286 Matching Annotations
  1. Last 7 days
    1. Adjiwanou, V., Alam, N., Alkema, L., Asiki, G., Bawah, A., Béguy, D., Cetorelli, V., Dube, A., Feehan, D., Fisker, A. B., Gage, A., Garcia, J., Gerland, P., Guillot, M., Gupta, A., Haider, M. M., Helleringer, S., Jasseh, M., Kabudula, C., … You, D. (2020). Measuring excess mortality during the COVID-19 pandemic in low- and lower-middle income countries: The need for mobile phone surveys [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/4bu3q

  2. May 2021
    1. (6) ReconfigBehSci on Twitter: “@MichaelPaulEdw1 @islaut1 @ToddHorowitz3 @richarddmorey @MaartenvSmeden and not just misguided (as too simplistic) but part of the problem....” / Twitter. (n.d.). Retrieved February 24, 2021, from https://twitter.com/SciBeh/status/1356528429211021319

    1. ReconfigBehSci. (2021, February 2). @MichaelPaulEdw1 @islaut1 @ToddHorowitz3 @richarddmorey @MaartenvSmeden as I just said to @islaut1 if you want to force the logical contradiction you move away entirely from all of the interesting cases of inference from absence in everyday life, including the interesting statistical cases of, for example, null findings—So I think we now agree? [Tweet]. @SciBeh. https://twitter.com/SciBeh/status/1356530759016792064

    1. ReconfigBehSci. (2021, February 1). @MaartenvSmeden @richarddmorey you absolutely did (and I would have been disappointed if you hadn’t ;-)! It was a general comment prompted by the fact that the title of the article you linked to doesn’t (as is widespread), and I actually genuinely think this is part of the “problem” in pedagogical terms. 1/2 [Tweet]. @SciBeh. https://twitter.com/SciBeh/status/1356227423067664384

    1. ReconfigBehSci. (2021, February 1). @islaut1 @richarddmorey I think of strength of inference resting on P(not E|not H) (for coronavirus case). Search determines the conditional probability (and by total probability of course prob of evidence) but it isn’t itself the evidence. So, was siding with R. against what I thought you meant ;-) [Tweet]. @SciBeh. https://twitter.com/SciBeh/status/1356216290847944706

    1. ReconfigBehSci. (2021, February 1). @islaut1 @richarddmorey I think diff. Is that your first response seemed to indicate the evidence was the search itself (contra Richard) so turning an inference from absence of something into a kind of positive evidence ('the search’). Let’s call absence of evidence “not E”. 1/2 [Tweet]. @SciBeh. https://twitter.com/SciBeh/status/1356215051238191104

    1. ReconfigBehSci. (2021, February 2). @MichaelPaulEdw1 @islaut1 @ToddHorowitz3 @richarddmorey as this account is focussed on COVID, maybe time to move the discussion elsewhere- happy to discuss further if you want to get in touch by email—U.hahn" "https://t.co/HOGwHragEb [Tweet]. @SciBeh. https://twitter.com/SciBeh/status/1356529368630239232

    1. ReconfigBehSci. (2021, February 1). @MaartenvSmeden @richarddmorey 2/2 Having conducted experiments on lay understanding of arguments from ignorance, in my experience, people intuitively understand probabilistic impact of factors, such as quality of search, that moderate strength. Rather than build on that, we work against it with slogan! [Tweet]. @SciBeh. https://twitter.com/SciBeh/status/1356228495714746370

    1. Maarten van Smeden. (2021, February 1). Personal top 10 fallacies and paradoxes in statistics 1. Absence of evidence fallacy 2. Ecological fallacy 3. Stein’s paradox 4. Lord’s paradox 5. Simpson’s paradox 6. Berkson’s paradox 7. Prosecutors fallacy 8. Gambler’s fallacy 9. Lindsey’s paradox 10. Low birthweight paradox [Tweet]. @MaartenvSmeden. https://twitter.com/MaartenvSmeden/status/1356147552362639366

  3. Apr 2021
    1. The complement of an event AAA in a sample space SSS, denoted AcAcA^c, is the collection of all outcomes in SSS that are not elements of the set AAA. It corresponds to negating any description in words of the event AAA.

      The complement of an event \(A\) in a sample space \(S\), denoted \(A^c\), is the collection of all outcomes in \(S\) that are not elements of the set \(A\). It corresponds to negating any description in words of the event \(A\).


