Objectiveto demonstrate the use of propensity scores to evaluate the comparative effectiveness of laparoscopic and open appendectomy. This is a introduction on propensity score matching on r, written in r markdown. An introduction to the augmented inverse propensity weighted. A critical appraisal of propensityscore matching in the medical literature between 1996 and 2003. Propensity scores for the estimation of average treatment. The article by king and nielsen 2016, also referenced below presents some limitations in propensity score matching and some remedies for when many individual cases remain after the matching attempt.
An important final point made is that the results do not necessarily imply problems with alternative approaches which use the propensity score, such as inverse weighting or regression. Matching procedures based on this balancing score are known as propensity score matching psm and will be the focus of this paper. Further, the use of propensity score matching as a means of controlling selection bias in observational studies is not the only method that can be used to control for selection bias, nor is the propensity score method consistently endorsed or used by all epidemiologists and statisticians who analyze observational data. In the statistical analysis of observational data, propensity score matching psm is a statistical matching technique that attempts to estimate the effect of a treatment, policy, or other intervention by accounting for the covariates that predict receiving the treatment. One possible balancing score is the propensity score, i. Mar, 2015 different propensity score techniques use these conditional probabilities in different ways. Propensity score matching allows one to estimate the att imbens, 2004. I mbens1 propensity score matching estimators rosenbaum and rubin 1983 are widely. Five steps to successfully implement and evaluate propensity. Sekhon uc berkeley abstract matching is an r package which provides functions for multivariate and propensity score matching and for nding optimal covariate balance based on a genetic search algorithm. The use of propensity scores as a method to promote causality in studies that cannot use random assignment has increased dramatically since its original publication in 1983. Estimate propensity scores propensity score model covariates common support b.
What, when, and how article pdf available in the journal of early adolescence 341. Propensity score matching iza institute of labor economics. Propensity score matching in observational studies propensity scores are an alternative method to estimate the effect of receiving treatment when random assignment of treatments to subjects is not feasible. Department of leadership, policy and adult and higher education. Is taking math lessons after school helpful in improving score. Propensity score matching psm has become a popular approach to estimate causal treatment effects. Propensity models depend on the potential outcomes model popularized by don rubin1. Psm uses a vector of observed variables to predict the probability of experiencing the event participation to create a counterfactual group. The propensity score is the probability of treatment assignment conditional on observed baseline characteristics.
This video provides an introduction to propensity score matching, and explains why it is a useful concept for deriving estimates for the average causal effect. Propensity score matching and related models examples in stata greedy matching and subsequent analysis of hazard rates optimal matching postfull matching analysis using the hodgeslehmann aligned rank test postpair matching analysis using regression of difference scores propensity score. Four different propensity score methods are used for removing the effects of confounding when estimating the effects of treatment on outcomes. Estimate distribution by splitting sample by quintiles or other strata of propensity score test whether mean of propensity score is equal in treatment and comparison groups within each quintile. Multivariate and propensity score matching software with. Estimate treatment effects average treatment effect on the treated att alternatives to matching d. With this case study in hand, you will feel confident that you have the tools necessary to begin answering some of your own research questions using propensity scores. This paper provides an introduction to propensity scores for evaluation practitioners. This study provides an introduction to propensity score analysis using the.
There are a total of 19 possible match combinations depicted, each of which would be considered further for possible inclusion in the final list of matches. The purpose of this training session is to introduce propensity score matching as a technique for addressing issues related to design. We addressed this issue by using a propensityscore matching procedure that enabled us to match early. These estimated propensity scores can then be used in various ways matching, stratification, covariateadjustment or inverseprobability weighting to obtain estimates of the exposure effect. Outline estimating causal e ects society for prevention. Introduction to propensity scores in stata learning objectives. Propensity score analysis methods have become a common choice for. To understand the use of propensity scores as a means for controlling for selection bias in observational studies of treatment effects. An introduction to the augmented inverse propensity. Multivariate and propensity score matching software with automated balance optimization. Stata has a function for tseffects for obtaining propensity scores, and the function of psmatch for propensity score matching.
