Propensity scores are useful when estimating a treatment’s effect on an outcome using observational data and when selection bias due to nonrandom treatment assignment is likely. In the following sections, we introduce situations in which propensity scores might be used in health services research and provide step-by-step instructions and Stata 13 code and output to illustrate (1) choice of variables to include in the propensity score (2) balance of propensity score across treatment and comparison groups (3) balance of covariates across treatment and comparison groups within blocks of the propensity score (4) choice of matching and weighting strategies (5) balance of covariates after matching or weighting the sample by a propensity score and (6) interpretation of treatment effect estimates. 2013a Stata YouTube channel, or provide disjointed information ( Here, we synthesize information on creation and assessment of propensity scores within one article. Other useful Stata references gloss over propensity score assessment (treatment effects manual, StataCorp. While the advantages and disadvantages of using propensity scores are well known (e.g., Stuart 2010 Brooks and Ohsfeldt 2013), it is difficult to find specific guidance with accompanying statistical code for the steps involved in creating and assessing propensity scores. The goal of creating a propensity score is to balance covariates between individuals who did and did not receive a treatment, making it easier to isolate the effect of a treatment. A propensity score is a single score that represents the probability of receiving a treatment, conditional on a set of observed covariates. Propensity score analysis is a useful tool to account for imbalance in covariates between treated and comparison groups. A difficulty in using observational data is that patient and provider characteristics may be associated with both treatment selection and outcome, leading to different distributions of covariates within treatment and comparison groups. A brief overview of the stochastic frontier literature, a description of the two commands and their options,Īnd examples using simulated and real data are provided.Recent national initiatives for comparative effectiveness research recommend harnessing the power of existing data to evaluate health-related treatment effects (Patient-Centered Outcomes Research Institute 2012). Similarly, sfpanel allows one to fit a much wider range of time-varying inefficiency models compared with the xtfrontier command, including the model of Cornwell, Schmidt, and Sickles (1990, Journal of Econometrics 46: 185–200) the model of Lee and Schmidt (1993, in The Measurement of Productive Efficiency: Techniques and Applications), a production frontier model with flexible temporal variation in technical efficiency the flexible model of Kumbhakar (1990, Journal of Econometrics 46: 201–211) the inefficiency effects model of Battese and Coelli (1995 Empirical Economics 20: 325–332) and the "true" fixed- and random-effects models of Greene (2005a, Journal of Econometrics 126: 269–303). sfcross extends the capabilities of the frontierĬommand by including additional models (Greene, 2003, Journal of Productivity Analysis 19: 179–190 Wang, 2002, Journal of Productivity Analysis 18: 241–253) and command functionality, suchĪs the possibility of managing complex survey data characteristics. Rome, This article describes sfcross and sfpanel, two new StataĬommands for the estimation of cross-sectional and panel-data stochasticįrontier models. Home > Archives > Volume 13 Number 4 > st0315Ĭentre for Economic and International StudiesĮconomic and Financial Statistics Department
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