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Suppose I have two propensity matched groups whose covariates are balanced. How do I analyze clinical outcomes between the groups. I would be interested in overall survival, one year survival, and incidence of adverse events such as stroke.

  • Can I use standard generalized logistic regression models and kaplan meier curves with log rank test?
  • Do I need to treat the clinical subjects as paired and adjust my analysis appropriately (i.e stratified log rank and Mcnemar test)

Thank you!

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This is debated. Peter Austin strongly recommends accounting for the paired nature of the matched groups. Other methodologies do not. I think it makes sense to do so. Generally, power will be increased if within-pair distances are low. The differences will not be stark, however.

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  • $\begingroup$ I have two matched sets with n = 50. I've computed standard kaplan meier curves and a logrank test with p ~ 0.7. After, I tried to account for matches in 2 ways. In the first, I utilized the survdiff() function in the survival package with a cluster(ID) variable to account for pairing. In the second method, I ran the same thing with a coxph function and a cluster(ID) variable. survdiff gives me a value of p <0.0001 whereas coxph gives me a p value close to the baseline 0.7 with a slightly different robust standard error. Which should I trust. Does cluster(ID) not work with survdiff? $\endgroup$ – chill_hakawati Mar 28 at 21:01

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