Personalized Chemotherapy: Understanding Clinical Trials – the Kaplan Meier Graph

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In this episode of Personalized Cancer Chemotherapy Dr. Weisenthal explains the Kaplan-Meier graph. The graph is often used in clinical trials to compare survival times among patients with the same type of cancer who received different chemotherapy treatments. Understanding the graph is easy and also very useful as it will enable you to cut through the clutter in published clinical trial manuscripts and see at glance if any chemotherapy regimen provided a superior survival benefit.

Comments

vik ram says:

started well….but then came the true motive – PROPAGANDA – shame on you

Brian Thiessen says:

Dr. Weisenthal should realize that in order to prove his personalized cancer chemotherapy hypothesis, he will need to do a randomized trial where one cohort of cancer patients get best standard treatment and the other cohort gets personalized cancer therapy. I think he might find that in that trial, the curves might again overlap.

Ashwin Konga says:

Thank you for your explanation on Kaplan-Meier graph.

prashant chaudhary says:

Thank you for presentation

superstar1732 says:

Great explanation! It really helped me prepare for my cancer biology college course. Although I could just make out what you were saying, there is still poor audio quality.

Gregory D. Pawelski says:

The thing about evidence-based medicine and the sanctimonious randomized clinical trial is the need for thousands, if not tens of thousands, of patients for these types of clinical trials. It's becoming increasingly difficult to justify very large and expensive clinical trials. Would we even do these kinds of trials when we have elegant matching of the biological defect and the specific drug intervention?

Ebony Kennedy says:

Thank you. You just taught my no-stats-knowing self what a Kaplan Meier curve is. I'm forever indebted.

Maggie Robertson says:

is this guy serious, randomization is essential to the validity of a clinical trial. It is our only way to reduce confounding that might bias results. If you randomize people with different molecular markers, or whatever, should be evenly distributed in both groups, sample size being large enough and provided you found a primary outcome that was valid, feel free to subgroup analyze. but no randomization, no sir, no thank you

Shah ktk says:

Please upload the next video….

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