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The Central Role of the Propensity Score in Sensitivity Analysis for Matched Observational Studies 倾向评分在匹配观察性研究敏感性分析中的核心作用
Observational studies Pub Date : 2023-01-23 DOI: 10.1353/obs.2023.0002
Siyu Heng
{"title":"The Central Role of the Propensity Score in Sensitivity Analysis for Matched Observational Studies","authors":"Siyu Heng","doi":"10.1353/obs.2023.0002","DOIUrl":"https://doi.org/10.1353/obs.2023.0002","url":null,"abstract":"Abstract:The propensity score, which was originally introduced in Rosenbaum and Rubin (1983), has been widely considered one of the most important concepts in the causal inference literature. This article briefly reviews some propensity score models involving both observed and unobserved covariates and discusses their applications in sensitivity analysis for matched observational studies.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45075047","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
Commentary on Rubin and Rosenbaum Seminal 1983 Paper on Propensity Scores: From Then to Now 鲁宾和罗森鲍姆1983年关于倾向得分的开创性论文:从那时到现在
Observational studies Pub Date : 2023-01-23 DOI: 10.1353/obs.2023.0000
Usha Govindarajulu
{"title":"Commentary on Rubin and Rosenbaum Seminal 1983 Paper on Propensity Scores: From Then to Now","authors":"Usha Govindarajulu","doi":"10.1353/obs.2023.0000","DOIUrl":"https://doi.org/10.1353/obs.2023.0000","url":null,"abstract":"Abstract:Rubin and Rosenbaum (1983) wrote about the theory and application of “propensity scores” in their landmark paper. Since that time, the method has still been in use or adapted for use in various contexts. In this commentary, I discuss their original paper and the latest in terms of criticisms and defense of the use of some of the theory they proposed for propensity score matching. Although the commentary is not exhaustive, I try to highlight important aspects of their theory as well as points made later for and against some of their originally proposed theory.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46521470","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
Bridging the design and modeling of causal inference: A Bayesian nonparametric perspective 连接因果推理的设计和建模:贝叶斯非参数视角
Observational studies Pub Date : 2023-01-23 DOI: 10.1353/obs.2023.0012
Xinyi Xu, S. MacEachern, Bo Lu
{"title":"Bridging the design and modeling of causal inference: A Bayesian nonparametric perspective","authors":"Xinyi Xu, S. MacEachern, Bo Lu","doi":"10.1353/obs.2023.0012","DOIUrl":"https://doi.org/10.1353/obs.2023.0012","url":null,"abstract":"Abstract:In their seminal paper first published 40 years ago, Rosenbaum and Rubin crafted the concept of the propensity score to tackle the challenging problem of causal inference in observational studies. The propensity score is set up mostly as a design tool to recreate a randomization like scenario, through matching or subclassification. Bayesian development over the past two decades has adopted a modeling framework to infer the causal effect. In this commentary, we highlight the connection between the design- and model-based perspectives to analysis. We briefly review a Bayesian nonparametric framework that utilizes Gaussian Process models on propensity scores to mimic matched designs. We also discuss the role of variation as well as bias in estimators arising from observational data.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47614346","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
The Uses of Propensity Scores in Randomized Controlled Trials 倾向性评分在随机对照试验中的应用
Observational studies Pub Date : 2023-01-23 DOI: 10.1353/obs.2023.0007
T. Loux, Yi Huang
{"title":"The Uses of Propensity Scores in Randomized Controlled Trials","authors":"T. Loux, Yi Huang","doi":"10.1353/obs.2023.0007","DOIUrl":"https://doi.org/10.1353/obs.2023.0007","url":null,"abstract":"Abstract:Propensity scores are a dimension-reduction technique used to quantify the differences between treatment groups. Though propensity scores were developed to address the issue of confounding in observational studies, they have also proven useful in randomized controlled trials where confounding is structurally absent. When applied to randomized controlled trials, propensity scores can ensure balance between groups at the time of randomization, account for chance imbalances in observed randomization, and generalize target results to target populations. In this article, we review propensity score methodology developed for randomized trials with these goals.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48717518","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
The Pursuit of Efficiency versus Robustness: A Learning Experience from Analyzing a Semiparametric Nonignorable Propensity Score Model 追求效率与稳健性:一个半参数不可忽略倾向评分模型的学习经验分析
Observational studies Pub Date : 2023-01-23 DOI: 10.1353/obs.2023.0009
Samidha Shetty, Yanyuan Ma, Jiwei Zhao
