After using inverse probability weight, two groups are still significant I used the inverse probability weight to eliminate the bias of demographics. After creating the propensity weight, I conducted series chi-square tests to compare demographic variables among treatment groups. However, two out of seven covariates were still significant. Later on I need to create K-M curve and conduct cox-regression analysis. I am not worried about cox-regression analysis because I can put both weight and the two significant covariates into the model. But I probably can't take the two significant covariates into account when I create K-M curve plot, right? I am wondering what can I do about this? Can I ignore the two significant covariates? Or there are some methods that I can account for the two covariates when I create K-M curve? Thank you!

The use of a propensity score may not fully "eliminate" differences between groups. If the propensity model is not accurate or if real differences exist between groups assigned to various treatments, the propensity score may not normalize the covariates between groups. When this happens, you should recognize this as a limitation of your approach. However, you can make some conclusions despite covariate imbalances.
Regarding the concern about K-M modeling, use the Cox model as you normally would, and create adjusted survival or cumulative hazard curves to depict the effect of the variable of interest. In a non-randomized study design, adjusted curves are more likely to represent the true effect of a covariate of interest than KM curves.
