International Journal of Biostatistics最新文献_第6页

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

Targeting the Optimal Design in Randomized Clinical Trials with Binary Outcomes and No Covariate: Simulation Study 二元结果无协变量随机临床试验的优化设计目标：模拟研究

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2011-01-24 DOI: 10.2202/1557-4679.1310

A. Chambaz, M. J. van der Laan

{"title":"Targeting the Optimal Design in Randomized Clinical Trials with Binary Outcomes and No Covariate: Simulation Study","authors":"A. Chambaz, M. J. van der Laan","doi":"10.2202/1557-4679.1310","DOIUrl":"https://doi.org/10.2202/1557-4679.1310","url":null,"abstract":"We undertake here a comprehensive simulation study of the theoretical properties that we derive in a companion article devoted to the asymptotic study of adaptive group sequential designs in the case of randomized clinical trials (RCTs) with binary treatment, binary outcome and no covariate. By adaptive design, we mean in this setting a RCT design that allows the investigator to dynamically modify its course through data-driven adjustment of the randomization probability based on data accrued so far without negatively impacting on the statistical integrity of the trial. By adaptive group sequential design, we refer to the fact that group sequential testing methods can be equally well applied on top of adaptive designs. The simulation study validates the theory. It notably shows in the estimation framework that the confidence intervals we obtain achieve the desired coverage even for moderate sample sizes. In addition, it shows in the testing framework that type I error control at the prescribed level is guaranteed and that all sampling procedures only suffer from a very slight increase of the type II error. A three-sentence take-home message is “Adaptive designs do learn the targeted optimal design and inference and testing can be carried out under adaptive sampling as they would under the targeted optimal randomization probability iid sampling. In particular, adaptive designs achieve the same efficiency as the fixed oracle design. This is confirmed by a simulation study, at least for moderate or large sample sizes, across a large collection of targeted randomization probabilities.”","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":"7 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2011-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2202/1557-4679.1310","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68718288","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

Relative Risk Estimation in Randomized Controlled Trials: A Comparison of Methods for Independent Observations 随机对照试验的相对风险估计:独立观察方法的比较

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2011-01-06 DOI: 10.2202/1557-4679.1278

L. Yelland, A. Salter, Philip Ryan

引用次数: 38

Classification of Stationary Signals with Mixed Spectrum 混合频谱平稳信号的分类

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2011-01-06 DOI: 10.2202/1557-4679.1288

P. Saavedra, A. Santana-del-Pino, C. N. Hernández-Flores, J. Artiles-Romero, J. J. González-Henríquez

引用次数: 0

An Improved Bland-Altman Method for Concordance Assessment 一种改进的Bland-Altman一致性评价方法

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2011-01-06 DOI: 10.2202/1557-4679.1295

Jason J. Z. Liao, R. Capen

引用次数: 10

A Dunnett-Type Procedure for Multiple Endpoints 多端点的dunnett型过程

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2011-01-06 DOI: 10.2202/1557-4679.1258

M. Hasler, L. Hothorn

引用次数: 26

Rejoinder to Nancy Cook's Comment on "Measures to Summarize and Compare the Predictive Capacity of Markers" 对Nancy Cook关于“总结和比较标记预测能力的措施”的回答

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2010-07-29 DOI: 10.2202/1557-4679.1280

M. Pepe

引用次数: 2