I have a dataset of patients with a grouping variable (groups A (control) and group B (treatment)). The two groups have sample sizes of 170 vs. 30. I would like to compare outcomes between the two treatment groups but they differ in baseline covariates. I tried propensity score matching and inverse probability treatment weights (IPTW) but both don't seem to achieve good covariate balance between the groups (as of SMD <0.1).
My guess is the sample size of the treatment group is too small. Does anybody have a recommendation how I can adjust for baseline covariates in such a small sample size?
