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Published benefits of ivermectin use in Itajaí, Brazil for COVID-19 infection, hospitalisation, and mortality are entirely explained by statistical artefacts

Mills et al., medRxiv, doi:10.1101/2023.08.10.23293924
Aug 2023  
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Highly flawed and unethical analysis, with multiple basic errors, invalid assumptions, highly biased discussion, failure to correct any of the issues for over two months, major changes without explanation, and repeating known major errors.
There are major changes to all tables and figures, without explanation. For example, in Table 2 the KC22 death rate for non-users has changed from 2.60% to 0.17%.
For example, authors claim that there were ""499 reported deaths - a citywide post-hospitalisation COVID death rate of 30.1% during the study period"", which is not possible based on official data, (B). Authors appear to have used a mismatched dataset, incorrectly including data from neighboring municipalities (C), (D). Author's R code appears to incorrectly select patients (E). Authors also do not appear to have read the original paper in full, missing details of the population analyzed (F). They also appear to have missed the distinction between an individual's residential city and the location of their medical care (C), (G). Authors assumptions are also invalid - participation for patients with symptoms may depend on multiple factors. For example, patients with more severe symptoms may be more likely to perceive serious risk and more likely to seek known effective treatment. Authors also only discuss potential biases in one direction - for example ignoring bias due to dropping out of the program. The assumption of no effect for treatment is contradicted by 17 out of 17 prophylaxis studies.
While it is possible the errors were accidental, two factors increase the severity.
The extreme mismatch in deaths with the original paper should have been a sign to check. The cause of the mismatch should have been relatively easy to determine, and in case authors were still stuck, they could have asked the original authors. Such an extreme and easily identifiable error in mortality numbers undermines author's credibility.
Further, authors were notified of the mortality error within 12 hours of announcing the preprint on Twitter and acknowledged the error, initially claiming a rapid correction would be forthcoming. However, the paper had still not been withdrawn over two months after publication, and was still available without any warning, note, or even partial correction. Leaving known highly incorrect information for so long raises ethical concerns.
Discussion in this paper is highly biased, supporting concern over bias from the team. Authors claim that ""rigorous randomised clinical trials have largely not found clinical benefit for ivermectin use in COVID-19"", but in reality all 17 of 17 prophylaxis studies at the time showed statistically significant benefits of ivermectin, including all 4 of 4 RCTs.
Authors reference none of the other prophylaxis studies, and cherry pick a few non-prophylaxis studies, and even then they ignore the signs of efficacy in those studies. While it's common for authors to misrepresent research, excluding all 16 other studies and cherry picking a few non-prophylaxis studies stands out as one of the most extreme cases.
Authors cite only 4 of the 99 studies, all with major issues, and none reporting prophylaxis results. Further, the 4 studies cited all show signs of efficacy - Lim shows 69% lower mortality, close to statistical significance; Bramante showed 61% lower hospitalization for ivermectin vs. placebo (not reported by authors, without statistical significance); the co-principal investigator of Reis has stated ""there is a clear signal that IVM works in COVID patients""; and in Naggie the posterior probability ivermectin was effective was 99%, 98%, 97% for mean time unwell and clinical progression @14 and 7 days, all exceeding the pre-specified threshold for superiority (clinical progression results were changed without explanation between the preprint and journal version, the primary outcome was not reported, with a new post-hoc highly biased outcome created).
All 4 of the cited studies are low quality studies with major issues. For example, one trial reports impossible data, refuses to release data despite pledging to, broke blinding and shared interim results externally, had randomization failure, blinding failure, and numerous protocol violations Marinos, Reis.
Author's summary is discordant with reality - 60 studies show statistically significant positive results for one or more outcomes (including 23 RCTs) Abbas, Ahmed, Ahsan, Alam, Aref, Aref (B), Babalola, Behera, Behera (B), Bernigaud, Biber, Borody, Budhiraja, Bukhari, Cadegiani, Carvallo, Carvallo (B), Chaccour, Chahla, Chahla (B), Chowdhury, de Jesús Ascencio-Montiel, de la Rocha, Desort-Henin, Efimenko, Elalfy, Espitia-Hernandez, Faisal, Ghauri, Gorial, Hashim, Hellwig, IVERCOR PREP, Kerr, Khan, Lim, Lima-Morales, Mahmud, Mayer, Merino, Mondal, Morgenstern, Mourya, Naggie, Okumuş, Osati, Ozer, Qadeer, Rajter, Rezai, Rezk, Samajdar, Seet, Shahbaznejad, Shimizu, Shouman, Soto-Becerra, Spoorthi, Tanioka, Thairu.
