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Famotidine for COVID-19: real-time meta analysis of 25 studies
Covid Analysis, December 2022
https://c19early.org/fmmeta.html
 
0 0.5 1 1.5+ All studies 15% 25 92,443 Improvement, Studies, Patients Relative Risk Mortality 17% 18 86,375 Ventilation 12% 1 178 ICU admission -11% 4 997 Hospitalization 15% 5 528 Recovery 10% 6 890 Cases 0% 3 307 Viral clearance 13% 1 151 RCTs 27% 4 461 RCT mortality 15% 2 386 Peer-reviewed 14% 23 76,455 Prophylaxis 13% 10 44,795 Early 48% 1 55 Late 12% 14 47,593 Famotidine for COVID-19 c19early.org/fm Dec 2022 Favorsfamotidine Favorscontrol after exclusions
Statistically significant improvements are seen for mortality, hospitalization, recovery, and viral clearance. 13 studies from 13 independent teams in 6 different countries show statistically significant improvements in isolation (8 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 15% [5‑25%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Early treatment is more effective than late treatment.
In exclusion sensitivity analysis, statistical significance is lost after excluding 2 of 25 studies in pooled analysis.
0 0.5 1 1.5+ All studies 15% 25 92,443 Improvement, Studies, Patients Relative Risk Mortality 17% 18 86,375 Ventilation 12% 1 178 ICU admission -11% 4 997 Hospitalization 15% 5 528 Recovery 10% 6 890 Cases 0% 3 307 Viral clearance 13% 1 151 RCTs 27% 4 461 RCT mortality 15% 2 386 Peer-reviewed 14% 23 76,455 Prophylaxis 13% 10 44,795 Early 48% 1 55 Late 12% 14 47,593 Famotidine for COVID-19 c19early.org/fm Dec 2022 Favorsfamotidine Favorscontrol after exclusions
No treatment, vaccine, or intervention is 100% effective and available. All practical, effective, and safe means should be used based on risk/benefit analysis. Multiple treatments are typically used in combination, and other treatments are significantly more effective. None of the famotidine studies show zero events with treatment.
All data to reproduce this paper and sources are in the appendix.
Highlights
Famotidine reduces risk for COVID-19 with very high confidence for mortality, hospitalization, recovery, and in pooled analysis, and low confidence for viral clearance, however increased risk is seen with very low confidence for ICU admission.
We show traditional outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor in COVID-19 studies.
Real-time updates and corrections, transparent analysis with all results in the same format, consistent protocol for 47 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Brennan (DB RCT) 48% 0.52 [0.20-1.32] no recov. 5/27 10/28 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.17 Early treatment 48% 0.52 [0.20-1.32] 5/27 10/28 48% improvement Shoaibi -3% 1.03 [0.89-1.18] death 1,816 (n) 26,820 (n) Improvement, RR [CI] Treatment Control Zhou (PSM) -81% 1.81 [1.28-2.58] severe case 72/519 198/2,595 Yeramaneni -59% 1.59 [0.94-2.71] death 410 (n) 746 (n) Mura (PSM) 21% 0.79 [0.65-0.96] death 563 (n) 563 (n) Samim.. (SB RCT) 33% 0.67 [0.45-0.98] hosp. time 10 (n) 10 (n) Elhadi (ICU) 7% 0.93 [0.73-1.17] death 34/60 247/405 ICU patients Taşdemir 45% 0.55 [0.20-1.55] death 5/85 10/94 OT​1 Kuno (PSM) 0% 1.00 [0.86-1.17] death 1,593 (n) 7,972 (n) Stolow -519% 6.19 [2.10-18.3] death 137 (n) 352 (n) Wagner 70% 0.30 [0.20-0.44] death 638 (n) 819 (n) Pahwani (RCT) 11% 0.89 [0.36-2.20] death 8/89 9/89 Siraj 36% 0.64 [0.48-0.83] death 183/711 122/289 Zangeneh (ICU) 39% 0.61 [0.42-0.90] death n/a n/a ICU patients Chowdhury (RCT) 16% 0.84 [0.54-1.31] death 26/104 31/104 ICU patients Tau​2 = 0.13, I​2 = 87.5%, p = 0.26 Late treatment 12% 0.88 [0.71-1.10] 328/6,735 617/40,858 12% improvement Freedberg (PSM) 57% 0.43 [0.21-0.86] death/int. 8/84 332/1,536 Improvement, RR [CI] Treatment Control Mather (PSM) 61% 0.39 [0.20-0.74] death 83 (n) 689 (n) Balouch 22% 0.78 [0.36-1.51] symp. case 18/80 49/227 Yeramaneni 51% 0.49 [0.16-1.52] death 351 (n) 6,807 (n) Cheung -34% 1.34 [0.24-6.06] severe case 23 (n) 929 (n) Fung 0% 1.00 [0.96-1.04] death population-based cohort Razjouyan 27% 0.73 [0.59-0.92] death 93 (n) 9,981 (n) Wallace -11% 1.11 [0.89-1.35] death 98/423 1,436/7,521 MacFadden 7% 0.93 [0.84-1.03] cases n/a n/a Loucera 18% 0.82 [0.59-1.15] death 207 (n) 15,761 (n) Tau​2 = 0.02, I​2 = 67.7%, p = 0.031 Prophylaxis 13% 0.87 [0.76-0.99] 124/1,344 1,817/43,451 13% improvement All studies 15% 0.85 [0.75-0.95] 457/8,106 2,444/84,337 15% improvement 25 famotidine COVID-19 studies c19early.org/fm Dec 2022 Tau​2 = 0.05, I​2 = 82.5%, p = 0.0054 Effect extraction pre-specified(most serious outcome, see appendix) 1 OT: comparison with other treatment Favors famotidine Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Brennan (DB RCT) 48% recovery Relative Risk [CI] Tau​2 = 0.00, I​2 = 0.0%, p = 0.17 Early treatment 48% 48% improvement Shoaibi -3% death Zhou (PSM) -81% severe case Yeramaneni -59% death Mura (PSM) 21% death Samim.. (SB RCT) 33% hospitalization Elhadi (ICU) 7% death ICU patients Taşdemir 45% death OT​1 Kuno (PSM) 0% death Stolow -519% death Wagner 70% death Pahwani (RCT) 11% death Siraj 36% death Zangeneh (ICU) 39% death ICU patients Chowdhury (RCT) 16% death ICU patients Tau​2 = 0.13, I​2 = 87.5%, p = 0.26 Late treatment 12% 12% improvement Freedberg (PSM) 57% death/intubation Mather (PSM) 61% death Balouch 22% symp. case Yeramaneni 51% death Cheung -34% severe case Fung 0% death Razjouyan 27% death Wallace -11% death MacFadden 7% case Loucera 18% death Tau​2 = 0.02, I​2 = 67.7%, p = 0.031 Prophylaxis 13% 13% improvement All studies 15% 15% improvement 25 famotidine COVID-19 studies c19early.org/fm Dec 2022 Tau​2 = 0.05, I​2 = 82.5%, p = 0.0054 Protocol pre-specified/rotate for details1 OT: comparison with other treatment Favors famotidine Favors control
B
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Figure 1. A. Random effects meta-analysis. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix. B. Scatter plot showing the most serious outcome in all studies, and for studies within each stage. Diamonds shows the results of random effects meta-analysis. C. Results within the context of multiple COVID-19 treatments. D. Timeline of results in famotidine studies.
We analyze all significant studies concerning the use of famotidine for COVID-19. Search methods, inclusion criteria, effect extraction criteria (more serious outcomes have priority), all individual study data, PRISMA answers, and statistical methods are detailed in Appendix 1. We present random effects meta-analysis results for all studies, studies within each treatment stage, individual outcomes, peer-reviewed studies, Randomized Controlled Trials (RCTs), and after exclusions.
Figure 2 shows stages of possible treatment for COVID-19. Prophylaxis refers to regularly taking medication before becoming sick, in order to prevent or minimize infection. Early Treatment refers to treatment immediately or soon after symptoms appear, while Late Treatment refers to more delayed treatment.
Figure 2. Treatment stages.
An In Vitro study supports the efficacy of famotidine [Loffredo].
Preclinical research is an important part of the development of treatments, however results may be very different in clinical trials. Preclinical results are not used in this paper.
Table 1 summarizes the results for all stages combined, with different exclusions, and for specific outcomes. Table 2 shows results by treatment stage. Figure 3, 4, 5, 6, 7, 8, 9, 10, and 11 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, recovery, cases, viral clearance, and peer reviewed studies.
Table 1. Random effects meta-analysis for all stages combined, with different exclusions, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval.
