Biometrics最新文献_第7页

Optimal adaptive SMART designs with binary outcomes. 具有二元结果的最优自适应SMART设计。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae140

Rik Ghosh, Bibhas Chakraborty, Inbal Nahum-Shani, Megan E Patrick, Palash Ghosh

{"title":"Optimal adaptive SMART designs with binary outcomes.","authors":"Rik Ghosh, Bibhas Chakraborty, Inbal Nahum-Shani, Megan E Patrick, Palash Ghosh","doi":"10.1093/biomtc/ujae140","DOIUrl":"10.1093/biomtc/ujae140","url":null,"abstract":"In a sequential multiple-assignment randomized trial (SMART), a sequence of treatments is given to a patient over multiple stages. In each stage, randomization may be done to allocate patients to different treatment groups. Even though SMART designs are getting popular among clinical researchers, the methodologies for adaptive randomization at different stages of a SMART are few and not sophisticated enough to handle the complexity of optimal allocation of treatments at every stage of a trial. Lack of optimal allocation methodologies can raise critical concerns about SMART designs from an ethical point of view. In this work, we develop an optimal adaptive allocation procedure using a constrained optimization that minimizes the total expected number of treatment failures for a SMART with a binary primary outcome, subject to a fixed asymptotic variance of a predefined objective function. Issues related to optimal adaptive allocations are explored theoretically with supporting simulations. The applicability of the proposed methodology is demonstrated using a recently conducted SMART study named M-bridge for developing universal and resource-efficient dynamic treatment regimes for incoming first-year college students as a bridge to desirable treatments to address alcohol-related risks.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11639531/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142827001","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

Graphical model inference with external network data. 图形模型推断与外部网络数据。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae151

Jack Jewson, Li Li, Laura Battaglia, Stephen Hansen, David Rossell, Piotr Zwiernik

引用次数: 0

Modeling longitudinal skewed functional data. 纵向倾斜功能数据建模。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae121

Mohammad Samsul Alam, Ana-Maria Staicu

引用次数: 0

Clustering computer mouse tracking data with informed hierarchical shrinkage partition priors. 利用有信息的分层收缩分区先验对计算机鼠标跟踪数据进行聚类。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae124

Ziyi Song, Weining Shen, Marina Vannucci, Alexandria Baldizon, Paul M Cinciripini, Francesco Versace, Michele Guindani

{"title":"Clustering computer mouse tracking data with informed hierarchical shrinkage partition priors.","authors":"Ziyi Song, Weining Shen, Marina Vannucci, Alexandria Baldizon, Paul M Cinciripini, Francesco Versace, Michele Guindani","doi":"10.1093/biomtc/ujae124","DOIUrl":"10.1093/biomtc/ujae124","url":null,"abstract":"Mouse-tracking data, which record computer mouse trajectories while participants perform an experimental task, provide valuable insights into subjects' underlying cognitive processes. Neuroscientists are interested in clustering the subjects' responses during computer mouse-tracking tasks to reveal patterns of individual decision-making behaviors and identify population subgroups with similar neurobehavioral responses. These data can be combined with neuroimaging data to provide additional information for personalized interventions. In this article, we develop a novel hierarchical shrinkage partition (HSP) prior for clustering summary statistics derived from the trajectories of mouse-tracking data. The HSP model defines a subjects' cluster as a set of subjects that gives rise to more similar (rather than identical) nested partitions of the conditions. The proposed model can incorporate prior information about the partitioning of either subjects or conditions to facilitate clustering, and it allows for deviations of the nested partitions within each subject group. These features distinguish the HSP model from other bi-clustering methods that typically create identical nested partitions of conditions within a subject group. Furthermore, it differs from existing nested clustering methods, which define clusters based on common parameters in the sampling model and identify subject groups by different distributions. We illustrate the unique features of the HSP model on a mouse tracking dataset from a pilot study and in simulation studies. Our results show the ability and effectiveness of the proposed exploratory framework in clustering and revealing possible different behavioral patterns across subject groups.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11523067/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142543342","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

Robust model averaging approach by Mallows-type criterion. 采用 Mallows 型标准的稳健模型平均法。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae128

