{"title":"Review and comparison of treatment effect estimators using propensity and prognostic scores.","authors":"Myoung-Jae Lee, Sanghyeok Lee","doi":"10.1515/ijb-2021-0005","DOIUrl":null,"url":null,"abstract":"<p><p>In finding effects of a binary treatment, practitioners use mostly either propensity score matching (PSM) or inverse probability weighting (IPW). However, many new treatment effect estimators are available now using propensity score and \"prognostic score\", and some of these estimators are much better than PSM and IPW in several aspects. In this paper, we review those recent treatment effect estimators to show how they are related to one another, and why they are better than PSM and IPW. We compare 26 estimators in total through extensive simulation and empirical studies. Based on these, we recommend recent treatment effect estimators using \"overlap weight\", and \"targeted MLE\" using statistical/machine learning, as well as a simple regression imputation/adjustment estimator using linear prognostic score models.</p>","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1515/ijb-2021-0005","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 3
Abstract
In finding effects of a binary treatment, practitioners use mostly either propensity score matching (PSM) or inverse probability weighting (IPW). However, many new treatment effect estimators are available now using propensity score and "prognostic score", and some of these estimators are much better than PSM and IPW in several aspects. In this paper, we review those recent treatment effect estimators to show how they are related to one another, and why they are better than PSM and IPW. We compare 26 estimators in total through extensive simulation and empirical studies. Based on these, we recommend recent treatment effect estimators using "overlap weight", and "targeted MLE" using statistical/machine learning, as well as a simple regression imputation/adjustment estimator using linear prognostic score models.
期刊介绍：
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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