International Journal of Biostatistics最新文献

A review of survival stacking: a method to cast survival regression analysis as a classification problem.

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2025-03-28 DOI: 10.1515/ijb-2022-0055

Erin Craig, Chenyang Zhong, Robert Tibshirani

引用次数: 0

A multivariate Bayesian learning approach for improved detection of doping in athletes using urinary steroid profiles.

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2025-03-28 DOI: 10.1515/ijb-2024-0019

Dimitra Eleftheriou, Thomas Piper, Mario Thevis, Tereza Neocleous

{"title":"A multivariate Bayesian learning approach for improved detection of doping in athletes using urinary steroid profiles.","authors":"Dimitra Eleftheriou, Thomas Piper, Mario Thevis, Tereza Neocleous","doi":"10.1515/ijb-2024-0019","DOIUrl":"https://doi.org/10.1515/ijb-2024-0019","url":null,"abstract":"Biomarker analysis of athletes' urinary steroid profiles is crucial for the success of anti-doping efforts. Current statistical analysis methods generate personalised limits for each athlete based on univariate modelling of longitudinal biomarker values from the urinary steroid profile. However, simultaneous modelling of multiple biomarkers has the potential to further enhance abnormality detection. In this study, we propose a multivariate Bayesian adaptive model for longitudinal data analysis, which extends the established single-biomarker model in forensic toxicology. The proposed approach employs Markov chain Monte Carlo sampling methods and addresses the scarcity of confirmed abnormal values through a one-class classification algorithm. By adapting decision boundaries as new measurements are obtained, the model provides robust and personalised detection thresholds for each athlete. We tested the proposed approach on a database of 229 athletes, which includes longitudinal steroid profiles containing samples classified as normal, atypical, or confirmed abnormal. Our results demonstrate improved detection performance, highlighting the potential value of a multivariate approach in doping detection.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732816","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

Regression analysis of clustered current status data with informative cluster size under a transformed survival model.

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2025-03-24 DOI: 10.1515/ijb-2023-0130

Yanqin Feng, Shijiao Yin, Jieli Ding

引用次数: 0

Prognostic adjustment with efficient estimators to unbiasedly leverage historical data in randomized trials.

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2025-03-11 DOI: 10.1515/ijb-2024-0018

Lauren D Liao, Emilie Højbjerre-Frandsen, Alan E Hubbard, Alejandro Schuler

{"title":"Prognostic adjustment with efficient estimators to unbiasedly leverage historical data in randomized trials.","authors":"Lauren D Liao, Emilie Højbjerre-Frandsen, Alan E Hubbard, Alejandro Schuler","doi":"10.1515/ijb-2024-0018","DOIUrl":"https://doi.org/10.1515/ijb-2024-0018","url":null,"abstract":"Although randomized controlled trials (RCTs) are a cornerstone of comparative effectiveness, they typically have much smaller sample size than observational studies due to financial and ethical considerations. Therefore there is interest in using plentiful historical data (either observational data or prior trials) to reduce trial sizes. Previous estimators developed for this purpose rely on unrealistic assumptions, without which the added data can bias the treatment effect estimate. Recent work proposed an alternative method (prognostic covariate adjustment) that imposes no additional assumptions and increases efficiency in trial analyses. The idea is to use historical data to learn a prognostic model: a regression of the outcome onto the covariates. The predictions from this model, generated from the RCT subjects' baseline variables, are then used as a covariate in a linear regression analysis of the trial data. In this work, we extend prognostic adjustment to trial analyses with nonparametric efficient estimators, which are more powerful than linear regression. We provide theory that explains why prognostic adjustment improves small-sample point estimation and inference without any possibility of bias. Simulations corroborate the theory: efficient estimators using prognostic adjustment compared to without provides greater power (i.e., smaller standard errors) when the trial is small. Population shifts between historical and trial data attenuate benefits but do not introduce bias. We showcase our estimator using clinical trial data provided by Novo Nordisk A/S that evaluates insulin therapy for individuals with type 2 diabetes.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143598241","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

Bayesian covariance regression in functional data analysis with applications to functional brain imaging. 贝叶斯协方差回归在功能数据分析中的应用，以及在脑功能成像中的应用。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2025-02-05 DOI: 10.1515/ijb-2023-0029

John Shamshoian, Nicholas Marco, Damla Şentürk, Shafali Jeste, Donatello Telesca

引用次数: 0

DsubCox: a fast subsampling algorithm for Cox model with distributed and massive survival data.

