Epidemiologic Methods最新文献_第8页

The mean prevalence 平均患病率

Epidemiologic Methods Pub Date : 2020-01-01 DOI: 10.1515/em-2019-0033

F. Habibzadeh, P. Habibzadeh

引用次数: 2

Population attributable fractions for continuously distributed exposures 连续分布暴露的人口归因分数

Epidemiologic Methods Pub Date : 2020-01-01 DOI: 10.1515/em-2019-0037

J. Ferguson, Fabrizio Maturo, S. Yusuf, M. O’Donnell

{"title":"Population attributable fractions for continuously distributed exposures","authors":"J. Ferguson, Fabrizio Maturo, S. Yusuf, M. O’Donnell","doi":"10.1515/em-2019-0037","DOIUrl":"https://doi.org/10.1515/em-2019-0037","url":null,"abstract":"Abstract When estimating population attributable fractions (PAF), it is common to partition a naturally continuous exposure into a categorical risk factor. While prior risk factor categorization can help estimation and interpretation, it can result in underestimation of the disease burden attributable to the exposure as well as biased comparisons across different exposures and risk factors. Here, we propose sensible PAF estimands for continuous exposures under a potential outcomes framework. In contrast to previous approaches, we incorporate estimation of the minimum risk exposure value (MREV) into our procedures. While for exposures such as tobacco usage, a sensible value of the MREV is known, often it is unknown and needs to be estimated. Second, in the setting that the MREV value is an extreme-value of the exposure lying in the distributional tail, we argue that the natural estimator of PAF may be both statistically biased and highly volatile; instead, we consider a family of modified PAFs which include the natural estimate of PAF as a limit. A graphical comparison of this set of modified PAF for differing risk factors may be a better way to rank risk factors as intervention targets, compared to the standard PAF calculation. Finally, we analyse the bias that may ensue from prior risk factor categorization, examining whether categorization is ever a good idea, and suggest interpretations of categorized-estimands within a causal inference setting.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75758425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Extrapolating sparse gold standard cause of death designations to characterize broader catchment areas 外推稀疏的金标准死因名称来描述更广泛的流域

Epidemiologic Methods Pub Date : 2020-01-01 DOI: 10.1515/em-2019-0031

R. Lyles, S. Cunningham, Suprateek Kundu, Q. Bassat, I. Mandomando, C. Sacoor, Victor Akelo, D. Onyango, Emily Zielinski-Gutierrez, Allan W. Taylor

{"title":"Extrapolating sparse gold standard cause of death designations to characterize broader catchment areas","authors":"R. Lyles, S. Cunningham, Suprateek Kundu, Q. Bassat, I. Mandomando, C. Sacoor, Victor Akelo, D. Onyango, Emily Zielinski-Gutierrez, Allan W. Taylor","doi":"10.1515/em-2019-0031","DOIUrl":"https://doi.org/10.1515/em-2019-0031","url":null,"abstract":"Abstract Objectives The Child Health and Mortality Prevention Surveillance (CHAMPS) Network is designed to elucidate and track causes of under-5 child mortality and stillbirth in multiple sites in sub-Saharan Africa and South Asia using advanced surveillance, laboratory and pathology methods. Expert panels provide an arguable gold standard determination of underlying cause of death (CoD) on a subset of child deaths, in part through examining tissue obtained via minimally invasive tissue sampling (MITS) procedures. We consider estimating a population-level distribution of CoDs based on this sparse but precise data, in conjunction with data on subgrouping characteristics that are measured on the broader population of cases and are potentially associated with selection for MITS and with cause-specific mortality. Methods We illustrate how estimation of each underlying CoD proportion using all available data can be addressed equivalently in terms of a Horvitz-Thompson adjustment or a direct standardization, uncovering insights relevant to the designation of appropriate subgroups to adjust for non-representative sampling. Taking advantage of the functional form of the result when expressed as a multinomial distribution-based maximum likelihood estimator, we propose small-sample adjustments to Bayesian credible intervals based on Jeffreys or related weakly informative Dirichlet prior distributions. Results Our analyses of early data from CHAMPS sites in Kenya and Mozambique and accompanying simulation studies demonstrate the validity of the adjustment approach under attendant assumptions, together with marked performance improvements associated with the proposed adjusted Bayesian credible intervals. Conclusions Adjustment for non-representative sampling of those validated via gold standard diagnostic methods is a critical endeavor for epidemiologic studies like CHAMPS that seek extrapolation of CoD proportion estimates.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90912114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Heterogeneous indirect effects for multiple mediators using interventional effect models 利用介入效应模型研究多种介质的异质性间接效应

