Astral Codex Ten

Dr. Bitterman

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Dr. Bitterman

Article

Dr. Bitterman is a recurring person in the Astral Codex Ten archive, appearing 4 times across 4 issues between November 17, 2021 and February 01, 2023. The archive places it in contexts such as “but Dr. Bitterman suggests that once you remove low quality trials and worm-related results”; “See further discussion by Dr. Bitterman here and here”; “Dr. Bitterman, one of the researchers who came up with the ivermectin-effects-are-from-worms hypothesis”. It most often appears alongside Alexandros Marinos, COVID, Gideon Meyerowitz-Katz.

Metadata

  • Category: People
  • Mention count: 4
  • Issue count: 4
  • First seen: November 17, 2021
  • Last seen: February 01, 2023

Appears In

Source Context

Recovered passages from the original issue text. When the raw archive preserved outbound links inside the source passage, they are listed directly under the quote.

Ivermectin: Much More Than You Wanted To Know
November 17, 2021 · Original source
Here’s the prevalence of roundworm infections by country (source). But alongside roundworms, there are threadworms, hookworms, blood flukes, liver flukes, nematodes, trematodes, all sorts of worms. Add them all up and somewhere between half and a quarter of people in the developing world have at least one parasitic worm in their body. Being full of worms may impact your ability to fight coronavirus. Gluchowska et al write: Helminth [ie worm] infections are among the most common infectious diseases. Bradbury et al. highlight the possible negative interactions between helminth infection and COVID-19 severity in helminth-endemic regions and note that alterations in the gut microbiome associated with helminth infection appear to have systemic immunomodulatory effects. It has also been proposed that helminth co-infection may increase the morbidity and mortality of COVID-19, because the immune system cannot efficiently respond to the virus; in addition, vaccines will be less effective for these patients, but treatment and prevention of helminth infections might reduce the negative effect of COVID-19. During millennia of parasite-host coevolution helminths evolved mechanisms suppressing the host immune responses, which may mitigate vaccine efficacy and increase severity of other infectious diseases. Treatment of worm infections might reduce the negative effect of COVID-19! And ivermectin is a deworming drug! You can see where this is going… The most relevant species of worm here is the roundworm Strongyloides stercoralis. Among the commonest treatments for COVID-19 is corticosteroids, a type of immunosuppresant drug. The types of immune responses it suppresses do more harm than good in coronavirus, so turning them off limits collateral damage and makes patients better on net. But these are also the types of immune responses that control Strongyloides. If you turn them off even very briefly, the worms multiply out of control, you get what’s called “Strongyloides hyperinfection”, and pretty often you die. According to the WHO: The current COVID-19 pandemic serves to highlight the risk of using systemic corticosteroids and, to a lesser extent, other immunosuppressive therapy, in populations with significant risk of underlying strongyloidiasis. Cases of strongyloidiasis hyperinfection in the setting of corticosteroid use as COVID-19 therapy have been described and draw attention to the necessity of addressing the risk of iatrogenic strongyloidiasis hyperinfection syndrome in infected individuals prior to corticosteroid administration. Although this has gained importance in the midst of a pandemic where corticosteroids are one of few therapies shown to improve mortality, its relevance is much broader given that corticosteroids and other immunosuppressive therapies have become increasingly common in treatment of chronic diseases (e.g. asthma or certain rheumatologic conditions). So you need to “address the risk” of strongyloides infection during COVID treatment in roundworm-endemic areas. And how might you address this, WHO? Treatment of chronic strongyloidiasis with ivermectin 200 µg/kg per day orally x 1-2 days is considered safe with potential contraindications including possible Loa loa infection (endemic in West and Central Africa), pregnancy, and weight <15kg. Given ivermectin’s safety profile, the United States has utilized presumptive treatment with ivermectin for strongyloidiasis in refugees resettling from endemic areas, and both Canada and the European Centre for Disease Prevention and Control have issued guidance on presumptive treatment to avoid hyperinfection in at risk populations. Screening and treatment, or where not available, addition of ivermectin to mass drug administration programs should be studied and considered. This is serious and common enough that, if you’re not going to screen for it, it might be worth “add[ing] ivermectin to mass drug administration