You can use a logistic or probit regression model to estimate the likelihood or propensity of treatment, and match on just this propensity. Introduction to propensity scores monash university. A comparison of outcomes is made between treated and control groups. Including the propensity score as a continuous explanatory variable in a model for the health outcome was the most widely used method, appearing in 14 50% of the studies, closely followed by propensity score matching used in 12 43% of studies. Propensity score methods achieve removal of bias by balancing covariate distributions between treated and untreated groups. The most common implementation of propensity score matching is onetoone or pair matching, in which pairs of treated and untreated subjects are formed, such that matched subjects have similar values of the propensity score. Some practical guidance for the implementation of propensity. An introduction to propensity score matching ut college of liberal. For more details on using r markdown see rmarkdown. My book practical propensity score methods using r was a product of my teaching and research, and it aims to facilitate the work of researchers and graduate students interested in estimating treatment effects with observational data.
Caliper, kernel, and local linear matching all require specifying a \bandwidth, i. Rosenbaum and rubin 1983 proposed propensity score matching. The trainers will present the theory behind the method, how it differs from other analyses of statistical control. While the utility of these approaches is important, the concepts underlying their use are complex. An introduction to propensity score methods for reducing the. An introduction to the augmented inverse propensity weighted estimator adam n. In observational studies, the true propensity score is not, in general, known. Treatment evaluation is the estimation of the average effect of a program or treatment on the outcome of interest. A case study on the comparative effectiveness of laparoscopic vs open appendectomy.
Introduction to matching and propensity score reweighting. If a treated unit has a propensity score close to 0 or a control unit has a propensity score close to 1, the resulting iptwate weight can be large. My book practical propensity score methods using r was a product of my teaching and research, and it aims to facilitate the work of researchers. Propensity scores rosenbaum and rubins 1983, 1985 major breakthrough was showing that the benefits of exact matching extend to matching on a propensity score. The score can then be used to do a number of things. Propensity score matching psm refers to the pairing of treatment and control units with similar values on. Arealevel public health interventions can be difficult to evaluate using natural experiments. Although both methods are more valid in terms of balancing study groups than simple matching or stratification based on baseline characteristics, they vary in their ability to minimize bias. The purpose of this training session is to introduce propensity score matching as a technique for addressing issues related to design methodologies and nonrandomization.
Propensity score is a balancing method to explore causal effect in observational studies. Why you shouldnt use propensity score matching the. It is widely applied when evaluating labour market policies, but empirical examples can be found in very diverse fields of study. Propensity score matching is a common technique used to estimate the effects of a treatment or program when you dont have a randomized controlled experiment. Mar 28, 2015 propensity score matching is a common technique used to estimate the effects of a treatment or program when you dont have a randomized controlled experiment. A probability of being exposed treated, vaccinated, etc. Propensity score matching entails forming matched sets of treated and untreated subjects who share a similar value of the propensity score. Quinn october 21, 2009 abstract in this paper we discuss an estimator for average treatment e. An output data set of each patients propensity score can be generated with sas using proc logistic, and a generalized sas macro can do optimized n. An introduction to propensity score methods for reducing.
Creating matched samples using exact matching 7 of 17. Implementation of propensity score matching marco caliendo iza, bonn sabine kopeinig university of cologne abstract. Matching has become a popular approach to estimate causal treatment effects. Proceed with analyses based on sample matched or weighted by propensity score calculating a propensity score is an iterative process. I have been teaching and doing research about propensity score methods at university of florida for over 10 years. Propensity score methods for bias reduction in the. If a few observations have very large weights, the resulting iptwate estimator has a large variance and is not approximately normally distributed robins, hernan, and brumback 2000, p. Some parts of the notes are taken from various sources listed below and no originality is claimed. An intuitive introduction to propensity score matching youtube. An intuitive introduction to propensity score matching. The central causal question in many research domains, the objective of an investigation is.