      The complement of an event \(A\) consists of all outcomes of the experiment that do not result in event \(A\).

      Complement formula:

      $$P(A^c)=1-P(A)$$

    1. Jeremy Faust MD MS (ER physician) on Twitter: “Let’s talk about the background risk of CVST (cerebral venous sinus thrombosis) versus in those who got J&J vaccine. We are going to focus in on women ages 20-50. We are going to compare the same time period and the same disease (CVST). DEEP DIVE🧵 KEY NUMBERS!” / Twitter. (n.d.). Retrieved April 15, 2021, from https://twitter.com/jeremyfaust/status/1382536833863651330

    1. Dr Lea Merone MBChB (hons) MPH&TM MSc FAFPHM Ⓥ. ‘I’m an Introvert and Being Thrust into the Centre of This Controversy Has Been Quite Confronting. I’ve Had a Little Processing Time Right Now and I Have a Few Things to Say. I Won’t Repeat @GidMK and His Wonderful Thread but I Will Say 1 This Slander of Us Both Has Been 1/n’. Tweet. @LeaMerone (blog), 29 March 2021. https://twitter.com/LeaMerone/status/1376365651892166658.

  4. Mar 2021
    1. Ashish K. Jha, MD, MPH. (2020, December 12). Michigan vs. Ohio State Football today postponed due to COVID But a comparison of MI vs OH on COVID is useful Why? While vaccines are coming, we have 6-8 hard weeks ahead And the big question is—Can we do anything to save lives? Lets look at MI, OH for insights Thread [Tweet]. @ashishkjha. https://twitter.com/ashishkjha/status/1337786831065264128

    1. Stefan Simanowitz. (2020, November 14). “Sweden hoped herd immunity would curb #COVID19. Don’t do what we did” write 25 leading Swedish scientists “Sweden’s approach to COVID has led to death, grief & suffering. The only example we’re setting is how not to deal with a deadly infectious disease” https://t.co/azOg6AxSYH https://t.co/u2IqU5iwEn [Tweet]. @StefSimanowitz. https://twitter.com/StefSimanowitz/status/1327670787617198087

  5. Feb 2021
    1. A fairly comprehensive list of problems and limitations that are often encountered with data as well as suggestions about who should be responsible for fixing them (from a journalistic perspective).

  6. Jan 2021
  7. Dec 2020
    1. In addition, for music and movies, we also normalize the resulting scores (akin to "grading on a curve" in college), which prevents scores from clumping together.
    1. Stuaert Rtchie [@StuartJRitchie] (2020) This encapsulates the problem nicely. Sure, there’s a paper. But actually read it & what do you find? p-values mostly juuuust under .05 (a red flag) and a sample size that’s FAR less than “25m”. If you think this is in any way compelling evidence, you’ve totally been sold a pup. Twitter. Retrieved from:https://twitter.com/StuartJRitchie/status/1305963050302877697

    1. Inferential statistics are the statistical procedures that are used to reach conclusions aboutassociations between variables. They differ from descriptive statistics in that they are explicitly designed to test hypotheses.

      Descriptive statistics are used specifically to test hypotheses.

  8. Nov 2020
  9. Oct 2020
    1. CDC reverses course on testing for asymptomatic people who had Covid-19 contact

      Take Away

      Transmission of viable SARS-CoV-2 RNA can occur even from an infected but asymptomatic individual. Some people never become symptomatic. That group usually becomes non-infectious after 14 days from initial infection. For persons displaying symptoms , the SARS-CoV-2 RNA can be detected for 1 to 2 days prior to symptomatology. (1)

      The Claim

      Asymptomatic people who had SARS-CoV-2 contact should be tested.

      The Evidence

      Yes, this is a reversal of August 2020 advice. What is the importance of asymptomatic testing?

      Studies show that asymptomatic individuals have infected others prior to displaying symptoms. (1)

      According to the CDC’s September 10th 2020 update approximately 40% of infected Americans are asymptomatic at time of testing. Those persons are still contagious and are estimated to have already transmitted the virus to some of their close contacts. (2)

      In a report appearing in the July 2020 Journal of Medical Virology, 15.6% of SARS-CoV-2 positive patients in China are asymptomatic at time of testing. (3)

      Asymptomatic infection also varies by age group as older persons often have more comorbidities causing them to be susceptible to displaying symptoms earlier. A larger percentage of children remain asymptomatic but are still able to transmit the virus to their contacts. (1) (3)

