International Journal of Biostatistics最新文献_第6页

Penalized logistic regression with prior information for microarray gene expression classification. 利用先验信息对微阵列基因表达分类进行有惩罚的逻辑回归。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2022-11-25 eCollection Date: 2024-05-01 DOI: 10.1515/ijb-2022-0025

Murat Genç

{"title":"Penalized logistic regression with prior information for microarray gene expression classification.","authors":"Murat Genç","doi":"10.1515/ijb-2022-0025","DOIUrl":"10.1515/ijb-2022-0025","url":null,"abstract":"Cancer classification and gene selection are important applications in DNA microarray gene expression data analysis. Since DNA microarray data suffers from the high-dimensionality problem, automatic gene selection methods are used to enhance the classification performance of expert classifier systems. In this paper, a new penalized logistic regression method that performs simultaneous gene coefficient estimation and variable selection in DNA microarray data is discussed. The method employs prior information about the gene coefficients to improve the classification accuracy of the underlying model. The coordinate descent algorithm with screening rules is given to obtain the gene coefficient estimates of the proposed method efficiently. The performance of the method is examined on five high-dimensional cancer classification datasets using the area under the curve, the number of selected genes, misclassification rate and F-score measures. The real data analysis results indicate that the proposed method achieves a good cancer classification performance with a small misclassification rate, large area under the curve and F-score by trading off some sparsity level of the underlying model. Hence, the proposed method can be seen as a reliable penalized logistic regression method in the scope of high-dimensional cancer classification.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40707390","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

The balanced discrete triplet Lindley model and its INAR(1) extension: properties and COVID-19 applications. 平衡离散三重态Lindley模型及其INAR(1)扩展:性质和COVID-19应用。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2022-11-24 eCollection Date: 2023-11-01 DOI: 10.1515/ijb-2022-0001

Masoumeh Shirozhan, Naushad A Mamode Khan, Célestin C Kokonendji

{"title":"The balanced discrete triplet Lindley model and its INAR(1) extension: properties and COVID-19 applications.","authors":"Masoumeh Shirozhan, Naushad A Mamode Khan, Célestin C Kokonendji","doi":"10.1515/ijb-2022-0001","DOIUrl":"10.1515/ijb-2022-0001","url":null,"abstract":"This paper proposes a new flexible discrete triplet Lindley model that is constructed from the balanced discretization principle of the extended Lindley distribution. This model has several appealing statistical properties in terms of providing exact and closed form moment expressions and handling all forms of dispersion. Due to these, this paper explores further the usage of the discrete triplet Lindley as an innovation distribution in the simple integer-valued autoregressive process (INAR(1)). This subsequently allows for the modeling of count time series observations. In this context, a novel INAR(1) process is developed under mixed Binomial and the Pegram thinning operators. The model parameters of the INAR(1) process are estimated using the conditional maximum likelihood and Yule-Walker approaches. Some Monte Carlo simulation experiments are executed to assess the consistency of the estimators under the two estimation approaches. Interestingly, the proposed INAR(1) process is applied to analyze the COVID-19 cases and death series of different countries where it yields reliable parameter estimates and suitable forecasts via the modified Sieve bootstrap technique. On the other side, the new INAR(1) with discrete triplet Lindley innovations competes comfortably with other established INAR(1)s in the literature.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40721904","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

Potential application of elastic nets for shared polygenicity detection with adapted threshold selection. 弹性网在自适应阈值选择共享多基因检测中的潜在应用。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2022-11-03 eCollection Date: 2023-11-01 DOI: 10.1515/ijb-2020-0108

Majnu John, Todd Lencz

{"title":"Potential application of elastic nets for shared polygenicity detection with adapted threshold selection.","authors":"Majnu John, Todd Lencz","doi":"10.1515/ijb-2020-0108","DOIUrl":"10.1515/ijb-2020-0108","url":null,"abstract":"Current research suggests that hundreds to thousands of single nucleotide polymorphisms (SNPs) with small to modest effect sizes contribute to the genetic basis of many disorders, a phenomenon labeled as polygenicity. Additionally, many such disorders demonstrate polygenic overlap, in which risk alleles are shared at associated genetic loci. A simple strategy to detect polygenic overlap between two phenotypes is based on rank-ordering the univariate p-values from two genome-wide association studies (GWASs). Although high-dimensional variable selection strategies such as Lasso and elastic nets have been utilized in other GWAS analysis settings, they are yet to be utilized for detecting shared polygenicity. In this paper, we illustrate how elastic nets, with polygenic scores as the dependent variable and with appropriate adaptation in selecting the penalty parameter, may be utilized for detecting a subset of SNPs involved in shared polygenicity. We provide theory to better understand our approaches, and illustrate their utility using synthetic datasets. Results from extensive simulations are presented comparing the elastic net approaches with the rank ordering approach, in various scenarios. Results from simulations studies exhibit one of the elastic net approaches to be superior when the correlations among the SNPs are high. Finally, we apply the methods on two real datasets to illustrate further the capabilities, limitations and differences among the methods.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10154439/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9401096","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: 0

A Bayesian semiparametric accelerate failure time mixture cure model. 贝叶斯半参数加速失效时间混合固化模型。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2022-11-01 DOI: 10.1515/ijb-2021-0012

Yijun Wang, Weiwei Wang, Yincai Tang

引用次数: 4

Doubly robust adaptive LASSO for effect modifier discovery. 双鲁棒自适应LASSO用于效果修正器的发现。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2022-11-01 DOI: 10.1515/ijb-2020-0073

