International Journal of Biostatistics最新文献_第4页

Summarizing causal differences in survival curves in the presence of unmeasured confounding. 在存在未测量的混杂因素时，总结生存曲线的因果差异。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2020-09-18 DOI: 10.1515/ijb-2019-0146

Pablo Martínez-Camblor, Todd A MacKenzie, Douglas O Staiger, Phillip P Goodney, A James O'Malley

{"title":"Summarizing causal differences in survival curves in the presence of unmeasured confounding.","authors":"Pablo Martínez-Camblor, Todd A MacKenzie, Douglas O Staiger, Phillip P Goodney, A James O'Malley","doi":"10.1515/ijb-2019-0146","DOIUrl":"https://doi.org/10.1515/ijb-2019-0146","url":null,"abstract":"Proportional hazard Cox regression models are frequently used to analyze the impact of different factors on time-to-event outcomes. Most practitioners are familiar with and interpret research results in terms of hazard ratios. Direct differences in survival curves are, however, easier to understand for the general population of users and to visualize graphically. Analyzing the difference among the survival curves for the population at risk allows easy interpretation of the impact of a therapy over the follow-up. When the available information is obtained from observational studies, the observed results are potentially subject to a plethora of measured and unmeasured confounders. Although there are procedures to adjust survival curves for measured covariates, the case of unmeasured confounders has not yet been considered in the literature. In this article we provide a semi-parametric procedure for adjusting survival curves for measured and unmeasured confounders. The method augments our novel instrumental variable estimation method for survival time data in the presence of unmeasured confounding with a procedure for mapping estimates onto the survival probability and the expected survival time scales.","PeriodicalId":49058,"journal":{"name":"International Journal of Biostatistics","volume":"17 2","pages":"223-240"},"PeriodicalIF":1.2,"publicationDate":"2020-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2019-0146","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38396427","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

Co-localization analysis in fluorescence microscopy via maximum entropy copula. 利用最大熵联结法分析荧光显微镜的共定位。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2020-09-18 DOI: 10.1515/ijb-2019-0019

Zahra Amini Farsani, Volker J Schmid

{"title":"Co-localization analysis in fluorescence microscopy via maximum entropy copula.","authors":"Zahra Amini Farsani, Volker J Schmid","doi":"10.1515/ijb-2019-0019","DOIUrl":"https://doi.org/10.1515/ijb-2019-0019","url":null,"abstract":"Co-localization analysis is a popular method for quantitative analysis in fluorescence microscopy imaging. The localization of marked proteins in the cell nucleus allows a deep insight into biological processes in the nucleus. Several metrics have been developed for measuring the co-localization of two markers, however, they depend on subjective thresholding of background and the assumption of linearity. We propose a robust method to estimate the bivariate distribution function of two color channels. From this, we can quantify their co- or anti-colocalization. The proposed method is a combination of the Maximum Entropy Method (MEM) and a Gaussian Copula, which we call the Maximum Entropy Copula (MEC). This new method can measure the spatial and nonlinear correlation of signals to determine the marker colocalization in fluorescence microscopy images. The proposed method is compared with MEM for bivariate probability distributions. The new colocalization metric is validated on simulated and real data. The results show that MEC can determine co- and anti-colocalization even in high background settings. MEC can, therefore, be used as a robust tool for colocalization analysis.","PeriodicalId":49058,"journal":{"name":"International Journal of Biostatistics","volume":"17 1","pages":"165-175"},"PeriodicalIF":1.2,"publicationDate":"2020-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2019-0019","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38493940","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

Integrative analysis with a system of semiparametric projection non-linear regression models. 半参数投影非线性回归模型系统的综合分析。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2020-09-16 DOI: 10.1515/ijb-2019-0124

Ao Yuan, Tianmin Wu, Hong-Bin Fang, Ming T Tan

引用次数: 0

Alternatives to the Kaplan-Meier estimator of progression-free survival. Kaplan-Meier无进展生存估计的替代方法。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2020-09-07 DOI: 10.1515/ijb-2019-0095

