Annals of Applied Statistics最新文献_第2页

HETEROGENEOUS TREATMENT AND SPILLOVER EFFECTS UNDER CLUSTERED NETWORK INTERFERENCE. 集群网络干扰下的异质性处理与溢出效应。

IF 1.3 4区数学

Annals of Applied Statistics Pub Date : 2025-03-01 Epub Date: 2025-03-17 DOI: 10.1214/24-aoas1913

Falco J Bargagli-Stoffi, Costanza Tortú, Laura Forastiere

{"title":"HETEROGENEOUS TREATMENT AND SPILLOVER EFFECTS UNDER CLUSTERED NETWORK INTERFERENCE.","authors":"Falco J Bargagli-Stoffi, Costanza Tortú, Laura Forastiere","doi":"10.1214/24-aoas1913","DOIUrl":"10.1214/24-aoas1913","url":null,"abstract":"The bulk of causal inference studies rule out the presence of interference between units. However, in many real-world scenarios, units are interconnected by social, physical, or virtual ties, and the effect of the treatment can spill from one unit to other connected individuals in the network. In this paper, we develop a machine learning method that uses tree-based algorithms and a Horvitz-Thompson estimator to assess the heterogeneity of treatment and spillover effects with respect to individual, neighborhood, and network characteristics in the context of clustered networks and interference within clusters. The proposed network causal tree (NCT) algorithm has several advantages. First, it allows the investigation of the heterogeneity of the treatment effect, avoiding potential bias due to the presence of interference. Second, understanding the heterogeneity of both treatment and spillover effects can guide policymakers in scaling up interventions, designing targeting strategies, and increasing cost-effectiveness. We investigate the performance of our NCT method using a Monte Carlo simulation study and illustrate its application to assess the heterogeneous effects of information sessions on the uptake of a new weather insurance policy in rural China.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"19 1","pages":"28-55"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12245184/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144610248","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

CAUSAL HEALTH IMPACTS OF POWER PLANT EMISSION CONTROLS UNDER MODELED AND UNCERTAIN PHYSICAL PROCESS INTERFERENCE. 模拟和不确定物理过程干扰下电厂排放控制的因果健康影响。

IF 1.3 4区数学

Annals of Applied Statistics Pub Date : 2024-12-01 Epub Date: 2024-10-31 DOI: 10.1214/24-aoas1904

Nathan B Wikle, Corwin M Zigler

{"title":"CAUSAL HEALTH IMPACTS OF POWER PLANT EMISSION CONTROLS UNDER MODELED AND UNCERTAIN PHYSICAL PROCESS INTERFERENCE.","authors":"Nathan B Wikle, Corwin M Zigler","doi":"10.1214/24-aoas1904","DOIUrl":"10.1214/24-aoas1904","url":null,"abstract":"Causal inference with spatial environmental data is often challenging due to the presence of interference: outcomes for observational units depend on some combination of local and nonlocal treatment. This is especially relevant when estimating the effect of power plant emissions controls on population health, as pollution exposure is dictated by: (i) the location of point-source emissions as well as (ii) the transport of pollutants across space via dynamic physical-chemical processes. In this work we estimate the effectiveness of air quality interventions at coal-fired power plants in reducing two adverse health outcomes in Texas in 2016: pediatric asthma ED visits and Medicare all-cause mortality. We develop methods for causal inference with interference when the underlying network structure is not known with certainty and instead must be estimated from ancillary data. Notably, uncertainty in the interference structure is propagated to the resulting causal effect estimates. We offer a Bayesian, spatial mechanistic model for the interference mapping, which we combine with a flexible nonparametric outcome model to marginalize estimates of causal effects over uncertainty in the structure of interference. our analysis finds some evidence that emissions controls at upwind power plants reduce asthma ED visits and all-cause mortality; however, accounting for uncertainty in the interference renders the results largely inconclusive.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"18 4","pages":"2753-2774"},"PeriodicalIF":1.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11619076/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142787678","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 LATENT VARIABLE MIXTURE MODEL FOR COMPOSITION-ON-COMPOSITION REGRESSION WITH APPLICATION TO CHEMICAL RECYCLING. 成分-成分回归的潜在变量混合模型及其在化工回收中的应用。