{"title":"The Pursuit of Efficiency versus Robustness: A Learning Experience from Analyzing a Semiparametric Nonignorable Propensity Score Model","authors":"Samidha Shetty, Yanyuan Ma, Jiwei Zhao","doi":"10.1353/obs.2023.0009","DOIUrl":"https://doi.org/10.1353/obs.2023.0009","url":null,"abstract":"Abstract:Rosenbaum and Rubin’s pioneering work on “The Central Role of the Propensity Score in Observational Studies for Causal Effects” has shaped the landscape of the literature in causal inference and missing data analysis. In the past decades, the concept of propensity score has been used not only under ignorability assumption, but also under nonignorability assumption. The nice properties of double robustness and semiparametric efficiency are well known under ignorability; however, the situation is a lot more sophisticated under nonignorability. In this paper, we summarize what we have learnt from analyzing a semi-parametric nonignorable propensity score model. It turns out that, under nonignorability, the efficient estimator for the quantity of interest might be too complicated to be practically implemented. On the other hand, by sacrificing the efficiency to some extent, one type of robust estimators is much easier to derive and implement; hence is recommended. This is a general tradeoff between efficiency and robustness in a typical semiparametric model.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44169085","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
RobustIV and controlfunctionIV: Causal Inference for Linear and Nonlinear Models with Invalid Instrumental Variables 鲁棒性IV和控制函数IV：具有无效工具变量的线性和非线性模型的因果推理
Observational studies Pub Date : 2023-01-11 DOI: 10.1353/obs.2023.a906625
Taehyeon Koo, Youjin Lee, Dylan S. Small, Zijian Guo
{"title":"RobustIV and controlfunctionIV: Causal Inference for Linear and Nonlinear Models with Invalid Instrumental Variables","authors":"Taehyeon Koo, Youjin Lee, Dylan S. Small, Zijian Guo","doi":"10.1353/obs.2023.a906625","DOIUrl":"https://doi.org/10.1353/obs.2023.a906625","url":null,"abstract":"Abstract:We present R software packages RobustIV and controlfunctionIV for causal inference with possibly invalid instrumental variables. RobustIV focuses on the linear outcome model. It implements the two-stage hard thresholding method to select valid instrumental variables from a set of candidate instrumental variables and make inferences for the causal effect in both low- and high-dimensional settings. Furthermore, RobustIV implements the high-dimensional endogeneity test and the searching and sampling method, a uniformly valid inference method robust to errors in instrumental variable selection. controlfunctionIV considers the nonlinear outcome model and makes inferences about the causal effect based on the control function method. Our packages are demonstrated using two publicly available economic data sets together with applications to the Framingham Heart Study.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45733657","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
Using Pilot Data for Power Analysis of Observational Studies for the Estimation of Dynamic Treatment Regimes. 使用试点数据进行观测研究的功率分析以估计动态治疗方案
Observational studies Pub Date : 2023-01-01 DOI: 10.1353/obs.2023.a906627
Eric J Rose, Erica E M Moodie, Susan Shortreed
{"title":"Using Pilot Data for Power Analysis of Observational Studies for the Estimation of Dynamic Treatment Regimes.","authors":"Eric J Rose, Erica E M Moodie, Susan Shortreed","doi":"10.1353/obs.2023.a906627","DOIUrl":"10.1353/obs.2023.a906627","url":null,"abstract":"<p><p>Significant attention has been given to developing data-driven methods for tailoring patient care based on individual patient characteristics. Dynamic treatment regimes formalize this approach through a sequence of decision rules that map patient information to a suggested treatment. The data for estimating and evaluating treatment regimes are ideally gathered through the use of Sequential Multiple Assignment Randomized Trials (SMARTs), though longitudinal observational studies are commonly used due to the potentially prohibitive costs of conducting a SMART. Observational studies are typically powered for simple comparisons of fixed treatment sequences; a priori power or sample size calculations for tailored strategies are rarely if ever undertaken. This has lead to many studies that fail to find a statistically significant benefit to tailoring treatment. We develop power analyses for the estimation of dynamic treatment regimes from observational studies. Our approach uses pilot data to estimate the power for comparing the value of the optimal regime, i.e., the expected outcome if all patients in the population were treated by following the optimal regime, with a known comparison mean. This allows for calculations that ensure a study has sufficient power to detect the need for tailoring, should it be present. Our approach also ensures the value of the estimated optimal treatment regime has a high probability of being within a range of the value of the true optimal regime, set a priori. We examine the performance of the proposed procedure with a simulation study and use it to size a study for reducing depressive symptoms using data from electronic health records.</p>","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11245299/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46457253","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
