International Journal of Biostatistics最新文献_第3页

Gradient boosting for linear mixed models. 线性混合模型的梯度增强。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2021-01-13 DOI: 10.1515/ijb-2020-0136

Colin Griesbach, Benjamin Säfken, Elisabeth Waldmann

引用次数: 8

The method of envelopes to concisely calculate semiparametric efficient scores under parametric restrictions. 在参数限制下，采用包络法简明地计算半参数有效分数。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2020-12-24 DOI: 10.1515/ijb-2019-0043

Constantine E Frangakis

{"title":"The method of envelopes to concisely calculate semiparametric efficient scores under parametric restrictions.","authors":"Constantine E Frangakis","doi":"10.1515/ijb-2019-0043","DOIUrl":"https://doi.org/10.1515/ijb-2019-0043","url":null,"abstract":"When addressing semiparametric problems with parametric restrictions (assumptions on the distribution), the efficient score (ES) of a parameter is often important for generating useful estimates. However, usual derivation of ES, although conceptually simple, is often lengthy and with many steps that do not help in understanding why its final form arises. This drawback often casts onto semiparametric estimation a mantle that can turn away otherwise able doctoral students or researchers. Here we show that many ESs can be obtained as a one-step derivation after we characterize those features (envelopes) of the unrestricted problem that are constrained in the restricted problem. We demonstrate our arguments in three problems with known ES but whose usual derivations are lengthy. We show that the envelope-based derivation is dramatically explanatory and compact, needing essentially two lines where the standard approach needs 10 or more pages. This suggests that the envelope method can add useful intuition and exegesis to both teaching and research of semiparametric estimation.","PeriodicalId":49058,"journal":{"name":"International Journal of Biostatistics","volume":"17 1","pages":"1-5"},"PeriodicalIF":1.2,"publicationDate":"2020-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2019-0043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38995775","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

A kernel- and optimal transport- based test of independence between covariates and right-censored lifetimes. 基于核和最优传输的协变量和右审查寿命之间的独立性检验。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2020-12-02 DOI: 10.1515/ijb-2020-0022

David Rindt, Dino Sejdinovic, David Steinsaltz

引用次数: 4

Effects of covariates on alternating recurrent events in accelerated failure time models. 加速失效时间模型中协变量对交替复发事件的影响。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2020-11-12 DOI: 10.1515/ijb-2019-0099

Moumita Chatterjee, Sugata Sen Roy

引用次数: 0

Bayesian information criterion approximations to Bayes factors for univariate and multivariate logistic regression models. 单变量和多变量逻辑回归模型的贝叶斯信息准则近似。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2020-10-29 DOI: 10.1515/ijb-2020-0045

Katharina Selig, Pamela Shaw, Donna Ankerst

{"title":"Bayesian information criterion approximations to Bayes factors for univariate and multivariate logistic regression models.","authors":"Katharina Selig, Pamela Shaw, Donna Ankerst","doi":"10.1515/ijb-2020-0045","DOIUrl":"https://doi.org/10.1515/ijb-2020-0045","url":null,"abstract":"Schwarz's criterion, also known as the Bayesian Information Criterion or BIC, is commonly used for model selection in logistic regression due to its simple intuitive formula. For tests of nested hypotheses in independent and identically distributed data as well as in Normal linear regression, previous results have motivated use of Schwarz's criterion by its consistent approximation to the Bayes factor (BF), defined as the ratio of posterior to prior model odds. Furthermore, under construction of an intuitive unit-information prior for the parameters of interest to test for inclusion in the nested models, previous results have shown that Schwarz's criterion approximates the BF to higher order in the neighborhood of the simpler nested model. This paper extends these results to univariate and multivariate logistic regression, providing approximations to the BF for arbitrary prior distributions and definitions of the unit-information prior corresponding to Schwarz's approximation. Simulations show accuracies of the approximations for small samples sizes as well as comparisons to conclusions from frequentist testing. We present an application in prostate cancer, the motivating setting for our work, which illustrates the approximation for large data sets in a practical example.","PeriodicalId":49058,"journal":{"name":"International Journal of Biostatistics","volume":"17 2","pages":"241-266"},"PeriodicalIF":1.2,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2020-0045","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38637717","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: 7

Parametric models for combined failure time data from an incident cohort study and a prevalent cohort study with follow-up. 来自事件队列研究和流行队列研究的合并失效时间数据的参数化模型。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2020-10-12 DOI: 10.1515/ijb-2020-0042

