International Journal of Biostatistics最新文献_第8页

Estimation and Inference for the Mediation Proportion. 中介比例的估计与推断。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2017-09-20 DOI: 10.1515/ijb-2017-0006

Daniel Nevo, Xiaomei Liao, Donna Spiegelman

{"title":"Estimation and Inference for the Mediation Proportion.","authors":"Daniel Nevo, Xiaomei Liao, Donna Spiegelman","doi":"10.1515/ijb-2017-0006","DOIUrl":"https://doi.org/10.1515/ijb-2017-0006","url":null,"abstract":"In epidemiology, public health and social science, mediation analysis is often undertaken to investigate the extent to which the effect of a risk factor on an outcome of interest is mediated by other covariates. A pivotal quantity of interest in such an analysis is the mediation proportion. A common method for estimating it, termed the \"difference method\", compares estimates from models with and without the hypothesized mediator. However, rigorous methodology for estimation and statistical inference for this quantity has not previously been available. We formulated the problem for the Cox model and generalized linear models, and utilize a data duplication algorithm together with a generalized estimation equations approach for estimating the mediation proportion and its variance. We further considered the assumption that the same link function hold for the marginal and conditional models, a property which we term \"g-linkability\". We show that our approach is valid whenever g-linkability holds, exactly or approximately, and present results from an extensive simulation study to explore finite sample properties. The methodology is illustrated by an analysis of pre-menopausal breast cancer incidence in the Nurses' Health Study. User-friendly publicly available software implementing those methods can be downloaded from the last author's website (SAS) or from CRAN (R).","PeriodicalId":49058,"journal":{"name":"International Journal of Biostatistics","volume":"13 2","pages":""},"PeriodicalIF":1.2,"publicationDate":"2017-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2017-0006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35372681","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: 43

Modelling Mixed Types of Outcomes in Additive Genetic Models. 加性遗传模型中混合类型结果的建模。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2017-07-07 DOI: 10.1515/ijb-2017-0001

Wagner Hugo Bonat

引用次数: 4

Kernel-Based Measure of Variable Importance for Genetic Association Studies. 遗传关联研究中基于核的变量重要度测度。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2017-06-17 DOI: 10.1515/ijb-2016-0087

Vicente Gallego, M Luz Calle, Ramon Oller

{"title":"Kernel-Based Measure of Variable Importance for Genetic Association Studies.","authors":"Vicente Gallego, M Luz Calle, Ramon Oller","doi":"10.1515/ijb-2016-0087","DOIUrl":"https://doi.org/10.1515/ijb-2016-0087","url":null,"abstract":"The identification of genetic variants that are associated with disease risk is an important goal of genetic association studies. Standard approaches perform univariate analysis where each genetic variant, usually Single Nucleotide Polymorphisms (SNPs), is tested for association with disease status. Though many genetic variants have been identified and validated so far using this univariate approach, for most complex diseases a large part of their genetic component is still unknown, the so called missing heritability. We propose a Kernel-based measure of variable importance (KVI) that provides the contribution of a SNP, or a group of SNPs, to the joint genetic effect of a set of genetic variants. KVI can be used for ranking genetic markers individually, sets of markers that form blocks of linkage disequilibrium or sets of genetic variants that lie in a gene or a genetic pathway. We prove that, unlike the univariate analysis, KVI captures the relationship with other genetic variants in the analysis, even when measured at the individual level for each genetic variable separately. This is specially relevant and powerful for detecting genetic interactions. We illustrate the results with data from an Alzheimer's disease study and show through simulations that the rankings based on KVI improve those rankings based on two measures of importance provided by the Random Forest. We also prove with a simulation study that KVI is very powerful for detecting genetic interactions.","PeriodicalId":49058,"journal":{"name":"International Journal of Biostatistics","volume":"13 2","pages":""},"PeriodicalIF":1.2,"publicationDate":"2017-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2016-0087","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35099476","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

