International Journal of Biostatistics最新文献

Best subset selection meets resampling: tackling selection instability at very low numbers of events per variable. 最佳子集选择满足重新采样：在每个变量的事件数非常低的情况下解决选择不稳定性。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2026-10-15 DOI: 10.1515/ijb-2025-0024

Ricardo Knauer, Nicholas M Brisson, Georg N Duda, Deborah Falla, David W Evans, Erik Rodner

{"title":"Best subset selection meets resampling: tackling selection instability at very low numbers of events per variable.","authors":"Ricardo Knauer, Nicholas M Brisson, Georg N Duda, Deborah Falla, David W Evans, Erik Rodner","doi":"10.1515/ijb-2025-0024","DOIUrl":"https://doi.org/10.1515/ijb-2025-0024","url":null,"abstract":"Trust is a basic necessity for translating machine learning models from bench to bedside. To foster clinicians' trust in predictive models, these models should be interpretable or explainable. A major prerequisite for interpretability or explainability is that feature subsets are stable, i.e., that predictors returned by feature or best subset selection approaches do not change dramatically when the data is perturbed only slightly. However, prior research has shown that stable selections are usually not possible when the number of labels in the smaller class per feature, or outcome events per variable (EPV), is very low, and it remains challenging to build stable, interpretable models in such cases. In this work, we conduct a narrative review in the very low EPV regime and propose to combine cardinality-constrained best subset selection for logistic regression with resampling to tackle the selection instability. We perform a comprehensive evaluation on both synthetic and real-world clinical data, and find that our novel resampling-based model selection procedure yields interpretable feature subsets with a better discriminative performance and stability compared to best subset selection without resampling as well as feature selection with and without resampling. A Python package implementing the proposed methods is available at https://github.com/ml-lab-htw/l0l2learn.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149197610","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

Estimating relapse time distribution from longitudinal biomarker trajectories using iterative regression and continuous time Markov processes. 利用迭代回归和连续时间马尔可夫过程估计纵向生物标志物轨迹的复发时间分布。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2026-10-14 DOI: 10.1515/ijb-2025-0054

Alice Cleynen, Benoîte de Saporta, Amélie Vernay

{"title":"Estimating relapse time distribution from longitudinal biomarker trajectories using iterative regression and continuous time Markov processes.","authors":"Alice Cleynen, Benoîte de Saporta, Amélie Vernay","doi":"10.1515/ijb-2025-0054","DOIUrl":"https://doi.org/10.1515/ijb-2025-0054","url":null,"abstract":"Biomarker measurements obtained by blood sampling are often used as a non-invasive means of monitoring tumour progression in cancer patients. Diseases evolve dynamically over time, and studying longitudinal observations of specific biomarkers can help to understand patients response to treatment and predict disease progression. We propose a novel iterative regression-based method to estimate changes in patients status within a cohort that includes censored patients, and illustrate it on clinical data from myeloma cases. We formulate the relapse time estimation problem in the framework of Piecewise Deterministic Markov processes (PDMP), where the Euclidean component is a surrogate biomarker for patient state. This approach enables continuous-time estimation of the status-change dates, which in turn allows for accurate inference of the relapse time distribution. A key challenge lies in the partial observability of the process, a complexity that has been rarely addressed in previous studies. We evaluate the performance of our procedure through a simulation study and compare it with different approaches. This work is a proof of concept on biomarker trajectories with simple behaviour, but our method can easily be extended to more complex dynamics.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149185633","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 longitudinal Bayesian framework for estimating causal dose-response relationships. 估计因果剂量-反应关系的纵向贝叶斯框架。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2026-10-12 DOI: 10.1515/ijb-2026-0010

Yu Luo, Kuan Liu, Ramandeep Singh, Daniel J Graham

引用次数: 0

Machine learning for estimating and comparing clinical rules for treating diarrheal illness with antibiotics. 用机器学习估计和比较抗生素治疗腹泻病的临床规则。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2026-10-09 DOI: 10.1515/ijb-2026-0026