programs” in affected areas! Dr. Avi Bitterman carries the hypothesis to the finish line: First two images are with all relevant studies; second two are a sensitivity analysis that removes some of the most dubious. The good ivermectin trials in areas with low Strongyloides prevalence, like Vallejos in Argentina, are mostly negative. The good ivermectin trials in areas with high Strongyloides prevalence, like Mahmud in Bangladesh, are mostly positive. Worms can’t explain the viral positivity outcomes (ie PCR), but Dr. Bitterman suggests that once you remove low quality trials and worm-related results, the rest looks like simple publication bias: This is still just a possibility. Maybe I’m over-focusing too hard on a couple positive results and this will all turn out to be nothing. Or who knows, maybe ivermectin does work against COVID a little - although it would have to be very little, fading to not at all in temperate worm-free countries. But this theory feels right to me. It feels right to me because it’s the most troll-ish possible solution. Everybody was wrong! The people who called it a miracle drug against COVID were wrong. The people who dismissed all the studies because they F@#king Love Science were wrong. Ivmmeta.com was wrong. Gideon Meyerowitz-Katz was…well, he was right, actually, I got the worm-related meta-analysis graphic above from his Twitter timeline. Still, an excellent troll. Also, the best part is that I ignorantly asked, in my description of Mahmud et al above: And it was! It was a fluke! A literal, physical, fluke! For my whole life, God has been placing terrible puns in my path to irritate me, and this would be the worst one ever! So it has to be true! The Scientific Takeaway 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.
Inline links: source, Gluchowska et al, the WHO, carries, https://substackcdn.com/image/fetch/$s_!xExE!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F5da21781-249c-4e59-b616-9f23d83cc044_2048x1184.jpeg, https://substackcdn.com/image/fetch/$s_!4SMr!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fdcd6e4b2-37f7-4602-93d5-2581c3b27a60_700x432.png, https://substackcdn.com/image/fetch/$s_!-6n2!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F7fd6e8f4-093e-4e02-bce7-363615146c9c_2228x1346.jpeg, https://substackcdn.com/image/fetch/$s_!CPZs!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fb0425847-198a-4bd3-a63b-149f15d147ba_700x432.png, https://substackcdn.com/image/fetch/$s_!H3rK!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F9972491b-25b0-4c06-8aca-86fce102ae63_666x147.png, even in 2014, The Carlisle-Stouffer-Fisher method
First two images are with all relevant studies; second two are a sensitivity analysis that removes some of the most dubious. The good ivermectin trials in areas with low Strongyloides prevalence, like Vallejos in Argentina, are mostly negative. The good ivermectin trials in areas with high Strongyloides prevalence, like Mahmud in Bangladesh, are mostly positive. Worms can’t explain the viral positivity outcomes (ie PCR), but Dr. Bitterman suggests that once you remove low quality trials and worm-related results, the rest looks like simple publication bias: This is still just a possibility. Maybe I’m over-focusing too hard on a couple positive results and this will all turn out to be nothing. Or who knows, maybe ivermectin does work against COVID a little - although it would have to be very little, fading to not at all in temperate worm-free countries. But this theory feels right to me. It feels right to me because it’s the most troll-ish possible solution. Everybody was wrong! The people who called it a miracle drug against COVID were wrong. The people who dismissed all the studies because they F@#king Love Science were wrong. Ivmmeta.com was wrong. Gideon Meyerowitz-Katz was…well, he was right, actually, I got the worm-related meta-analysis graphic above from his Twitter timeline. Still, an excellent troll. Also, the best part is that I ignorantly asked, in my description of Mahmud et al above: And it was! It was a fluke! A literal, physical, fluke! For my whole life, God has been placing terrible puns in my path to irritate me, and this would be the worst one ever! So it has to be true! The Scientific Takeaway 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.
Inline links: https://substackcdn.com/image/fetch/$s_!H3rK!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F9972491b-25b0-4c06-8aca-86fce102ae63_666x147.png, even in 2014, The Carlisle-Stouffer-Fisher method
Highlights From The Comments On Ivermectin
November 23, 2021 · Original source
But I’m also not as skeptical as Rzztmass. We don’t have to speculate about whether doctors in parasite-prone areas would give steroids - we know they did! Dr. Bitterman asked and lots of these trials admitted giving steroids to their patients. Ravakirti gave steroids to the entire control group, Lopez-Medina gave it to some controls. It happened! We know it happened!
