[FRIAM] Could this possibly be true?
Pieter Steenekamp
pieters at randcontrols.co.za
Thu Sep 16 12:00:31 EDT 2021
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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