Melissa cites and Bruce gives an online reference for a pretty good article on
propensity scoring. I want to add that it does seem to be a bit light on the
theoretical side. Consider,
"Exclude any covariates that predict treatment status perfectly,
as distributions of covariates need to overlap between treatment
and comparison groups (see Step 2)."
The need for overlap is perfectly true. However, if you have a fine candidate Var
for confounding which confounds perfectly, you have a FLAWED STUDY, and
this flaw deserves a prominent footnote. Yes, you have to drop the Var from the
propensity scoring; but you have to give recognition to a major outside
influence which must be considered in the discussion section of the study.
Elsewhere, there's a related problem treated concretely. The authors suggest that
when age as a continuous variable results in too much non-overlap, a diagnostic
warning might be turned off by dichotomizing age. As solutions go, that's like un-
screwing a warning light when you can't fix what is wrong. "Throwing away information"
needs further consideration/justification, and discussion.
- - - - - - - -
Bruce posted --
That article appears to be freely available here:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4213057/
I assume you are referring to Figure 1.
Ives, Melissa L wrote
> Is there a way to get a graph in SPSS that is similar to the graph that
> results from Stata's 'psgraph' command?
>
>
> (example in: Garrido, M. M., Kelley, A. S., Paris, J., Roza, K., Meier, D.
> E., Morrison, R. S., et al. (2014). Methods for constructing and assessing
> propensity scores. Health Services Research, 49(5), 1701-1720.
...
--
Rich Ulrich
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