Jenny J Zhang, Zhuoxin Sun, Han Yuan, Molin Wang

{"title":"Alternatives to the Kaplan-Meier estimator of progression-free survival.","authors":"Jenny J Zhang, Zhuoxin Sun, Han Yuan, Molin Wang","doi":"10.1515/ijb-2019-0095","DOIUrl":"https://doi.org/10.1515/ijb-2019-0095","url":null,"abstract":"Progression-free survival (PFS), defined as the time from randomization to progression of disease or death, has been indicated as an endpoint to support accelerated approval of certain cancer drugs by the U.S. FDA. The standard Kaplan-Meier (KM) estimator of PFS, however, can result in significantly biased estimates. A major source for the bias results from the substitution of censored progression times with death times. Currently, to ameliorate this bias, several sensitivity analyses based on rather arbitrary definitions of PFS censoring are usually conducted. In addition, especially in the advanced cancer setting, patients with censored progression and observed death times have the potential to experience disease progression between those two times, in which case their true PFS time is actually between those times. In this paper, we present two alternative nonparametric estimators of PFS, which statistically incorporate survival data often available for those patients who are censored with respect to progression to obtain less biased estimates. Through extensive simulations, we show that these estimators greatly reduce the bias of the standard KM estimator and can also be utilized as alternative sensitivity analyses with a solid statistical basis in lieu of the arbitrarily defined analyses currently used. An example is also given using an ECOG-ACRIN Cancer Research Group advanced breast cancer study.","PeriodicalId":49058,"journal":{"name":"International Journal of Biostatistics","volume":"17 1","pages":"99-115"},"PeriodicalIF":1.2,"publicationDate":"2020-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2019-0095","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38350338","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

Modelling ethnic differences in the distribution of insulin resistance via Bayesian nonparametric processes: an application to the SABRE cohort study. 通过贝叶斯非参数过程模拟胰岛素抵抗分布的种族差异：在SABRE队列研究中的应用。

IF 1 4区数学

International Journal of Biostatistics Pub Date : 2020-08-31 DOI: 10.1515/ijb-2019-0108

Marco Molinari, Maria de Iorio, Nishi Chaturvedi, Alun Hughes, Therese Tillin

引用次数: 0

Two-stage receiver operating-characteristic curve estimator for cohort studies. 队列研究的两阶段受试者工作特征曲线估计器。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2020-08-21 DOI: 10.1515/ijb-2019-0097

Susana Díaz-Coto, Norberto Octavio Corral-Blanco, Pablo Martínez-Camblor

{"title":"Two-stage receiver operating-characteristic curve estimator for cohort studies.","authors":"Susana Díaz-Coto, Norberto Octavio Corral-Blanco, Pablo Martínez-Camblor","doi":"10.1515/ijb-2019-0097","DOIUrl":"https://doi.org/10.1515/ijb-2019-0097","url":null,"abstract":"The receiver operating-characteristic (ROC) curve is a graphical statistical tool routinely used for studying the classification accuracy in both, diagnostic and prognosis problems. Given the different nature of these situations, ROC curve estimation has been separately considered for binary (diagnostic) and time-to-event (prognosis) outcomes, even for data coming from the same study design. In this work, the authors propose a two-stage ROC curve estimator which allows to link both contexts through a general prediction model (first-stage) and the empirical cumulative estimator of the distribution function (second-stage) of the considered test (marker) on the total population. The so-called two-stage Mixed-Subject (sMS) approach proves its behavior on both, large-samples (theoretically) and finite-samples (via Monte Carlo simulations). Besides, a useful asymptotic distribution for the concomitant area under the curve is also computed. Results show the ability of the proposed estimator to fit non-standard situations by considering flexible predictive models. Two real-world examples, one with binary and one with time-dependent outcomes, help us to a better understanding of the proposed methodology on usual practical circumstances. The R code used for the practical implementation of the proposed methodology and its documentation is provided as supplementary material.","PeriodicalId":49058,"journal":{"name":"International Journal of Biostatistics","volume":"17 1","pages":"117-137"},"PeriodicalIF":1.2,"publicationDate":"2020-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2019-0097","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38324619","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: 1

A machine learning-based approach for estimating and testing associations with multivariate outcomes. 一种基于机器学习的方法，用于估计和测试与多变量结果的关联。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2020-08-13 DOI: 10.1515/ijb-2019-0061

