Steve Kirsch says unpublished statistics from 182 patient records establish causation. The numbers may justify further study, but they cannot support the conclusion he draws.
Steve, the clustering is not in dispute. The question is whether it exceeds what we would expect without a causal effect. “You can’t explain it” does not provide that comparison.
I’m willing to examine parent reports and test your “Andrews effect,” but let’s define it and agree on the protocol before selecting cases. Contacting parents can verify dates and histories. It cannot create the missing denominator or fix selection bias.
If the goal is truth, I’m in. Let’s specify in advance what result would support or weaken each explanation.
Steve, I’d start by agreeing on exactly what we’re testing and what result would count against the hypothesis before looking at more data.
My suggested next steps:
Define the “Andrews effect” precisely and specify the predicted pattern.
Randomly select a prespecified sample of the clinic cases and independently verify vaccination dates, onset dates, and the nature of the regression with the parents and, where possible, records.
Establish the comparison we’re currently missing: how often children of the same ages and vaccination patterns would be expected to have a vaccination within the same two-day window absent a causal effect.
Preregister the analysis and the criteria for what would strengthen or weaken the vaccine hypothesis.
Publish the verified data, methods, and results so either of us can be wrong.
I’m happy to participate. The key for me is that we agree on the test before we see the answers, rather than interpreting the pattern afterward.
the tl;dr here is that you cannot explain the clustering.
https://alter.systems/p/a29f5a5a-9956-4660-b9bc-bc16ab2b06e2
Would you like to contact the parents directly to verify an Andrews effect because I'm sure you don't trust me to do that.
Or you can randomly pick a few of my followers and see if there is an Andrews effect.
Let me know if you are more interested in throwing darts or finding truth!
Steve, the clustering is not in dispute. The question is whether it exceeds what we would expect without a causal effect. “You can’t explain it” does not provide that comparison.
I’m willing to examine parent reports and test your “Andrews effect,” but let’s define it and agree on the protocol before selecting cases. Contacting parents can verify dates and histories. It cannot create the missing denominator or fix selection bias.
If the goal is truth, I’m in. Let’s specify in advance what result would support or weaken each explanation.
what do you suggest for next steps?
Steve, I’d start by agreeing on exactly what we’re testing and what result would count against the hypothesis before looking at more data.
My suggested next steps:
Define the “Andrews effect” precisely and specify the predicted pattern.
Randomly select a prespecified sample of the clinic cases and independently verify vaccination dates, onset dates, and the nature of the regression with the parents and, where possible, records.
Establish the comparison we’re currently missing: how often children of the same ages and vaccination patterns would be expected to have a vaccination within the same two-day window absent a causal effect.
Preregister the analysis and the criteria for what would strengthen or weaken the vaccine hypothesis.
Publish the verified data, methods, and results so either of us can be wrong.
I’m happy to participate. The key for me is that we agree on the test before we see the answers, rather than interpreting the pattern afterward.
see: https://github.com/skirsch/autism/tree/main/protocol/v2 for the protocol and the survey and the process. This is not finished yet.
Now have a 3 prong approach to test the hypothesis.