International Journal of Biostatistics最新文献_第6页

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

A Lower Bound Model for Multiple Record Systems Estimation with Heterogeneous Catchability 具有异构可捕获性的多记录系统估计下界模型

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2011-05-18 DOI: 10.2202/1557-4679.1283

L. Rivest

引用次数: 5

Reaction to Pearl's Critique of Principal Stratification 对珀尔《主要分层批判》的反应

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2011-04-13 DOI: 10.2202/1557-4679.1324

Arvid Sjolander

引用次数: 11

Bayesian Variable Selection in Semiparametric Proportional Hazards Model for High Dimensional Survival Data 高维生存数据半参数比例风险模型中的贝叶斯变量选择

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2011-04-07 DOI: 10.2202/1557-4679.1301

Kyu Ha Lee, S. Chakraborty, Jianguo Sun

{"title":"Bayesian Variable Selection in Semiparametric Proportional Hazards Model for High Dimensional Survival Data","authors":"Kyu Ha Lee, S. Chakraborty, Jianguo Sun","doi":"10.2202/1557-4679.1301","DOIUrl":"https://doi.org/10.2202/1557-4679.1301","url":null,"abstract":"Variable selection for high dimensional data has recently received a great deal of attention. However, due to the complex structure of the likelihood, only limited developments have been made for time-to-event data where censoring is present. In this paper, we propose a Bayesian variable selection scheme for a Bayesian semiparametric survival model for right censored survival data sets. A special shrinkage prior on the coefficients corresponding to the predictor variables is used to handle cases when the explanatory variables are of very high-dimension. The shrinkage prior is obtained through a scale mixture representation of Normal and Gamma distributions. Our proposed variable selection prior corresponds to the well known lasso penalty. The likelihood function is based on the Cox proportional hazards model framework, where the cumulative baseline hazard function is modeled a priori by a gamma process. We assign a prior on the tuning parameter of the shrinkage prior and adaptively control the sparsity of our model. The primary use of the proposed model is to identify the important covariates relating to the survival curves. To implement our methodology, we have developed a fast Markov chain Monte Carlo algorithm with an adaptive jumping rule. We have successfully applied our method on simulated data sets under two different settings and real microarray data sets which contain right censored survival time. The performance of our Bayesian variable selection model compared with other competing methods is also provided to demonstrate the superiority of our method. A short description of the biological relevance of the selected genes in the real data sets is provided, further strengthening our claims.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":"7 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2011-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2202/1557-4679.1301","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68717639","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: 25

An Alternative to Pooling Kaplan-Meier Curves in Time-to-Event Meta-Analysis 时间-事件元分析中Kaplan-Meier曲线池化的替代方法

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2011-03-30 DOI: 10.2202/1557-4679.1289

D. Rubin

{"title":"An Alternative to Pooling Kaplan-Meier Curves in Time-to-Event Meta-Analysis","authors":"D. Rubin","doi":"10.2202/1557-4679.1289","DOIUrl":"https://doi.org/10.2202/1557-4679.1289","url":null,"abstract":"A meta-analysis that uses individual-level data instead of study-level data is widely considered to be a gold standard approach, in part because it allows a time-to-event analysis. Unfortunately, with the common practice of presenting Kaplan-Meier survival curves after pooling subjects across randomized trials, using individual-level data can actually be a step backwards; a Simpson's paradox can occur in which pooling incorrectly reverses the direction of an association. We introduce a nonparametric procedure for synthesizing survival curves across studies that is designed to avoid this difficulty and preserve the integrity of randomization. The technique is based on a counterfactual formulation in which we ask what pooled survival curves would look like if all subjects in all studies had been assigned treatment, or if all subjects had been assigned to control arms. The method is related to a Kaplan-Meier adjustment proposed in 2005 by Xie and Liu to correct for confounding in nonrandomized studies, but is formulated for the meta-analysis setting. The procedure is discussed in the context of examining rosiglitazone and cardiovascular adverse events.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":"7 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2011-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2202/1557-4679.1289","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68717777","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

Marginal Models for Censored Longitudinal Cost Data: Appropriate Working Variance Matrices in Inverse-Probability-Weighted GEEs Can Improve Precision 删减纵向成本数据的边际模型:在反概率加权GEEs中适当的工作方差矩阵可以提高精度

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2011-02-07 DOI: 10.2202/1557-4679.1170

E. Pullenayegum, A. Willan

引用次数: 1

HingeBoost: ROC-Based Boost for Classification and Variable Selection HingeBoost:基于roc的分类和变量选择Boost

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2011-02-04 DOI: 10.2202/1557-4679.1304

Zhuo Wang

{"title":"HingeBoost: ROC-Based Boost for Classification and Variable Selection","authors":"Zhuo Wang","doi":"10.2202/1557-4679.1304","DOIUrl":"https://doi.org/10.2202/1557-4679.1304","url":null,"abstract":"In disease classification, a traditional technique is the receiver operative characteristic (ROC) curve and the area under the curve (AUC). With high-dimensional data, the ROC techniques are needed to conduct classification and variable selection. The current ROC methods do not explicitly incorporate unequal misclassification costs or do not have a theoretical grounding for optimizing the AUC. Empirical studies in the literature have demonstrated that optimizing the hinge loss can maximize the AUC approximately. In theory, minimizing the hinge rank loss is equivalent to minimizing the AUC in the asymptotic limit. In this article, we propose a novel nonparametric method HingeBoost to optimize a weighted hinge loss incorporating misclassification costs. HingeBoost can be used to construct linear and nonlinear classifiers. The estimation and variable selection for the hinge loss are addressed by a new boosting algorithm. Furthermore, the proposed twin HingeBoost can select more sparse predictors. Some properties of HingeBoost are studied as well. To compare HingeBoost with existing classification methods, we present empirical study results using data from simulations and a prostate cancer study with mass spectrometry-based proteomics.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":"7 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2011-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2202/1557-4679.1304","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68717746","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: 30