Further, author's discussion of preclinical data is highly cherry-picked and highly misleading, with inaccurate interpretation of the concentration required based on Caly (see discussion) and other studies, and no mention of the majority of proposed mechanisms and supporting data.
2.5 months later authors released an updated preprint which has very different data, but unethically does not detail or mention the authors' errors. Note that GTK pre-announced that there would be no change in the results on Twitter before it was possible to have made corrections. The analysis is unreliable because the conclusion came before the analysis, and the analysis includes assumptions that can be easily tweaked for any desired result. Moreover, many errors have clearly not been fixed. As before, the authors reference none of the other prophylaxis studies, and cherry pick a few low quality non-prophylaxis studies with many critical issues, and even then they ignore the signs of efficacy in those studies. While it's common for authors to misrepresent research, excluding all 16 other studies and cherry picking a few non-prophylaxis studies stands out as one of the most extreme cases. All 17 of 17 prophylaxis studies at the time showed statistically significant benefits of ivermectin, including all 4 of 4 RCTs, contradictory to the author's claims. Further, it is known that author's were aware of these errors.
Author's extreme bias, silent major changes without explanation, and failure to correct known errors suggests their goal is not to accurately evaluate the clinical evidence.
Mills et al., 15 Aug 2023, retrospective, Brazil, preprint, 3 authors, study period 7 July, 2020 - 2 December, 2020.
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Published benefits of ivermectin use in Itajaí, Brazil for COVID-19 infection, hospitalisation, and mortality are entirely explained by statistical artefacts
Robin Mills, Ana Carolina Peçanha Antonio, Greg Tucker-Kellogg
Background Two recent publications by Kerr et al. (Cureus 14(1):e21272; Cureus 14(8): e28624) reported dramatic effects of prophylactic ivermectin use for both prevention of COVID-19 and reduction of COVID-19-related hospitalisation and mortality, including a dose-dependent effect of ivermectin prophylaxis. These papers have gained an unusually large public influence: they were incorporated into debates around COVID-19 policies and may have contributed to decreased trust in vaccine efficacy and public health authorities more broadly. Both studies were based on retrospective observational analysis of city-wide registry data from the city of Itajaí, Brazil from July-December 2020. Methods Starting with initially identified sources of error, we conducted a revised statistical analysis of available data, including data made available with the original papers and public data from the Brazil Ministry of Health. We identified additional uncorrected sources of bias and errors from the original analysis, including incorrect subject exclusion and missing subjects, an enrolment time bias, and multiple sources of immortal time bias. In models assuming no actual effect from ivermectin use, we conducted Monte Carlo simulations to estimate the contribution of these biases to any observed effect. Results Untreated statistical artefacts and methodological errors alone lead to dramatic apparent risk reduction associated with Ivermectin use in both studies. The magnitude of apparent risk reduction from these artefacts is comparable to the results reported by the studies themselves, including apparent protection from infection, hospitalisation, and death, and including the reported apparent dose-response relationship. Conclusions The inference of ivermectin efficacy reported in both papers is unsupported, as the observed effects are entirely explained by untreated statistical artefacts and methodological errors. Our re-analysis calls for caution in interpreting highly publicised observational studies and highlights the importance of common sources of bias in clinical research.
Author contributions RM conducted the research required to uncover all fallacies mentioned in the manuscript (delayed registrations, missing data, incorrect inclusion of prior infections, all biases). GTK independently uncovered the immortal time bias, enrolment bias, and attrition bias. RM and GTK wrote the simulation and analysis code. GTK in particular, RM, and ACPA contributed to writing and reviewing the manuscript. ACPA acquired the data from the Brazilian Health Ministry. ACPA has been in frequent contact with Itajaí City Hall in an attempt to get access to missing raw data. RM has been in frequent contact with the KC22 and KB22 authors in an attempt to discuss the issues in their work before publishing this manuscript.
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