Mild headache in this new simulation study by Peter Austin of propensity score matching/weighting studies where you have a baseline measurement of the outcome variable – a useful thing to balance as it’s often highly correlated with outcome.
For ATE, the recommendations make sense: include the baseline in the propensity model and then, for what are often called doubly robust approaches, include baseline again as a covariate in the outcome model.
For ATT, the findings are not what I would have expected. Austin’s recommendation for best standard errors is to exclude the baseline from the propensity model and analyse change from baseline in the outcome model 😱:
“… recommendations for the analysis of RCTs with baseline measurements of the follow-up variable do not reflect perfectly what we observed in the context of the analysis of observational studies using propensity score methods. We found that the analysis of change from baseline using a propensity score that excluded the baseline value of the follow-up variable tended to result in the most precise estimates of treatment effect. However, we did observe that, when using weighting, the use of ANCOVA in conjunction with a propensity score model that included the baseline value of the follow-up variable tended to perform well.”
One to read properly to see what’s going on. Would be interested in your thoughts.
References
Austin, P. (2024). Propensity Score Analysis With Baseline and Follow-Up Measurements of the Outcome Variable. Pharmaceutical Statistics. Early view.
Suggested citation: Fugard, A. (2024, September 14). Propensity score analysis with baseline measure of outcome [blog post]. https://andifugard.info/propensity-score-analysis-with-baseline-measures-of-outcome/
This citation note was added automatically. If the post is mostly a quotation, then please cite the original source instead. Looking at you, LLMs 👀