David Benkeser, Andrew Mertens, John M Colford, Alan Hubbard, Benjamin F Arnold, Aryeh Stein, Mark J van der Laan

{"title":"A machine learning-based approach for estimating and testing associations with multivariate outcomes.","authors":"David Benkeser, Andrew Mertens, John M Colford, Alan Hubbard, Benjamin F Arnold, Aryeh Stein, Mark J van der Laan","doi":"10.1515/ijb-2019-0061","DOIUrl":"https://doi.org/10.1515/ijb-2019-0061","url":null,"abstract":"We propose a method for summarizing the strength of association between a set of variables and a multivariate outcome. Classical summary measures are appropriate when linear relationships exist between covariates and outcomes, while our approach provides an alternative that is useful in situations where complex relationships may be present. We utilize machine learning to detect nonlinear relationships and covariate interactions and propose a measure of association that captures these relationships. A hypothesis test about the proposed associative measure can be used to test the strong null hypothesis of no association between a set of variables and a multivariate outcome. Simulations demonstrate that this hypothesis test has greater power than existing methods against alternatives where covariates have nonlinear relationships with outcomes. We additionally propose measures of variable importance for groups of variables, which summarize each groups' association with the outcome. We demonstrate our methodology using data from a birth cohort study on childhood health and nutrition in the Philippines.","PeriodicalId":49058,"journal":{"name":"International Journal of Biostatistics","volume":"17 1","pages":"7-21"},"PeriodicalIF":1.2,"publicationDate":"2020-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2019-0061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38255057","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

Shrinkage estimation applied to a semi-nonparametric regression model. 收缩估计应用于半非参数回归模型。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2020-08-10 DOI: 10.1515/ijb-2018-0109

Hossein Zareamoghaddam, Syed E Ahmed, Serge B Provost

引用次数: 0

Estimating the area under a receiver operating characteristic curve using partially ordered sets. 用部分有序集估计接收机工作特性曲线下的面积。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2020-08-07 DOI: 10.1515/ijb-2019-0127

Ehsan Zamanzade, Xinlei Wang

引用次数: 2

Multivariate quasi-beta regression models for continuous bounded data. 连续有界数据的多元拟回归模型。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2020-08-01 DOI: 10.1515/ijb-2019-0163

Ricardo R Petterle, Wagner H Bonat, Cassius T Scarpin, Thaísa Jonasson, Victória Z C Borba

{"title":"Multivariate quasi-beta regression models for continuous bounded data.","authors":"Ricardo R Petterle, Wagner H Bonat, Cassius T Scarpin, Thaísa Jonasson, Victória Z C Borba","doi":"10.1515/ijb-2019-0163","DOIUrl":"https://doi.org/10.1515/ijb-2019-0163","url":null,"abstract":"We propose a multivariate regression model to deal with multiple continuous bounded data. The proposed model is based on second-moment assumptions, only. We adopted the quasi-score and Pearson estimating functions for estimation of the regression and dispersion parameters, respectively. Thus, the proposed approach does not require a multivariate probability distribution for the variable response vector. The multivariate quasi-beta regression model can easily handle multiple continuous bounded outcomes taking into account the correlation between the response variables. Furthermore, the model allows us to analyze continuous bounded data on the interval [0, 1], including zeros and/or ones. Simulation studies were conducted to investigate the behavior of the NORmal To Anything (NORTA) algorithm and to check the properties of the estimating function estimators to deal with multiple correlated response variables generated from marginal beta distributions. The model was motivated by a data set concerning the body fat percentage, which was measured at five regions of the body and represent the response variables. We analyze each response variable separately and compare it with the fit of the multivariate proposed model. The multivariate quasi-beta regression model provides better fit than its univariate counterparts, as well as allows us to measure the correlation between response variables. Finally, we adapted diagnostic tools to the proposed model. In the supplementary material, we provide the data set and R code.","PeriodicalId":49058,"journal":{"name":"International Journal of Biostatistics","volume":"17 1","pages":"39-53"},"PeriodicalIF":1.2,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/ijb-2019-0163","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38222036","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: 6