Biostatistics最新文献_第7页

A Bayesian pharmacokinetics integrated phase I–II design to optimize dose-schedule regimes 贝叶斯药代动力学 I-II 期综合设计优化剂量安排方案

IF 2.1 3区数学

Biostatistics Pub Date : 2024-09-14 DOI: 10.1093/biostatistics/kxae034

Mengyi Lu, Ying Yuan, Suyu Liu

{"title":"A Bayesian pharmacokinetics integrated phase I–II design to optimize dose-schedule regimes","authors":"Mengyi Lu, Ying Yuan, Suyu Liu","doi":"10.1093/biostatistics/kxae034","DOIUrl":"https://doi.org/10.1093/biostatistics/kxae034","url":null,"abstract":"The schedule of administering a drug has profound impact on the toxicity and efficacy profiles of the drug through changing its pharmacokinetics (PK). PK is an innate and indispensable component of the dose-schedule optimization. Motivated by this, we propose a Bayesian PK integrated dose-schedule finding (PKIDS) design to identify the optimal dose-schedule regime by integrating PK, toxicity, and efficacy data. Based on the causal pathway that dose and schedule affect PK, which in turn affects efficacy and toxicity, we jointly model the three endpoints by first specifying a Bayesian hierarchical model for the marginal distribution of the longitudinal dose-concentration process. Conditional on the drug concentration in plasma, we jointly model toxicity and efficacy as a function of the concentration. We quantify the risk-benefit of regimes using utility—continuously updating the estimates of PK, toxicity, and efficacy based on interim data—and make adaptive decisions to assign new patients to appropriate dose-schedule regimes via adaptive randomization. The simulation study shows that the PKIDS design has desirable operating characteristics.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"34 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

On the Addams family of discrete frailty distributions for modeling multivariate case I interval-censored data 关于用于多变量病例 I 区间删失数据建模的离散虚弱分布的 Addams 系列

IF 2.1 3区数学

Biostatistics Pub Date : 2024-09-10 DOI: 10.1093/biostatistics/kxae035

Maximilian Bardo, Niel Hens, Steffen Unkel

{"title":"On the Addams family of discrete frailty distributions for modeling multivariate case I interval-censored data","authors":"Maximilian Bardo, Niel Hens, Steffen Unkel","doi":"10.1093/biostatistics/kxae035","DOIUrl":"https://doi.org/10.1093/biostatistics/kxae035","url":null,"abstract":"Random effect models for time-to-event data, also known as frailty models, provide a conceptually appealing way of quantifying association between survival times and of representing heterogeneities resulting from factors which may be difficult or impossible to measure. In the literature, the random effect is usually assumed to have a continuous distribution. However, in some areas of application, discrete frailty distributions may be more appropriate. The present paper is about the implementation and interpretation of the Addams family of discrete frailty distributions. We propose methods of estimation for this family of densities in the context of shared frailty models for the hazard rates for case I interval-censored data. Our optimization framework allows for stratification of random effect distributions by covariates. We highlight interpretational advantages of the Addams family of discrete frailty distributions and theK-point distribution as compared to other frailty distributions. A unique feature of the Addams family and the K-point distribution is that the support of the frailty distribution depends on its parameters. This feature is best exploited by imposing a model on the distributional parameters, resulting in a model with non-homogeneous covariate effects that can be analyzed using standard measures such as the hazard ratio. Our methods are illustrated with applications to multivariate case I interval-censored infection data.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"26 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Pooling controls from nested case–control studies with the proportional risks model 利用比例风险模型汇集嵌套病例对照研究的对照结果

IF 2.1 3区数学

Biostatistics Pub Date : 2024-09-10 DOI: 10.1093/biostatistics/kxae032

Yen Chang, Anastasia Ivanova, Demetrius Albanes, Jason P Fine, Yei Eun Shin

引用次数: 0

Dynamic and concordance-assisted learning for risk stratification with application to Alzheimer's disease. 应用于阿尔茨海默病风险分层的动态和一致性辅助学习。

IF 2.1 3区数学

Biostatistics Pub Date : 2024-09-10 DOI: 10.1093/biostatistics/kxae036

Wen Li,Ruosha Li,Ziding Feng,Jing Ning,

{"title":"Dynamic and concordance-assisted learning for risk stratification with application to Alzheimer's disease.","authors":"Wen Li,Ruosha Li,Ziding Feng,Jing Ning,","doi":"10.1093/biostatistics/kxae036","DOIUrl":"https://doi.org/10.1093/biostatistics/kxae036","url":null,"abstract":"Dynamic prediction models capable of retaining accuracy by evolving over time could play a significant role for monitoring disease progression in clinical practice. In biomedical studies with long-term follow up, participants are often monitored through periodic clinical visits with repeat measurements until an occurrence of the event of interest (e.g. disease onset) or the study end. Acknowledging the dynamic nature of disease risk and clinical information contained in the longitudinal markers, we propose an innovative concordance-assisted learning algorithm to derive a real-time risk stratification score. The proposed approach bypasses the need to fit regression models, such as joint models of the longitudinal markers and time-to-event outcome, and hence enjoys the desirable property of model robustness. Simulation studies confirmed that the proposed method has satisfactory performance in dynamically monitoring the risk of developing disease and differentiating high-risk and low-risk population over time. We apply the proposed method to the Alzheimer's Disease Neuroimaging Initiative data and develop a dynamic risk score of Alzheimer's Disease for patients with mild cognitive impairment using multiple longitudinal markers and baseline prognostic factors.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":"110 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Multivariate spatiotemporal functional principal component analysis for modeling hospitalization and mortality rates in the dialysis population. 透析人群住院率和死亡率建模的多变量时空功能主成分分析