Improvement Studies Patients Authors
All studies15% [5‑25%]25 92,443 210
After exclusions17% [4‑29%]22 91,799 176
Peer-reviewed studiesPeer-reviewed14% [3‑24%]23 76,455 196
Randomized Controlled TrialsRCTs27% [5‑44%]4 461 56
Mortality17% [5‑28%]18 86,375 134
ICU admissionICU-11% [-112‑42%]4 997 35
HospitalizationHosp.15% [7‑22%]5 528 38
Recovery10% [5‑14%]6 890 68
Cases0% [-19‑16%]3 307 20
RCT mortality15% [-26‑43%]2 386 19
RCT hospitalizationRCT hosp.17% [12‑22%]3 349 25
Table 2. Random effects meta-analysis results by treatment stage. Results show the percentage improvement with treatment, the 95% confidence interval, and the number of studies for the stage.treatment and the 95% confidence interval.
Early treatment Late treatment Prophylaxis
All studies48% [-32‑80%] 112% [-10‑29%] 1413% [1‑24%] 10
After exclusions48% [-32‑80%] 110% [-14‑30%] 1220% [4‑34%] 9
Peer-reviewed studiesPeer-reviewed48% [-32‑80%] 110% [-14‑28%] 1313% [0‑24%] 9
Randomized Controlled TrialsRCTs48% [-32‑80%] 125% [1‑43%] 3-
Mortality-17% [-3‑33%] 1215% [-4‑29%] 6
ICU admissionICU--11% [-112‑42%] 4-
HospitalizationHosp.-17% [13‑21%] 46% [3‑9%] 1
Recovery48% [-32‑80%] 110% [5‑14%] 437% [-54‑74%] 1
Cases--0% [-19‑16%] 3
RCT mortality-15% [-26‑43%] 2-
RCT hospitalizationRCT hosp.-17% [12‑22%] 3-
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Figure 3. Random effects meta-analysis for all studies with pooled effects. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
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Figure 4. Random effects meta-analysis for mortality results.
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Figure 5. Random effects meta-analysis for ventilation.
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Figure 6. Random effects meta-analysis for ICU admission.
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Figure 7. Random effects meta-analysis for hospitalization.
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Figure 8. Random effects meta-analysis for recovery.
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Figure 9. Random effects meta-analysis for cases.
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Figure 10. Random effects meta-analysis for viral clearance.
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Figure 11. Random effects meta-analysis for peer reviewed studies. [Zeraatkar] analyze 356 COVID-19 trials, finding no significant evidence that peer-reviewed studies are more trustworthy. They also show extremely slow review times during the pandemic. Authors recommend using preprint evidence, with appropriate checks for potential falsified data, which provides higher certainty much earlier. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Figure 12 shows a comparison of results for RCTs and non-RCT studies. The median effect size for RCTs is 25% improvement, compared to 18% for other studies. Figure 13, 14, and 15 show forest plots for random effects meta-analysis of all Randomized Controlled Trials, RCT mortality results, and RCT hospitalization results. RCT results are included in Table 1 and Table 2.
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Figure 12. Results for RCTs and non-RCT studies.
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Figure 13. Random effects meta-analysis for all Randomized Controlled Trials. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
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Figure 14. Random effects meta-analysis for RCT mortality results.
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Figure 15. Random effects meta-analysis for RCT hospitalization results.
To avoid bias in the selection of studies, we analyze all non-retracted studies. Here we show the results after excluding studies with major issues likely to alter results, non-standard studies, and studies where very minimal detail is currently available. Our bias evaluation is based on analysis of each study and identifying when there is a significant chance that limitations will substantially change the outcome of the study. We believe this can be more valuable than checklist-based approaches such as Cochrane GRADE, which may underemphasize serious issues not captured in the checklists, overemphasize issues unlikely to alter outcomes in specific cases (for example, lack of blinding for an objective mortality outcome, or certain specifics of randomization with a very large effect size), or be easily influenced by potential bias. However, they can also be very high quality.
The studies excluded are as below. Figure 16 shows a forest plot for random effects meta-analysis of all studies after exclusions.
[Elhadi], unadjusted results with no group details.
[Fung], not fully adjusting for the different baseline risk of systemic autoimmune patients.
[Taşdemir], excessive unadjusted differences between groups.
