https://ivmmeta.com/ Ivermectin is effective for COVID-19: meta analysis of 26 studies Covid Analysis, November 26, 2020 (Version 6, December 16, 2020) @CovidAnalysis PDF •Ivermectin is effective for COVID-19. 100% of studies report positive effects. The probability that an ineffective treatment generated results as positive as the 26 studies to date is estimated to be 1 in 67 million (p = 0.000000015). •Early treatment is most successful, with an estimated reduction of 87% in the effect measured using a random effects meta-analysis, RR 0.13 [0.04-0.40]. •100% of the 10 Randomized Controlled Trials (RCTs) report positive effects, with an estimated reduction of 74% in the effect measured using a random effects meta-analysis, RR 0.26 [0.12-0.56]. A B C Figure 1. A. Scatter plot showing the distribution of effects reported in early treatment studies and in all studies (the vertical lines and shaded boxes show the median and interquartile range). Early treatment is more effective. B and C. Study results ordered by date, with the line showing the probability that the observed frequency of positive results occurred due to random chance from an ineffective treatment. Introduction We analyze all significant studies concerning the use of ivermectin for COVID-19. Search methods, inclusion criteria, effect extraction criteria (more serious outcomes have priority), PRISMA answers, statistical methods, and individual study results are detailed in Appendix 1. We present random-effects meta-analysis results for all studies, for studies within each treatment stage, for mortality results only, and for Randomized Controlled Trials (RCTs) only. We also perform a simple analysis of the distribution of study effects. If treatment was not effective, the observed effects would be randomly distributed (or more likely to be negative if treatment is harmful). We can compute the probability that the observed percentage of positive results (or higher) could occur due to chance with an ineffective treatment (the probability of >= k heads in n coin tosses, or the one-sided sign test / binomial test). Analysis of publication bias is important and adjustments may be needed if there is a bias toward publishing positive results. Figure 2 shows stages of possible treatment for COVID-19. Pre-Exposure Prophylaxis (PrEP) refers to regularly taking medication before being infected, in order to prevent or minimize infection. In Post-Exposure Prophylaxis (PEP), medication is taken after exposure but before symptoms appear. Early Treatment refers to treatment immediately or soon after symptoms appear, while Late Treatment refers to more delayed treatment. Figure 2. Treatment stages. Results Figure 3, Figure 4 and Table 1 show results by treatment stage. Figure 5 and Figure 6 show forest plots for a random effects meta-analysis of all studies and for mortality results only. Treatment time Number of studies reporting positive results Total number of studies Percentage of studies reporting positive results Probability of an equal or greater percentage of positive results from an ineffective treatment Random effects meta-analysis results Early treatment 6 6 100% 0.016 1 in 64 87% improvement RR 0.13 [0.04‑0.40] p = 0.00052 Late treatment 13 13 100% 0.00012 1 in 8 thousand 48% improvement RR 0.52 [0.36‑0.74] p = 0.0003 Pre‑Exposure Prophylaxis 5 5 100% 0.031 1 in 32 96% improvement RR 0.04 [0.01‑0.31] p = 0.0021 Post‑Exposure Prophylaxis 2 2 100% 0.25 1 in 4 90% improvement RR 0.10 [0.06‑0.17] p < 0.0001 All studies 26 26 100% 0.000000015 1 in 67 million 77% improvement RR 0.23 [0.15‑0.35] p < 0.0001 Table 1. Results by treatment stage. Figure 3. Results by treatment stage. Figure 4. Results by treatment stage. Study results are ordered by date, with the line showing the probability that the observed frequency of positive results occurred due to random chance from an ineffective treatment. Figure 5. Forest plot (random effects model). Figure 6. Forest plot (random effects model) for mortality results only. Randomized Controlled Trials (RCTs) RCTs are very valuable and minimize potential bias, however they are neither necessary or sufficient. [Concato] find that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. [Anglemyer] summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. [Lee] shows that only 14% of the guidelines of the Infectious Diseases Society of America were based on RCTs. Limitations in an RCT can easily outweigh the benefits, for example excessive dosages, excessive treatment delays, or Internet survey bias could easily have a greater effect on results. Ethical issues may prevent running RCTs for known effective treatments. For more on the problems with RCTs see [Deaton, Nichol]. Results restricted to RCTs are shown in Figure 7, Figure 8, Figure 9, and Table 2. Figure 7. Randomized Controlled Trials. The distribution of results for RCTs is similar to the distribution for all other studies. Figure 8. RCTs excluding late treatment. Treatment time Number of studies reporting positive results Total number of studies Percentage of studies reporting positive results Probability of an equal or greater percentage of positive results from an ineffective treatment Random effects meta-analysis results Randomized Controlled Trials 10 10 100% 0.00098 1 in 1 thousand 74% improvement RR 0.26 [0.12‑0.56] p = 0.00053 Table 2. Summary of RCT results. Figure 9. Forest plot (random effects model) for Randomized Controlled Trials only. Discussion Publishing is often biased towards positive results, which we would need to adjust for when analyzing the percentage of positive results. For ivermectin, there is currently not enough data to evaluate publication bias with high confidence. One method to evaluate bias is to look at prospective vs. retrospective studies. Prospective studies are likely to be published regardless of the result, while retrospective studies are more likely to exhibit bias. For example, researchers may perform preliminary analysis with minimal effort and the results may influence their decision to continue. Retrospective studies also provide more opportunities for the specifics of data extraction and adjustments to influence results. While some effects are not statistically significant when considered alone, currently all ivermectin studies report positive effects. We note that 15 of the 26 studies are prospective studies. Typical meta analyses involve subjective selection criteria, effect extraction rules, and study bias evaluation, which can be used to bias results towards a specific outcome. In order to avoid bias we include all studies and use a pre-specified method to extract results from all studies. We note that the positive results are relatively insensitive to potential selection criteria, effect extraction rules, and/or bias evaluation. Conclusion Ivermectin is an effective treatment for COVID-19. The probability that an ineffective treatment generated results as positive as the 26 studies to date is estimated to be 1 in 67 million (p = 0.000000015). Early treatment is most successful, with an estimated reduction of 87% in the effect measured using a random effects meta-analysis, RR 0.13 [0.04-0.40].