James McVittie, David Wolfson, David Stephens, Vittorio Addona, David Buckeridge

引用次数: 3

Targeted design for adaptive clinical trials via semiparametric model. 基于半参数模型的适应性临床试验目标设计。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2020-10-08 DOI: 10.1515/ijb-2018-0100

Hongbin Zhang, Ao Yuan, Ming T Tan

引用次数: 1

Causal mediation analysis in presence of multiple mediators uncausally related. 存在非因果相关的多个中介的因果中介分析。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2020-09-30 DOI: 10.1515/ijb-2019-0088

Allan Jérolon, Laura Baglietto, Etienne Birmelé, Flora Alarcon, Vittorio Perduca

{"title":"Causal mediation analysis in presence of multiple mediators uncausally related.","authors":"Allan Jérolon, Laura Baglietto, Etienne Birmelé, Flora Alarcon, Vittorio Perduca","doi":"10.1515/ijb-2019-0088","DOIUrl":"https://doi.org/10.1515/ijb-2019-0088","url":null,"abstract":"Mediation analysis aims at disentangling the effects of a treatment on an outcome through alternative causal mechanisms and has become a popular practice in biomedical and social science applications. The causal framework based on counterfactuals is currently the standard approach to mediation, with important methodological advances introduced in the literature in the last decade, especially for simple mediation, that is with one mediator at the time. Among a variety of alternative approaches, Imai et al. showed theoretical results and developed an R package to deal with simple mediation as well as with multiple mediation involving multiple mediators conditionally independent given the treatment and baseline covariates. This approach does not allow to consider the often encountered situation in which an unobserved common cause induces a spurious correlation between the mediators. In this context, which we refer to as mediation with uncausally related mediators, we show that, under appropriate hypothesis, the natural direct and joint indirect effects are non-parametrically identifiable. Moreover, we adopt the quasi-Bayesian algorithm developed by Imai et al. and propose a procedure based on the simulation of counterfactual distributions to estimate not only the direct and joint indirect effects but also the indirect effects through individual mediators. We study the properties of the proposed estimators through simulations. As an illustration, we apply our method on a real data set from a large cohort to assess the effect of hormone replacement treatment on breast cancer risk through three mediators, namely dense mammographic area, nondense area and body mass index.","PeriodicalId":49058,"journal":{"name":"International Journal of Biostatistics","volume":"17 2","pages":"191-221"},"PeriodicalIF":1.2,"publicationDate":"2020-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2019-0088","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38432917","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

Marginal quantile regression for longitudinal data analysis in the presence of time-dependent covariates. 存在时变协变量的纵向数据分析的边际分位数回归。

IF 1 4区数学

International Journal of Biostatistics Pub Date : 2020-09-28 DOI: 10.1515/ijb-2020-0010

I-Chen Chen, Philip M Westgate

引用次数: 0

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2020-09-21 DOI: 10.1515/ijb-2019-0120

Georges Bresson, Anoop Chaturvedi, Mohammad Arshad Rahman, Shalabh

{"title":"Seemingly unrelated regression with measurement error: estimation via Markov Chain Monte Carlo and mean field variational Bayes approximation.","authors":"Georges Bresson, Anoop Chaturvedi, Mohammad Arshad Rahman, Shalabh","doi":"10.1515/ijb-2019-0120","DOIUrl":"https://doi.org/10.1515/ijb-2019-0120","url":null,"abstract":"Linear regression with measurement error in the covariates is a heavily studied topic, however, the statistics/econometrics literature is almost silent to estimating a multi-equation model with measurement error. This paper considers a seemingly unrelated regression model with measurement error in the covariates and introduces two novel estimation methods: a pure Bayesian algorithm (based on Markov chain Monte Carlo techniques) and its mean field variational Bayes (MFVB) approximation. The MFVB method has the added advantage of being computationally fast and can handle big data. An issue pertinent to measurement error models is parameter identification, and this is resolved by employing a prior distribution on the measurement error variance. The methods are shown to perform well in multiple simulation studies, where we analyze the impact on posterior estimates for different values of reliability ratio or variance of the true unobserved quantity used in the data generating process. The paper further implements the proposed algorithms in an application drawn from the health literature and shows that modeling measurement error in the data can improve model fitting.","PeriodicalId":49058,"journal":{"name":"International Journal of Biostatistics","volume":"17 1","pages":"75-97"},"PeriodicalIF":1.2,"publicationDate":"2020-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2019-0120","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38396067","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