Epidemiologic Methods Pub Date : 2020-01-01 DOI: 10.1515/em-2020-0023

W. W. Loh, B. Moerkerke, T. Loeys, S. Vansteelandt

{"title":"Heterogeneous indirect effects for multiple mediators using interventional effect models","authors":"W. W. Loh, B. Moerkerke, T. Loeys, S. Vansteelandt","doi":"10.1515/em-2020-0023","DOIUrl":"https://doi.org/10.1515/em-2020-0023","url":null,"abstract":"Abstract Decomposing an exposure effect on an outcome into separate natural indirect effects through multiple mediators requires strict assumptions, such as correctly postulating the causal structure of the mediators, and no unmeasured confounding among the mediators. In contrast, interventional indirect effects for multiple mediators can be identified even when – as often – the mediators either have an unknown causal structure, or share unmeasured common causes, or both. Existing estimation methods for interventional indirect effects require calculating each distinct indirect effect in turn. This can quickly become unwieldy or unfeasible, especially when investigating indirect effect measures that may be modified by observed baseline characteristics. In this article, we introduce simplified estimation procedures for such heterogeneous interventional indirect effects using interventional effect models. Interventional effect models are a class of marginal structural models that encode the interventional indirect effects as causal model parameters, thus readily permitting effect modification by baseline covariates using (statistical) interaction terms. The mediators and outcome can be continuous or noncontinuous. We propose two estimation procedures: one using inverse weighting by the counterfactual mediator density or mass functions, and another using Monte Carlo integration. The former has the advantage of not requiring an outcome model, but is susceptible to finite sample biases due to highly variable weights. The latter has the advantage of consistent estimation under a correctly specified (parametric) outcome model, but is susceptible to biases due to extrapolation. The estimators are illustrated using publicly available data assessing whether the indirect effects of self-efficacy on fatigue via self-reported post-traumatic stress disorder symptoms vary across different levels of negative coping among health care workers during the COVID-19 outbreak.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77992838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

A comparison of cause-specific and competing risk models to assess risk factors for dementia 评估痴呆危险因素的病因特异性和竞争风险模型的比较

Epidemiologic Methods Pub Date : 2020-01-01 DOI: 10.1515/em-2019-0036

M. Waller, G. Mishra, A. Dobson

{"title":"A comparison of cause-specific and competing risk models to assess risk factors for dementia","authors":"M. Waller, G. Mishra, A. Dobson","doi":"10.1515/em-2019-0036","DOIUrl":"https://doi.org/10.1515/em-2019-0036","url":null,"abstract":"Abstract The study of dementia risk factors is complicated by the competing risk of dying. The standard approaches are the cause-specific Cox proportional hazard model with deaths treated as censoring events (and removed from the risk set) and the Fine and Gray sub-distribution hazard model in which those who die remain in the risk set. An alternative approach is to modify the risk set between these extremes. We propose a novel method of doing this based on estimating the time at which the person might have been diagnosed if they had not died using a parametric survival model, and then applying the cause-specific and Fine and Gray models to the modified dataset. We compare these methods using data on dementia from the Australian Longitudinal Study on Women’s Health and discuss the assumptions and limitations of each model. The results from survival models to assess risk factors for dementia varied considerably between the cause-specific model and the models designed to account for competing risks. Therefore, when assessing risk factors in the presence of competing risks it is important to examine results from: the cause-specific model, different models which account for competing risks, and the model which assesses risk factors associated with the competing risk.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89724042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Modeling of Clinical Phenotypes Assessed at Discrete Study Visits. 在离散研究访问中评估临床表型的建模。

Epidemiologic Methods Pub Date : 2019-12-01 Epub Date: 2019-08-02 DOI: 10.1515/em-2018-0011

Emily J Huang, Ravi Varadhan, Michelle C Carlson

{"title":"Modeling of Clinical Phenotypes Assessed at Discrete Study Visits.","authors":"Emily J Huang, Ravi Varadhan, Michelle C Carlson","doi":"10.1515/em-2018-0011","DOIUrl":"https://doi.org/10.1515/em-2018-0011","url":null,"abstract":"In studies of clinical phenotypes, such as dementia, disability, and frailty, participants are typically assessed at in-person clinic visits. Thus, the precise time of onset for the phenotype is unknown. The discreteness of the clinic visits yields grouped event time data. We investigate how to perform a risk factor analysis in the case of grouped data. Since visits can be months to years apart, numbers of ties can be large, causing the exact tie-handling method of the Cox model to be computationally infeasible. We propose two, new, computationally efficient approximations to the exact method: Laplace approximation and an analytic approximation. Through extensive simulation studies, we compare these new methods to the Prentice-Gloeckler model and the Cox model using Efron's and Breslow's tie-handling methods. In addition, we compare the methods in an application to a large cohort study (N = 3,605) on the development of clinical frailty in older adults. In our simulations, the Laplace approximation has low bias in all settings, and the analytic approximation has low bias in settings where the regression coefficient is not large in magnitude. Their corresponding confidence intervals also have approximately the nominal coverage probability. In the data application, the results from the approximations are nearly identical to that of the Prentice-Gloeckler model.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/em-2018-0011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38821278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","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 Mediation Analysis in the Presence of a Misclassified Binary Exposure 存在误分类二元暴露的因果中介分析

