International Journal of Biostatistics最新文献

An enhanced approximate Bayesian computation method for stage-structured development models. 阶段结构开发模型的改进近似贝叶斯计算方法。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2025-09-23 DOI: 10.1515/ijb-2025-0065

Hoa Pham, Huong T T Pham, Kai Siong Yow

{"title":"An enhanced approximate Bayesian computation method for stage-structured development models.","authors":"Hoa Pham, Huong T T Pham, Kai Siong Yow","doi":"10.1515/ijb-2025-0065","DOIUrl":"https://doi.org/10.1515/ijb-2025-0065","url":null,"abstract":"Multi-stage models for cohort data are widely used in various fields, including disease progression, the biological development of plants and animals, and laboratory studies of life cycle development. However, the likelihood functions of these models are often intractable and complex. These complexities in the likelihood functions frequently result in significant biases and high computational costs when estimating parameters using current Bayesian methods. This paper aims to address these challenges by applying the enhanced Sequential Monte Carlo approximate Bayesian computation (ABC-SMC) method, which does not rely on explicit likelihood functions, to stage-structured development models with non-hazard rates and stage-wise constant hazard rates. Instead of using a likelihood function, the proposed method determines parameter estimates based on matching vector summary statistics. It incorporates stage-wise parameter estimations and retains accepted parameters across stages. This approach not only reduces model biases but also improves the computational efficiency of parameter estimations, despite the computational intractability of the likelihood functions. The proposed ABC-SMC method is validated through simulation studies on stage-structured development models and applied to a case study of breast development in New Zealand schoolgirls. The results demonstrate that the proposed methods effectively reduce biases in later-stage estimates for stage-structured models, enhance computational efficiency, and maintain accuracy and reliability in parameter estimations compared to the current methods.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145126234","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

Leveraging external information by guided adaptive shrinkage to improve variable selection in high-dimensional regression settings. 利用外部信息引导自适应收缩，以提高变量选择在高维回归设置。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2025-09-08 DOI: 10.1515/ijb-2024-0108

Mark A van de Wiel, Wessel N van Wieringen

{"title":"Leveraging external information by guided adaptive shrinkage to improve variable selection in high-dimensional regression settings.","authors":"Mark A van de Wiel, Wessel N van Wieringen","doi":"10.1515/ijb-2024-0108","DOIUrl":"https://doi.org/10.1515/ijb-2024-0108","url":null,"abstract":"Variable selection is challenging for high-dimensional data, in particular when sample size is low. It is widely recognized that external information in the form of complementary data on the variables, 'co-data', may improve results. Examples are known variable groups or p-values from a related study. Such co-data are ubiquitous in genomics settings due to the availability of public repositories, and is likely equally relevant for other applications. Yet, the uptake of prediction methods that structurally use such co-data is limited. We review guided adaptive shrinkage methods: a class of regression-based learners that use co-data to adapt the shrinkage parameters, crucial for the performance of those learners. We discuss technical aspects, but also the applicability in terms of types of co-data that can be handled. This class of methods is contrasted with several others. In particular, group-adaptive shrinkage is compared with the better-known sparse group-lasso by evaluating variable selection. Moreover, we demonstrate the versatility of the guided shrinkage methodology by showing how to 'do-it-yourself': we integrate implementations of a co-data learner and the spike-and-slab prior for the purpose of improving variable selection in genetics studies. We conclude with a real data example.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145076513","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

Two-sample empirical likelihood method for right censored data. 右截尾数据的两样本经验似然方法。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2025-09-05 DOI: 10.1515/ijb-2024-0120

Leonora Pahirko, Janis Valeinis, Deivids Jēkabsons

引用次数: 0

Inference on overlap index: with an application to cancer data. 重叠指数的推理：与癌症数据的应用。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2025-09-05 DOI: 10.1515/ijb-2024-0106

Raju Dey, Arne C Bathke, Somesh Kumar

引用次数: 0

Forecasting mortality rates in hyponatremia: a statistical approach using Holt-Winters models. 预测低钠血症死亡率：使用霍尔特-温特斯模型的统计方法

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2025-09-02 DOI: 10.1515/ijb-2024-0075

Rawiyah Muneer Alraddadi, Mohamed Abd Allah El-Hadidy, Qin Shao, Qu Xianggui, Sadik Khuder

引用次数: 0

Regression analysis of interval-censored failure time data under semiparametric transformation models with missing covariates. 缺失协变量半参数变换模型下间隔截尾失效时间数据的回归分析。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2025-08-29 DOI: 10.1515/ijb-2024-0016

Yichen Lou, Mingyue Du

{"title":"Regression analysis of interval-censored failure time data under semiparametric transformation models with missing covariates.","authors":"Yichen Lou, Mingyue Du","doi":"10.1515/ijb-2024-0016","DOIUrl":"https://doi.org/10.1515/ijb-2024-0016","url":null,"abstract":"This paper discusses regression analysis of interval-censored failure time data arising from semiparametric transformation models in the presence of covariates that are missing at random (MAR). We define a specific formulation of the MAR mechanism tailored to the interval censoring, where the timing of observation adds complexity to handling missing covariates. To overcome the limitations and computational challenges present in the existing methods, we propose a multiple imputation procedure that can be easily implemented with the use of the standard software. The proposed method makes use of two predictive scores for each individual and the distance defined by these scores. Furthermore, it utilizes partial information from incomplete observations and thus yields more efficient estimators than the complete-case analysis and the inverse probability weighting approach. An extensive simulation study is conducted to assess the performance of the proposed method and indicates that it performs well in practical situations. Finally we apply the proposed approach to an Alzheimer's Disease study that motivated this work.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144976071","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