IF 1.4 4区数学

Annals of Applied Statistics Pub Date : 2024-12-01 Epub Date: 2024-10-31 DOI: 10.1214/24-aoas1935

Nicholas Rios, Lingzhou Xue, Xiang Zhan

{"title":"A LATENT VARIABLE MIXTURE MODEL FOR COMPOSITION-ON-COMPOSITION REGRESSION WITH APPLICATION TO CHEMICAL RECYCLING.","authors":"Nicholas Rios, Lingzhou Xue, Xiang Zhan","doi":"10.1214/24-aoas1935","DOIUrl":"10.1214/24-aoas1935","url":null,"abstract":"It is quite common to encounter compositional data in a regression framework in data analysis. When both responses and predictors are compositional, most existing models rely on a family of log-ratio based transformations to move the analysis from the simplex to the reals. This often makes the interpretation of the model more complex. A transformation-free regression model was recently developed, but it only allows for a single compositional predictor. However, many datasets include multiple compositional predictors of interest. Motivated by an application to hydrothermal liquefaction (HTL) data, a novel extension of this transformation-free regression model is provided that allows for two (or more) compositional predictors to be used via a latent variable mixture. A modified expectation-maximization algorithm is proposed to estimate model parameters, which are shown to have natural interpretations. Conformal inference is used to obtain prediction limits on the compositional response. The resulting methodology is applied to the HTL dataset. Extensions to multiple predictors are discussed.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"18 4","pages":"3253-3273"},"PeriodicalIF":1.4,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12448131/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145114836","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

MODELING TRAJECTORIES USING FUNCTIONAL LINEAR DIFFERENTIAL EQUATIONS. 利用函数线性微分方程建模轨迹。

IF 1.3 4区数学

Annals of Applied Statistics Pub Date : 2024-12-01 Epub Date: 2024-10-31 DOI: 10.1214/24-aoas1943

Julia Wrobel, Britton Sauerbrei, Eric A Kirk, Jian-Zhong Guo, Adam Hantman, Jeff Goldsmith

{"title":"MODELING TRAJECTORIES USING FUNCTIONAL LINEAR DIFFERENTIAL EQUATIONS.","authors":"Julia Wrobel, Britton Sauerbrei, Eric A Kirk, Jian-Zhong Guo, Adam Hantman, Jeff Goldsmith","doi":"10.1214/24-aoas1943","DOIUrl":"10.1214/24-aoas1943","url":null,"abstract":"We are motivated by a study that seeks to better understand the dynamic relationship between muscle activation and paw position during locomotion. For each gait cycle in this experiment, activation in the biceps and triceps is measured continuously and in parallel with paw position as a mouse trotted on a treadmill. We propose an innovative general regression method that draws from both ordinary differential equations and functional data analysis to model the relationship between these functional inputs and responses as a dynamical system that evolves over time. Specifically, our model addresses gaps in both literatures and borrows strength across curves estimating ODE parameters across all curves simultaneously rather than separately modeling each functional observation. Our approach compares favorably to related functional data methods in simulations and in cross-validated predictive accuracy of paw position in the gait data. In the analysis of the gait cycles, we find that paw speed and position are dynamically influenced by inputs from the biceps and triceps muscles and that the effect of muscle activation persists beyond the activation itself.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"18 4","pages":"3425-3443"},"PeriodicalIF":1.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11934208/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143711989","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 SEMIPARAMETRIC METHOD FOR RISK PREDICTION USING INTEGRATED ELECTRONIC HEALTH RECORD DATA. 综合电子病历数据风险预测的半参数方法。

IF 1.3 4区数学

Annals of Applied Statistics Pub Date : 2024-12-01 Epub Date: 2024-10-31 DOI: 10.1214/24-AOAS1938

