Journal of the Royal Statistical Society Series C-Applied Statistics最新文献_第3页

Joint modelling of survival and backwards recurrence outcomes: an analysis of factors associated with fertility treatment in the U.S. 存活率和逆向复发结果的联合建模：美国生育治疗相关因素分析

IF 1.3 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2024-08-19 eCollection Date: 2024-11-01 DOI: 10.1093/jrsssc/qlae039

Siyuan Guo, Jiajia Zhang, Alexander C McLain

{"title":"Joint modelling of survival and backwards recurrence outcomes: an analysis of factors associated with fertility treatment in the U.S.","authors":"Siyuan Guo, Jiajia Zhang, Alexander C McLain","doi":"10.1093/jrsssc/qlae039","DOIUrl":"10.1093/jrsssc/qlae039","url":null,"abstract":"The motivation for this paper is to determine factors associated with time-to-fertility treatment (TTFT) among women currently attempting pregnancy in a cross-sectional sample. Challenges arise due to dependence between time-to-pregnancy (TTP) and TTFT. We propose appending a marginal accelerated failure time model to identify risk factors of TTFT with a model for TTP where fertility treatment is included as a time-varying treatment to account for their dependence. The latter requires extending backwards recurrence survival methods to incorporate time-varying covariates with time-varying coefficients. Since backwards recurrence survival methods are a function of mean survival, computational difficulties arise in formulating mean survival when fertility treatment is unobserved, i.e. when TTFT is censored. We address these challenges by developing computationally friendly forms for the double expectation of TTP and TTFT. The performance is validated via comprehensive simulation studies. We apply our approach to the National Survey of Family Growth and explore factors related to prolonged TTFT in the U.S.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":"73 5","pages":"1355-1369"},"PeriodicalIF":1.3,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11561729/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142649543","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

Walking fingerprinting. 行走指纹识别

IF 1.3 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2024-07-29 eCollection Date: 2024-11-01 DOI: 10.1093/jrsssc/qlae033

Lily Koffman, Ciprian Crainiceanu, Andrew Leroux

{"title":"Walking fingerprinting.","authors":"Lily Koffman, Ciprian Crainiceanu, Andrew Leroux","doi":"10.1093/jrsssc/qlae033","DOIUrl":"10.1093/jrsssc/qlae033","url":null,"abstract":"We consider the problem of predicting an individual's identity from accelerometry data collected during walking. In a previous paper, we transformed the accelerometry time series into an image by constructing the joint distribution of the acceleration and lagged acceleration for a vector of lags. Predictors derived by partitioning this image into grid cells were used in logistic regression to predict individuals. Here, we (a) implement machine learning methods for prediction using the grid cell-derived predictors; (b) derive inferential methods to screen for the most predictive grid cells while adjusting for correlation and multiple comparisons; and (c) develop a novel multivariate functional regression model that avoids partitioning the predictor space. Prediction methods are compared on two open source acceleometry data sets collected from: (a) 32 individuals walking on a <math><mn>1.06</mn></math> km path; and (b) six repetitions of walking on a 20 m path on two occasions at least 1 week apart for 153 study participants. In the 32-individual study, all methods achieve at least 95% rank-1 accuracy, while in the 153-individual study, accuracy varies from 41% to 98%, depending on the method and prediction task. Methods provide insights into why some individuals are easier to predict than others.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":"73 5","pages":"1221-1241"},"PeriodicalIF":1.3,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11561731/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650551","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 spatially varying health effects of wildland fire smoke using mobile health data. 利用移动健康数据估算野外火灾烟雾对健康的空间影响。

IF 1 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2024-07-16 eCollection Date: 2024-11-01 DOI: 10.1093/jrsssc/qlae034