Inline links: the entire control group
See further discussion by Dr. Bitterman here and here..
Inline links: here, here
Open Thread 200
November 28, 2021 · Original source
4: Dr. Bitterman, one of the researchers who came up with the ivermectin-effects-are-from-worms hypothesis, is defending his idea from some of the concerns you guys brought up in the comments. For example, in response to a comment that hyperinfection syndrome is rare, he writes:
Inline links: you guys brought up in the comments, writes
Response To Alexandros Contra Me On Ivermectin
February 01, 2023 · Original source
I thought the most plausible explanation for the discrepancy was Dr. Avi Bitterman’s hypothesis (now written up here) that ivermectin worked for its official indication of treating parasitic worms. COVID is frequently treated with steroids, steroids prevent the immune system from fighting a common parasitic worm called Strongyloides, and sometimes people getting treated for COVID died of Strongyloides hyperinfection. Ivermectin could prevent these deaths, which would mean fewer deaths in the treatment group than the control group, which would look like ivermectin preventing deaths from COVID in high-parasite-load areas (like the tropics) but not low-parasite-load areas (like temperate zones). This explained some of the mortality results, with the other endpoints likely being because of publication bias.
Inline links: here
“Synthetic control groups” - ie comparing people in a trial to some previously-known understanding of how a disease progresses - are a standard practice, and basically fine. Borody et al indeed have had amazing careers with many things they can be proud of. But I continue to believe that this paper is not among them. Synthetic control groups are more common in social sciences, but have occasionally been used in pharmacology when it would be unethical or extremely difficult to use a real control group. The most common use case is rare cancers, where it takes years to get enough patients to test a drug and it also seems kind of unethical to delay. Another good thing about rare cancers is that they're pretty discrete; you don't have to worry about things like "well, 90% of leukemias never make it to a doctor anyway, so maybe we're only seeing the serious leukemias" or "these guys counted the leukemias that get dealt with by the local doctors' office, but those other guys counted the leukemias that have to go to the hospital". More important, studies with synthetic control groups usually go above and beyond to justify why their synthetic control group should be a fair comparison to the treatment group. Here's an example, from a paper about a rare leukemia. They start by getting a synthetic control group from a previous randomized controlled trial of leukemia drugs (not the general population!) Then they throw out more than half their patients for not being a good match for the selection criteria of the current study. Then they investigate whether there are significant differences on five important demographic factors, and find a few. Then they re-weight the patients in the historical comaprator study to adjust out the differences between the previous population and the current population. Then they do some analyses to check if they re-weighted everything correctly. Then they apologize profusely for having to use this vastly inferior methodology at all: In special cases when a disease is rare, prognosis is very poor, and there are limited therapeutic options available, single-arm clinical trials may be used as evidence for accelerated drug approvals. Comprehensive evaluation of historical comparator or reference data can provide an additional approach for putting the efficacy of a new therapy into perspective.11, 12 In this study, we applied different statistical methods and sensitivity analyses to evaluate the clinical efficacy of blinatumomab against historical data. Concerns often raised regarding the use of historical comparator data are the influence of potential biases related to selection, misclassification and confounding.12 The requirement of rigorous eligibility criteria in the blinatumomab clinical study—such as Eastern Cooperative Oncology Group status of two or lower and absence of abnormal lab values during screening—may increase the chance of better outcomes in the clinical study than the historical data. While it may be possible to use unadjusted historical data when patient populations are sufficiently similar,27 the disproportionate number of advanced-stage patients in the blinatumomab trial required methods applied to individual-level data to minimize bias. Selection bias was minimized by use of stringent inclusion criteria into the historical data set and by weighting or adjusting for known prognostic factors. In addition, the historical data set represented adult R/R patients who received standard of care (excluding palliative care patients where possible), without any restrictions to any patient subgroups. Residual confounding may still remain and be difficult to control for, particularly in data sets where differences in important prognostic factors are unknown or not measured in one data set. In this study, nearly all known important prognostic factors were adjusted for in the weighted or propensity score analyses. Missing data on key covariates lead to exclusion of some records from the analyses (Figure 1), which may theoretically bias the overall results. However, our examination of records with missing covariates did not identify significant differences by patient demographic characteristics compared with patients who had complete data (data not shown). Misclassification bias was limited by harmonization of patient-level data in the pooled analysis, which employed common data definitions for disease classification and outcomes characterization. Compare this to how the Borody study discusses its synthetic control group: The control data was from contemporary infected subjects in Australia obtained from published Covid Tracking Data. I hesitate to say “they didn’t even say which tracking