Allison Codi, Sara Kim, Elizabeth Rogawski McQuade, David Benkeser

{"title":"Machine learning for estimating and comparing clinical rules for treating diarrheal illness with antibiotics.","authors":"Allison Codi, Sara Kim, Elizabeth Rogawski McQuade, David Benkeser","doi":"10.1515/ijb-2026-0026","DOIUrl":"https://doi.org/10.1515/ijb-2026-0026","url":null,"abstract":"Acute diarrheal disease is a leading cause of death in children under age 5, disproportionately impacting children in low-resource settings. Although many cases could respond to antibiotic treatment, the benefits of widely prescribing antibiotics must be weighed against the risks of antimicrobial resistance. These challenges motivate development of individualized treatment guidelines for diarrheal disease. In this study, we utilize a framework for creation and evaluation of individualized treatment rules that leverage diagnostic and clinical information to make treatment recommendations. In contrast to many applications of pipelines for creating and evaluating treatment rules, we (i) explicitly create rules that limit the proportion of children treated based on expected clinical benefit, to limit risks for overtreatment and the emergence of antimicrobial resistance, and (ii) propose methods to compare rules based on different sets of input covariates, allowing for quantification of the impact of measuring additional biomarkers in clinical settings. We use a nested cross-validation procedure with ensemble machine learning and doubly-robust estimation to derive, evaluate, and compare rules. We demonstrate that our proposed method yields appropriate inference in a realistic simulation study and apply our method to data from the AntiBiotics for Children with severe Diarrhea (ABCD) trial.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149149789","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

Doubly robust estimation of monotonic survival curves for time-varying treatments in observational studies. 观察性研究中时变治疗单调生存曲线的双稳健估计。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2026-09-18 DOI: 10.1515/ijb-2025-0101

Lan Wen

引用次数: 0

Using non-targeted HPV infections in studies with risk compensation. 在风险补偿研究中使用非靶向HPV感染。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2026-07-16 DOI: 10.1515/ijb-2025-0087

Lola Etiévant

{"title":"Using non-targeted HPV infections in studies with risk compensation.","authors":"Lola Etiévant","doi":"10.1515/ijb-2025-0087","DOIUrl":"https://doi.org/10.1515/ijb-2025-0087","url":null,"abstract":"Studies of HPV vaccine efficacy usually record infections with vaccine targeted and non-targeted strains. Contrary to blinded randomized controlled trials, confounding bias can be a threat and risk compensation may occur in observational studies. Etievant et al. (Biometrics, 2023) proposed to use cervical infections with non-targeted HPV strains to remove or reduce confounding bias in estimates of vaccine efficacy on targeted strains. However, they assumed that vaccinated women could not change their behavior after vaccination. This work investigates if the quantity estimated in practice with the method of Etievant et al. has a clear causal meaning under a more plausible setting where unmeasured sexual behavior acts as both a confounder and a mediator. Under certain assumptions, using non-targeted HPV infections can remove both confounding bias and the portion of the vaccine effect on the targeted HPV strains that is mediated through the change of behavior. In that case, the estimated quantity has a clear causal interpretation as it represents the direct immunological effect of the vaccine. Infections with non-targeted HPV strains could also be used to isolate the indirect behavioral effect of the vaccine in unblinded randomized controlled trial.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2026-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148473745","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

Targeted maximum likelihood estimation (TMLE) in regulatory submissions and research: a landscape analysis. 法规提交和研究中的目标最大似然估计（TMLE）：景观分析。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2026-06-17 DOI: 10.1515/ijb-2026-0055

Hana Lee, Menglun Wang, Spencer Haupert, Gabriel K Innes, Sarah K Dutcher, Susan Gruber, Qi Liu

引用次数: 0

Predicting birth weight by multivariate functional principal component regressions. 多变量主成分回归预测出生体重。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2026-06-03 eCollection Date: 2026-05-01 DOI: 10.1515/ijb-2025-0131

Yaeji Lim, Ruijin Lu, Madeleine St Ville, Katherine L Grantz, Zhen Chen

{"title":"Predicting birth weight by multivariate functional principal component regressions.","authors":"Yaeji Lim, Ruijin Lu, Madeleine St Ville, Katherine L Grantz, Zhen Chen","doi":"10.1515/ijb-2025-0131","DOIUrl":"10.1515/ijb-2025-0131","url":null,"abstract":"Functional data analysis (FDA) provides a powerful statistical framework for analyzing complex data, such as curves or functions, over high-dimensional domains. In this paper, we focus on functional predictor regression (scalar-on-function) models applied to the prediction of birth weight using maternal dietary patterns during pregnancy. Specifically, we analyze trajectories of nine components of the Alternative Healthy Eating Index (AHEI) as multivariate functional predictors. We adopt Multivariate Functional Principal Component Analysis (MVFPCA) to obtain low-dimensional, uncorrelated representations of the multivariate functional predictors through their FPC scores. Building on MVFPCA, we develop a novel multivariate functional principal component regression (MVFPCR) model to predict birth weight effectively. Our method accommodates various regression approaches, including linear and quantile regression, depending on the distribution of birth weights. Through simulation studies and the application to a fetal growth study dataset, we demonstrate the effectiveness of our proposed model in functional predictor regression.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":" ","pages":"169-184"},"PeriodicalIF":1.2,"publicationDate":"2026-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148165290","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