Lili Wu, Chenyin Gao, Shu Yang, Brian J Reich, Ana G Rappold

{"title":"Estimating spatially varying health effects of wildland fire smoke using mobile health data.","authors":"Lili Wu, Chenyin Gao, Shu Yang, Brian J Reich, Ana G Rappold","doi":"10.1093/jrsssc/qlae034","DOIUrl":"10.1093/jrsssc/qlae034","url":null,"abstract":"Wildland fire smoke exposures are an increasing threat to public health, highlighting the need for studying the effects of protective behaviours on reducing health outcomes. Emerging smartphone applications provide unprecedented opportunities to deliver health risk communication messages to a large number of individuals in real-time and subsequently study the effectiveness, but also pose methodological challenges. Smoke Sense, a citizen science project, provides an interactive smartphone app platform for participants to engage with information about air quality, and ways to record their own health symptoms and actions taken to reduce smoke exposure. We propose a doubly robust estimator of the structural nested mean model that accounts for spatially and time-varying effects via a local estimating equation approach with geographical kernel weighting. Moreover, our analytical framework also handles informative missingness by inverse probability weighting of estimating functions. We evaluate the method using extensive simulation studies and apply it to Smoke Sense data to increase the knowledge base about the relationship between health preventive measures and health-related outcomes. Our results show that the protective behaviours' effects vary over space and time and find that protective behaviours have more significant effects on reducing health symptoms in the Southwest than the Northwest region of the U.S.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":"73 5","pages":"1242-1261"},"PeriodicalIF":1.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11561730/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142649541","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

Inverse set estimation and inversion of simultaneous confidence intervals. 反集估计和同时置信区间反演。

IF 1 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2024-05-31 eCollection Date: 2024-08-01 DOI: 10.1093/jrsssc/qlae027

Junting Ren, Fabian J E Telschow, Armin Schwartzman

引用次数: 0

Population-level task-evoked functional connectivity via Fourier analysis. 通过傅立叶分析实现群体级任务诱发功能连接。

IF 1 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2024-03-14 eCollection Date: 2024-08-01 DOI: 10.1093/jrsssc/qlae015

Kun Meng, Ani Eloyan

引用次数: 0

Testing unit root non-stationarity in the presence of missing data in univariate time series of mobile health studies. 测试移动健康研究单变量时间序列中存在缺失数据时的单位根非平稳性。

IF 1.4 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2024-02-29 eCollection Date: 2024-06-01 DOI: 10.1093/jrsssc/qlae010

Charlotte Fowler, Xiaoxuan Cai, Justin T Baker, Jukka-Pekka Onnela, Linda Valeri

{"title":"Testing unit root non-stationarity in the presence of missing data in univariate time series of mobile health studies.","authors":"Charlotte Fowler, Xiaoxuan Cai, Justin T Baker, Jukka-Pekka Onnela, Linda Valeri","doi":"10.1093/jrsssc/qlae010","DOIUrl":"10.1093/jrsssc/qlae010","url":null,"abstract":"The use of digital devices to collect data in mobile health studies introduces a novel application of time series methods, with the constraint of potential data missing at random or missing not at random (MNAR). In time-series analysis, testing for stationarity is an important preliminary step to inform appropriate subsequent analyses. The Dickey-Fuller test evaluates the null hypothesis of unit root non-stationarity, under no missing data. Beyond recommendations under data missing completely at random for complete case analysis or last observation carry forward imputation, researchers have not extended unit root non-stationarity testing to more complex missing data mechanisms. Multiple imputation with chained equations, Kalman smoothing imputation, and linear interpolation have also been used for time-series data, however such methods impose constraints on the autocorrelation structure and impact unit root testing. We propose maximum likelihood estimation and multiple imputation using state space model approaches to adapt the augmented Dickey-Fuller test to a context with missing data. We further develop sensitivity analyses to examine the impact of MNAR data. We evaluate the performance of existing and proposed methods across missing mechanisms in extensive simulations and in their application to a multi-year smartphone study of bipolar patients.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":"73 3","pages":"755-773"},"PeriodicalIF":1.4,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11175825/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332377","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

Revisiting the effects of maternal education on adolescents' academic performance: Doubly robust estimation in a network-based observational study. 重新审视母亲教育对青少年学习成绩的影响：基于网络的观察研究中的双稳健估计。

IF 1.3 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2024-02-13 eCollection Date: 2024-06-01 DOI: 10.1093/jrsssc/qlae008