data”, because in the past I’ve said things like that and just missed it. But I can’t find them saying which tracking data. In Borody et al’s synthetic control group, 70/600 (11.5%) patients required hospitalization. But the US hospitalization rate appears to be about 1% for unvaccinated individuals. So Borody’s synthetic control group got 10x the expected hospitalization rate. This seems very relevant to this study finding that ivermectin decreases hospitalization by 90%! I’m not claiming this is fraudulent, or impossible, or means the study couldn’t have been good. And Borody claim to have used an “equivalent” control group, so maybe there was some adjustment done for this. But this is why we usually use more than one word to describe our control groups! Or use real control groups that don’t ruin your study if you do a finicky adjustment slightly wrong! I feel like these are the kinds of questions Alexandros needs to be asking, instead of just giving a link to a Stat News article about how sometimes synthetic control groups are okay. Also other questions, like “how come this found a 90% decrease in hospitalization and mortality, but lots of other studies found smaller decreases, and the biggest and best studies found none at all?” I know Alexandros’ answers are to find lots of flaws with the biggest and best studies, but these flaws wouldn’t be enough to cover up a 90% cure rate. And if you’re in the business of calling out flaws in studies I genuinely think having your control group be “we used some group of people somewhere in Australia, they had 10x the normal hospitalization rate, we won’t tell you anything else” would be the sort of flaw you would call out! Thomas Borody is a genuinely brilliant gastroenterologist and I am very grateful for his life-saving discoveries. But Elon Musk is a genuinely brilliant engineer and I am very grateful for his low-cost reusable rockets - and this doesn’t mean he never does crazy inexplicable things. Maybe Borody and his collaborators have a point from this study, but I don’t feel like it makes sense as written. If they ever explain what they were doing in more detail and it’s some sort of amazing 4D-chess move that makes total sense, I will apologize to them. Otherwise, stick to inventing amazing life-saving digestive therapies. In response to this section, Alexandros stresses that he is not necessarily saying Borody et al is incorrect or challenging my decision to leave it out. He writes: I will repeat that my strong objection, is that you wrote " this is not how you control group, @#!% you". I therefore pointed to stat news to support my case that, yes, this can indeed be how you control group. That's all. In the article I even noted that this aversion towards disrespect to elders may even be a cultural difference between us. To be clear, if I were making a case for ivermectin, I would not be relying on this study as my starting point. III. Hokey Meta-Analysis Alexandros points out that I used the wrong statistical test when analyzing the overall picture gleaned from this studies. He’s right. The right statistical test would make ivermectin look stronger, without changing the sign of the conclusion. After getting a core group of potentially trustworthy studies, I tried to see whether ivermectin still had a statistically significant positive effect in them. I tried to be honest that I didn’t really know how to do formal meta-analyses: Probably I’m forgetting some reason I can’t just do simple summary statistics to this, but whatever. It is p = 0.15, not significant . . . What happens if I unprincipledly pick whatever I think the most reasonable outcome to use from each study is? . . . Now it’s p = 0.04, seemingly significant I in fact could not do simple summary statistics to this. Alexandros describes the test I should have used, a DerSimonian-Laird test, and applies it to the same data. Now the numbers are p = 0.03 and p < 0.0001. I accept that I was wrong, he is right, and this is more accurate. My original conclusion to this section is that although you couldn’t be absolutely sure from the numbers, eyeballing things it definitely looked like ivermectin had an effect. I then went on to try to explain that effect. With Marinos’ corrections, you can be sure from the numbers, but the rest of the post - an attempt to explain the effect - still stands. IV. Worms Alexandros brings up issues with the Strongyloides hypothesis; Dr. Bitterman graciously responds. I find the issues real enough to lower my credence in the idea, but not to completely rule it out. Even if it is true, I probably overestimated how important it was. My original explanation for the effect was Dr. Avi Bitterman’s theory of Strongyloides hyperinfection. Many people in certain tropical regions are infected with the parasitic worm Strongyloides. Usually a person’s immune system keeps this worm under control, and the parasites cause only limited problems. But under certain situations - especially when people take immune-suppressing corticosteroids - the immune system fails, the worms multiply, and the patient can potentially die of sudden worm overgrowth (“hyperinfection”). Corticosteroids are a common COVID treatment. So plausibly some people in tropical areas fighting COVID are at risk of dying from worm hyperinfection. Ivermectin was originally an anti-parasitic-worm medication before being repurposed to fight COVID, and everyone agrees it is very good at this. So if many people in COVID trials are dying of worm infections, then ivermectin could help them. This would look like ivermectin reducing mortality in COVID trials, and make people wrongly conclude that ivermectin treats COVID. Alexandros responds to this theory here, again I’ll try to summarize: The original Bitterman paper concludes that ivermectin trials show stronger results in high-Strongyloides-prevalence regions. But it mixes prevalence data from two different papers with different methodologies. Correcting for this, the findings no longer clear a formal bar for statistical significance, and don’t really look significant either.