Generalized Confidence Intervals for Intra- and Inter-subject Coefficients of Variation in Linear Mixed-effects Models. 线性混合效应模型中主体内和主体间变异系数的广义置信区间。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2017-06-15 DOI: 10.1515/ijb-2016-0093

Johannes Forkman

引用次数: 0

Big Data, Small Sample. 大数据，小样本。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2017-05-20 DOI: 10.1515/ijb-2017-0012

Inna Gerlovina, Mark J van der Laan, Alan Hubbard

{"title":"Big Data, Small Sample.","authors":"Inna Gerlovina, Mark J van der Laan, Alan Hubbard","doi":"10.1515/ijb-2017-0012","DOIUrl":"https://doi.org/10.1515/ijb-2017-0012","url":null,"abstract":"Multiple comparisons and small sample size, common characteristics of many types of \"Big Data\" including those that are produced by genomic studies, present specific challenges that affect reliability of inference. Use of multiple testing procedures necessitates calculation of very small tail probabilities of a test statistic distribution. Results based on large deviation theory provide a formal condition that is necessary to guarantee error rate control given practical sample sizes, linking the number of tests and the sample size; this condition, however, is rarely satisfied. Using methods that are based on Edgeworth expansions (relying especially on the work of Peter Hall), we explore the impact of departures of sampling distributions from typical assumptions on actual error rates. Our investigation illustrates how far the actual error rates can be from the declared nominal levels, suggesting potentially wide-spread problems with error rate control, specifically excessive false positives. This is an important factor that contributes to \"reproducibility crisis\". We also review some other commonly used methods (such as permutation and methods based on finite sampling inequalities) in their application to multiple testing/small sample data. We point out that Edgeworth expansions, providing higher order approximations to the sampling distribution, offer a promising direction for data analysis that could improve reliability of studies relying on large numbers of comparisons with modest sample sizes.","PeriodicalId":49058,"journal":{"name":"International Journal of Biostatistics","volume":"13 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2017-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2017-0012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35076952","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: 4

Improvement Screening for Ultra-High Dimensional Data with Censored Survival Outcomes and Varying Coefficients. 具有截尾生存结果和变系数的超高维数据的改进筛选。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2017-05-18 DOI: 10.1515/ijb-2017-0024

Mu Yue, Jialiang Li

{"title":"Improvement Screening for Ultra-High Dimensional Data with Censored Survival Outcomes and Varying Coefficients.","authors":"Mu Yue, Jialiang Li","doi":"10.1515/ijb-2017-0024","DOIUrl":"https://doi.org/10.1515/ijb-2017-0024","url":null,"abstract":"Motivated by risk prediction studies with ultra-high dimensional bio markers, we propose a novel improvement screening methodology. Accurate risk prediction can be quite useful for patient treatment selection, prevention strategy or disease management in evidence-based medicine. The question of how to choose new markers in addition to the conventional ones is especially important. In the past decade, a number of new measures for quantifying the added value from the new markers were proposed, among which the integrated discrimination improvement (IDI) and net reclassification improvement (NRI) stand out. Meanwhile, C-statistics are routinely used to quantify the capacity of the estimated risk score in discriminating among subjects with different event times. In this paper, we will examine these improvement statistics as well as the norm-based approach for evaluating the incremental values of new markers and compare these four measures by analyzing ultra-high dimensional censored survival data. In particular, we consider Cox proportional hazards models with varying coefficients. All measures perform very well in simulations and we illustrate our methods in an application to a lung cancer study.","PeriodicalId":49058,"journal":{"name":"International Journal of Biostatistics","volume":"13 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2017-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2017-0024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35027552","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: 9

Comparing Four Methods for Estimating Tree-Based Treatment Regimes. 比较四种评估树基处理方案的方法。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2017-05-12 DOI: 10.1515/ijb-2016-0068

