[FRIAM] Could this possibly be true?
Pieter Steenekamp
pieters at randcontrols.co.za
Thu Sep 16 13:17:27 EDT 2021
Marcus,
The data are fresh from the horse's mouth, Phizer themselves.
Their main report
https://www.medrxiv.org/content/10.1101/2021.07.28.21261159v1.full.pdf and
their supplementary data
https://www.medrxiv.org/content/medrxiv/early/2021/07/28/2021.07.28.21261159/DC1/embed/media-1.pdf
Maybe you want to read the very first email in this thread again?
Pieter
On Thu, 16 Sept 2021 at 18:37, <thompnickson2 at gmail.com> wrote:
> Just so’s you know,
>
>
>
> I took it from this email thread, where it pretty much stood alone.
>
>
>
> And remember. Y ou (we) aren’t just writing to one another. You (we) are
> writing to 300 other people.
>
>
>
> n
>
>
>
> Nick Thompson
>
> ThompNickSon2 at gmail.com
>
> https://wordpress.clarku.edu/nthompson/
>
>
>
> *From:* Friam <friam-bounces at redfish.com> *On Behalf Of *Marcus Daniels
> *Sent:* Thursday, September 16, 2021 12:32 PM
> *To:* The Friday Morning Applied Complexity Coffee Group <
> friam at redfish.com>
> *Subject:* Re: [FRIAM] Could this possibly be true?
>
>
>
> Trials are done in phases. First they have to establish that the
> treatment does no harm. That’s what this was about. Some anti-vax jerk
> probably dug up this reference and started quoting it out of context, like
> you are.
>
>
>
> *From:* Friam <friam-bounces at redfish.com> *On Behalf Of *
> thompnickson2 at gmail.com
> *Sent:* Thursday, September 16, 2021 9:28 AM
> *To:* 'The Friday Morning Applied Complexity Coffee Group' <
> friam at redfish.com>
> *Subject:* Re: [FRIAM] Could this possibly be true?
>
>
>
> You guys keep proving my point about the 100th Meridian and irony.
>
>
>
> 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!
>
>
>
> n
>
>
>
> Nick Thompson
>
> ThompNickSon2 at gmail.com
>
> https://wordpress.clarku.edu/nthompson/
>
>
>
> *From:* Friam <friam-bounces at redfish.com> *On Behalf Of *Roger Critchlow
> *Sent:* Thursday, September 16, 2021 12:16 PM
> *To:* The Friday Morning Applied Complexity Coffee Group <
> friam at redfish.com>
> *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 <
> pieters at randcontrols.co.za> 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
>
> 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 <wimberly3 at gmail.com> 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 <thompnickson2 at gmail.com> 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
>
> ThompNickSon2 at gmail.com
>
> https://wordpress.clarku.edu/nthompson/
>
>
>
> *From:* Friam <friam-bounces at redfish.com> *On Behalf Of *Pieter Steenekamp
> *Sent:* Thursday, September 16, 2021 7:34 AM
> *To:* The Friday Morning Applied Complexity Coffee Group <
> friam at redfish.com>
> *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 <rec at elf.org> 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, 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
> 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 <
> pieters at randcontrols.co.za> 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) ,
> 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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