Propensity Score Methods for Creating Covariate Balance in Observational Studies

2011 ◽  
Vol 64 (10) ◽  
pp. 897-903 ◽  
Author(s):  
Cassandra W. Pattanayak ◽  
Donald B. Rubin ◽  
Elizabeth R. Zell
Keyword(s):  
Propensity Score ◽  
Observational Studies ◽  
Propensity Score Methods ◽  
Covariate Balance

scholarly journals Entropy Balancing is Doubly Robust

2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Qingyuan Zhao ◽  
Daniel Percival
Keyword(s):  
Propensity Score ◽  
Observational Studies ◽  
Optimization Problem ◽  
Entropy Maximization ◽  
Entropy Balancing ◽  
Covariate Balance ◽  
Original Proposal ◽  
Variance Bound ◽  
Doubly Robust ◽  
Theoretical Results

AbstractCovariate balance is a conventional key diagnostic for methods estimating causal effects from observational studies. Recently, there is an emerging interest in directly incorporating covariate balance in the estimation. We study a recently proposed entropy maximization method called Entropy Balancing (EB), which exactly matches the covariate moments for the different experimental groups in its optimization problem. We show EB is doubly robust with respect to linear outcome regression and logistic propensity score regression, and it reaches the asymptotic semiparametric variance bound when both regressions are correctly specified. This is surprising to us because there is no attempt to model the outcome or the treatment assignment in the original proposal of EB. Our theoretical results and simulations suggest that EB is a very appealing alternative to the conventional weighting estimators that estimate the propensity score by maximum likelihood.

scholarly journals Assessing covariate balance when using the generalized propensity score with quantitative or continuous exposures

2018 ◽  
Vol 28 (5) ◽  
pp. 1365-1377 ◽  
Author(s):  
Peter C Austin
Keyword(s):  
Propensity Score ◽  
Propensity Score Analysis ◽  
Balance Assessment ◽  
Propensity Score Method ◽  
Propensity Score Methods ◽  
Covariate Balance ◽  
Crucial Component ◽  
Score Analysis ◽  
Generalized Propensity Score ◽  
Using Data

Propensity score methods are increasingly being used to estimate the effects of treatments and exposures when using observational data. The propensity score was initially developed for use with binary exposures (e.g., active treatment vs. control). The generalized propensity score is an extension of the propensity score for use with quantitative exposures (e.g., dose or quantity of medication, income, years of education). A crucial component of any propensity score analysis is that of balance assessment. This entails assessing the degree to which conditioning on the propensity score (via matching, weighting, or stratification) has balanced measured baseline covariates between exposure groups. Methods for balance assessment have been well described and are frequently implemented when using the propensity score with binary exposures. However, there is a paucity of information on how to assess baseline covariate balance when using the generalized propensity score. We describe how methods based on the standardized difference can be adapted for use with quantitative exposures when using the generalized propensity score. We also describe a method based on assessing the correlation between the quantitative exposure and each covariate in the sample when weighted using generalized propensity score -based weights. We conducted a series of Monte Carlo simulations to evaluate the performance of these methods. We also compared two different methods of estimating the generalized propensity score: ordinary least squared regression and the covariate balancing propensity score method. We illustrate the application of these methods using data on patients hospitalized with a heart attack with the quantitative exposure being creatinine level.

Modern Algorithms for Matching in Observational Studies

2020 ◽  
Vol 7 (1) ◽  
pp. 143-176 ◽  
Author(s):  
Paul R. Rosenbaum
Keyword(s):  
Sensitivity Analysis ◽  
Propensity Score ◽  
Observational Studies ◽  
Propensity Scores ◽  
Variable Number ◽  
Exact Matching ◽  
Covariate Balance ◽  
Software Packages ◽  
Effective Use ◽  
Entire Number

Using a small example as an illustration, this article reviews multivariate matching from the perspective of a working scientist who wishes to make effective use of available methods. The several goals of multivariate matching are discussed. Matching tools are reviewed, including propensity scores, covariate distances, fine balance, and related methods such as near-fine and refined balance, exact and near-exact matching, tactics addressing missing covariate values, the entire number, and checks of covariate balance. Matching structures are described, such as matching with a variable number of controls, full matching, subset matching and risk-set matching. Software packages in R are described. A brief review is given of the theory underlying propensity scores and the associated sensitivity analysis concerning an unobserved covariate omitted from the propensity score.

scholarly journals Propensity score matching in otolaryngologic literature: A systematic review and critical appraisal

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244423
Author(s):  
Aman Prasad ◽  
Max Shin ◽  
Ryan M. Carey ◽  
Kevin Chorath ◽  
Harman Parhar ◽  
...  
Keyword(s):  
Systematic Review ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Observational Studies ◽  
Critical Appraisal ◽  
Propensity Scores ◽  
Final Analysis ◽  
Publication History ◽  
Covariate Balance

Background Propensity score techniques can reduce confounding and bias in observational studies. Such analyses are able to measure and balance pre-determined covariates between treated and untreated groups, leading to results that can approximate those generated by randomized prospective studies when such trials are not feasible. The most commonly used propensity score -based analytic technique is propensity score matching (PSM). Although PSM popularity has continued to increase in medical literature, improper methodology or methodological reporting may lead to biased interpretation of treatment effects or limited scientific reproducibility and generalizability. In this study, we aim to characterize and assess the quality of PSM methodology reporting in high-impact otolaryngologic literature. Methods PubMed and Embase based systematic review of the top 20 journals in otolaryngology, as measured by impact factor from the Journal Citations Reports from 2012 to 2018, for articles using PSM analysis throughout their publication history. Eligible articles were reviewed and assessed for quality and reporting of PSM methodology. Results Our search yielded 101 studies, of which 92 were eligible for final analysis and review. The proportion of studies utilizing PSM increased significantly over time (p < 0.001). Nearly all studies (96.7%, n = 89) specified the covariates used to calculate propensity scores. Covariate balance was illustrated in 67.4% (n = 62) of studies, most frequently through p-values. A minority (17.4%, n = 16) of studies were found to be fully reproducible according to previously established criteria. Conclusions While PSM analysis is becoming increasingly prevalent in otolaryngologic literature, the quality of PSM methodology reporting can be improved. We provide potential recommendations for authors regarding optimal reporting for analyses using PSM.

Sign in / Sign up

Export Citation Format

Share Document