Ivermectin: Much More Than You Wanted To Know




About ten years ago, when the replication crisis started, we learned a certain set of tools for examining studies.

Check for selection bias. Distrust “adjusting for confounders”. Check for p-hacking and forking paths. Make teams preregister their analyses. Do forest plots to find publication bias. Stop accepting p-values of 0.049. Wait for replications. Trust reviews and meta-analyses, instead of individual small studies.

These were good tools. Having them was infinitely better than not having them. But even in 2014, I was writing about how many bad studies seemed to slip through the cracks even when we pushed this toolbox to its limits. We needed new tools.

I think the methods that Meyerowitz-Katz, Sheldrake, Heathers, Brown, Lawrence and others brought to the limelight this year are some of the new tools we were waiting for.

Part of this new toolset is to check for fraud. About 10 – 15% of the seemingly-good studies on ivermectin ended up extremely suspicious for fraud. Elgazzar, Carvallo, Niaee, Cadegiani, Samaha. There are ways to check for this even when you don’t have the raw data. Like:

The Carlisle-Stouffer-Fisher method: Check some large group of comparisons, usually the Table 1 of an RCT where they compare the demographic characteristics of the control and experimental groups, for reasonable p-values. Real data will have p-values all over the map; one in every ten comparisons will have a p-value of 0.1 or less. Fakers seem bad at this and usually give everything a nice safe p-value like 0.8 or 0.9.

GRIM – make sure means are possible given the number of numbers involved. For example, if a paper reports analyzing 10 patients and finding that 27% of them recovered, something has gone wrong. One possible thing that could have gone wrong is that the data are made up. Another possible thing is that they’re not giving the full story about how many patients dropped out when. But something is wrong.

But having the raw data is much better, and lets you notice if, for example, there are just ten patients who have been copy-pasted over and over again to make a hundred patients. Or if the distribution of values in a certain variable is unrealistic, like the Ariely study where cars drove a number of miles that was perfectly evenly distributed from 0 to 50,000 and then never above 50,000.

Author(s): Scott Alexander

Publication Date: 17 Nov 2021

Publication Site: Astral Codex Ten at substack

Sharp Reductions in COVID-19 Case Fatalities and Excess Deaths in Peru in Close Time Conjunction, State-By-State, with Ivermectin Treatments

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3765018



On May 8, 2020, Peru’s Ministry of Health approved ivermectin (IVM) for the treatment of COVID-19. A drug of Nobel Prize-honored distinction, IVM has been safely distributed in 3.7 billion doses worldwide since 1987. It has exhibited major, statistically significant reductions in case mortality and severity in 11 clinical trials for COVID-19, three with randomized controls. The indicated biological mechanism of IVM is the same as that of antiviral antibodies generated by vaccines—binding to SARS-CoV-2 viral spike protein, blocking viral attachment to host cells.

Mass distributions of IVM for COVID-19 treatments, inpatient and outpatient, were conducted in different timeframes with local autonomy in the 25 states (departamentos) of Peru. These treatments were conducted early in the pandemic’s first wave in 24 states, in some cases beginning even a few weeks before the May 8 national authorization, but delayed four months in Lima. Analysis was performed using Peruvian public health data for all-cause deaths and for COVID-19 case fatalities, as independently tracked for ages 60 and above. These daily figures were retrieved and analyzed by state. Case incidence data were not analyzed due to variations in testing methods and other confounding factors. These clinical data associated with IVM treatments beginning in different time periods, April through August 2020, in each of 25 Peruvian states, spanning an area equivalent to that from Denmark to Italy and Greece in Europe or from north to south along the US, with a total population of 33 million, provided a rich source for analysis.

For the 24 states with early IVM treatment (and Lima), excess deaths dropped 59% (25%) at +30 days and 75% (25%) at +45 days after day of peak deaths. Case fatalities likewise dropped sharply in all states but Lima, yet six indices of Google-tracked community mobility rose over the same period. For nine states having mass distributions of IVM in a short timeframe through a national program, Mega-Operación Tayta (MOT), excess deaths at +30 days dropped by a population-weighted mean of 74%, each drop beginning within 11 day after MOT start. Extraneous causes of mortality reductions were ruled out. These sharp major reductions in COVID-19 mortality following IVM treatment thus occurred in each of Peru’s states, with such especially sharp reductions in close time conjunction with IVM treatments in each of the nine states of operation MOT. Its safety well established even at high doses, IVM is a compelling option for immediate, large scale national deployments as an interim measure and complement to pandemic control through vaccinations.

Author(s): Juan J Chamie-Quintero, Jennifer Hibberd, David Scheim

Publication Date: 27 January 2021

Publication Site: SSRN