Observational studies最新文献

Does matching introduce confounding or selection bias into the matched case-control design? 匹配病例对照设计是否会引入混杂或选择偏差？

Observational studies Pub Date : 2024-06-06 DOI: 10.1353/obs.2024.a929114

Fei Wan, S. Sutcliffe, Jeffrey Zhang, Dylan Small

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引用次数: 0

Using a difference-in-difference control trial to test an intervention aimed at increasing the take-up of a welfare payment in New Zealand 使用差异中差异对照试验来测试旨在增加新西兰福利支付的干预措施

Observational studies Pub Date : 2023-09-07 DOI: 10.1353/obs.2023.a906626

David Rea, Dean R. Hyslop

引用次数: 0

Size-biased sensitivity analysis for matched pairs design to assess the impact of healthcare-associated infections 对配对设计进行大小偏倚敏感性分析，以评估医疗保健相关感染的影响

Observational studies Pub Date : 2023-09-07 DOI: 10.1353/obs.2023.a906628

David Watson

{"title":"Size-biased sensitivity analysis for matched pairs design to assess the impact of healthcare-associated infections","authors":"David Watson","doi":"10.1353/obs.2023.a906628","DOIUrl":"https://doi.org/10.1353/obs.2023.a906628","url":null,"abstract":"Abstract:Healthcare-associated infections are serious adverse events that occur during a hospital admission. Quantifying the impact of these infections on inpatient length of stay and cost has important policy implications due to the Hospital-Acquired Conditions Reduction Program in the United States. However, most studies on this topic are flawed because they do not account for when a healthcare-associated infection occurred during a hospital admission. Such an approach leads to selection bias because patients with longer hospital stays are more likely to experience an infection due to their increased exposure time. Time of infection is often not incorporated into the estimation strategy because this information is unknown, yet there are no methods that account for the selection bias in this scenario. To address this problem, we propose a sensitivity analysis for matched pairs designs for assessing the effect of healthcare-associated infections on length of stay and cost when time of infection is unknown. The approach models the probability of infection, or the assignment mechanism, as proportional to a power function of the uninfected length of stay, where the sensitivity parameter is the value of the power. The general idea is to incorporate the degree of exposure into the probability of an infection occurring. Under this size-biased assignment mechanism, we develop hypothesis tests under a sharp null hypothesis of constant multiplicative effects. The approach is demonstrated on a pediatric cohort of inpatient encounters and compared to benchmark estimates that properly account for time of infection. The results reaffirm the severe degree of bias when not accounting for time of infection and also show that the proposed sensitivity analysis captures the benchmark estimates for plausible and theoretically justified values of the sensitivity parameter.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42324694","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

A Software Tutorial for Matching in Clustered Observational Studies 集群观测研究中的匹配软件教程

Observational studies Pub Date : 2023-09-07 DOI: 10.1353/obs.2023.a906624

Luke Keele, Matthew Lenard, Luke Miratrix, Lindsay Page

引用次数: 0

Doubly Robust Estimation of Average Treatment Effects on the Treated through Marginal Structural Models 通过边际结构模型的平均治疗效果的双稳健估计

Observational studies Pub Date : 2023-05-11 DOI: 10.1353/obs.2023.0025

M. Schomaker, Philipp F. M. Baumann

引用次数: 0

Causal Methods Madness: Lessons Learned from the 2022 ACIC Competition to Estimate Health Policy Impacts 因果方法疯狂:从2022年ACIC竞赛中获得的经验教训，以评估卫生政策的影响

Observational studies Pub Date : 2023-05-11 DOI: 10.1353/obs.2023.0023

Daniel Thal, M. Finucane

{"title":"Causal Methods Madness: Lessons Learned from the 2022 ACIC Competition to Estimate Health Policy Impacts","authors":"Daniel Thal, M. Finucane","doi":"10.1353/obs.2023.0023","DOIUrl":"https://doi.org/10.1353/obs.2023.0023","url":null,"abstract":"Abstract:Introducing novel causal estimators usually involves simulation studies run by the statistician developing the estimator, but this traditional approach can be fraught: simulation design is often favorable to the new method, unfavorable results might never be published, and comparison across estimators is difficult. The American Causal Inference Conference (ACIC) data challenges offer an alternative. As organizers of the 2022 challenge, we generated thousands of data sets similar to real-world policy evaluations and baked in true causal impacts unknown to participants. Participating teams then competed on an even playing field, using their cutting-edge methods to estimate those effects. In total, 20 teams submitted results from 58 estimators that used a range of approaches. We found several important factors driving performance that are not commonly used in business-as-usual applied policy evaluations, pointing to ways future evaluations could achieve more precise and nuanced estimates of policy impacts. Top-performing methods used flexible modeling of outcome-covariate and outcome-participation relationships as well as regularization of subgroup estimates. Furthermore, we found that model-based uncertainty intervals tended to outperform bootstrap-based ones. Lastly, and counter to our expectations, we found that analyzing large-n patient-level data does not improve performance relative to analyzing smaller-n data aggregated to the primary care practice level, given that in our simulated data sets practices (not individual patients) decided whether to join the intervention. Ultimately, we hope this competition helped identify methods that are best suited for evaluating which social policies move the needle for the individuals and communities they serve.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44338192","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

Estimating Treatment Effect with Propensity Score Weighted Regression and Double Machine Learning 用倾向评分加权回归和双机器学习估计治疗效果

Observational studies Pub Date : 2023-05-11 DOI: 10.1353/obs.2023.0028

Jun Xue, Wei Zhong Goh, Dana Rotz

引用次数: 0

Estimating Treatment Effects over Time with Causal Forests: An application to the ACIC 2022 Data Challenge 利用因果森林估算随时间推移的治疗效果:在ACIC 2022数据挑战中的应用

Observational studies Pub Date : 2023-05-11 DOI: 10.1353/obs.2023.0026

Shu Wan, Guanghui Zhang

引用次数: 0

Inverse Probability Weighting Difference-in-Differences (IPWDID) 反向概率加权差值（IPWDID）

Observational studies Pub Date : 2023-05-11 DOI: 10.1353/obs.2023.0027

Yuqin Wei, M. Epland, Jingyuan Liu