IF 1.8 3区数学

Biostatistics Pub Date : 2024-07-01 DOI: 10.1093/biostatistics/kxad013

Qi Qian, Danh V Nguyen, Donatello Telesca, Esra Kurum, Connie M Rhee, Sudipto Banerjee, Yihao Li, Damla Senturk

{"title":"Multivariate spatiotemporal functional principal component analysis for modeling hospitalization and mortality rates in the dialysis population.","authors":"Qi Qian, Danh V Nguyen, Donatello Telesca, Esra Kurum, Connie M Rhee, Sudipto Banerjee, Yihao Li, Damla Senturk","doi":"10.1093/biostatistics/kxad013","DOIUrl":"10.1093/biostatistics/kxad013","url":null,"abstract":"Dialysis patients experience frequent hospitalizations and a higher mortality rate compared to other Medicare populations, in whom hospitalizations are a major contributor to morbidity, mortality, and healthcare costs. Patients also typically remain on dialysis for the duration of their lives or until kidney transplantation. Hence, there is growing interest in studying the spatiotemporal trends in the correlated outcomes of hospitalization and mortality among dialysis patients as a function of time starting from transition to dialysis across the United States Utilizing national data from the United States Renal Data System (USRDS), we propose a novel multivariate spatiotemporal functional principal component analysis model to study the joint spatiotemporal patterns of hospitalization and mortality rates among dialysis patients. The proposal is based on a multivariate Karhunen-Loéve expansion that describes leading directions of variation across time and induces spatial correlations among region-specific scores. An efficient estimation procedure is proposed using only univariate principal components decompositions and a Markov Chain Monte Carlo framework for targeting the spatial correlations. The finite sample performance of the proposed method is studied through simulations. Novel applications to the USRDS data highlight hot spots across the United States with higher hospitalization and/or mortality rates and time periods of elevated risk.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"718-735"},"PeriodicalIF":1.8,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11358256/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10019524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 scalable approach for continuous time Markov models with covariates. 带有协变量的连续时间马尔可夫模型的可扩展方法

IF 1.8 3区数学

Biostatistics Pub Date : 2024-07-01 DOI: 10.1093/biostatistics/kxad012

Farhad Hatami, Alex Ocampo, Gordon Graham, Thomas E Nichols, Habib Ganjgahi

引用次数: 0

Semi-supervised mixture multi-source exchangeability model for leveraging real-world data in clinical trials. 用于在临床试验中利用真实世界数据的半监督混合多源可交换性模型。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-07-01 DOI: 10.1093/biostatistics/kxad024

Lillian M F Haine, Thomas A Murry, Raquel Nahra, Giota Touloumi, Eduardo Fernández-Cruz, Kathy Petoumenos, Joseph S Koopmeiners

{"title":"Semi-supervised mixture multi-source exchangeability model for leveraging real-world data in clinical trials.","authors":"Lillian M F Haine, Thomas A Murry, Raquel Nahra, Giota Touloumi, Eduardo Fernández-Cruz, Kathy Petoumenos, Joseph S Koopmeiners","doi":"10.1093/biostatistics/kxad024","DOIUrl":"10.1093/biostatistics/kxad024","url":null,"abstract":"The traditional trial paradigm is often criticized as being slow, inefficient, and costly. Statistical approaches that leverage external trial data have emerged to make trials more efficient by augmenting the sample size. However, these approaches assume that external data are from previously conducted trials, leaving a rich source of untapped real-world data (RWD) that cannot yet be effectively leveraged. We propose a semi-supervised mixture (SS-MIX) multisource exchangeability model (MEM); a flexible, two-step Bayesian approach for incorporating RWD into randomized controlled trial analyses. The first step is a SS-MIX model on a modified propensity score and the second step is a MEM. The first step targets a representative subgroup of individuals from the trial population and the second step avoids borrowing when there are substantial differences in outcomes among the trial sample and the representative observational sample. When comparing the proposed approach to competing borrowing approaches in a simulation study, we find that our approach borrows efficiently when the trial and RWD are consistent, while mitigating bias when the trial and external data differ on either measured or unmeasured covariates. We illustrate the proposed approach with an application to a randomized controlled trial investigating intravenous hyperimmune immunoglobulin in hospitalized patients with influenza, while leveraging data from an external observational study to supplement a subgroup analysis by influenza subtype.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"617-632"},"PeriodicalIF":1.8,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11247180/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10268326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Joint modeling in presence of informative censoring on the retrospective time scale with application to palliative care research. 在回顾性时间尺度上存在信息审查的情况下进行联合建模，并应用于姑息治疗研究。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-07-01 DOI: 10.1093/biostatistics/kxad028

