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 < piet...@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 <wimber...@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 <thompnicks...@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 >>> >>> thompnicks...@gmail.com >>> >>> https://wordpress.clarku.edu/nthompson/ >>> >>> >>> >>> *From:* Friam <friam-boun...@redfish.com> *On Behalf Of *Pieter >>> Steenekamp >>> *Sent:* Thursday, September 16, 2021 7:34 AM >>> *To:* The Friday Morning Applied Complexity Coffee Group < >>> friam@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 <r...@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 < >>> piet...@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% >>> >>> - .... . -..-. . -. -.. -..-. .. ... -..-. .... . .-. . >>> FRIAM Applied Complexity Group listserv >>> Zoom Fridays 9:30a-12p Mtn GMT-6 bit.ly/virtualfriam >>> un/subscribe http://redfish.com/mailman/listinfo/friam_redfish.com >>> FRIAM-COMIC http://friam-comic.blogspot.com/ >>> archives: http://friam.471366.n2.nabble.com/ >>> >>> - .... . -..-. . -. -.. -..-. .. ... -..-. .... . .-. . >>> FRIAM Applied Complexity Group listserv >>> Zoom Fridays 9:30a-12p Mtn GMT-6 bit.ly/virtualfriam >>> un/subscribe http://redfish.com/mailman/listinfo/friam_redfish.com >>> FRIAM-COMIC http://friam-comic.blogspot.com/ >>> archives: http://friam.471366.n2.nabble.com/ >>> >>> >>> .-- .- -. - / .- -.-. - .. --- -. ..--.. / -.-. --- -. .--- ..- --. .- - >>> . >>> FRIAM Applied Complexity Group listserv >>> Zoom Fridays 9:30a-12p Mtn UTC-6 bit.ly/virtualfriam >>> un/subscribe http://redfish.com/mailman/listinfo/friam_redfish.com >>> FRIAM-COMIC http://friam-comic.blogspot.com/ >>> archives: >>> 5/2017 thru present https://redfish.com/pipermail/friam_redfish.com/ >>> 1/2003 thru 6/2021 http://friam.383.s1.nabble.com/ >>> >> >> .-- .- -. - / .- -.-. - .. --- -. ..--.. / -.-. --- -. .--- ..- --. .- - . >> FRIAM Applied Complexity Group listserv >> Zoom Fridays 9:30a-12p Mtn UTC-6 bit.ly/virtualfriam >> un/subscribe http://redfish.com/mailman/listinfo/friam_redfish.com >> FRIAM-COMIC http://friam-comic.blogspot.com/ >> archives: >> 5/2017 thru present https://redfish.com/pipermail/friam_redfish.com/ >> 1/2003 thru 6/2021 http://friam.383.s1.nabble.com/ >> > > .-- .- -. - / .- -.-. - .. --- -. ..--.. / -.-. --- -. .--- ..- --. .- - . > FRIAM Applied Complexity Group listserv > Zoom Fridays 9:30a-12p Mtn UTC-6 bit.ly/virtualfriam > un/subscribe http://redfish.com/mailman/listinfo/friam_redfish.com > FRIAM-COMIC http://friam-comic.blogspot.com/ > archives: > 5/2017 thru present https://redfish.com/pipermail/friam_redfish.com/ > 1/2003 thru 6/2021 http://friam.383.s1.nabble.com/ >
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