In examples 7.34 and 7.35 we described methods using propensity scores to account for possible confounding factors in an observational study.In addition to adjusting for the propensity score in a multiple regression and matching on the propensity score...
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Example 7.35: Propensity score matching
As discussed in example 7.34, it's sometimes preferable to match on propensity scores, rather than adjust for them as a covariate.SASWe use a suite of macros written by Jon Kosanke and Erik Bergstralh at the Mayo Clinic. The dist macro calculates the ...
Example 7.34: Propensity scores and causal inference from observational studies
Propensity scores can be used to help make causal interpretation of observational data more plausible, by adjusting for other factors that may responsible for differences between groups. Heuristically, we estimate the probability of exposure, rather t...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