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Figure 16. Random effects meta-analysis for all studies after exclusions. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
Heterogeneity in COVID-19 studies arises from many factors including:
The time between infection or the onset of symptoms and treatment may critically affect how well a treatment works. For example an antiviral may be very effective when used early but may not be effective in late stage disease, and may even be harmful. Oseltamivir, for example, is generally only considered effective for influenza when used within 0-36 or 0-48 hours [McLean, Treanor]. Baloxavir studies for influenza also show that treatment delay is critical — [Ikematsu] report an 86% reduction in cases for post-exposure prophylaxis, [Hayden] show a 33 hour reduction in the time to alleviation of symptoms for treatment within 24 hours and a reduction of 13 hours for treatment within 24-48 hours, and [Kumar] report only 2.5 hours improvement for inpatient treatment.
Table 3. Studies of baloxavir for influenza show that early treatment is more effective.
Treatment delayResult
Post exposure prophylaxis86% fewer cases [Ikematsu]
<24 hours-33 hours symptoms [Hayden]
24-48 hours-13 hours symptoms [Hayden]
Inpatients-2.5 hours to improvement [Kumar]
Figure 17 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 47 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 17. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 47 treatments.
Details of the patient population including age and comorbidities may critically affect how well a treatment works. For example, many COVID-19 studies with relatively young low-comorbidity patients show all patients recovering quickly with or without treatment. In such cases, there is little room for an effective treatment to improve results (as in [López-Medina]).
Efficacy may differ significantly depending on the effect measured, for example a treatment may be very effective at reducing mortality, but less effective at minimizing cases or hospitalization. Or a treatment may have no effect on viral clearance while still being effective at reducing mortality.
There are many different variants of SARS-CoV-2 and efficacy may depend critically on the distribution of variants encountered by the patients in a study. For example, the Gamma variant shows significantly different characteristics [Faria, Karita, Nonaka, Zavascki]. Different mechanisms of action may be more or less effective depending on variants, for example the viral entry process for the omicron variant has moved towards TMPRSS2-independent fusion, suggesting that TMPRSS2 inhibitors may be less effective [Peacock, Willett].
Effectiveness may depend strongly on the dosage and treatment regimen.
The use of other treatments may significantly affect outcomes, including anything from supplements, other medications, or other kinds of treatment such as prone positioning.
The quality of medications may vary significantly between manufacturers and production batches, which may significantly affect efficacy and safety. [Williams] analyze ivermectin from 11 different sources, showing highly variable antiparasitic efficacy across different manufacturers. [Xu] analyze a treatment from two different manufacturers, showing 9 different impurities, with significantly different concentrations for each manufacturer.
We present both pooled analyses and specific outcome analyses. Notably, pooled analysis often results in earlier detection of efficacy as shown in Figure 18. For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, etc. An antiviral tested with a low-risk population may report zero mortality in both arms, however a reduction in severity and improved viral clearance may translate into lower mortality among a high-risk population, and including these results in pooled analysis allows faster detection of efficacy. Trials with high-risk patients may also be restricted due to ethical concerns for treatments that are known or expected to be effective.
Pooled analysis enables using more of the available information. While there is much more information available, for example dose-response relationships, the advantage of the method used here is simplicity and transparency. Note that pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral replication or early stage disease could show no efficacy in pooled analysis if most studies only examine viral clearance. While we present pooled results, we also present individual outcome analyses, which may be more informative for specific use cases.
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Figure 18. The time when studies showed that treatments were effective, defined as statistically significant improvement of ≥10% from ≥3 studies. Pooled results typically show efficacy earlier than specific outcome results. Results from all studies often shows efficacy much earlier than when restricting to RCTs. Results reflect conditions as used in trials to date, these depend on the population treated, treatment delay, and treatment regimen.
The distribution of studies will alter the outcome of a meta analysis. Consider a simplified example where everything is equal except for the treatment delay, and effectiveness decreases to zero or below with increasing delay. If there are many studies using very late treatment, the outcome may be negative, even though early treatment is very effective. This may have a greater effect than pooling different outcomes such as mortality and hospitalization. For example a treatment may have 50% efficacy for mortality but only 40% for hospitalization when used within 48 hours. However efficacy could be 0% when used late.
All meta analyses combine heterogeneous studies, varying in population, variants, and potentially all factors above, and therefore may obscure efficacy by including studies where treatment is less effective. Generally, we expect the estimated effect size from meta analysis to be less than that for the optimal case. Looking at all studies is valuable for providing an overview of all research, important to avoid cherry-picking, and informative when a positive result is found despite combining less-optimal situations. However, the resulting estimate does not apply to specific cases such as early treatment in high-risk populations. While we present results for all studies, we also present treatment time and individual outcome analyses, which may be more informative for specific use cases.
