In my study, I am using propensity score matching to balance the effects of covariates on the impact of prednisolone on death outcomes among COVID-19 patients. 92 covariates have been considered, including demographic factors, signs and symptoms, other drugs, laboratory tests, and vital indicators. A problem arises when I remove one of the variables (the ALT). This changes the final results significantly.
How can I ensure whether I should remove that variable?
I'm not sure I comprehended your query. This is the article that may be of assistance to you. "Adelson JL, McCoach DB, Rogers HJ, Adelson JA, Sauer TM. Developing and applying the propensity score to make causal inferences: variable selection and stratification. Frontiers in psychology. 2017 Aug 17;8:1413."
Hi,
Refer to this book which gives good information:
Propensity score analysis: statistical methods and applications / Shenyang Guo & Mark W. Fraser. — 2nd edition.ISBN 978-1-4522-3500-4. SAGE Publications, Inc
Hi,
you want to include all variables associated with either selection into treatment or the outcome into the propensity score model but at the same time keep your model simple. If you have a big number of covariates inluding them all in the propensity score model might make it harder to achieve balance. You might want to take a look into a feature selection procedure such as the stepAIC in R.
These articles (see screen shot) maybe useful. Include all related variables that influence participation (treated or untreated)
Chinakorn Sujimongkol Bhogaraju Anand Julia Gerb Proloy Barua Thank you.
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