Miaomiao Wang, Kang You, Lixing Zhu, Guohua Zou

{"title":"Robust model averaging approach by Mallows-type criterion.","authors":"Miaomiao Wang, Kang You, Lixing Zhu, Guohua Zou","doi":"10.1093/biomtc/ujae128","DOIUrl":"https://doi.org/10.1093/biomtc/ujae128","url":null,"abstract":"Model averaging is an important tool for treating uncertainty from model selection process and fusing information from different models, and has been widely used in various fields. However, the most existing model averaging criteria are proposed based on the methods of ordinary least squares or maximum likelihood, which possess high sensitivity to outliers or violation of certain model assumption. For the mean regression, no optimal robust methods are developed. To fill this gap, in our paper, we propose an outlier-robust model averaging approach by Mallows-type criterion. The idea is that we first construct a generalized M (GM) estimator for each candidate model, and then build robust weighting schemes by the asymptotic expansion of the final prediction error based on the GM-type loss function. So, we can still achieve a trustworthy result even if the dataset is contaminated by outliers in response and/or covariates. Asymptotic properties of the proposed robust model averaging estimators are established under some regularity conditions. The consistency of our weight estimators tending to the theoretically optimal weight vectors is also derived. We prove that our model averaging estimator is robust in terms of having bounded influence function. Further, we define the empirical prediction influence function to evaluate the quantitative robustness of the model averaging estimator. A simulation study and a real data analysis are conducted to demonstrate the finite sample performance of our estimators and compare them with other commonly used model selection and averaging methods.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142614075","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

Large-scale survival analysis with a cure fraction. 利用治愈率进行大规模生存分析

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae138

Bo Han, Xiaoguang Wang, Liuquan Sun

{"title":"Large-scale survival analysis with a cure fraction.","authors":"Bo Han, Xiaoguang Wang, Liuquan Sun","doi":"10.1093/biomtc/ujae138","DOIUrl":"https://doi.org/10.1093/biomtc/ujae138","url":null,"abstract":"With the advent of massive survival data with a cure fraction, large-scale regression for analyzing the effects of risk factors on a general population has become an emerging challenge. This article proposes a new probability-weighted method for estimation and inference for semiparametric cure regression models. We develop a flexible formulation of the mixture cure model consisting of the model-free incidence and the latency assumed by the semiparametric proportional hazards model. The susceptible probability assesses the concordance between the observations and the latency. With the susceptible probability as weight, we propose a weighted estimating equation method in a small-scale setting. Robust nonparametric estimation of the weight permits stable implementation of the estimation of regression parameters. A recursive probability-weighted estimation method based on data blocks with smaller sizes is further proposed, which achieves computational and memory efficiency in a large-scale or online setting. Asymptotic properties of the proposed estimators are established. We conduct simulation studies and a real data application to demonstrate the empirical performance of the proposed method.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142692670","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

Temporal generative models for learning heterogeneous group dynamics of ecological momentary assessment data. 用于学习生态瞬时评估数据的异质群体动态的时间生成模型。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae115

Soohyun Kim, Young-Geun Kim, Yuanjia Wang

{"title":"Temporal generative models for learning heterogeneous group dynamics of ecological momentary assessment data.","authors":"Soohyun Kim, Young-Geun Kim, Yuanjia Wang","doi":"10.1093/biomtc/ujae115","DOIUrl":"10.1093/biomtc/ujae115","url":null,"abstract":"One of the goals of precision psychiatry is to characterize mental disorders in an individualized manner, taking into account the underlying dynamic processes. Recent advances in mobile technologies have enabled the collection of ecological momentary assessments that capture multiple responses in real-time at high frequency. However, ecological momentary assessment data are often multi-dimensional, correlated, and hierarchical. Mixed-effect models are commonly used but may require restrictive assumptions about the fixed and random effects and the correlation structure. The recurrent temporal restricted Boltzmann machine (RTRBM) is a generative neural network that can be used to model temporal data, but most existing RTRBM approaches do not account for the potential heterogeneity of group dynamics within a population based on available covariates. In this paper, we propose a new temporal generative model, the HDRBM, to learn the heterogeneous group dynamics and demonstrate the effectiveness of this approach on simulated and real-world ecological momentary assessment datasets. We show that by incorporating covariates, HDRBM can improve accuracy and interpretability, explore the underlying drivers of the group dynamics of participants, and serve as a generative model for ecological momentary assessment studies.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11472390/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142457177","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

