Biometrics最新文献_第10页

The central role of the identifying assumption in population size estimation. 识别假设在种群数量估计中的核心作用。

IF 1.9 4区数学

Biometrics Pub Date : 2024-01-29 DOI: 10.1093/biomtc/ujad028

Serge Aleshin-Guendel, Mauricio Sadinle, Jon Wakefield

{"title":"The central role of the identifying assumption in population size estimation.","authors":"Serge Aleshin-Guendel, Mauricio Sadinle, Jon Wakefield","doi":"10.1093/biomtc/ujad028","DOIUrl":"10.1093/biomtc/ujad028","url":null,"abstract":"The problem of estimating the size of a population based on a subset of individuals observed across multiple data sources is often referred to as capture-recapture or multiple-systems estimation. This is fundamentally a missing data problem, where the number of unobserved individuals represents the missing data. As with any missing data problem, multiple-systems estimation requires users to make an untestable identifying assumption in order to estimate the population size from the observed data. If an appropriate identifying assumption cannot be found for a data set, no estimate of the population size should be produced based on that data set, as models with different identifying assumptions can produce arbitrarily different population size estimates-even with identical observed data fits. Approaches to multiple-systems estimation often do not explicitly specify identifying assumptions. This makes it difficult to decouple the specification of the model for the observed data from the identifying assumption and to provide justification for the identifying assumption. We present a re-framing of the multiple-systems estimation problem that leads to an approach that decouples the specification of the observed-data model from the identifying assumption, and discuss how common models fit into this framing. This approach takes advantage of existing software and facilitates various sensitivity analyses. We demonstrate our approach in a case study estimating the number of civilian casualties in the Kosovo war.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140058585","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

Simultaneous variable selection and estimation in semiparametric regression of mixed panel count data. 混合面板计数数据半参数回归中的同步变量选择和估计。

IF 1.9 4区数学

Biometrics Pub Date : 2024-01-29 DOI: 10.1093/biomtc/ujad041

Lei Ge, Tao Hu, Yang Li

引用次数: 0

A flexible framework for spatial capture-recapture with unknown identities. 身份未知的空间捕获-再捕获灵活框架。

IF 1.9 4区数学

Biometrics Pub Date : 2024-01-29 DOI: 10.1093/biomtc/ujad019

Paul van Dam-Bates, Michail Papathomas, Ben C Stevenson, Rachel M Fewster, Daniel Turek, Frances E C Stewart, David L Borchers

{"title":"A flexible framework for spatial capture-recapture with unknown identities.","authors":"Paul van Dam-Bates, Michail Papathomas, Ben C Stevenson, Rachel M Fewster, Daniel Turek, Frances E C Stewart, David L Borchers","doi":"10.1093/biomtc/ujad019","DOIUrl":"10.1093/biomtc/ujad019","url":null,"abstract":"Camera traps or acoustic recorders are often used to sample wildlife populations. When animals can be individually identified, these data can be used with spatial capture-recapture (SCR) methods to assess populations. However, obtaining animal identities is often labor-intensive and not always possible for all detected animals. To address this problem, we formulate SCR, including acoustic SCR, as a marked Poisson process, comprising a single counting process for the detections of all animals and a mark distribution for what is observed (eg, animal identity, detector location). The counting process applies equally when it is animals appearing in front of camera traps and when vocalizations are captured by microphones, although the definition of a mark changes. When animals cannot be uniquely identified, the observed marks arise from a mixture of mark distributions defined by the animal activity centers and additional characteristics. Our method generalizes existing latent identity SCR models and provides an integrated framework that includes acoustic SCR. We apply our method to estimate density from a camera trap study of fisher (Pekania pennanti) and an acoustic survey of Cape Peninsula moss frog (Arthroleptella lightfooti). We also test it through simulation. We find latent identity SCR with additional marks such as sex or time of arrival to be a reliable method for estimating animal density.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139899314","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

Longitudinal varying coefficient single-index model with censored covariates. 带有删减协变量的纵向变化系数单指数模型。

