Observational studies最新文献_第2页

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":"9 1","pages":"1 - 24"},"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":"9 1","pages":"27 - 3"},"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

引用次数: 0

lmtp: An R Package for Estimating the Causal Effects of Modified Treatment Policies lmtp：一个用于估计改良治疗政策因果影响的R包

Observational studies Pub Date : 2023-03-01 DOI: 10.1353/obs.2023.0019

Nicholas T Williams, I. Díaz

引用次数: 2

Doubly-Robust Inference in R using drtmle 基于drtmle的R中的双稳健推理

Observational studies Pub Date : 2023-03-01 DOI: 10.1353/obs.2023.0017

D. Benkeser, N. Hejazi

{"title":"Doubly-Robust Inference in R using drtmle","authors":"D. Benkeser, N. Hejazi","doi":"10.1353/obs.2023.0017","DOIUrl":"https://doi.org/10.1353/obs.2023.0017","url":null,"abstract":"Abstract:Inverse probability of treatment weighted estimators and doubly robust estimators (including augmented inverse probability of treatment weight and targeted minimum loss estimators) are widely used in causal inference to estimate and draw inference about the average effect of a treatment. As an intermediate step, these estimators require estimation of key nuisance parameters, which are often regression functions. Typically, regressions are estimated using maximum likelihood and parametric models. Confidence intervals and p-values may be computed based on standard asymptotic results, such as the central limit theorem, the delta method, and the nonparametric bootstrap. However, in high-dimensional settings, maximum likelihood estimation often breaks down and standard procedures no longer yield correct inference. Instead, we may rely on adaptive estimators of nuisance parameters to construct flexible regression estimators. However, use of adaptive estimators poses a challenge for performing statistical inference about an estimated treatment effect. While doubly robust estimators facilitate inference when all relevant regression functions are consistently estimated, the same cannot be said when at least one nuisance estimator is inconsistent. drtmle implements doubly robust confidence intervals and hypothesis tests for targeted minimum loss estimates of the average treatment effect, in addition to several other recently proposed estimators of the average treatment effect.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"9 1","pages":"43 - 78"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41508466","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: 3

Comparison of dimension reduction methods for the identification of heart-healthy dietary patterns 降维方法识别心脏健康饮食模式的比较

Observational studies Pub Date : 2023-03-01 DOI: 10.1353/obs.2023.0020

Natalie C. Gasca, R. McClelland

{"title":"Comparison of dimension reduction methods for the identification of heart-healthy dietary patterns","authors":"Natalie C. Gasca, R. McClelland","doi":"10.1353/obs.2023.0020","DOIUrl":"https://doi.org/10.1353/obs.2023.0020","url":null,"abstract":"Abstract:Most nutritional epidemiology studies investigating diet-disease trends use unsupervised dimension reduction methods, like principal component regression (PCR) and sparse PCR (SPCR), to create dietary patterns. Supervised methods, such as partial least squares (PLS), sparse PLS (SPLS), and Lasso, offer the possibility of more concisely summarizing the foods most related to a disease. In this study we evaluate these five methods for interpretable reduction of food frequency questionnaire (FFQ) data when analyzing a univariate continuous cardiac-related outcome via a simulation study and data application. We also demonstrate that to control for covariates, various scientific premises require different adjustment approaches when using PLS. To emulate food groups, we generated blocks of normally distributed predictors with varying intra-block covariances; only nine of 24 predictors contributed to the normal response. When block covariances were informed by FFQ data, the only methods that performed variable selection were Lasso and SPLS, which selected two and four irrelevant variables, respectively. SPLS had the lowest prediction error, and both PLS-based methods constructed four patterns, while PCR and SPCR created 24 patterns. These methods were applied to 120 FFQ variables and baseline body mass index (BMI) from the Multi-Ethnic Study of Atherosclerosis, which includes 6814 participants aged 45-84, and we adjusted for age, gender, race/ethnicity, exercise, and total energy intake. From 120 variables, PCR created 17 BMI-related patterns and PLS selected one pattern; SPLS only used five variables to create two patterns. All methods exhibited similar predictive performance. Specifically, SPLS’s first pattern highlighted hamburger and diet soda intake (positive associations with BMI), reflecting a fast food diet. By selecting fewer patterns and foods, SPLS can create interpretable dietary patterns while maintaining predictive ability.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"9 1","pages":"123 - 156"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49570747","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