Asma Bahamyirou, Mireille E Schnitzer, Edward H Kennedy, Lucie Blais, Yi Yang

引用次数: 8

Causal inference under interference with prognostic scores for dynamic group therapy studies. 动态群体治疗研究中预后评分干扰下的因果推断。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2022-11-01 DOI: 10.1515/ijb-2019-0126

Bing Han, Susan M Paddock, Lane Burgette

{"title":"Causal inference under interference with prognostic scores for dynamic group therapy studies.","authors":"Bing Han, Susan M Paddock, Lane Burgette","doi":"10.1515/ijb-2019-0126","DOIUrl":"https://doi.org/10.1515/ijb-2019-0126","url":null,"abstract":"Group therapy is a common treatment modality for behavioral health conditions. Patients often enter and exit groups on an ongoing basis, leading to dynamic therapy groups. Examining the effect of high versus low session attendance on patient outcomes is a research question of interest. However, there are several challenges to identifying causal effects in this setting, including the lack of randomization, interference among patients, and the interrelatedness of patient participation. Dynamic therapy groups motivate a unique causal inference scenario, as the treatment statuses are completely defined by the patient attendance record for the therapy session, which is also the structure inducing interference. We adopt the Rubin causal model framework to define the causal effect of high versus low session attendance of group therapy at both the individual patient and peer levels. We propose a strategy to identify individual, peer, and total effects of high attendance versus low attendance on patient outcomes by the prognostic score stratification. We examine performance of our approach via simulation and apply it to data from a group cognitive behavioral therapy trial for treating depression among patients in a substance use disorders treatment setting.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9973534/pdf/nihms-1876458.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10866225","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: 0

The number of response categories in ordered response models. 有序响应模型中响应类别的数量。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2022-11-01 DOI: 10.1515/ijb-2021-0013

Maria Iannario, Anna Clara Monti, Pietro Scalera

{"title":"The number of response categories in ordered response models.","authors":"Maria Iannario, Anna Clara Monti, Pietro Scalera","doi":"10.1515/ijb-2021-0013","DOIUrl":"https://doi.org/10.1515/ijb-2021-0013","url":null,"abstract":"The choice of the number m of response categories is a crucial issue in categorization of a continuous response. The paper exploits the Proportional Odds Models' property which allows to generate ordinal responses with a different number of categories from the same underlying variable. It investigates the asymptotic efficiency of the estimators of the regression coefficients and the accuracy of the derived inferential procedures when m varies. The analysis is based on models with closed-form information matrices so that the asymptotic efficiency can be analytically evaluated without need of simulations. The paper proves that a finer categorization augments the information content of the data and consequently shows that the asymptotic efficiency and the power of the tests on the regression coefficients increase with m. The impact of the loss of information produced by merging categories on the efficiency of the estimators is also considered, highlighting its risks especially when performed in its extreme form of dichotomization. Furthermore, the appropriate value of m for various sample sizes is explored, pointing out that a large number of categories can offset the limited amount of information of a small sample by a better quality of the data. Finally, two case studies on the quality of life of chemotherapy patients and on the perception of pain, based on discretized continuous scales, illustrate the main findings of the paper.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10844729","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

Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score. 通过对预后评分进行线性调整来提高随机试验评估的效率。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2022-11-01 DOI: 10.1515/ijb-2021-0072

Alejandro Schuler, David Walsh, Diana Hall, Jon Walsh, Charles Fisher

{"title":"Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score.","authors":"Alejandro Schuler, David Walsh, Diana Hall, Jon Walsh, Charles Fisher","doi":"10.1515/ijb-2021-0072","DOIUrl":"https://doi.org/10.1515/ijb-2021-0072","url":null,"abstract":"Estimating causal effects from randomized experiments is central to clinical research. Reducing the statistical uncertainty in these analyses is an important objective for statisticians. Registries, prior trials, and health records constitute a growing compendium of historical data on patients under standard-of-care that may be exploitable to this end. However, most methods for historical borrowing achieve reductions in variance by sacrificing strict type-I error rate control. Here, we propose a use of historical data that exploits linear covariate adjustment to improve the efficiency of trial analyses without incurring bias. Specifically, we train a prognostic model on the historical data, then estimate the treatment effect using a linear regression while adjusting for the trial subjects' predicted outcomes (their prognostic scores). We prove that, under certain conditions, this prognostic covariate adjustment procedure attains the minimum variance possible among a large class of estimators. When those conditions are not met, prognostic covariate adjustment is still more efficient than raw covariate adjustment and the gain in efficiency is proportional to a measure of the predictive accuracy of the prognostic model above and beyond the linear relationship with the raw covariates. We demonstrate the approach using simulations and a reanalysis of an Alzheimer's disease clinical trial and observe meaningful reductions in mean-squared error and the estimated variance. Lastly, we provide a simplified formula for asymptotic variance that enables power calculations that account for these gains. Sample size reductions between 10% and 30% are attainable when using prognostic models that explain a clinically realistic percentage of the outcome variance.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10844739","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: 18

Spike detection for calcium activity. 钙活性的峰值检测。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2022-11-01 DOI: 10.1515/ijb-2020-0043

Hermine Biermé, Camille Constant, Anne Duittoz, Christine Georgelin

引用次数: 0

Bayesian multi-response nonlinear mixed-effect model: application of two recent HIV infection biomarkers. 贝叶斯多响应非线性混合效应模型:两个新近的HIV感染生物标志物的应用。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2022-11-01 DOI: 10.1515/ijb-2021-0030

Charlotte Castel, Cécile Sommen, Edouard Chatignoux, Yann Le Strat, Ahmadou Alioum

引用次数: 0