Stata 13 help code#
Stata 13 help trial#

As variables are added to the matching process, however, it becomes more and more difficult to find exact matches for individuals (i.e., it is unlikely to find individuals in both the treatment and comparison groups with identical gender, age, race, comorbidity level, and insurance status). For instance, if one were concerned that age might affect both treatment selection and outcome, one strategy would be to compare individuals of similar age in both treatment and comparison groups. Propensity scores can be thought of as an advanced matching technique. Propensity scores provide a way to balance measured covariates across treatment and comparison groups and better approximate the counterfactual for treated individuals. This leads to selection bias, where measured and unmeasured characteristics of individuals are associated with likelihood of receiving treatment and with the outcome. In observational data, however, treatment assignment is not random. Because only one treatment state can be observed at a time for each individual, control individuals that are similar to treated individuals in everything but treatment receipt are used as proxies for the counterfactual.

Stata 13 help trial#

The classic experimental design for estimating treatment effects is a randomized controlled trial (RCT), where random assignment to treatment balances individualsвЂ™ observed and unobserved characteristics across treatment and control groups. Propensity scores are useful when estimating a treatmentвЂ™s effect on an outcome using observational data and when selection bias due to nonrandom treatment assignment is likely.

Stata 13 help code#

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. 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).