Publishing is often biased towards positive results, however evidence suggests that there may be a negative bias for inexpensive treatments for COVID-19. Both negative and positive results are very important for COVID-19, media in many countries prioritizes negative results for inexpensive treatments (inverting the typical incentive for scientists that value media recognition), and there are many reports of difficulty publishing positive results [Boulware, Meeus, Meneguesso]. For famotidine, there is currently not enough data to evaluate publication bias with high confidence.
One method to evaluate bias is to compare prospective vs. retrospective studies. Prospective studies are more 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.
45% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 80% of prospective studies, consistent with a bias toward publishing negative results. The median effect size for retrospective studies is 19% improvement, compared to 16% for prospective studies, showing similar results. Figure 19 shows a scatter plot of results for prospective and retrospective studies.
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Figure 19. Prospective vs. retrospective studies.
Funnel plots have traditionally been used for analyzing publication bias. This is invalid for COVID-19 acute treatment trials — the underlying assumptions are invalid, which we can demonstrate with a simple example. Consider a set of hypothetical perfect trials with no bias. Figure 20 plot A shows a funnel plot for a simulation of 80 perfect trials, with random group sizes, and each patient's outcome randomly sampled (10% control event probability, and a 30% effect size for treatment). Analysis shows no asymmetry (p > 0.05). In plot B, we add a single typical variation in COVID-19 treatment trials — treatment delay. Consider that efficacy varies from 90% for treatment within 24 hours, reducing to 10% when treatment is delayed 3 days. In plot B, each trial's treatment delay is randomly selected. Analysis now shows highly significant asymmetry, p < 0.0001, with six variants of Egger's test all showing p < 0.05 [Egger, Harbord, Macaskill, Moreno, Peters, Rothstein, Rücker, Stanley]. Note that these tests fail even though treatment delay is uniformly distributed. In reality treatment delay is more complex — each trial has a different distribution of delays across patients, and the distribution across trials may be biased (e.g., late treatment trials may be more common). Similarly, many other variations in trials may produce asymmetry, including dose, administration, duration of treatment, differences in SOC, comorbidities, age, variants, and bias in design, implementation, analysis, and reporting.
Figure 20. Example funnel plot analysis for simulated perfect trials.
Pharmaceutical drug trials often have conflicts of interest whereby sponsors or trial staff have a financial interest in the outcome being positive. Famotidine for COVID-19 lacks this because it is off-patent, has multiple manufacturers, and is very low cost. In contrast, most COVID-19 famotidine trials have been run by physicians on the front lines with the primary goal of finding the best methods to save human lives and minimize the collateral damage caused by COVID-19. While pharmaceutical companies are careful to run trials under optimal conditions (for example, restricting patients to those most likely to benefit, only including patients that can be treated soon after onset when necessary, and ensuring accurate dosing), not all famotidine trials represent the optimal conditions for efficacy.
Some analyses classify treatment based on early/late administration (as we do here), while others distinguish between mild/moderate/severe cases. We note that viral load does not indicate degree of symptoms — for example patients may have a high viral load while being asymptomatic. With regard to treatments that have antiviral properties, timing of treatment is critical — late administration may be less helpful regardless of severity.
1 of the 25 studies compare against other treatments, which may reduce the effect seen.
Summary statistics from meta analysis necessarily lose information. As with all meta analyses, studies are heterogeneous, with differences in treatment delay, treatment regimen, patient demographics, variants, conflicts of interest, standard of care, and other factors. We provide analyses by specific outcomes and by treatment delay, and we aim to identify key characteristics in the forest plots and summaries. Results should be viewed in the context of study characteristics.
Details of treatment delay per patient is often not available. For example, a study may treat 90% of patients relatively early, but the events driving the outcome may come from 10% of patients treated very late. Our 5 day cutoff for early treatment may be too conservative, 5 days may be too late in many cases.
Comparison across treatments is confounded by differences in the studies performed, for example dose, variants, and conflicts of interest. Trials affiliated with special interests may use designs better suited to the preferred outcome.
In some cases, the most serious outcome has very few events, resulting in lower confidence results being used in pooled analysis, however the method is simpler and more transparent. This is less critical as the number of studies increases. Restriction to outcomes with sufficient power may be beneficial in pooled analysis and improve accuracy when there are few studies, however we maintain our pre-specified method to avoid any retrospective changes.