Introduction in the evaluation problems, data often do not come from randomized trials but from nonrandomized observational studies. Apr 06, 2018 the article by king and nielsen 2016, also referenced below presents some limitations in propensity score matching and some remedies for when many individual cases remain after the matching attempt. Propensity score matching and related methods a number of different propensity score matching methods can be used to match units on their propensity scores. Markdown is a simple formatting syntax for authoring html, pdf, and ms word documents. School of social work university of north carolina at chapel hill january 28, 2005 for workshop conducted at the school of social work, university of illinois urbanachampaign. Propensity score matching produced matched samples of students who not only were unbalanced in terms of student sex. These propensity score methods include many variations of weighting, matching, and stratification. We describe the use of propensity score matching psm to select control local authority areas lau to evaluate the public health impact of alcohol policies for 1 prospective evaluation of alcohol policies using arealevel data, and 2 a novel twostage quasi casecontrol design. Propensity score matching and related models examples in stata greedy matching and subsequent analysis of hazard rates optimal matching postfull matching analysis using the hodgeslehmann aligned rank test postpair matching analysis using regression of difference scores propensity score weighting. This estimates average treatment effect on the treated only. To learn how to create propensity scores apply them in a variety of analytic approaches in stata. Practical propensity score methods using r introduction. Rubin, 1983 is a widely used tool for determining causal effects from observational data.
Feb 06, 2014 this video provides an introduction to propensity score matching, and explains why it is a useful concept for deriving estimates for the average causal effec. Propensity score rosenbaum and rubin 1983 realized the bias from covariates can be eliminated by controlling for a scalarvalued function a balancing score calculated from the baseline covariates, i. Jun 08, 2011 propensity score matching allows one to estimate the att imbens, 2004. Propensity score matching for selection of local areas as. The most common implementation of propensity score matching is one to one or pair matching, in which pairs of treated and untreated subjects are formed, such that matched subjects have similar values of the propensity score. The use of propensity score matching in the evaluation of active labour market policies a study carried out on behalf of the department for work and pensions by alex bryson, richard dorsett and susan purdon policy studies institute and national centre for social research. Propensity score matching is a valuable tool for dealing with observational data and nonrandom treatment assignment, which often results in groups that differ systematically in numerous measured and unmeasured variables. Propensity score matching in observational studies author. The propensity score research, methods, statistics. The propensity score allows one to design and analyze an observational nonrandomized study so that it mimics some of the particular characteristics of a randomized controlled trial. As such, it is argued that propensity score matching can increase confounder imbalance, thereby leading to estimates of exposure effects with greater bias. For example, similar to the idea of matching patients on specific characteristics, propensity score techniques can match patients on their likelihood of being in a certain group, but without being limited to just a few variables. When these systematically different variables are associated with both group assignment and the outcomes of interest, bias is introduced. Propensity score methods, models and adjustment dr david a.
Estimation of average treatment effects based on propensity scores. May 29, 2014 including the propensity score as a continuous explanatory variable in a model for the health outcome was the most widely used method, appearing in 14 50% of the studies, closely followed by propensity score matching used in 12 43% of studies. Each person in our study is assigned a score that ranges from 0 never exposed to 1 always exposed. A practical guide to getting started with propensity scores. An introduction to propensity score methods for reducing the effects of confounding in observational studies. There are three ways to use the propensity score to do this balancing. Ensure that covariates are balanced across treatment and comparison groups in sample matched or weighted by propensity score step 6. The why, when, and how of propensity score methods for estimating causal e. An introduction to implementing propensity score matching. Propensity score matching is used when a group of subjects receive a treatment and wed like to compare their outcomes with the outcomes of a control group. I aside from pair matching, all of these approaches require specifying a \tuning parameter. Eight studies used some form of propensity score stratification. The purpose of this paper is to provide the reader with a conceptual and practical introduction to propensity scores, matching using propensity scores, and its implementation using statistical r programsoftware.
Check balance of propensity score across groups does the propensity score have a similar distribution across treatment and comparison groups. Outline 1 observational studies and propensity score 2 motivating example. Propensity score matching psm aims to equate treatment groups with respect to. Lecture notes on propensity score matching jinlung lin this lecture note is intended solely for teaching.
479 1614 272 407 1345 1388 1442 1628 749 1321 1598 1374 69 525 3 1593 791 185 790 1397 95 216 17 1646 638 1630 45 830 405 995 615 863 721 661 957 696 503 1365