Vanessa McNealis, Erica E M Moodie, Nema Dean

{"title":"Revisiting the effects of maternal education on adolescents' academic performance: Doubly robust estimation in a network-based observational study.","authors":"Vanessa McNealis, Erica E M Moodie, Nema Dean","doi":"10.1093/jrsssc/qlae008","DOIUrl":"10.1093/jrsssc/qlae008","url":null,"abstract":"In many contexts, particularly when study subjects are adolescents, peer effects can invalidate typical statistical requirements in the data. For instance, it is plausible that a student's academic performance is influenced both by their own mother's educational level as well as that of their peers. Since the underlying social network is measured, the Add Health study provides a unique opportunity to examine the impact of maternal college education on adolescent school performance, both direct and indirect. However, causal inference on populations embedded in social networks poses technical challenges, since the typical no interference assumption no longer holds. While inverse probability-of-treatment weighted (IPW) estimators have been developed for this setting, they are often highly unstable. Motivated by the question of maternal education, we propose doubly robust (DR) estimators combining models for treatment and outcome that are consistent and asymptotically normal if either model is correctly specified. We present empirical results that illustrate the DR property and the efficiency gain of DR over IPW estimators even when the treatment model is misspecified. Contrary to previous studies, our robust analysis does not provide evidence of an indirect effect of maternal education on academic performance within adolescents' social circles in Add Health.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":"73 3","pages":"715-734"},"PeriodicalIF":1.3,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11175826/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332376","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

Unsupervised Bayesian classification for models with scalar and functional covariates. 针对标量和功能协变量模型的无监督贝叶斯分类。

IF 1 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2024-02-07 eCollection Date: 2024-06-01 DOI: 10.1093/jrsssc/qlae006

Nancy L Garcia, Mariana Rodrigues-Motta, Helio S Migon, Eva Petkova, Thaddeus Tarpey, R Todd Ogden, Julio O Giordano, Martin M Perez

{"title":"Unsupervised Bayesian classification for models with scalar and functional covariates.","authors":"Nancy L Garcia, Mariana Rodrigues-Motta, Helio S Migon, Eva Petkova, Thaddeus Tarpey, R Todd Ogden, Julio O Giordano, Martin M Perez","doi":"10.1093/jrsssc/qlae006","DOIUrl":"10.1093/jrsssc/qlae006","url":null,"abstract":"We consider unsupervised classification by means of a latent multinomial variable which categorizes a scalar response into one of the L components of a mixture model which incorporates scalar and functional covariates. This process can be thought as a hierarchical model with the first level modelling a scalar response according to a mixture of parametric distributions and the second level modelling the mixture probabilities by means of a generalized linear model with functional and scalar covariates. The traditional approach of treating functional covariates as vectors not only suffers from the curse of dimensionality, since functional covariates can be measured at very small intervals leading to a highly parametrized model, but also does not take into account the nature of the data. We use basis expansions to reduce the dimensionality and a Bayesian approach for estimating the parameters while providing predictions of the latent classification vector. The method is motivated by two data examples that are not easily handled by existing methods. The first example concerns identifying placebo responders on a clinical trial (normal mixture model) and the other predicting illness for milking cows (zero-inflated mixture of the Poisson model).","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":"73 3","pages":"658-681"},"PeriodicalIF":1.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11271982/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141789691","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

Bayesian semi-parametric inference for clustered recurrent events with zero inflation and a terminal event. 针对零膨胀和终结事件的群集重复事件的贝叶斯半参数推断。

IF 1.4 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2024-02-01 eCollection Date: 2024-06-01 DOI: 10.1093/jrsssc/qlae003

Xinyuan Tian, Maria Ciarleglio, Jiachen Cai, Erich J Greene, Denise Esserman, Fan Li, Yize Zhao

引用次数: 0

A Bayesian latent class model for integrating multi-source longitudinal data: application to the CHILD cohort study 整合多源纵向数据的贝叶斯潜类模型:在儿童队列研究中的应用

4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-11-13 DOI: 10.1093/jrsssc/qlad100

Zihang Lu, Padmaja Subbarao, Wendy Lou

引用次数: 1