Yup K-M Curve can be misleading or uselessly incorrect if the basic assumptions are violated. https://www.quality-control-plan.com/StatGuide/kaplan_ass_viol.htm
Unfortunately many people are not aware of it believing they can use this tool to make the most important decision that will impact their lives. It is really sad.
I am glad that those who worked in the DCVax-L clinical trial took the road less traveled which is more difficult with a lot of agony but the end result will be gold standard which everyone including patients especially can rely on.
If the populations from which data for a Kaplan-Meier estimation were sampled violate one or more of the Kaplan-Meier assumptions, the results of the analysis may be incorrect or misleading. For example, if the assumption of independence of censoring times is violated, then the estimates for survival may be biased and unreliable. If there are factors unaccounted for in the analysis that affect survival and/or censoring times, then the Kaplan-Meier calculations may not give useful estimates for survival.
Some small violations may have little practical effect on the analysis, while other violations may render the Kaplan-Meier results uselessly incorrect or uninterpretable. In particular, small sample sizes may increase the effect of assumption violations. Heavy censoring may also affect the reliability of the Kaplan-Meier estimates.
Potential assumption violations include: Implicit factors: lack of independence within the sample Lack of independence of censoring: lack of independence of censoring Lack of uniformity: lack of uniformity within a time interval Many censored values: problems caused by a large number of censored values Patterns in plots of data: detecting violations of assumptions graphically Special problems with small sample sizes