Aniek Sies, Iven Van Mechelen

{"title":"Comparing Four Methods for Estimating Tree-Based Treatment Regimes.","authors":"Aniek Sies, Iven Van Mechelen","doi":"10.1515/ijb-2016-0068","DOIUrl":"https://doi.org/10.1515/ijb-2016-0068","url":null,"abstract":"When multiple treatment alternatives are available for a certain psychological or medical problem, an important challenge is to find an optimal treatment regime, which specifies for each patient the most effective treatment alternative given his or her pattern of pretreatment characteristics. The focus of this paper is on tree-based treatment regimes, which link an optimal treatment alternative to each leaf of a tree; as such they provide an insightful representation of the decision structure underlying the regime. This paper compares the absolute and relative performance of four methods for estimating regimes of that sort (viz., Interaction Trees, Model-based Recursive Partitioning, an approach developed by Zhang et al. and Qualitative Interaction Trees) in an extensive simulation study. The evaluation criteria were, on the one hand, the expected outcome if the entire population would be subjected to the treatment regime resulting from each method under study and the proportion of clients assigned to the truly best treatment alternative, and, on the other hand, the Type I and Type II error probabilities of each method. The method of Zhang et al. was superior regarding the first two outcome measures and the Type II error probabilities, but performed worst in some conditions of the simulation study regarding Type I error probabilities.","PeriodicalId":49058,"journal":{"name":"International Journal of Biostatistics","volume":"13 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2017-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2016-0068","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35013211","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: 16

Group Tests for High-dimensional Failure Time Data with the Additive Hazards Models. 用加性危险模型对高维失效时间数据进行分组试验。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2017-05-09 DOI: 10.1515/ijb-2016-0085

Dandan Jiang, Jianguo Sun

引用次数: 0

Median Analysis of Repeated Measures Associated with Recurrent Events in Presence of Terminal Event. 存在终末期事件时与复发事件相关的重复测量的中位数分析。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2017-04-28 DOI: 10.1515/ijb-2016-0057

Rajeshwari Sundaram, Ling Ma, Subhashis Ghoshal

引用次数: 0

Empirical Likelihood in Nonignorable Covariate-Missing Data Problems. 不可忽略协变量缺失数据问题的经验似然。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2017-04-20 DOI: 10.1515/ijb-2016-0053

Yanmei Xie, Biao Zhang

{"title":"Empirical Likelihood in Nonignorable Covariate-Missing Data Problems.","authors":"Yanmei Xie, Biao Zhang","doi":"10.1515/ijb-2016-0053","DOIUrl":"https://doi.org/10.1515/ijb-2016-0053","url":null,"abstract":"Abstract: Missing covariate data occurs often in regression analysis, which frequently arises in the health and social sciences as well as in survey sampling. We study methods for the analysis of a nonignorable covariate-missing data problem in an assumed conditional mean function when some covariates are completely observed but other covariates are missing for some subjects. We adopt the semiparametric perspective of Bartlett et al. (Improving upon the efficiency of complete case analysis when covariates are MNAR. Biostatistics 2014;15:719–30) on regression analyses with nonignorable missing covariates, in which they have introduced the use of two working models, the working probability model of missingness and the working conditional score model. In this paper, we study an empirical likelihood approach to nonignorable covariate-missing data problems with the objective of effectively utilizing the two working models in the analysis of covariate-missing data. We propose a unified approach to constructing a system of unbiased estimating equations, where there are more equations than unknown parameters of interest. One useful feature of these unbiased estimating equations is that they naturally incorporate the incomplete data into the data analysis, making it possible to seek efficient estimation of the parameter of interest even when the working regression function is not specified to be the optimal regression function. We apply the general methodology of empirical likelihood to optimally combine these unbiased estimating equations. We propose three maximum empirical likelihood estimators of the underlying regression parameters and compare their efficiencies with other existing competitors. We present a simulation study to compare the finite-sample performance of various methods with respect to bias, efficiency, and robustness to model misspecification. The proposed empirical likelihood method is also illustrated by an analysis of a data set from the US National Health and Nutrition Examination Survey (NHANES).","PeriodicalId":49058,"journal":{"name":"International Journal of Biostatistics","volume":"13 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2017-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2016-0053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34940139","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: 5