Leveraging Contact Network Information in Clustered Randomized Studies of Contagion Processes. 在传染过程的聚类随机研究中利用接触网络信息
Observational studies Pub Date : 2023-01-01 DOI: 10.1353/obs.2023.0021
Maxwell H Wang, Patrick Staples, Mélanie Prague, Ravi Goyal, Victor DeGruttola, Jukka-Pekka Onnela
{"title":"Leveraging Contact Network Information in Clustered Randomized Studies of Contagion Processes.","authors":"Maxwell H Wang, Patrick Staples, Mélanie Prague, Ravi Goyal, Victor DeGruttola, Jukka-Pekka Onnela","doi":"10.1353/obs.2023.0021","DOIUrl":"10.1353/obs.2023.0021","url":null,"abstract":"<p><p>In a randomized study, leveraging covariates related to the outcome (e.g. disease status) may produce less variable estimates of the effect of exposure. For contagion processes operating on a contact network, transmission can only occur through ties that connect affected and unaffected individuals; the outcome of such a process is known to depend intimately on the structure of the network. In this paper, we investigate the use of contact network features as efficiency covariates in exposure effect estimation. Using augmented generalized estimating equations (GEE), we estimate how gains in efficiency depend on the network structure and spread of the contagious agent or behavior. We apply this approach to simulated randomized trials using a stochastic compartmental contagion model on a collection of model-based contact networks and compare the bias, power, and variance of the estimated exposure effects using an assortment of network covariate adjustment strategies. We also demonstrate the use of network-augmented GEEs on a clustered randomized trial evaluating the effects of wastewater monitoring on COVID-19 cases in residential buildings at the the University of California San Diego.</p>","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10270696/pdf/nihms-1870046.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9722502","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
Bayesian Causal Forests & the 2022 ACIC Data Challenge: Scalability and Sensitivity 贝叶斯因果森林与2022 ACIC数据挑战：可扩展性和敏感性
Observational studies Pub Date : 2022-11-03 DOI: 10.1353/obs.2023.0024
Ajinkya Kokandakar, Hyunseung Kang, Sameer K. Deshpande
{"title":"Bayesian Causal Forests & the 2022 ACIC Data Challenge: Scalability and Sensitivity","authors":"Ajinkya Kokandakar, Hyunseung Kang, Sameer K. Deshpande","doi":"10.1353/obs.2023.0024","DOIUrl":"https://doi.org/10.1353/obs.2023.0024","url":null,"abstract":"Abstract:We demonstrate how Hahn et al.'s Bayesian Causal Forests model (BCF) can be used to estimate conditional average treatment effects for the longitudinal dataset in the 2022 American Causal Inference Conference Data Challenge. Unfortunately, existing implementations of BCF do not scale to the size of the challenge data. Therefore, we developed flexBCF—a more scalable and flexible implementation of BCF— and used it in our challenge submission. We investigate the sensitivity of our results to the choice of propensity score estimation method and the use of sparsity-inducing regression tree priors. While we found that our overall point predictions were not especially sensitive to these modeling choices, we did observe that running BCF with flexibly estimated propensity scores often yielded better-calibrated uncertainty intervals.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48070886","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
Causal Inference: History, Perspectives, Adventures, and Unification (An Interview with Judea Pearl) 因果推理:历史、视角、冒险与统一(朱迪亚·珀尔访谈)
Observational studies Pub Date : 2022-10-01 DOI: 10.1353/obs.2022.0007
J. Pearl
{"title":"Causal Inference: History, Perspectives, Adventures, and Unification (An Interview with Judea Pearl)","authors":"J. Pearl","doi":"10.1353/obs.2022.0007","DOIUrl":"https://doi.org/10.1353/obs.2022.0007","url":null,"abstract":"In October 2022, the journal Observational Studies published interviews with 4 causal inference contributors, James Heckman, Jamie Robins, Don Rubin and myself [Observational Studies, 2022, 8(2):7–94. https://muse.jhu.edu/issue/48885]. My interview (with Ian Shrier) was conducted in June 2019, and is provided below as published. The only change made is the References section, which was incomplete in the published version. Fundamental disagreements with the other three interviewees and commentaries will be further discussed and posted on my blog.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43435750","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
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