IF 1.4 4区数学

Biometrics Pub Date : 2024-01-29 DOI: 10.1093/biomtc/ujad006

Shikun Wang, Jing Ning, Ying Xu, Ya-Chen Tina Shih, Yu Shen, Liang Li

{"title":"Longitudinal varying coefficient single-index model with censored covariates.","authors":"Shikun Wang, Jing Ning, Ying Xu, Ya-Chen Tina Shih, Yu Shen, Liang Li","doi":"10.1093/biomtc/ujad006","DOIUrl":"10.1093/biomtc/ujad006","url":null,"abstract":"It is of interest to health policy research to estimate the population-averaged longitudinal medical cost trajectory from initial cancer diagnosis to death, and understand how the trajectory curve is affected by patient characteristics. This research question leads to a number of statistical challenges because the longitudinal cost data are often non-normally distributed with skewness, zero-inflation, and heteroscedasticity. The trajectory is nonlinear, and its length and shape depend on survival, which are subject to censoring. Modeling the association between multiple patient characteristics and nonlinear cost trajectory curves of varying lengths should take into consideration parsimony, flexibility, and interpretation. We propose a novel longitudinal varying coefficient single-index model. Multiple patient characteristics are summarized in a single-index, representing a patient's overall propensity for healthcare use. The effects of this index on various segments of the cost trajectory depend on both time and survival, which is flexibly modeled by a bivariate varying coefficient function. The model is estimated by generalized estimating equations with an extended marginal mean structure to accommodate censored survival time as a covariate. We established the pointwise confidence interval of the varying coefficient and a test for the covariate effect. The numerical performance was extensively studied in simulations. We applied the proposed methodology to medical cost data of prostate cancer patients from the Surveillance, Epidemiology, and End Results-Medicare-Linked Database.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10871868/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139745911","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

Adaptive sequential surveillance with network and temporal dependence. 具有网络和时间依赖性的自适应顺序监控。

IF 1.9 4区数学

Biometrics Pub Date : 2024-01-29 DOI: 10.1093/biomtc/ujad007

Ivana Malenica, Jeremy R Coyle, Mark J van der Laan, Maya L Petersen

{"title":"Adaptive sequential surveillance with network and temporal dependence.","authors":"Ivana Malenica, Jeremy R Coyle, Mark J van der Laan, Maya L Petersen","doi":"10.1093/biomtc/ujad007","DOIUrl":"10.1093/biomtc/ujad007","url":null,"abstract":"Strategic test allocation is important for control of both emerging and existing pandemics (eg, COVID-19, HIV). It supports effective epidemic control by (1) reducing transmission via identifying cases and (2) tracking outbreak dynamics to inform targeted interventions. However, infectious disease surveillance presents unique statistical challenges. For instance, the true outcome of interest (positive infection status) is often a latent variable. In addition, presence of both network and temporal dependence reduces data to a single observation. In this work, we study an adaptive sequential design, which allows for unspecified dependence among individuals and across time. Our causal parameter is the mean latent outcome we would have obtained, if, starting at time t given the observed past, we had carried out a stochastic intervention that maximizes the outcome under a resource constraint. The key strength of the method is that we do not have to model network and time dependence: a short-term performance Online Super Learner is used to select among dependence models and randomization schemes. The proposed strategy learns the optimal choice of testing over time while adapting to the current state of the outbreak and learning across samples, through time, or both. We demonstrate the superior performance of the proposed strategy in an agent-based simulation modeling a residential university environment during the COVID-19 pandemic.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10826884/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139568924","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

Efficient estimation for left-truncated competing risks regression for case-cohort studies. 病例队列研究中左截断竞争风险回归的有效估计。