Epidemiologic Methods Pub Date : 2019-11-29 DOI: 10.1515/em-2016-0006

Zhichao Jiang, T. VanderWeele

引用次数: 3

Regression analysis of unmeasured confounding 未测量混杂因素的回归分析

Epidemiologic Methods Pub Date : 2019-08-22 DOI: 10.1515/em-2019-0028

B. Knaeble, B. Osting, M. Abramson

{"title":"Regression analysis of unmeasured confounding","authors":"B. Knaeble, B. Osting, M. Abramson","doi":"10.1515/em-2019-0028","DOIUrl":"https://doi.org/10.1515/em-2019-0028","url":null,"abstract":"Abstract When studying the causal effect of x on y, researchers may conduct regression and report a confidence interval for the slope coefficient β x ${beta }_{x}$ . This common confidence interval provides an assessment of uncertainty from sampling error, but it does not assess uncertainty from confounding. An intervention on x may produce a response in y that is unexpected, and our misinterpretation of the slope happens when there are confounding factors w. When w are measured we may conduct multiple regression, but when w are unmeasured it is common practice to include a precautionary statement when reporting the confidence interval, warning against unwarranted causal interpretation. If the goal is robust causal interpretation then we can do something more informative. Uncertainty, in the specification of three confounding parameters can be propagated through an equation to produce a confounding interval. Here, we develop supporting mathematical theory and describe an example application. Our proposed methodology applies well to studies of a continuous response or rare outcome. It is a general method for quantifying error from model uncertainty. Whereas, confidence intervals are used to assess uncertainty from unmeasured individuals, confounding intervals can be used to assess uncertainty from unmeasured attributes.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81842128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Instrumental Variable Estimation with the R Package ivtools 工具变量估计与R包ivtools

Epidemiologic Methods Pub Date : 2019-07-20 DOI: 10.1515/EM-2018-0024

Arvid Sjolander, T. Martinussen

引用次数: 33

Posterior predictive treatment assignment methods for causal inference in the context of time-varying treatments 时变治疗背景下因果推理的后验预测治疗分配方法

Epidemiologic Methods Pub Date : 2019-07-15 DOI: 10.1515/em-2019-0024

Shirley X Liao, Lucas R. F. Henneman, C. Zigler

{"title":"Posterior predictive treatment assignment methods for causal inference in the context of time-varying treatments","authors":"Shirley X Liao, Lucas R. F. Henneman, C. Zigler","doi":"10.1515/em-2019-0024","DOIUrl":"https://doi.org/10.1515/em-2019-0024","url":null,"abstract":"Abstract Marginal structural models (MSM) with inverse probability weighting (IPW) are used to estimate causal effects of time-varying treatments, but can result in erratic finite-sample performance when there is low overlap in covariate distributions across different treatment patterns. Modifications to IPW which target the average treatment effect (ATE) estimand either introduce bias or rely on unverifiable parametric assumptions and extrapolation. This paper extends an alternate estimand, the ATE on the overlap population (ATO) which is estimated on a sub-population with a reasonable probability of receiving alternate treatment patterns in time-varying treatment settings. To estimate the ATO within an MSM framework, this paper extends a stochastic pruning method based on the posterior predictive treatment assignment (PPTA) (Zigler, C. M., and M. Cefalu. 2017. “Posterior Predictive Treatment Assignment for Estimating Causal Effects with Limited Overlap.” eprint arXiv:1710.08749.) as well as a weighting analog (Li, F., K. L. Morgan, and A. M. Zaslavsky. 2018. “Balancing Covariates via Propensity Score Weighting.” Journal of the American Statistical Association 113: 390–400, https://doi.org/10.1080/01621459.2016.1260466.) to the time-varying treatment setting. Simulations demonstrate the performance of these extensions compared against IPW and stabilized weighting with regard to bias, efficiency, and coverage. Finally, an analysis using these methods is performed on Medicare beneficiaries residing across 18,480 ZIP codes in the U.S. to evaluate the effect of coal-fired power plant emissions exposure on ischemic heart disease (IHD) hospitalization, accounting for seasonal patterns that lead to change in treatment over time.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90796155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0