{"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}
{"title":"ivmte: An R Package for Extrapolating Instrumental Variable Estimates Away From Compliers*","authors":"Joshua Shea, Alexander Torgovitsky","doi":"10.1353/obs.2023.0016","DOIUrl":"https://doi.org/10.1353/obs.2023.0016","url":null,"abstract":"Abstract:Instrumental variable (IV) strategies are widely used to estimate causal effects in economics, political science, epidemiology, sociology, psychology, and other fields. When there is unobserved heterogeneity in causal effects, standard linear IV estimators only represent effects for complier subpopulations (Imbens and Angrist, 1994). Marginal treatment effect (MTE) methods (Heckman and Vytlacil, 1999, 2005) allow researchers to use additional assumptions to extrapolate beyond complier subpopulations. We discuss a flexible framework for MTE methods based on linear regression and the generalized method of moments. We show how to implement the framework using the ivmte package for R.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"9 1","pages":"1 - 42"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45602939","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}
{"title":"The central role of the propensity score in epidemiology","authors":"Brian K. Lee","doi":"10.1353/obs.2023.0004","DOIUrl":"https://doi.org/10.1353/obs.2023.0004","url":null,"abstract":"Abstract:In this commentary, I provide a personal perspective on how the propensity score has become important to epidemiology.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"19 1","pages":"55 - 57"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66460632","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}
{"title":"Some Reflections on Rosenbaum and Rubin’s Propensity Score Paper","authors":"R. Little","doi":"10.1353/obs.2023.0006","DOIUrl":"https://doi.org/10.1353/obs.2023.0006","url":null,"abstract":"Abstract:Rosenbaum and Rubin’s paper is highly cited because the basic idea is simple and insightful, and it has applications to important practical problems in treatment comparisons with observational data, and selection bias and nonresponse in surveys. I discuss several issues related to the method, including use of the propensity score for weighting or prediction, and two robust methods that use the propensity score as a covariate and can be more efficient that weighting when the weights are highly variable, namely Penalized Spline of Propensity Prediction (PSPP) and Penalized Spline of Propensity for Treatment Comparisons (PENCOMP). Approaches to addressing highly variable weights are discussed, including omitting variables in the propensity model that are unrelated to outcomes, and redefining the estimand.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"9 1","pages":"69 - 75"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49646766","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}
{"title":"What is a propensity score? Applications and extensions of balancing score methods","authors":"E. Stuart","doi":"10.1353/obs.2023.0011","DOIUrl":"https://doi.org/10.1353/obs.2023.0011","url":null,"abstract":"Abstract:The foundational propensity score paper by Rosenbaum and Rubin (1983a) laid the foundation for a set of methods widely used in the design of non-experimental studies. This commentary reflects on the theoretical contributions of that paper –especially the idea of the propensity score as a balancing score –as well as on the wide variety of contexts in which the general idea of a balancing score has since been applied. Areas in which the fundamental ideas of a balancing score –which can help equate two groups on the basis of a set of covariates –have been extended include mediation analysis and generalizability. The commentary also touches on common misperceptions regarding propensity scores, and on the key role of the “other” Rosenbaum and Rubin (1983b) paper, which laid out a method for assessing the sensitivity of study results to violation of the key assumption underlying most uses of propensity scores –that of no unmeasured confounding. All together, this body of work has changed how many fields conduct non-experimental studies, and other related types of studies, and with many applications and extensions yet to come.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"9 1","pages":"113 - 117"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45664986","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}
{"title":"A Visual Diagnostic Tool for Causal Inference","authors":"Lucy D’Agostino McGowan, Ralph B. D’Agostino","doi":"10.1353/obs.2023.0008","DOIUrl":"https://doi.org/10.1353/obs.2023.0008","url":null,"abstract":"Abstract:Rosenbaum and Rubin (1983) suggested a visual representation, that can be used as a diagnostic tool, for examining whether the relationships between confounders and outcomes are sufficiently controlled, or whether there is a more complex relationship that requires further adjustment. This short commentary highlights this simple tool, providing an example of its utility along with relevant R code.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"9 1","pages":"87 - 95"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46392615","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}
{"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":"9 1","pages":"19 - 22"},"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}
{"title":"The Central Role of Rosenbaum and Rubin’s Seminal Work","authors":"A. Spieker","doi":"10.1353/obs.2023.0010","DOIUrl":"https://doi.org/10.1353/obs.2023.0010","url":null,"abstract":"Abstract:Rosenbaum and Rubin’s seminal work on the propensity score set the stage for decades of subsequent developments in causal inference methodology for use in observational studies. In this commentary, I discuss two specific aspects of their work with particular emphasis on how they have shaped my understanding of causal inference: (1) the propensity score as a data reduction technique, and (2) the importance of drawing parallels between the observational study and the randomized experiment.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"9 1","pages":"105 - 112"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48592462","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}
{"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":"9 1","pages":"35 - 41"},"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}