Observational studies最新文献_第2页

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":null,"pages":null},"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":null,"pages":null},"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

ivmte: An R Package for Extrapolating Instrumental Variable Estimates Away From Compliers* ivmte：一个用于从Compliers外推仪器变量估计的R包*

Observational studies Pub Date : 2023-03-01 DOI: 10.1353/obs.2023.0016

Joshua Shea, Alexander Torgovitsky

引用次数: 0

Editor’s Note Editor’s音符

Observational studies Pub Date : 2023-01-23 DOI: 10.1353/obs.2023.0014

Nandita Mitra

引用次数: 0

The central role of the propensity score in epidemiology 倾向性评分在流行病学中的核心作用

Observational studies Pub Date : 2023-01-23 DOI: 10.1353/obs.2023.0004

Brian K. Lee