> On Sep 17, 2021, at 1:28 AM, <[email protected]> > <[email protected]> wrote: > > Would any of you buy a seat belt that was marketed to not cause deaths? You > are so lost in your point about small numbers that you’ve lost your sense of > the plain meaning of words. Sheesh!
Yes. Like “goal”. And “function”. I pass judgment on you all! > > n > > Nick Thompson > [email protected] <mailto:[email protected]> > https://wordpress.clarku.edu/nthompson/ > <https://linkprotect.cudasvc.com/url?a=https%3a%2f%2fwordpress.clarku.edu%2fnthompson%2f&c=E,1,V8E2wZpn-KPf9gL7JM5R_8ZQ1y7CVgBHbZFHfUw3Pvz0r-NeNdGkNJ05amvJ2Bxa1FHd29HdLu7TLpj5rk7kmiCgj4bHkG9mOB1pMT8_x5ShAPvGlDJUxsw,&typo=1> > > From: Friam <[email protected]> On Behalf Of Roger Critchlow > Sent: Thursday, September 16, 2021 12:16 PM > To: The Friday Morning Applied Complexity Coffee Group <[email protected]> > Subject: Re: [FRIAM] Could this possibly be true? > > sum(reasons_for_death) != number_of_deaths, and Death itself is listed as a > reported cause of death. > > -- rec -- > > On Thu, Sep 16, 2021 at 12:01 PM Pieter Steenekamp > <[email protected] <mailto:[email protected]>> wrote: >> For what it's worth, from table S4 in the supplementary data >> https://www.medrxiv.org/content/medrxiv/early/2021/07/28/2021.07.28.21261159/DC1/embed/media-1.pdf >> >> <https://linkprotect.cudasvc.com/url?a=https%3a%2f%2fwww.medrxiv.org%2fcontent%2fmedrxiv%2fearly%2f2021%2f07%2f28%2f2021.07.28.21261159%2fDC1%2fembed%2fmedia-1.pdf&c=E,1,R0pU8wvkUlcpkdfQRbJVL2Uobg6euwmwfSU6g4LRKfs-VLiHrFCDbNjXgMMPRyPfm42KJL9AMm8eL2ExfYTPfkrm5KpUZMaXGtSnYygX9XwQzCM,&typo=1> >> >> Reported Cause of Death BNT162b2 (N=21,926) Placebo (N=21,921) >> >> Deaths 15 >> 14 >> Acute respiratory failure 0 >> 1 >> Aortic rupture 0 >> 1 >> Arteriosclerosis 2 >> 0 >> Biliary cancer metastatic 0 >> 1 >> COVID-19 0 >> 2 >> COVID-19 pneumonia 1 >> 0 >> Cardiac arrest 4 >> 1 >> Cardiac failure congestive 1 >> 0 >> Cardiorespiratory arrest 1 >> 1 >> Chronic obstructive pulmonary >> disease 1 >> 0 >> Death 0 >> 1 >> Dementia 0 >> 1 >> Emphysematous cholecystitis 1 >> 0 >> Hemorrhagic stroke 0 >> 1 >> Hypertensive heart disease 1 >> 0 >> Lung cancer metastatic 1 >> 0 >> Metastases to liver 0 >> 1 >> Missing 0 >> 1 >> Multiple organ dysfunction >> syndrome 0 >> 2 >> Myocardial infarction 0 >> 2 >> Overdose 0 >> 1 >> Pneumonia 0 >> 2 >> Sepsis 1 >> 0 >> Septic shock 1 >> 0 >> Shigella sepsis 1 >> 0 >> Unevaluable event 1 >> 0 >> >> On Thu, 16 Sept 2021 at 17:37, Frank Wimberly <[email protected] >> <mailto:[email protected]>> wrote: >>> Pittsburgh irony: Ooh. Yinz are rill tough. I'm skeered. Cf. Kasich, >>> who is from McKees Rocks which is across the river from "dahntahn" >>> Pittsburgh. >>> >>> Yinz = "you ones" similar to "y'all" in the South. >>> >>> --- >>> Frank C. Wimberly >>> 140 Calle Ojo Feliz, >>> Santa Fe, NM 87505 >>> >>> 505 670-9918 >>> Santa Fe, NM >>> >>> On Thu, Sep 16, 2021, 8:41 AM <[email protected] >>> <mailto:[email protected]>> wrote: >>>> Then we can say with a 99% probability that the vaccination does not >>>> increase the total (again all causes) death rate with more than a factor >>>> of 1.6. >>>> Oh I am so glad. So reassuring*. >>>> >>>> You guys are scaring the total crap out of us citizens. >>>> >>>> N >>>> >>>> PS to Frank. There’s lot’s of irony in Pittsburgh. I count on you to >>>> recognize it. >>>> Nick Thompson >>>> [email protected] <mailto:[email protected]> >>>> https://wordpress.clarku.edu/nthompson/ >>>> <https://linkprotect.cudasvc.com/url?a=https%3a%2f%2fwordpress.clarku.edu%2fnthompson%2f&c=E,1,Uk0Gxx8q36tE_C-kHBzMVjgCMuBRANvruepskNc7L5-3myGWDDOxZgQPU2qaZylkNGoethdSQZpfXzILbdOcAgCWV4wta4XDLwVwiDbMoElJCbamWK5Qrtfwt6Zp&typo=1> >>>> >>>> From: Friam <[email protected] <mailto:[email protected]>> >>>> On Behalf Of Pieter Steenekamp >>>> Sent: Thursday, September 16, 2021 7:34 AM >>>> To: