Tag Archives: variance

Revelation re Covid

Occasionally you are putting your thoughts into words and realise you finally “get” something. That happened today when explaining why I was suspicious of two papers “explaining” Sars-Cov2 (aka Covid-19) that were linked to by NakedCapitalism.com. NB NC were not “endorsing” these studies, merely putting them out there for discussion and critique. I duly did and had a revelation.

I know whether SARS-COV-2 has primarily mean or variance effects. It is mostly about variances. Which is the nightmare scenario. How did I come to this revelation? Well, as usual, it was by absorbing the wise words and  experience of those who are “at the front line”.

Here is the deal.

  • We know none of the vaccines for SARS-COV-2 are sterilising.
  • Thus you “catch” it more than once.
  • We know from breakthrough cases and rapid emergence of variants (that respond at differential rates to existing vaccines) that people don’t follow a binary model [0,1] – be protected through chance/vaccine or get Covid. They can get it 2+ times.
  • Thus we have a logit/probit model with variances – when it comes to a “latent scale of susceptibility to infection” people do not have a “mountain” that is shifted following a bout or a vaccine. The vaccine just flattens the mountain into a gentle hill. Less likely to get horrifically ill but high variance – they can get it multiple times.
  • The papers referred to, as do all the papers I’ve read so far, assume the vaccine effects are ENTIRELY BASED IN MEANS.
  • This is conceptually incompatible with what we know from the vaccines and what their manufacturers state (albeit in small print sometimes) – the vaccines are non-sterilising. They reduce symptom severity but don’t stop you getting SARS-Cov-2 again.
  • THUS A MODEL ASSUMING THE ODDS/RISK RATIOS ARE HEAVILY INFLUENCED BY VARIANCES NOT MEANS IS THE ONLY VAGUELY VALID ONE. MEAN-BASED ONES ARE AUTOMATICALLY WRONG. THEIR ESTIMATES ARE BIASED.
  • YET ALL THE PAPERS ARE ASSUMING MEANS, i.e. STERILISING VACCINES. WTF?

 

So what will be the final outcome? Basically ANY piece that doesn’t attempt (even in a rudimentary way) to separate, or at least comment on, the mean-variance confound and note that the evidence favours variances is not going to be read by me. It goes into the same class as “papers that try to explain flights via flat earth paradigms”. Garbage. Nice to finally have a good rule that enables me to implement a policy I’ve rarely had enough “concrete data” to support. However, the data and interpretation from the good people at places like NC have “solved” the mean-variance confound for me.

Any paper that quotes risk/odds ratios without discussing variances is trash. I’m not reading or commenting on it. Maybe I’ll print it out for use in the next toilet paper shortage? Full stop.

DCE references

I’ve promised academic references for certain statements in last few blog entries. Here they are, numbered according to numbers in the articles elsewhere:

[1] Specification Error in Probit Models. Adonis Yatchew and Zvi Griliches. The Review of Economics and Statistics: Vol. 67, No. 1 (Feb., 1985), pp. 134-139 (6 pages) – THIS PAPER SHOWS WHY YOU MUST ADJUST FOR DIFFERENT VARIANCES BEFORE AGGREGATING HUMANS ELSE YOU GET BIAS NOT SIMPLY INCONSISTENCY. RELEVANT TO LOGIT OR PROBIT MODELS.

[2] Combining sources of preference data. Journal of Econometrics. David Hensher, Jordan Louviere, Joffre Swait. Journal of Econometrics: Volume 89, Issues 1–2, 26 November 1998, Pages 197-221- THIS PAPER SHOWS THEORETICALLY AND EMPIRICALLY WHY YOU MUST NET OUT VARIANCE DIFFERENCES BETWEEN DATA SOURCES (INCLUDING SUBJECTS) BEFORE AGGREGATING THEM.

[3] Confound it! That Pesky little scale constant messes up our convenient assumptions. Jordan Louviere & Thomas Eagle. USER-ACCESSIBLE EXPLANATION OF VARIANCE ISSUE IF [1] AND [2] UNAVAILABLE.

[4] Best-Worst Scaling: Theory, Methods and Applications. Jordan Louviere, Terry N Flynn, Anthony AJ Marley. Cambridge University Press (2015).

[5] The role of the scale parameter in estimation and comparison of multinomial logit models. Joffre Swait & Jordan Louviere. JMR 30(3): 305-314.