      Transmission modes

      Droplet transmission is the primary proven mode of transmission of the SARS-CoV-2 virus, although it is believed that touching a contaminated surface then touching mucous membranes, for example, the mouth and nose can also serve to transmit the virus. (1)

      It is still unclear how big or small a dose of exposure to viable viral particles is needed for transmission; more research is needed to elucidate this. (1)

      Citations

      (1) https://www.who.int/news- room/commentaries/detail/transmission-of-sars-cov-2- implications-for-infection-prevention-precautions

      (2) https://www.cdc.gov/coronavirus/2019- ncov/hcp/planning-scenarios.html

      (3) He J, Guo Y, Mao R, Zhang J. Proportion of asymptomatic coronavirus disease 2019: A systematic review and metaanalysis. J Med Virol. 2020;1– 11.https://doi.org/10.1002/jmv.26326

  10. Sep 2020
    1. The lowest value for false positive rate was 0.8%. Allow me to explain the impact of a false positive rate of 0.8% on Pillar 2. We return to our 10,000 people who’ve volunteered to get tested, and the expected ten with virus (0.1% prevalence or 1:1000) have been identified by the PCR test. But now we’ve to calculate how many false positives are to accompanying them. The shocking answer is 80. 80 is 0.8% of 10,000. That’s how many false positives you’d get every time you were to use a Pillar 2 test on a group of that size.

      Take Away: The exact frequency of false positive test results for COVID-19 is unknown. Real world data on COVID-19 testing suggests that rigorous testing regimes likely produce fewer than 1 in 10,000 (<0.01%) false positives, orders of magnitude below the frequency proposed here.

      The Claim: The reported numbers for new COVID-19 cases are overblown due to a false positive rate of 0.8%

      The Evidence: In this opinion article, the author correctly conveys the concern that for large testing strategies, case rates could become inflated if there is (a) a high false positive rate for the test and (b) there is a very low prevalence of the virus within the population. The false positive rate proposed by the author is 0.8%, based on the "lowest value" for similar tests given by a briefing to the UK's Scientific Advisory Group for Emergencies (1).

      In fact, the briefing states that, based on another analysis, among false positive rates for 43 external quality assessments, the interquartile range for false positive rate was 0.8-4.0%. The actual lowest value for false positive rate from this study was 0% (2).

      An upper limit for false positive rate can also be estimated from the number of tests conducted per confirmed COVID-19 case. In countries with low infection rates that have conducted widespread testing, such as Vietnam and New Zealand, at multiple periods throughout the pandemic they have achieved over 10,000 tests per positive case (3). Even if every single positive was false, the false positive rate would be below 0.01%.

      The prevalence of the virus within a population being tested can affect the positive predictive value of a test, which is the likelihood that a positive result is due to a true infection. The author here assumes the current prevalence of COVID-19 in the UK is 1 in 1,000 and the expected rate of positive results is 0.1%. Data from the University of Oxford and the Global Change Data Lab show that the current (Sept. 22, 2020) share of daily COVID-19 tests that are positive in the UK is around 1.7% (4). Therefore, based on real world data, the probability that a patient is positive for the test and does have the disease is 99.4%.

      Sources: (1) https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/895843/S0519_Impact_of_false_positives_and_negatives.pdf

      (2) https://www.medrxiv.org/content/10.1101/2020.04.26.20080911v3.full.pdf+html

      (3) https://ourworldindata.org/coronavirus-data-explorer?yScale=log&zoomToSelection=true&country=USA~DEU~IND~ITA~AUS~VNM~FIN~NZL~GBR&region=World&testsPerCaseMetric=true&interval=smoothed&aligned=true&smoothing=7&pickerMetric=location&pickerSort=asc

      (4) https://ourworldindata.org/coronavirus-data-explorer?zoomToSelection=true&country=USA~DEU~IND~ITA~AUS~VNM~FIN~NZL~GBR&region=World&positiveTestRate=true&interval=smoothed&aligned=true&smoothing=7&pickerMetric=location&pickerSort=asc

    1. H not

      I'm sorry but this is kind of lazy from the author. Either write H0, \(H_0\) or H naught. H not sounds like you're saying H "not" (negation)

  11. Aug 2020
    1. Diewert, W. Erwin, and Kevin J Fox. ‘Measuring Real Consumption and CPI Bias under Lockdown Conditions’. Working Paper. Working Paper Series. National Bureau of Economic Research, May 2020. https://doi.org/10.3386/w27144.