Quran Wu, Michael Daniels, Areej El-Jawahri, Marie Bakitas, Zhigang Li

{"title":"Joint modeling in presence of informative censoring on the retrospective time scale with application to palliative care research.","authors":"Quran Wu, Michael Daniels, Areej El-Jawahri, Marie Bakitas, Zhigang Li","doi":"10.1093/biostatistics/kxad028","DOIUrl":"10.1093/biostatistics/kxad028","url":null,"abstract":"Joint modeling of longitudinal data such as quality of life data and survival data is important for palliative care researchers to draw efficient inferences because it can account for the associations between those two types of data. Modeling quality of life on a retrospective from death time scale is useful for investigators to interpret the analysis results of palliative care studies which have relatively short life expectancies. However, informative censoring remains a complex challenge for modeling quality of life on the retrospective time scale although it has been addressed for joint models on the prospective time scale. To fill this gap, we develop a novel joint modeling approach that can address the challenge by allowing informative censoring events to be dependent on patients' quality of life and survival through a random effect. There are two sub-models in our approach: a linear mixed effect model for the longitudinal quality of life and a competing-risk model for the death time and dropout time that share the same random effect as the longitudinal model. Our approach can provide unbiased estimates for parameters of interest by appropriately modeling the informative censoring time. Model performance is assessed with a simulation study and compared with existing approaches. A real-world study is presented to illustrate the application of the new approach.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"754-768"},"PeriodicalIF":1.8,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11247190/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41161763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Improved fMRI-based pain prediction using Bayesian group-wise functional registration. 使用贝叶斯分组功能配准改进了基于fMRI的疼痛预测。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-07-01 DOI: 10.1093/biostatistics/kxad026

Guoqing Wang, Abhirup Datta, Martin A Lindquist

{"title":"Improved fMRI-based pain prediction using Bayesian group-wise functional registration.","authors":"Guoqing Wang, Abhirup Datta, Martin A Lindquist","doi":"10.1093/biostatistics/kxad026","DOIUrl":"10.1093/biostatistics/kxad026","url":null,"abstract":"In recent years, the field of neuroimaging has undergone a paradigm shift, moving away from the traditional brain mapping approach towards the development of integrated, multivariate brain models that can predict categories of mental events. However, large interindividual differences in both brain anatomy and functional localization after standard anatomical alignment remain a major limitation in performing this type of analysis, as it leads to feature misalignment across subjects in subsequent predictive models. This article addresses this problem by developing and validating a new computational technique for reducing misalignment across individuals in functional brain systems by spatially transforming each subject's functional data to a common latent template map. Our proposed Bayesian functional group-wise registration approach allows us to assess differences in brain function across subjects and individual differences in activation topology. We achieve the probabilistic registration with inverse-consistency by utilizing the generalized Bayes framework with a loss function for the symmetric group-wise registration. It models the latent template with a Gaussian process, which helps capture spatial features in the template, producing a more precise estimation. We evaluate the method in simulation studies and apply it to data from an fMRI study of thermal pain, with the goal of using functional brain activity to predict physical pain. We find that the proposed approach allows for improved prediction of reported pain scores over conventional approaches. Received on 2 January 2017. Editorial decision on 8 June 2021.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"885-903"},"PeriodicalIF":1.8,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41170769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

An intersectional framework for counterfactual fairness in risk prediction. 风险预测中反事实公平性的交叉框架。

IF 1.8 3区数学

Biostatistics Pub Date : 2024-07-01 DOI: 10.1093/biostatistics/kxad021

Solvejg Wastvedt, Jared D Huling, Julian Wolfson

{"title":"An intersectional framework for counterfactual fairness in risk prediction.","authors":"Solvejg Wastvedt, Jared D Huling, Julian Wolfson","doi":"10.1093/biostatistics/kxad021","DOIUrl":"10.1093/biostatistics/kxad021","url":null,"abstract":"Along with the increasing availability of health data has come the rise of data-driven models to inform decision making and policy. These models have the potential to benefit both patients and health care providers but can also exacerbate health inequities. Existing \"algorithmic fairness\" methods for measuring and correcting model bias fall short of what is needed for health policy in two key ways. First, methods typically focus on a single grouping along which discrimination may occur rather than considering multiple, intersecting groups. Second, in clinical applications, risk prediction is typically used to guide treatment, creating distinct statistical issues that invalidate most existing techniques. We present novel unfairness metrics that address both challenges. We also develop a complete framework of estimation and inference tools for our metrics, including the unfairness value (\"u-value\"), used to determine the relative extremity of unfairness, and standard errors and confidence intervals employing an alternative to the standard bootstrap. We demonstrate application of our framework to a COVID-19 risk prediction model deployed in a major Midwestern health system.","PeriodicalId":55357,"journal":{"name":"Biostatistics","volume":" ","pages":"702-717"},"PeriodicalIF":1.8,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10499741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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