Robust median regression for count data with general lower truncation using a contaminated discrete Weibull model. 使用受污染的离散威布尔模型对具有一般下截断的计数数据进行稳健中位数回归。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2026-06-01 DOI: 10.1515/ijb-2025-0066

Divan A Burger, Janet van Niekerk, Emmanuel Lesaffre

{"title":"Robust median regression for count data with general lower truncation using a contaminated discrete Weibull model.","authors":"Divan A Burger, Janet van Niekerk, Emmanuel Lesaffre","doi":"10.1515/ijb-2025-0066","DOIUrl":"https://doi.org/10.1515/ijb-2025-0066","url":null,"abstract":"In right-skewed count data, the mean is disproportionately affected by a long upper tail, whereas the median remains a more representative measure of central tendency. Discrete Weibull (DW) regression links covariates to a shifted median, which in turn induces the exact integer median; however, a single DW component can fit poorly when the observed count distribution has a markedly heavier upper tail than a single-component model can accommodate. We propose a contaminated DW (cDW) regression that augments the baseline DW distribution with a more dispersed secondary component within a finite mixture while retaining a single shifted-median link. This mixture accommodates extreme counts more effectively, thereby stabilizing the median-based regression coefficients. The model accommodates general lower truncation at an arbitrary threshold c, including c = 1 for strictly positive outcomes and c = 0 for nonnegative counts, and is estimated using a straightforward Bayesian Markov chain Monte Carlo algorithm implemented in JAGS; R code accompanies the paper. Applied to hospital length-of-stay data, the cDW regression reduces the influence of outliers and achieves superior predictive performance relative to a single-component DW model, as demonstrated by leave-one-out cross-validation and a Kullback-Leibler influence diagnostic. Simulation experiments show that, under strongly heavy-tailed mixture settings, the cTDW model accurately recovers the regression coefficients and improves on the single-component TDW model. Because the added tail component can increase probability mass at both extremes, we further recommend embedding the cDW in a hurdle framework when structural zeros are present: the zero probability is modeled separately, and the heavy-tail mixture is applied only to positive counts. The cDW regression model provides a robust, median-centered alternative for analyzing skewed, possibly truncated count outcomes.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148145365","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

Handling the uncertainty issue of missingness via a mixture-structure-based method. 通过基于混合结构的方法处理缺失的不确定性问题。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2026-05-28 eCollection Date: 2026-05-01 DOI: 10.1515/ijb-2025-0091

Wenxiao Zhou, Bo Fu

{"title":"Handling the uncertainty issue of missingness via a mixture-structure-based method.","authors":"Wenxiao Zhou, Bo Fu","doi":"10.1515/ijb-2025-0091","DOIUrl":"10.1515/ijb-2025-0091","url":null,"abstract":"Missing data are common in real-world studies, yet the underlying missingness structure is often unknown, bringing additional uncertainty before an appropriate inference method can be applied. In this paper, we systematically examine two sources of such uncertainty: (1) the missing mechanism(s) involved and (2) the specific functional form of the missing model within a given mechanism. Focusing particularly on settings involving missing not at random (MNAR) data, we propose a two-step mixture-structure-based method, including a model filtering pre-screening step. The tasks of handling both sources of uncertainty and conducting reliable inference are then unified within a single EM-based framework. The core of our method lies in constructing a two-layer postulated mixture, which can be viewed as deliberately introducing an overfitted mixture - thereby enhancing flexibility and robustness to uncertainty. We consider two general scenarios in which the true data follow either a mixture or a non-mixture structure, and establish an identification framework for continuous finite mixtures potentially subject to MNAR. Simulation studies and a real-data application to the Medical Expenditure Panel Survey (MEPS) are utilized to demonstrate the performance of our method.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":" ","pages":"1-45"},"PeriodicalIF":1.2,"publicationDate":"2026-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148044542","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