Penalized regression splines in Mixture Density Networks. 混合密度网络中的惩罚回归样条。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2025-06-05 eCollection Date: 2025-05-01 DOI: 10.1515/ijb-2023-0134

Quentin Edward Seifert, Anton Thielmann, Elisabeth Bergherr, Benjamin Säfken, Jakob Zierk, Manfred Rauh, Tobias Hepp

引用次数: 0

Early completion based on adjacent dose information for model-assisted designs to accelerate maximum tolerated dose finding. 早期完成基于相邻剂量信息的模型辅助设计，以加速最大耐受剂量的发现。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2025-06-03 DOI: 10.1515/ijb-2023-0040

Masahiro Kojima

{"title":"Early completion based on adjacent dose information for model-assisted designs to accelerate maximum tolerated dose finding.","authors":"Masahiro Kojima","doi":"10.1515/ijb-2023-0040","DOIUrl":"https://doi.org/10.1515/ijb-2023-0040","url":null,"abstract":"Phase I trials aim to identify the maximum tolerated dose (MTD) early and proceed quickly to an expansion cohort or a Phase II trial to assess the efficacy of the treatment. We present an early completion method based on multiple dosages (adjacent dose information) to accelerate the identification of the MTD in model-assisted designs. By using not only toxicity data for the current dose but also toxicity data for the next higher and lower doses, the MTD can be identified early without compromising accuracy. The early completion method is performed based on dose-assignment probabilities for multiple dosages. These probabilities are straightforward to calculate. We evaluated the early completion method using from an actual clinical trial. In a simulation study, we evaluated the percentage of correct MTD selection and the impact of early completion on trial outcomes. The results indicate that our proposed early completion method maintains a high level of accuracy in MTD selection, with minimal reduction compared to the standard approach. In certain scenarios, the accuracy of MTD selection even improves under the early completion framework. We conclude that the use of this early completion method poses no issue when applied to model-assisted designs.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144217433","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

Efficiency for evaluation of disease etiologic heterogeneity in case-case and case-control studies. 在病例-病例和病例-对照研究中评估疾病病因异质性的效率。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2025-05-30 DOI: 10.1515/ijb-2023-0027

Aya Kuchiba, Ran Gao, Molin Wang

{"title":"Efficiency for evaluation of disease etiologic heterogeneity in case-case and case-control studies.","authors":"Aya Kuchiba, Ran Gao, Molin Wang","doi":"10.1515/ijb-2023-0027","DOIUrl":"https://doi.org/10.1515/ijb-2023-0027","url":null,"abstract":"A disease of interest can often be classified into subtypes based on its various molecular or pathological characteristics. Recent epidemiological studies have increasingly provided evidence that some molecular subtypes in a disease may have distinct etiologies, by assessing whether the associations of a potential risk factor vary by disease subtypes (i.e., etiologic heterogeneity). Case-control and case-case studies are popular study designs in molecular epidemiology, and both can be validly applied in studies of etiologic heterogeneity. This study compared the efficiency of the etiologic heterogeneity parameter estimation between these two study designs by theoretical and numerical examinations. In settings where the two study designs have the same number of cases, the results showed that, compared with the case-case study, case-control studies always provided more efficient estimates or estimates with at least equivalent efficiency for heterogeneity parameters. In addition, we illustrated both approaches in a study for aiming to evaluate the association between plasma free estradiol and breast cancer risk according to the status of tumor estrogen and progesterone receptors, the results of which were originally provided through case-control study data.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210099","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

Weighted Euclidean balancing for a matrix exposure in estimating causal effect. 估计因果效应中矩阵暴露的加权欧几里得平衡。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2025-05-23 eCollection Date: 2025-05-01 DOI: 10.1515/ijb-2024-0021

Juan Chen, Yingchun Zhou

{"title":"Weighted Euclidean balancing for a matrix exposure in estimating causal effect.","authors":"Juan Chen, Yingchun Zhou","doi":"10.1515/ijb-2024-0021","DOIUrl":"10.1515/ijb-2024-0021","url":null,"abstract":"With the increasing complexity of data, researchers in various fields have become increasingly interested in estimating the causal effect of a matrix exposure, which involves complex multivariate treatments, on an outcome. Balancing covariates for the matrix exposure is essential to achieve this goal. While exact balancing and approximate balancing methods have been proposed for multiple balancing constraints, dealing with a matrix treatment introduces a large number of constraints, making it challenging to achieve exact balance or select suitable threshold parameters for approximate balancing methods. To address this challenge, the weighted Euclidean balancing method is proposed, which offers an approximate balance of covariates from an overall perspective. In this study, both parametric and nonparametric methods for estimating the causal effect of a matrix treatment is proposed, along with providing theoretical properties of the two estimations. To validate the effectiveness of our approach, extensive simulation results demonstrate that the proposed method outperforms alternative approaches across various scenarios. Finally, we apply the method to analyze the causal impact of the omics variables on the drug sensitivity of Vandetanib. The results indicate that EGFR CNV has a significant positive causal effect on Vandetanib efficacy, whereas EGFR methylation exerts a significant negative causal effect.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":" ","pages":"219-237"},"PeriodicalIF":1.2,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144152240","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