Jill Hasler, Yanyuan Ma, Yizheng Wei, Ravi Parikh, Jinbo Chen

{"title":"A SEMIPARAMETRIC METHOD FOR RISK PREDICTION USING INTEGRATED ELECTRONIC HEALTH RECORD DATA.","authors":"Jill Hasler, Yanyuan Ma, Yizheng Wei, Ravi Parikh, Jinbo Chen","doi":"10.1214/24-AOAS1938","DOIUrl":"10.1214/24-AOAS1938","url":null,"abstract":"When using electronic health records (EHRs) for clinical and translational research, additional data is often available from external sources to enrich the information extracted from EHRs. For example, academic biobanks have more granular data available, and patient reported data is often collected through small-scale surveys. It is common that the external data is available only for a small subset of patients who have EHR information. We propose efficient and robust methods for building and evaluating models for predicting the risk of binary outcomes using such integrated EHR data. Our method is built upon an idea derived from the two-phase design literature that modeling the availability of a patient's external data as a function of an EHR-based preliminary predictive score leads to effective utilization of the EHR data. Through both theoretical and simulation studies, we show that our method has high efficiency for estimating log-odds ratio parameters, the area under the ROC curve, as well as other measures for quantifying predictive accuracy. We apply our method to develop a model for predicting the short-term mortality risk of oncology patients, where the data was extracted from the University of Pennsylvania hospital system EHR and combined with survey-based patient reported outcome data.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"18 4","pages":"3318-3337"},"PeriodicalIF":1.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11934126/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143711932","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

INDIVIDUAL DYNAMIC PREDICTION FOR CURE AND SURVIVAL BASED ON LONGITUDINAL BIOMARKERS. 基于纵向生物标志物的个体动态治愈和生存预测。

IF 1.3 4区数学

Annals of Applied Statistics Pub Date : 2024-12-01 Epub Date: 2024-10-31 DOI: 10.1214/24-aoas1906

Can Xie, Xuelin Huang, Ruosha Li, Alexander Tsodikov, Kapil Bhalla

{"title":"INDIVIDUAL DYNAMIC PREDICTION FOR CURE AND SURVIVAL BASED ON LONGITUDINAL BIOMARKERS.","authors":"Can Xie, Xuelin Huang, Ruosha Li, Alexander Tsodikov, Kapil Bhalla","doi":"10.1214/24-aoas1906","DOIUrl":"10.1214/24-aoas1906","url":null,"abstract":"To optimize personalized treatment strategies and extend patients' survival times, it is critical to accurately predict patients' prognoses at all stages, from disease diagnosis to follow-up visits. The longitudinal biomarker measurements during visits are essential for this prediction purpose. Patients' ultimate concerns are cure and survival. However, in many situations, there is no clear biomarker indicator for cure. We propose a comprehensive joint model of longitudinal and survival data and a landmark cure model, incorporating proportions of potentially cured patients. The survival distributions in the joint and landmark models are specified through flexible hazard functions with the proportional hazards as a special case, allowing other patterns such as crossing hazard and survival functions. Formulas are provided for predicting each individual's probabilities of future cure and survival at any time point based on his or her current biomarker history. Simulations show that, with these comprehensive and flexible properties, the proposed cure models outperform standard cure models in terms of predictive performance, measured by the time-dependent area under the curve of receiver operating characteristic, Brier score, and integrated Brier score. The use and advantages of the proposed models are illustrated by their application to a study of patients with chronic myeloid leukemia.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"18 4","pages":"2796-2817"},"PeriodicalIF":1.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11864788/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143525086","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

STATISTICAL CURVE MODELS FOR INFERRING 3D CHROMATIN ARCHITECTURE. 用于推断三维染色质结构的统计曲线模型。

IF 1.3 4区数学

Annals of Applied Statistics Pub Date : 2024-12-01 Epub Date: 2024-10-31 DOI: 10.1214/24-AOAS1917

