Added PSA flow diagram.

jbryer · Nov 13, 2023 · a59e489 · a59e489
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diff --git a/Slides/Intro_PSA.Rmd b/Slides/Intro_PSA.Rmd
@@ -392,6 +392,13 @@ ggplot(dat, aes(x = x1, y = x2, shape = x3, color = factor(treatment))) +
 ```
 
 
+---
+# Steps for Implementing Propensity Score Analysis
+
+```{r, echo=FALSE, out.width = '90%'}
+knitr::include_graphics('images/PSA_Flow.png')
+```
+
 ---
 # Propensity score methods
 
@@ -523,20 +530,6 @@ knitr::include_graphics('images/psa_simulation_screenshot.png')
 ```
 
 
----
-# Steps for Implementing Propensity Score Analysis
-
-Stuart and Rubin (2008) outline the following steps for matching, but the same approach can be used for stratification and weighting as well.
-
-1. Choose the covariates to be used.
-
-2. Define a distance measure (i.e. what constitutes similar).
-
-3. Choose the matching algorithm.
-
-4. Diagnose the matches (or strata) obtained (iterating through steps 2 and 3 as well).
-
-5. Estimate the treatment effect using the matches (or strata) found in step 4.
 
 ---
 # Phase I: Estimate Propensity Scores