[FRIAM] Could this possibly be true?
Roger Critchlow
rec at elf.org
Thu Sep 16 12:15:57 EDT 2021
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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