Elena Tuzhilina, Trevor Hastie, Mark Segal

{"title":"STATISTICAL CURVE MODELS FOR INFERRING 3D CHROMATIN ARCHITECTURE.","authors":"Elena Tuzhilina, Trevor Hastie, Mark Segal","doi":"10.1214/24-AOAS1917","DOIUrl":"10.1214/24-AOAS1917","url":null,"abstract":"Reconstructing three-dimensional (3D) chromatin structure from conformation capture assays (such as Hi-C) is a critical task in computational biology, since chromatin spatial architecture plays a vital role in numerous cellular processes and direct imaging is challenging. Most existing algorithms that operate on Hi-C contact matrices produce reconstructed 3D configurations in the form of a polygonal chain. However, none of the methods exploit the fact that the target solution is a (smooth) curve in 3D: this contiguity attribute is either ignored or indirectly addressed by imposing spatial constraints that are challenging to formulate. In this paper we develop both B-spline and smoothing spline techniques for directly capturing this potentially complex 1D curve. We subsequently combine these techniques with a Poisson model for contact counts and compare their performance on a real data example. In addition, motivated by the sparsity of Hi-C contact data, especially when obtained from single-cell assays, we appreciably extend the class of distributions used to model contact counts. We build a general distribution-based metric scaling ( <math><mi>DBMS</mi></math> ) framework from which we develop zero-inflated and Hurdle Poisson models as well as negative binomial applications. Illustrative applications make recourse to bulk Hi-C data from IMR90 cells and single-cell Hi-C data from mouse embryonic stem cells.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"18 4","pages":"2979-3006"},"PeriodicalIF":1.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209861/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144545926","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 SPATIALLY VARYING HIERARCHICAL RANDOM EFFECTS MODEL FOR LONGITUDINAL MACULAR STRUCTURAL DATA IN GLAUCOMA PATIENTS. 青光眼患者纵向黄斑结构数据的空间变化分层随机效应模型。

IF 1.3 4区数学

Annals of Applied Statistics Pub Date : 2024-12-01 Epub Date: 2024-10-31 DOI: 10.1214/24-aoas1944

By Erica Su, Robert E Weiss, Kouros Nouri-Mahdavi, Andrew J Holbrook

{"title":"A SPATIALLY VARYING HIERARCHICAL RANDOM EFFECTS MODEL FOR LONGITUDINAL MACULAR STRUCTURAL DATA IN GLAUCOMA PATIENTS.","authors":"By Erica Su, Robert E Weiss, Kouros Nouri-Mahdavi, Andrew J Holbrook","doi":"10.1214/24-aoas1944","DOIUrl":"10.1214/24-aoas1944","url":null,"abstract":"We model longitudinal macular thickness measurements to monitor the course of glaucoma and prevent vision loss due to disease progression. The macular thickness varies over a 6 × 6 grid of locations on the retina, with additional variability arising from the imaging process at each visit. currently, ophthalmologists estimate slopes using repeated simple linear regression for each subject and location. To estimate slopes more precisely, we develop a novel Bayesian hierarchical model for multiple subjects with spatially varying population-level and subject-level coefficients, borrowing information over subjects and measurement locations. We augment the model with visit effects to account for observed spatially correlated visit-specific errors. We model spatially varying: (a) intercepts, (b) slopes, and (c) log-residual standard deviations (SD) with multivariate Gaussian process priors with Matérn cross-covariance functions. Each marginal process assumes an exponential kernel with its own SD and spatial correlation matrix. We develop our models for and apply them to data from the Advanced Glaucoma Progression Study. We show that including visit effects in the model reduces error in predicting future thickness measurements and greatly improves model fit.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"18 4","pages":"3444-3466"},"PeriodicalIF":1.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11864210/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143525083","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

UTILIZING A CAPTURE-RECAPTURE STRATEGY TO ACCELERATE INFECTIOUS DISEASE SURVEILLANCE. 利用捕获-再捕获战略加速传染病监测。

IF 1.3 4区数学

Annals of Applied Statistics Pub Date : 2024-12-01 Epub Date: 2024-10-31 DOI: 10.1214/24-aoas1927