Inline links: are a standard practice, Here's an example, 11, 12, 27, Figure 1, about 1% for unvaccinated individuals, here
The control groups in high-worm-prevalence-area studies had no more deaths than in low-worm-prevalence-area studies. If the worms were killing people in the control groups (who ivermectin was then saving in the treatment groups) you would expect more deaths. You can find arguments for all these points at the link. (One additional thing Alexandros does that I really like: he compares the Strongyloides hypothesis - as an attempt to explain why these studies keep getting such different results - to other hypotheses. For example, studies in Latin America get negative results more often than others. This really feels like confronting the real question. He finds that Latin American studies do find lower efficacy for ivermectin than the other mostly Asian studies, and hypothesizes that this is because ivermectin is very popular in Latin America, the “control” group illicitly takes it without telling the researchers, and so these studies are inadvertantly comparing two ivermectin groups. This is another clever and elegant theory. Unfortunately, the recent spate of negative American studies sink it6. Still, I agree there is a strong geographic element here; worms are one possible explanation, but there are others - including the scientific culture in different countries. I appreciate Alexandros highlighting how much this is true.) I asked Dr. Bitterman for his thoughts. He reiterates that although steroids are one major cause of Strongyloides hyperinfection, another is eosinopenia, a decrease in the immune cells that fight parasites. COVID can cause eosinopenia directly, so just because a COVID patient didn’t get steroids, or was only on steroids for a short period, doesn’t prove that the patient couldn’t have had hyperinfection. On the mixing of different sources to get Strongyloides prevalence data, he said: As mentioned in the paper, when available we attempted to granulate by regional prevalence. This was often not possible because robust data did not available for a given country. Brazil is a large country (and multiple different studies in our analysis were in Brazil) with variability and Paula was a robust study. We decided to attempt to granulate instead of stacking the Brazil countries with the same prevalence even though they are in very different regions. His re-analysis is a crude one, and he often switches between using that analysis and the ecological model study. At the time of out paper's publication, the ecological model was not available. I offered to re-do the whole analysis with that study's raw data with a mutually agreed upon methodology, which we have not fully ironed down yet. On the long delay before hyperinfection kills: I don't think it happens in 1 or 2 weeks. But 3-4 weeks (within almost all study durations) is certainly not unheard of (again, without being treated). Even 1/3rd of the [untreated animals in a marmoset study Alexandros cites] died within the range of the study durations." On the more general argument: I have a higher credence of the effect modifier than he does. Perhaps the main thing I don't think he fully appreciates is just how few deaths need to be explained by this in order to substantially shift the RR. Even if this is the case for just a handful of control group deaths, the RR change is not trivial simply because of how low events the entire supposed benefit was in the first place. Furthermore, the new trials (ACTIV-6 400, ACTIV-6 600, COVID-OUT), while they all have very low mortality rates, all tip the needle in favor of the hypothesis. As expected, in the USA where the prevalence is near 0, all the deaths of those trials were in the ivermectin group (again, small event rates though). I could re-do the analysis with the new data even with his critiques of how he thinks it should be done (which is highly debatable) but I didn't end up doing it because at this point it would be an historical debate since the world has moved on from the topic. There are other points of course, there's quibbles line by line. I find Alexandros’ adjustment for Brazil somewhat convincing - not necessarily as a good adjustment, just in the sense that some adjustment needed to be done. I think the broader point is that results on the border of “statistical significance” often appear or go away depending on ambiguous decisions about coding single cases. Alexandros realizes this and includes a more gestalt style chart directly showing the correlation, which he says goes below the significance threshold when you recode the Brazilian studies. This chart seems to be missing some studies which might change its conclusions; it was made by a third party and Alexandros is going to get back to me with more information. Dr. Bitterman adds that more recent American studies strengthen his hypothesis. More discussion