Password Please enter your Password. Forgot password? Don't have an account? Sign in via your Institution. You could not be signed in, please check and try again. Sign in with your library card Please enter your library card number. Search within In This Article Go to page:. All rights reserved. Sign in to annotate. PSA helps us to mimic an experimental study using data from an observational study. Decide on the set of covariates you want to include.
Use logistic regression to obtain a PS for each subject. Match exposed and unexposed subjects on the PS. Check the balance of covariates in the exposed and unexposed groups after matching on PS. Calculate the effect estimate and standard errors with this match population.
This is the critical step to your PSA. We use these covariates to predict our probability of exposure. We want to include all predictors of the exposure and none of the effects of the exposure.
We do not consider the outcome in deciding upon our covariates. We may include confounders and interaction variables. If we are in doubt of the covariate, we include it in our set of covariates unless we think that it is an effect of the exposure. We use the covariates to predict the probability of being exposed which is the PS. The more true covariates we use, the better our prediction of the probability of being exposed.
We calculate a PS for all subjects, exposed and unexposed. We want to match the exposed and unexposed subjects on their probability of being exposed their PS. If we cannot find a suitable match, then that subject is discarded. Discarding a subject can introduce bias into our analysis. Several methods for matching exist. Most common is the nearest neighbor within calipers. The nearest neighbor would be the unexposed subject that has a PS nearest to the PS for our exposed subject.
We may not be able to find an exact match, so we say that we will accept a PS score within certain caliper bounds. We set an apriori value for the calipers. Below 0. If we go past 0. Typically, 0. The ratio of exposed to unexposed subjects is variable. Matching with replacement allows for the unexposed subject that has been matched with an exposed subject to be returned to the pool of unexposed subjects available for matching.
There is a trade-off in bias and precision between matching with replacement and without Matching with replacement allows for reduced bias because of better matching between subjects.
Matching without replacement has better precision because more subjects are used. Substantial overlap in covariates between the exposed and unexposed groups must exist for us to make causal inferences from our data. This is true in all models, but in PSA, it becomes visually very apparent. If there is no overlap in covariates i. We can use a couple of tools to assess our balance of covariates.
First, we can create a histogram of the PS for exposed and unexposed groups. Second, we can assess the standardized difference.
Viewed times. What is the semantic difference between propensity and probability? Improve this question. I've put in an answer, but do note you can just glance in a dictionary for this one! I have a probability for mischief As mentioned in a comment to you answer. The reason that I did not trust the dictionary is that in some fields propensity is used seemingly in ways that are more closely related to probability. Add a comment. Active Oldest Votes. They're totally, utterly, different and totally unrelated.
A "propensity" is a quality of a person. It has nothing at all to do with describing a person's propensities. Improve this answer. Fattie Fattie I think what got me confused is stuff like propensity probability , propensity score , where propensity clearly used in a sense that is not related to people.
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