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2025-02-04 DOI: 10.1515/ijb-2024-0042

Haixiang Zhang, Yang Li, HaiYing Wang

引用次数: 0

Hypothesis testing for detecting outlier evaluators. 检测离群评估员的假设检验。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2024-11-04 eCollection Date: 2024-11-01 DOI: 10.1515/ijb-2023-0004

Li Xu, David M Zucker, Molin Wang

引用次数: 0

Optimizing personalized treatments for targeted patient populations across multiple domains. 跨领域优化针对目标患者群体的个性化治疗。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2024-09-26 eCollection Date: 2024-11-01 DOI: 10.1515/ijb-2024-0068

Yuan Chen, Donglin Zeng, Yuanjia Wang

{"title":"Optimizing personalized treatments for targeted patient populations across multiple domains.","authors":"Yuan Chen, Donglin Zeng, Yuanjia Wang","doi":"10.1515/ijb-2024-0068","DOIUrl":"10.1515/ijb-2024-0068","url":null,"abstract":"Learning individualized treatment rules (ITRs) for a target patient population with mental disorders is confronted with many challenges. First, the target population may be different from the training population that provided data for learning ITRs. Ignoring differences between the training patient data and the target population can result in sub-optimal treatment strategies for the target population. Second, for mental disorders, a patient's underlying mental state is not observed but can be inferred from measures of high-dimensional combinations of symptomatology. Treatment mechanisms are unknown and can be complex, and thus treatment effect moderation can take complicated forms. To address these challenges, we propose a novel method that connects measurement models, efficient weighting schemes, and flexible neural network architecture through latent variables to tailor treatments for a target population. Patients' underlying mental states are represented by a compact set of latent state variables while preserving interpretability. Weighting schemes are designed based on lower-dimensional latent variables to efficiently balance population differences so that biases in learning the latent structure and treatment effects are mitigated. Extensive simulation studies demonstrated consistent superiority of the proposed method and the weighting approach. Applications to two real-world studies of patients with major depressive disorder have shown a broad utility of the proposed method in improving treatment outcomes in the target population.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":" ","pages":"437-453"},"PeriodicalIF":1.2,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11661560/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142331579","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

History-restricted marginal structural model and latent class growth analysis of treatment trajectories for a time-dependent outcome. 针对随时间变化的结果，对治疗轨迹进行历史限制边际结构模型和潜类增长分析。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2024-08-12 eCollection Date: 2024-11-01 DOI: 10.1515/ijb-2023-0116

Awa Diop, Caroline Sirois, Jason R Guertin, Mireille E Schnitzer, James M Brophy, Claudia Blais, Denis Talbot

{"title":"History-restricted marginal structural model and latent class growth analysis of treatment trajectories for a time-dependent outcome.","authors":"Awa Diop, Caroline Sirois, Jason R Guertin, Mireille E Schnitzer, James M Brophy, Claudia Blais, Denis Talbot","doi":"10.1515/ijb-2023-0116","DOIUrl":"10.1515/ijb-2023-0116","url":null,"abstract":"In previous work, we introduced a framework that combines latent class growth analysis (LCGA) with marginal structural models (LCGA-MSM). LCGA-MSM first summarizes the numerous time-varying treatment patterns into a few trajectory groups and then allows for a population-level causal interpretation of the group differences. However, the LCGA-MSM framework is not suitable when the outcome is time-dependent. In this study, we propose combining a nonparametric history-restricted marginal structural model (HRMSM) with LCGA. HRMSMs can be seen as an application of standard MSMs on multiple time intervals. To the best of our knowledge, we also present the first application of HRMSMs with a time-to-event outcome. It was previously noted that HRMSMs could pose interpretation problems in survival analysis when either targeting a hazard ratio or a survival curve. We propose a causal parameter that bypasses these interpretation challenges. We consider three different estimators of the parameters: inverse probability of treatment weighting (IPTW), g-computation, and a pooled longitudinal targeted maximum likelihood estimator (pooled LTMLE). We conduct simulation studies to measure the performance of the proposed LCGA-HRMSM. For all scenarios, we obtain unbiased estimates when using either g-computation or pooled LTMLE. IPTW produced estimates with slightly larger bias in some scenarios. Overall, all approaches have good coverage of the 95 % confidence interval. We applied our approach to a population of older Quebecers composed of 57,211 statin initiators and found that a greater adherence to statins was associated with a lower combined risk of cardiovascular disease or all-cause mortality.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":" ","pages":"467-490"},"PeriodicalIF":1.2,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11661564/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141972255","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

Hybrid classical-Bayesian approach to sample size determination for two-arm superiority clinical trials. 经典-贝叶斯混合法确定双臂优势临床试验的样本量。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2024-07-01 eCollection Date: 2024-11-01 DOI: 10.1515/ijb-2023-0050

Valeria Sambucini

引用次数: 0