{"title":"The mean prevalence","authors":"F. Habibzadeh, P. Habibzadeh","doi":"10.1515/em-2019-0033","DOIUrl":"https://doi.org/10.1515/em-2019-0033","url":null,"abstract":"","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":"81165656","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}
J. Ferguson, Fabrizio Maturo, S. Yusuf, M. O’Donnell
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.
{"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}
R. Lyles, S. Cunningham, Suprateek Kundu, Q. Bassat, I. Mandomando, C. Sacoor, Victor Akelo, D. Onyango, Emily Zielinski-Gutierrez, Allan W. Taylor
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.
{"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}
W. W. Loh, B. Moerkerke, T. Loeys, S. Vansteelandt
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.
{"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}
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.
死亡的竞争风险使痴呆危险因素的研究变得复杂。标准方法是病因特异性Cox比例风险模型，其中死亡被视为审查事件(并从风险集中删除)，以及Fine和Gray子分布风险模型，其中死亡的人仍在风险集中。另一种方法是修改这两个极端之间的风险设置。我们提出了一种新的方法，该方法基于使用参数生存模型估计如果患者没有死亡，则患者可能被诊断的时间，然后将原因特定模型和Fine and Gray模型应用于修改后的数据集。我们使用澳大利亚妇女健康纵向研究的痴呆数据对这些方法进行比较，并讨论每个模型的假设和局限性。用于评估痴呆风险因素的生存模型的结果在病因特异性模型和用于考虑竞争风险的模型之间差异很大。因此，在评估存在竞争风险的风险因素时，重要的是要检查以下结果:特定原因模型，考虑竞争风险的不同模型，以及评估与竞争风险相关的风险因素的模型。
{"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}
Pub Date : 2019-12-01Epub Date: 2019-08-02DOI: 10.1515/em-2018-0011
Emily J Huang, Ravi Varadhan, Michelle C Carlson
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.
{"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":"<p><p>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 (<i>N</i> = 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.</p>","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}
Abstract Mediation analysis is popular in examining the extent to which the effect of an exposure on an outcome is through an intermediate variable. When the exposure is subject to misclassification, the effects estimated can be severely biased. In this paper, when the mediator is binary, we first study the bias on traditional direct and indirect effect estimates in the presence of conditional non-differential misclassification of a binary exposure. We show that in the absence of interaction, the misclassification of the exposure will bias the direct effect towards the null but can bias the indirect effect in either direction. We then develop an EM algorithm approach to correcting for the misclassification, and conduct simulation studies to assess the performance of the correction approach. Finally, we apply the approach to National Center for Health Statistics birth certificate data to study the effect of smoking status on the preterm birth mediated through pre-eclampsia.
{"title":"Causal Mediation Analysis in the Presence of a Misclassified Binary Exposure","authors":"Zhichao Jiang, T. VanderWeele","doi":"10.1515/em-2016-0006","DOIUrl":"https://doi.org/10.1515/em-2016-0006","url":null,"abstract":"Abstract Mediation analysis is popular in examining the extent to which the effect of an exposure on an outcome is through an intermediate variable. When the exposure is subject to misclassification, the effects estimated can be severely biased. In this paper, when the mediator is binary, we first study the bias on traditional direct and indirect effect estimates in the presence of conditional non-differential misclassification of a binary exposure. We show that in the absence of interaction, the misclassification of the exposure will bias the direct effect towards the null but can bias the indirect effect in either direction. We then develop an EM algorithm approach to correcting for the misclassification, and conduct simulation studies to assess the performance of the correction approach. Finally, we apply the approach to National Center for Health Statistics birth certificate data to study the effect of smoking status on the preterm birth mediated through pre-eclampsia.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89314487","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}
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.
在研究x对y的因果关系时，研究人员可以进行回归并报告斜率系数β x ${beta}_{x}$的置信区间。这个通用置信区间提供了抽样误差不确定性的评估，但它不能评估混杂的不确定性。对x的干预可能会在y中产生意想不到的响应，当存在混淆因素w时，我们对斜率的误解就会发生。当w被测量时，我们可能会进行多元回归，但当w未被测量时，通常的做法是在报告置信区间时包括预防性声明，警告不合理的因果解释。如果目标是健全的因果解释，那么我们可以做一些更有信息量的事情。不确定性，在规定的三个混杂参数可以通过一个方程传播产生一个混杂区间。在这里，我们开发了支持数学理论并描述了一个示例应用程序。我们提出的方法适用于连续反应或罕见结果的研究。这是对模型不确定性误差进行量化的一般方法。然而，置信区间用于评估来自未测量个体的不确定性，混淆区间可用于评估来自未测量属性的不确定性。
{"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}
Abstract Instrumental variables is a popular method in epidemiology and related fields, to estimate causal effects in the presence of unmeasured confounding. Traditionally, instrumental variable analyses have been confined to linear models, in which the causal parameter of interest is typically estimated with two-stage least squares. Recently, the methodology has been extended in several directions, including two-stage estimation and so-called G-estimation in nonlinear (e. g. logistic and Cox proportional hazards) models. This paper presents a new R package, ivtools, which implements many of these new instrumental variable methods. We briefly review the theory of two-stage estimation and G-estimation, and illustrate the functionality of the ivtools package by analyzing publicly available data from a cohort study on vitamin D and mortality.
{"title":"Instrumental Variable Estimation with the R Package ivtools","authors":"Arvid Sjolander, T. Martinussen","doi":"10.1515/EM-2018-0024","DOIUrl":"https://doi.org/10.1515/EM-2018-0024","url":null,"abstract":"Abstract Instrumental variables is a popular method in epidemiology and related fields, to estimate causal effects in the presence of unmeasured confounding. Traditionally, instrumental variable analyses have been confined to linear models, in which the causal parameter of interest is typically estimated with two-stage least squares. Recently, the methodology has been extended in several directions, including two-stage estimation and so-called G-estimation in nonlinear (e. g. logistic and Cox proportional hazards) models. This paper presents a new R package, ivtools, which implements many of these new instrumental variable methods. We briefly review the theory of two-stage estimation and G-estimation, and illustrate the functionality of the ivtools package by analyzing publicly available data from a cohort study on vitamin D and mortality.","PeriodicalId":37999,"journal":{"name":"Epidemiologic Methods","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81261198","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}
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.
具有逆概率加权(IPW)的边际结构模型(MSM)用于估计时变处理的因果效应，但当不同处理模式的协变量分布重叠度较低时，可能导致有限样本性能不稳定。针对平均治疗效果(ATE)估计的IPW修改要么引入偏差，要么依赖于无法验证的参数假设和外推。本文扩展了一个替代估计，即重叠群体(ATO)的ATE，该估计是在时变治疗设置中接受替代治疗模式的合理概率的亚群体上估计的。为了在MSM框架内估计ATO，本文扩展了一种基于后检预测处理分配(PPTA)的随机修剪方法(Zigler, C. M.和M. Cefalu. 2017)。“估计有限重叠因果效应的后验预测治疗分配”。)以及加权模拟(Li, F.， K. L. Morgan, and a . M. Zaslavsky. 2018)。“通过倾向得分加权平衡协变量。”美国统计协会杂志113:390-400,https://doi.org/10.1080/01621459.2016.1260466.)的时变治疗设置。仿真证明了这些扩展与IPW和稳定加权相比在偏置、效率和覆盖方面的性能。最后，使用这些方法对居住在美国18480个邮政编码的医疗保险受益人进行了分析，以评估燃煤电厂排放暴露对缺血性心脏病(IHD)住院治疗的影响，并考虑了导致治疗随时间变化的季节性模式。
{"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}
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