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Bivariate Analysis of Birth Weight and Gestational Age by Bayesian Distributional Regression with Copulas 基于copula的贝叶斯分布回归分析出生体重和胎龄的双变量分析

Statistics in Biosciences Pub Date : 2023-10-27 DOI: 10.1007/s12561-023-09396-4

Jonathan Rathjens, Arthur Kolbe, Jürgen Hölzer, Katja Ickstadt, Nadja Klein

{"title":"Bivariate Analysis of Birth Weight and Gestational Age by Bayesian Distributional Regression with Copulas","authors":"Jonathan Rathjens, Arthur Kolbe, Jürgen Hölzer, Katja Ickstadt, Nadja Klein","doi":"10.1007/s12561-023-09396-4","DOIUrl":"https://doi.org/10.1007/s12561-023-09396-4","url":null,"abstract":"Abstract We analyze perinatal data including biometric and obstetric information as well as data on maternal smoking, among others. Birth weight is the primarily interesting response variable. Gestational age is usually an important covariate and included in polynomial form. However, in opposition to this univariate regression, bivariate modeling of birth weight and gestational age is recommended to distinguish effects on each, on both, and between them. Rather than a parametric bivariate distribution, we apply conditional copula regression, where the marginal distributions of birth weight and gestational age (not necessarily of the same form) and the dependence structure are modeled conditionally on covariates. In the resulting distributional regression model, all parameters of the two marginals and the copula parameter are observation specific. While the Gaussian distribution is suitable for birth weight, the skewed gestational age data are better modeled by the three-parameter Dagum distribution. The Clayton copula performs better than the Gumbel and the symmetric Gaussian copula, indicating lower tail dependence (stronger dependence when both variables are low), although this non-linear dependence between birth weight and gestational age is surprisingly weak and only influenced by Cesarean section. A non-linear trend of birth weight on gestational age is detected by a univariate model that is polynomial with respect to the effect of gestational age. Covariate effects on the expected birth weight are similar in our copula regression model and a univariate regression model, while distributional copula regression reveals further insights, such as effects of covariates on the association between birth weight and gestational age.","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"122 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136264066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI-Powered Bayesian Statistics in Biomedicine 生物医学中的人工智能贝叶斯统计

Statistics in Biosciences Pub Date : 2023-10-26 DOI: 10.1007/s12561-023-09400-x

Qiwei Li

引用次数: 0

Efficient Estimation of Semiparametric Transformation Model with Interval-Censored Data in Two-Phase Cohort Studies 两期队列研究中具有区间截尾数据的半参数转换模型的有效估计

Statistics in Biosciences Pub Date : 2023-10-25 DOI: 10.1007/s12561-023-09392-8

Fei Gao, Kwun Chuen Gary Chan

引用次数: 0

Variable Selection for Nonlinear Covariate Effects with Interval-Censored Failure Time Data 失效时间间隔截尾非线性协变量效应的变量选择

Statistics in Biosciences Pub Date : 2023-10-20 DOI: 10.1007/s12561-023-09391-9

Tian Tian, Jianguo Sun

引用次数: 0

A Resampling Approach for Causal Inference on Novel Two-Point Time-Series with Application to Identify Risk Factors for Type-2 Diabetes and Cardiovascular Disease 新两点时间序列因果推断的重采样方法及其在2型糖尿病和心血管疾病危险因素识别中的应用

Statistics in Biosciences Pub Date : 2023-10-16 DOI: 10.1007/s12561-023-09390-w

Xiaowu Dai, Saad Mouti, Marjorie Lima do Vale, Sumantra Ray, Jeffrey Bohn, Lisa Goldberg

{"title":"A Resampling Approach for Causal Inference on Novel Two-Point Time-Series with Application to Identify Risk Factors for Type-2 Diabetes and Cardiovascular Disease","authors":"Xiaowu Dai, Saad Mouti, Marjorie Lima do Vale, Sumantra Ray, Jeffrey Bohn, Lisa Goldberg","doi":"10.1007/s12561-023-09390-w","DOIUrl":"https://doi.org/10.1007/s12561-023-09390-w","url":null,"abstract":"Abstract Two-point time-series data, characterized by baseline and follow-up observations, are frequently encountered in health research. We study a novel two-point time-series structure without a control group, which is driven by an observational routine clinical dataset collected to monitor key risk markers of type-2 diabetes (T2D) and cardiovascular disease (CVD). We propose a resampling approach called “I-Rand” for independently sampling one of the two-time points for each individual and making inferences on the estimated causal effects based on matching methods. The proposed method is illustrated with data from a service-based dietary intervention to promote a low-carbohydrate diet (LCD), designed to impact risk of T2D and CVD. Baseline data contain a pre-intervention health record of study participants, and health data after LCD intervention are recorded at the follow-up visit, providing a two-point time-series pattern without a parallel control group. Using this approach we find that obesity is a significant risk factor of T2D and CVD, and an LCD approach can significantly mitigate the risks of T2D and CVD. We provide code that implements our method.","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136113434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Tweedie Distributions for Biological Sequences Alignments 生物序列比对的Tweedie分布

Statistics in Biosciences Pub Date : 2023-10-09 DOI: 10.1007/s12561-023-09388-4

Ben Hassen Hanen, Masmoudi Khalil, Masmoudi Afif

引用次数: 0

Unlocking Cellular Insights Through Cell-Type Decomposition 通过细胞类型分解解锁细胞洞察力

Statistics in Biosciences Pub Date : 2023-10-04 DOI: 10.1007/s12561-023-09389-3

Xiaoyu Song

引用次数: 0

Power Analysis of Exposure Mixture Studies Via Monte Carlo Simulations 基于蒙特卡罗模拟的暴露混合物功率分析研究

Statistics in Biosciences Pub Date : 2023-10-01 DOI: 10.1007/s12561-023-09385-7

Phuc H. Nguyen, Amy H. Herring, Stephanie M. Engel

引用次数: 1

Improving the Power to Detect Indirect Effects in Mediation Analysis 提高中介分析中间接影响的检测能力

Statistics in Biosciences Pub Date : 2023-09-27 DOI: 10.1007/s12561-023-09386-6

John Kidd, Dan-Yu Lin

引用次数: 0

Detecting Disease Outbreak Regions Using Multiple Data Streams 使用多个数据流检测疾病爆发区域

Statistics in Biosciences Pub Date : 2023-09-16 DOI: 10.1007/s12561-023-09387-5

Sesha Dassanayake, Joshua P. French

引用次数: 0