Lin Ge, Yuzi Zhang, Lance Waller, Robert Lyles

{"title":"UTILIZING A CAPTURE-RECAPTURE STRATEGY TO ACCELERATE INFECTIOUS DISEASE SURVEILLANCE.","authors":"Lin Ge, Yuzi Zhang, Lance Waller, Robert Lyles","doi":"10.1214/24-aoas1927","DOIUrl":"10.1214/24-aoas1927","url":null,"abstract":"Monitoring key elements of disease dynamics (e.g., prevalence, case counts) is of great importance in infectious disease prevention and control, as emphasized during the COVID-19 pandemic. To facilitate this effort, we propose a new capture-recapture (CRC) analysis strategy that adjusts for misclassification stemming from the use of easily administered but imperfect diagnostic test kits, such as rapid antigen test-kits or saliva tests. Our method is based on a recently proposed \"anchor stream\" design, whereby an existing voluntary surveillance data stream is augmented by a smaller and judiciously drawn random sample. It incorporates manufacturer-specified sensitivity and specificity parameters to account for imperfect diagnostic results in one or both data streams. For inference to accompany case count estimation, we improve upon traditional Wald-type confidence intervals by developing an adapted Bayesian credible interval for the CRC estimator that yields favorable frequentist coverage properties. When feasible, the proposed design and analytic strategy provides a more efficient solution than traditional CRC methods or random sampling-based bias-corrected estimation to monitor disease prevalence while accounting for misclassification. We demonstrate the benefits of this approach through simulation studies and a numerical example that underscore its potential utility in practice for economical disease monitoring among a registered closed population.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"18 4","pages":"3130-3145"},"PeriodicalIF":1.3,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12273866/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144676401","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

EXPOSURE EFFECTS ON COUNT OUTCOMES WITH OBSERVATIONAL DATA, WITH APPLICATION TO INCARCERATED WOMEN. 通过观察数据分析暴露对计数结果的影响，并将其应用于被监禁妇女。

IF 1.4 4区数学

Annals of Applied Statistics Pub Date : 2024-09-01 Epub Date: 2024-08-05 DOI: 10.1214/24-aoas1874

Bonnie E Shook-Sa, Michael G Hudgens, Andrea K Knittel, Andrew Edmonds, Catalina Ramirez, Stephen R Cole, Mardge Cohen, Adebola Adedimeji, Tonya Taylor, Katherine G Michel, Andrea Kovacs, Jennifer Cohen, Jessica Donohue, Antonina Foster, Margaret A Fischl, Dustin Long, Adaora A Adimora

{"title":"EXPOSURE EFFECTS ON COUNT OUTCOMES WITH OBSERVATIONAL DATA, WITH APPLICATION TO INCARCERATED WOMEN.","authors":"Bonnie E Shook-Sa, Michael G Hudgens, Andrea K Knittel, Andrew Edmonds, Catalina Ramirez, Stephen R Cole, Mardge Cohen, Adebola Adedimeji, Tonya Taylor, Katherine G Michel, Andrea Kovacs, Jennifer Cohen, Jessica Donohue, Antonina Foster, Margaret A Fischl, Dustin Long, Adaora A Adimora","doi":"10.1214/24-aoas1874","DOIUrl":"10.1214/24-aoas1874","url":null,"abstract":"Causal inference methods can be applied to estimate the effect of a point exposure or treatment on an outcome of interest using data from observational studies. For example, in the Women's Interagency HIV Study, it is of interest to understand the effects of incarceration on the number of sexual partners and the number of cigarettes smoked after incarceration. In settings like this where the outcome is a count, the estimand is often the causal mean ratio, i.e., the ratio of the counterfactual mean count under exposure to the counterfactual mean count under no exposure. This paper considers estimators of the causal mean ratio based on inverse probability of treatment weights, the parametric g-formula, and doubly robust estimation, each of which can account for overdispersion, zero-inflation, and heaping in the measured outcome. Methods are compared in simulations and are applied to data from the Women's Interagency HIV Study.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"18 3","pages":"2147-2165"},"PeriodicalIF":1.4,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11526847/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142570975","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