IF 1.9 4区数学

Biometrics Pub Date : 2024-01-29 DOI: 10.1093/biomtc/ujad008

Xi Fang, Kwang Woo Ahn, Jianwen Cai, Soyoung Kim

{"title":"Efficient estimation for left-truncated competing risks regression for case-cohort studies.","authors":"Xi Fang, Kwang Woo Ahn, Jianwen Cai, Soyoung Kim","doi":"10.1093/biomtc/ujad008","DOIUrl":"10.1093/biomtc/ujad008","url":null,"abstract":"The case-cohort study design provides a cost-effective study design for a large cohort study with competing risk outcomes. The proportional subdistribution hazards model is widely used to estimate direct covariate effects on the cumulative incidence function for competing risk data. In biomedical studies, left truncation often occurs and brings extra challenges to the analysis. Existing inverse probability weighting methods for case-cohort studies with competing risk data not only have not addressed left truncation, but also are inefficient in regression parameter estimation for fully observed covariates. We propose an augmented inverse probability-weighted estimating equation for left-truncated competing risk data to address these limitations of the current literature. We further propose a more efficient estimator when extra information from the other causes is available. The proposed estimators are consistent and asymptotically normally distributed. Simulation studies show that the proposed estimator is unbiased and leads to estimation efficiency gain in the regression parameter estimation. We analyze the Atherosclerosis Risk in Communities study data using the proposed methods.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10826882/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139568927","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

A Bayesian survival treed hazards model using latent Gaussian processes. 使用潜在高斯过程的贝叶斯生存树状危害模型。

IF 1.9 4区数学

Biometrics Pub Date : 2024-01-29 DOI: 10.1093/biomtc/ujad009

Richard D Payne, Nilabja Guha, Bani K Mallick

引用次数: 0

A boosting method to select the random effects in linear mixed models. 在线性混合模型中选择随机效应的提升方法。

IF 1.9 4区数学

Biometrics Pub Date : 2024-01-29 DOI: 10.1093/biomtc/ujae010

Michela Battauz, Paolo Vidoni

引用次数: 0

Bayesian two-stage modeling of longitudinal and time-to-event data with an integrated fractional Brownian motion covariance structure. 具有积分布朗运动协方差结构的纵向和时间到事件数据的贝叶斯两阶段建模。

IF 1.9 4区数学

Biometrics Pub Date : 2024-01-29 DOI: 10.1093/biomtc/ujae011

Anushka Palipana, Seongho Song, Nishant Gupta, Rhonda Szczesniak

{"title":"Bayesian two-stage modeling of longitudinal and time-to-event data with an integrated fractional Brownian motion covariance structure.","authors":"Anushka Palipana, Seongho Song, Nishant Gupta, Rhonda Szczesniak","doi":"10.1093/biomtc/ujae011","DOIUrl":"10.1093/biomtc/ujae011","url":null,"abstract":"It is difficult to characterize complex variations of biological processes, often longitudinally measured using biomarkers that yield noisy data. While joint modeling with a longitudinal submodel for the biomarker measurements and a survival submodel for assessing the hazard of events can alleviate measurement error issues, the continuous longitudinal submodel often uses random intercepts and slopes to estimate both between- and within-patient heterogeneity in biomarker trajectories. To overcome longitudinal submodel challenges, we replace random slopes with scaled integrated fractional Brownian motion (IFBM). As a more generalized version of integrated Brownian motion, IFBM reasonably depicts noisily measured biological processes. From this longitudinal IFBM model, we derive novel target functions to monitor the risk of rapid disease progression as real-time predictive probabilities. Predicted biomarker values from the IFBM submodel are used as inputs in a Cox submodel to estimate event hazard. This two-stage approach to fit the submodels is performed via Bayesian posterior computation and inference. We use the proposed approach to predict dynamic lung disease progression and mortality in women with a rare disease called lymphangioleiomyomatosis who were followed in a national patient registry. We compare our approach to those using integrated Ornstein-Uhlenbeck or conventional random intercepts-and-slopes terms for the longitudinal submodel. In the comparative analysis, the IFBM model consistently demonstrated superior predictive performance.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10938548/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140118683","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 "The central role of the identifying assumption in population size estimation" by Serge Aleshin-Guendel, Mauricio Sadinle, and Jon Wakefield. Serge Aleshin-Guendel、Mauricio Sadinle 和 Jon Wakefield 关于 "识别假设在种群规模估计中的核心作用 "的讨论。

IF 1.9 4区数学

Biometrics Pub Date : 2024-01-29 DOI: 10.1093/biomtc/ujad031

John Whitehead

引用次数: 0