with Dr. Bitterman has also helped me better understand the context of this theory. Ivermectin does worst in studies of intermediate clinical endpoints: hospitalization, ICU admission, recovery time. It does best in studies of viral clearance rate and mortality. Viral clearance rate is a weak preclinical endpoint: not only is it especially susceptible to biases and file drawer effects, but it’s not that interesting unless it affects later clinical outcomes; many drugs change secondary endpoints but fail to change the things we care. Mortality is (usually) a strong and important endpoint; apparent positive results of ivermectin here require an explanation. The Strongyloides hypothesis tries to provide it. But I erred on my earlier post by holding it up as “the” explanation for a large and heterogenous group of studies which were mostly looking at endpoints other than mortality, or as a counter to ivmmeta’s analysis which found positive results everywhere for everything through statistical incompetence. I think I implicitly believed a stronger version of the worm hypothesis - that even in places without literal Strongyloides literally killing you, some people had some parasitic worms that were holding them back, ivermectin killed those worms, and that made them healthier overall and better able to deal with COVID. But nobody has asserted or defended that hypothesis and there’s no evidence for it. When I asked Dr. Bitterman, he pointed out that the opposite was at least as credible: parasitic worms depress the immune system, but immune overreaction is a major cause of death in COVID, so getting rid of them could make things worse rather than better. The original post should have explained this hypothesis better, devoted less emphasis to it, and focused more on publication bias and other issues that could explain the overall result. In some cases, these issues would have shed more light on the mortality statistics too. On my original post, I wrote: Parasitic worms are a significant confounder in some ivermectin studies, such that they made them get a positive result even when honest and methodologically sound: 50% confidence In retrospect this is framed too weakly - “significant” in “some” studies is compatible with irrelevant overall. Still, sticking to the spirit of what I meant, I think I would lower this guess to more like 35% now , and lower my overall estimate of how much of the mystery it explains even further. I’m not an expert on this, you shouldn’t care about my exact probability, and I’m only mentioning it to communicate clearly and try to hold myself accountable. V. Publication Bias Alexandros has various arguments against funnel plots in general, and Dr. Bitterman’s funnel plot in particular. Some of these arguments are reasonable, but taken together they would discredit 95 - 100% of all funnel plots everywhere. Trying to destroy the whole institution of funnel plots just because one of them disagrees with your hypothesis is . . . honestly a move I have to respect. I agree that these provide Bayesian evidence, rather than 100% irrefutable evidence, of publication bias, and need to be considered in the context of everything else going on. After doing that, I still think they’re publication bias. That makes publication bias more important. In the original post, I included this funnel plot from Dr. Bitterman: In case you haven’t seen one of these before: this plots how big an effect the study found (horizontal axis) against study size (vertical axis). Studies that find ivermectin had no effect are at the center (RR = 1), studies that find a strong curative effect are to the left, studies that find a strong harmful effect are to the right. When all studies are good, we have no reason to expect a correlation between study size and ivermectin efficacy - any deviations from the true effect should be random. This would look like a triangle centered around the true effect of the drug, with an equal number of studies on both sides. When there is a lot of publication bias, we should expect that small studies get published only if they find exciting results, and big studies get published regardless (because a lot of work went into them, someone will want to publish them, and journals will accept them regardless of how exciting they are). So here you would expect to see big studies around zero, and an asymmetric tail of smaller studies heading in the more-exciting direction. This is what we see on Dr. Bitterman’s plot, suggesting strong publication bias for ivermectin results. Alexandros’ full counterargument is here. Trying to sum it up: Funnel plots can sometimes look deceptive, or be misreported, and are generally suspect.
Inline links: 6, studies of intermediate clinical endpoints, https://substackcdn.com/image/fetch/$s_!rlgm!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F174e569d-8a2c-4c11-a2ff-d355f4bce5b1_1200x808.jpeg, here