I invented a new method, Kirsch Cumulative Outcomes Ratio (KCOR) for analyzing vaccine data. It took me a while to perfect the method. Various factors limit the degree of perfection, but it’s pretty hard to get any better than this.
KCOR showed the COVID vaccines were a net harm for all ages. I validated it with 3 other methods and also I have sensitivity and negative control tests.
KCOR uses record level data. Only dates of birth, vaccination, and death are need.
The four key steps are:
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Define fixed cohorts based on each person’s vaccine status on a specific date (the “enrollment date”)
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Slope normalize each cohort death curve over time using an exponential slope computed between two “quiet” points in time. This adjusts for cohorts being on different parts of the Gompertz curve.
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Take the ratio of the cumulative hazards of the slope normalized cohorts
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Normalize that ratio relative to the ratio during the baseline period (“quiet period” with no COVID)
I will be writing this up for a peer-reviewed journal, but in the meantime, I’d like to subject it to peer review to validate what I’ve done and suggest additional improvements before I publish.
The method is fully described both at a high level and in detail on my Github (where it can be easily replicated from scratch). The KCOR method is also described in this article which is a longer read and less current.
Links:
Bad news if you got the COVID vaccine. This is the age standardized graph with 95% CIs:
Values >1 indicate net harm. By the end of 2022, we had over 20% net harm on an age standardized basis. It wasn’t helpful for any age group.
If you have professional credentials in data analysis (math, statistics, data science, epidemiology, etc.), please leave your review in the comments in this article including your name and qualifications and permission to add your comments to the peer review section of the Github README file.
Thanks!
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