The Friday Morning Applied Complexity Coffee Group <[email protected] >>>> <mailto:[email protected]>> >>>> Subject: Re: [FRIAM] Could this possibly be true? >>>> >>>> Thank you Roger, >>>> >>>> Using the numbers from Phizer's report, I did a sort of quick and dirty >>>> manual iteration process to get to the following Monte Carlo testing >>>> conclusion >>>> >>>> If: >>>> a) the total death rate of the unvaccinated is 14/22000 (all causes) and >>>> b) a total of 15 out of 22000 (again all causes) of the vaccinated group >>>> died >>>> Then we can say with a 99% probability that the vaccination does not >>>> increase the total (again all causes) death rate with more than a factor >>>> of 1.6. >>>> >>>> My Python program to do this is as follows: >>>> import random >>>> total_of_tentousand_samples_less_than_16=0 >>>> r=1.6 # manually iterate this number until the answer is less than 100, >>>> with 1000 test runs for a probability of 99% >>>> numberList = [0, 1] # 0 = live, 1=dead >>>> for i in range(1000): >>>> x=(random.choices(numberList, weights=((1-r*14/22000), r*14/22000), >>>> k=22000)) >>>> if( sum(x)<16): >>>> >>>> total_of_tentousand_samples_less_than_16=total_of_tentousand_samples_less_than_16+1 >>>> >>>> print(total_of_tentousand_samples_less_than_16) >>>> >>>> # iteration tally: >>>> # with r=1.5 then total_of_tentousand_samples_less_than_16=105 >>>> # with r=1.6 then total_of_tentousand_samples_less_than_16=69 >>>> >>>> Pieter >>>> >>>> On Wed, 15 Sept 2021 at 22:26, Roger Critchlow <[email protected] >>>> <mailto:[email protected]>> wrote: >>>>> Pieter - >>>>> >>>>> The initial safety and efficacy report was published in the New England >>>>> Journal of Medicine at the end of 2020, >>>>> https://www.nejm.org/doi/full/10.1056/nejmoa2034577 >>>>> <https://linkprotect.cudasvc.com/url?a=https%3a%2f%2fwww.nejm.org%2fdoi%2ffull%2f10.1056%2fnejmoa2034577&c=E,1,bGwez3AcanjP41grxMhQDMKLPTbXFuXUAcJwKbX1WtfUb9BQEOz-XC2qEphGwUw6Ag0y_xXdyYLvC7ZZkKGohLuPua_ZVOfz4-mfmAQR8XJK&typo=1>, >>>>> it has smoother language and inline graphics. It also has fewer deaths >>>>> in the treatment group than in the control group, but it is only >>>>> reporting the first two months of the study. >>>>> >>>>> The numbers of deaths reported in the "Adverse Reactions" section of >>>>> these reports will eventually track the expected death rate of the >>>>> population in the trial, and apparently they do, since there is no >>>>> comment to indicate otherwise. Every clinical trial that tests the >>>>> safety of a treatment is expected to agree with the baseline mortality >>>>> statistics for the population in the trial. >>>>> >>>>> If you see 14 and 15 deaths out of 22000 participants and your immediate >>>>> response is that 15 is bigger than 14, then you should probably stop >>>>> torturing yourself with statistical data. You're making and agonizing >>>>> over distinctions that the data can never support. The number of deaths >>>>> in a population over a period of time has an average value and a variance >>>>> which are found by looking at large populations over long periods of >>>>> time. In any particular population and period of time there are a lot >>>>> trajectories that the death count can take that will be consistent with >>>>> the long term average even as they wander above and below the average. >>>>> >>>>> I append a simple simulation in julia that you can think about. >>>>> >>>>> -- rec -- >>>>> >>>>> # from https://www.cdc.gov/nchs/fastats/deaths.htm >>>>> <https://www.cdc.gov/nchs/fastats/deaths.htm> >>>>> death_rate = 869.7 # raw deaths per 100000 per year >>>>> >>>>> # simulate the action of a 'death rate' on a population of 'sample' >>>>> individuals for 'days' of time. >>>>> # convert the raw death rate to the death_rate_per_individual_per_day, ie >>>>> death_rate/100000/365.25, >>>>> # allocate an array of size sample*days, size coerced to an integer value, >>>>> # fill the array with uniform random numbers. >>>>> # if an array value is less than the death rate per person per day, score >>>>> 1 death. >>>>> # this overcounts because individuals can be scored as dying more than >>>>> once, YODO! >>>>> >>>>> simulate(death_rate, sample, days) = >>>>> sum(rand(Int(sample*days)) .