Leveraging information from secondary endpoints to enhance dynamic borrowing across subpopulations. 利用次要终点的信息，加强亚人群的动态借用。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae118

Jack M Wolf, David M Vock, Xianghua Luo, Dorothy K Hatsukami, F Joseph McClernon, Joseph S Koopmeiners

{"title":"Leveraging information from secondary endpoints to enhance dynamic borrowing across subpopulations.","authors":"Jack M Wolf, David M Vock, Xianghua Luo, Dorothy K Hatsukami, F Joseph McClernon, Joseph S Koopmeiners","doi":"10.1093/biomtc/ujae118","DOIUrl":"10.1093/biomtc/ujae118","url":null,"abstract":"Randomized trials seek efficient treatment effect estimation within target populations, yet scientific interest often also centers on subpopulations. Although there are typically too few subjects within each subpopulation to efficiently estimate these subpopulation treatment effects, one can gain precision by borrowing strength across subpopulations, as is the case in a basket trial. While dynamic borrowing has been proposed as an efficient approach to estimating subpopulation treatment effects on primary endpoints, additional efficiency could be gained by leveraging the information found in secondary endpoints. We propose a multisource exchangeability model (MEM) that incorporates secondary endpoints to more efficiently assess subpopulation exchangeability. Across simulation studies, our proposed model almost uniformly reduces the mean squared error when compared to the standard MEM that only considers data from the primary endpoint by gaining efficiency when subpopulations respond similarly to the treatment and reducing the magnitude of bias when the subpopulations are heterogeneous. We illustrate our model's feasibility using data from a recently completed trial of very low nicotine content cigarettes to estimate the effect on abstinence from smoking within three priority subpopulations. Our proposed model led to increases in the effective sample size two to four times greater than under the standard MEM.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11498028/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142494173","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

Estimating marginal treatment effect in cluster randomized trials with multi-level missing outcomes. 估计具有多级缺失结果的聚类随机试验的边际治疗效果。

IF 1.4 4区数学

Biometrics Pub Date : 2024-10-03 DOI: 10.1093/biomtc/ujae135

Chia-Rui Chang, Rui Wang

{"title":"Estimating marginal treatment effect in cluster randomized trials with multi-level missing outcomes.","authors":"Chia-Rui Chang, Rui Wang","doi":"10.1093/biomtc/ujae135","DOIUrl":"10.1093/biomtc/ujae135","url":null,"abstract":"Analyses of cluster randomized trials (CRTs) can be complicated by informative missing outcome data. Methods such as inverse probability weighted generalized estimating equations have been proposed to account for informative missingness by weighing the observed individual outcome data in each cluster. These existing methods have focused on settings where missingness occurs at the individual level and each cluster has partially or fully observed individual outcomes. In the presence of missing clusters, for example, all outcomes from a cluster are missing due to drop-out of the cluster, these approaches ignore this cluster-level missingness and can lead to biased inference if the cluster-level missingness is informative. Informative missingness at multiple levels can also occur in CRTs with a multi-level structure where study participants are nested in subclusters such as healthcare providers, and the subclusters are nested in clusters such as clinics. In this paper, we propose new estimators for estimating the marginal treatment effect in CRTs accounting for missing outcome data at multiple levels based on weighted generalized estimating equations. We show that the proposed multi-level multiply robust estimator is consistent and asymptotically normally distributed provided that one of the multiple propensity score models postulated at each clustering level is correctly specified. We evaluate the performance of the proposed method through extensive simulations and illustrate its use with a CRT evaluating a Malaria risk-reduction intervention in rural Madagascar.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11629964/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142827276","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

Discussion on "LEAP: the latent exchangeability prior for borrowing information from historical data" by Ethan M. Alt, Xiuya Chang, Xun Jiang, Qing Liu, May Mo, H. Amy Xia, and Joseph G. Ibrahim. 关于 Ethan M. Alt、Xiuya Chang、Xun Jiang、Qing Liu、May Mo、H. Amy Xia 和 Joseph G. Ibrahim 所著《LEAP：从历史数据中借用信息的潜在可交换性先验》的讨论。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae084

Harlan Campbell, Paul Gustafson

引用次数: 0