This can be achieved with a survival regression model (if you have a reasonable assumption about the hazard function) or Cox proportional hazards regression model (if you don't like to specify a hazard function but can assume that the hazards between the groups are proportional). There are Wald z-tests available to compare hazards between groups as for ANOVA models (which are also regression models under the hood).

Take care about error inflation by multiple testing. If you are testing a family of tests (that is: if you screen for any difference between a couple of groups), then you'll need to correct for that (-> Bonferroni, Holm).

https://www.graphpad.com/guides/prism/latest/statistics/stat_cox_regression_model.htm

https://www.graphpad.com/guides/prism/latest/statistics/stat_how_to_cox_regression.htm

https://www.graphpad.com/guides/prism/latest/statistics/stat_cox_reg_results.htm

@Jochen Wilhelm

Thanks very much, dear Jochen :) Thanks for your detailed reply, as always. I ll read the links & come back to your answer again. Thanks a lot. Very very helpful for advising me on what to do in this situation.

When comparing survival curves for 4-8 treatment groups, you can explore alternatives to the log-rank test. Consider using statistical methods designed for multiple groups, such as the stratified log-rank test, Mantel-Haenszel test, or Cox proportional hazards models. In R, for instance, you can utilize functions like survdiff for stratified tests and coxph for proportional hazards models. Additionally, visualize Kaplan-Meier curves with confidence intervals for each group. These approaches offer a comprehensive analysis, addressing the challenges posed by multiple treatment groups in survival data.

Thank you. Much appreciated.