< death_rate/100000/365.25) >>>>> >>>>> # accumulate an ensemble of death rate simulation results. >>>>> # run 'trials' simulations of 'death_rate' for 'sample' individuals for >>>>> 'days' time. >>>>> # accumulate an array with the number of deaths in each simulation >>>>> accumulate(death_rate, sample, days, trials) = >>>>> [simulate(death_rate, sample, days) for i in 1:trials] >>>>> >>>>> # check the model: run the simulation with death_rate for 100000 >>>>> individuals and 365.25 days, >>>>> # the result averaged over multiple simulations should tend to the >>>>> original death_rate. >>>>> # we report the mean and standard error of the accumulated death counts >>>>> julia> mean_and_std(accumulate(death_rate, 100000, 365.25, 50)) >>>>> (868.34, 31.64188002361066) >>>>> >>>>> # That's in the ball park >>>>> # Now what are the expected deaths per 22000 over 180 days >>>>> julia> mean_and_std(accumulate(death_rate, 22000, 180, 50)) >>>>> (94.3, 10.272312697891614) >>>>> >>>>> # that's nowhere close to the 14 and 15 found in the report. >>>>> # Probably the trial population was chosen to be young and healthy, >>>>> # so they have a lower death rate than the general population. >>>>> # let's use 14.5 deaths per 22000 per 180 days as an estimated trial >>>>> population death rate >>>>> # but convert the value to per_100000_per_year. >>>>> julia> est_death_rate = 14.5/22000*100000/180*365.25 >>>>> 133.74053030303028 >>>>> >>>>> # check the model: >>>>> julia> mean_and_std(accumulate(est_death_rate, 22000, 180, 50)) >>>>> (14.96, 3.6419326558007294) >>>>> >>>>> # in the ball park again. >>>>> >>>>> # So the point of this simulation isn't the exact result, it's the pairs >>>>> of results that this process can generate >>>>> # let's stack up two sets of simulations, call the top one 'treatment' >>>>> and the bottom one 'control' >>>>> # treatment and control are being generated by the exact same model, >>>>> # but their mutual relation is bouncing all over the place. >>>>> # That treatment>control or vice versa is just luck of the draw >>>>> >>>>> julia> [accumulate(est_death_rate, 22000, 180, 20), >>>>> accumulate(est_death_rate, 22000, 180, 20) ] >>>>> 2-element Vector{Vector{Int64}}: >>>>> [12, 12, 13, 11, 22, 13, 14, 16, 13, 14, 21, 17, 13, 14, 19, 11, 20, 11, >>>>> 9, 19] >>>>> [11, 14, 15, 17, 11, 19, 17, 12, 16, 14, 18, 16, 11, 16, 12, 16, 10, 14, >>>>> 17, 13] >>>>> >>>>> >>>>> On Wed, Sep 15, 2021 at 2:25 AM Pieter Steenekamp >>>>> <[email protected] <mailto:[email protected]>> wrote: >>>>>> In the Phizer report "Six Month Safety and Efficacy of the BNT162b2 mRNA >>>>>> COVID-19 Vaccine" >>>>>> (https://www.medrxiv.org/content/10.1101/2021.07.28.21261159v1.full.pdf >>>>>> <https://linkprotect.cudasvc.com/url?a=https%3a%2f%2fwww.medrxiv.org%2fcontent%2f10.1101%2f2021.07.28.21261159v1.full.pdf&c=E,1,rUTwn-4YDjMTIitGYgtzaVWCCSKfKXcEoh7SDtRStMsusyrW7lr8BiE_S6NFaQMPxiL5yVecYuPfxIM30IYWMgDDhUPyvvZ8ckhykGWfuHH8Ysfz-q4,&typo=1>) >>>>>> , I picked up the following: >>>>>> >>>>>> "During the blinded, controlled period, 15 BNT162b2 and 14 placebo >>>>>> recipients died" >>>>>> >>>>>> Does this mean the Phizer vaccine did not result in fewer total deaths >>>>>> in the vaccinated group compared to the placebo unvaccinated group? >>>>>> >>>>>> I sort of can't believe this, I obviously miss something. >>>>>> >>>>>> But of course, there are clear benefits in that the reported vaccine >>>>>> efficacy was 91.3% >>>>>> - 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