other medical experiments.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":"30 1","pages":"273 - 284"},"PeriodicalIF":1.2,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2015-0002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66987590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Multiple-Objective Optimal Designs for Studying the Dose Response Function and Interesting Dose Levels 剂量响应函数和感兴趣剂量水平研究的多目标优化设计

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2015-11-01 DOI: 10.1515/ijb-2015-0044

Seung Won Hyun, W. Wong

{"title":"Multiple-Objective Optimal Designs for Studying the Dose Response Function and Interesting Dose Levels","authors":"Seung Won Hyun, W. Wong","doi":"10.1515/ijb-2015-0044","DOIUrl":"https://doi.org/10.1515/ijb-2015-0044","url":null,"abstract":"Abstract We construct an optimal design to simultaneously estimate three common interesting features in a dose-finding trial with possibly different emphasis on each feature. These features are (1) the shape of the dose-response curve, (2) the median effective dose and (3) the minimum effective dose level. A main difficulty of this task is that an optimal design for a single objective may not perform well for other objectives. There are optimal designs for dual objectives in the literature but we were unable to find optimal designs for 3 or more objectives to date with a concrete application. A reason for this is that the approach for finding a dual-objective optimal design does not work well for a 3 or more multiple-objective design problem. We propose a method for finding multiple-objective optimal designs that estimate the three features with user-specified higher efficiencies for the more important objectives. We use the flexible 4-parameter logistic model to illustrate the methodology but our approach is applicable to find multiple-objective optimal designs for other types of objectives and models. We also investigate robustness properties of multiple-objective optimal designs to mis-specification in the nominal parameter values and to a variation in the optimality criterion. We also provide computer code for generating tailor made multiple-objective optimal designs.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":"11 1","pages":"253 - 271"},"PeriodicalIF":1.2,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2015-0044","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66988220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Multiple Comparisons Using Composite Likelihood in Clustered Data 在聚类数据中使用复合似然的多重比较

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2014-11-05 DOI: 10.1515/ijb-2016-0004

M. Azadbakhsh, Xin Gao, H. Jankowski

引用次数: 2

Functional and Parametric Estimation in a Semi- and Nonparametric Model with Application to Mass-Spectrometry Data 半参数和非参数模型的函数和参数估计及其在质谱数据中的应用

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2013-05-07 DOI: 10.1515/ijb-2014-0066

Weiping Ma, Yang Feng, Kani Chen, Z. Ying

引用次数: 2

Relative Risk Estimation in Cluster Randomized Trials: A Comparison of Generalized Estimating Equation Methods 聚类随机试验的相对风险估计:广义估计方程方法的比较

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2011-05-21 DOI: 10.2202/1557-4679.1323

L. Yelland, A. Salter, Philip Ryan

{"title":"Relative Risk Estimation in Cluster Randomized Trials: A Comparison of Generalized Estimating Equation Methods","authors":"L. Yelland, A. Salter, Philip Ryan","doi":"10.2202/1557-4679.1323","DOIUrl":"https://doi.org/10.2202/1557-4679.1323","url":null,"abstract":"Relative risks have become a popular measure of treatment effect for binary outcomes in randomized controlled trials (RCTs). Relative risks can be estimated directly using log binomial regression but the model may fail to converge. Alternative methods are available for estimating relative risks but these have generally only been evaluated for independent data. As some of these methods are now being applied in cluster RCTs, investigation of their performance in this context is needed. We compare log binomial regression and three alternative methods (expanded logistic regression, log Poisson regression and log normal regression) for estimating relative risks in cluster RCTs. Clustering is taken into account using generalized estimating equations (GEEs) with an independence or exchangeable working correlation structure. The results of our large simulation study show that the log binomial GEE generally performs well for clustered data but suffers from convergence problems, as expected. Both the log Poisson GEE and log normal GEE have advantages in certain settings in terms of type I error, bias and coverage. The expanded logistic GEE can perform poorly and is sensitive to the chosen working correlation structure. Conclusions about the effectiveness of treatment often differ depending on the method used, highlighting the need to pre-specify an analysis approach. We recommend pre-specifying that either the log Poisson GEE or log normal GEE will be used in the event that the log binomial GEE fails to converge.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":"7 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2011-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2202/1557-4679.1323","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68718384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 19

A First Passage Time Model for Long-Term Survivors with Competing Risks 具有竞争风险的长期幸存者的首次通过时间模型

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2011-05-21 DOI: 10.2202/1557-4679.1224

Ruimin Xu, P. McNicholas, A. Desmond, G. Darlington

引用次数: 5