Studies show that combinations of treatments can be highly synergistic and may result in many times greater efficacy than individual treatments alone [Alsaidi, Andreani, Biancatelli, De Forni, Gasmi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Thairu]. Therefore standard of care may be critical and benefits may diminish or disappear if standard of care does not include certain treatments.
This real-time analysis is constantly updated based on submissions. Accuracy benefits from widespread review and submission of updates and corrections from reviewers. Less popular treatments may receive fewer reviews.
No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. Efficacy may vary significantly with different variants and within different populations. All treatments have potential side effects. Propensity to experience side effects may be predicted in advance by qualified physicians. We do not provide medical advice. Before taking any medication, consult a qualified physician who can compare all options, provide personalized advice, and provide details of risks and benefits based on individual medical history and situations.
Statistically significant improvements are seen for mortality, hospitalization, recovery, and viral clearance. 13 studies from 13 independent teams in 6 different countries show statistically significant improvements in isolation (8 for the most serious outcome). Meta analysis using the most serious outcome reported shows 15% [5‑25%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Early treatment is more effective than late treatment. In exclusion sensitivity analysis, statistical significance is lost after excluding 2 of 25 studies in pooled analysis.
0 0.5 1 1.5 2+ Symptomatic case 22% Improvement Relative Risk Recovery time 37% c19early.org/fm Balouch et al. Famotidine for COVID-19 Prophylaxis Favors famotidine Favors control
[Balouch] Survey of 307 patients in the USA, showing no significant difference in COVID-19 cases with famotidine use.
0 0.5 1 1.5 2+ Recovery 48% Improvement Relative Risk Recovery (b) 43% Estimated time to 50% r.. 28% c19early.org/fm Brennan et al. NCT04724720 Famotidine RCT EARLY TREATMENT Favors famotidine Favors control
[Brennan] Small RCT with 27 famotidine and 28 placebo patients, showing improved recovery with treatment. Recovery was faster with treatment for 14 of 16 symptoms. There was no mortality or hospitalization. NCT04724720.
0 0.5 1 1.5 2+ Severe case -34% Improvement Relative Risk c19early.org/fm Cheung et al. Famotidine for COVID-19 Prophylaxis Favors famotidine Favors control
[Cheung] Retrospective 952 COVID-19 patients in Hong Kong, showing no significant difference in severe disease with famotidine use.
0 0.5 1 1.5 2+ Mortality 16% Improvement Relative Risk ICU time 9% Time to improvement 33% Recovery time 7% Hospitalization time 17% Time to viral- 13% c19early.org/fm Chowdhury et al. NCT04504240 Famotidine RCT ICU Favors famotidine Favors control
[Chowdhury] RCT 208 ICU patients in Bangladesh, showing improved recovery with famotidine. Famotidine 40mg (<60kg) or 60mg every 8 hours.
0 0.5 1 1.5 2+ Mortality 7% Improvement Relative Risk c19early.org/fm Elhadi et al. Famotidine for COVID-19 ICU PATIENTS Favors famotidine Favors control
[Elhadi] Prospective study of 465 COVID-19 ICU patients in Libya showing no significant differences with treatment.
0 0.5 1 1.5 2+ Death/intubation 57% Improvement Relative Risk c19early.org/fm Freedberg et al. Famotidine for COVID-19 Prophylaxis Favors famotidine Favors control
[Freedberg] PSM retrospective 1,620 hospitalized patients in the USA, 84 with existing famotidine use, showing lower risk of combined death/intubation with treatment.
0 0.5 1 1.5 2+ Mortality 0% Improvement Relative Risk Hospitalization 6% Case -12% c19early.org/fm Fung et al. Famotidine for COVID-19 Prophylaxis Favors famotidine Favors control
[Fung] Retrospective database analysis of 374,229 patients in the USA, showing higher cases, lower hospitalizations, and no change in mortality with famotidine use.
0 0.5 1 1.5 2+ Mortality 0% Improvement Relative Risk c19early.org/fm Kuno et al. Famotidine for COVID-19 LATE TREATMENT Favors famotidine Favors control
[Kuno] PSM retrospective 9,565 COVID-19 hospitalized patients in the USA, 1,593 receiving famotidine, showing no significant difference in mortality.
0 0.5 1 1.5 2+ Mortality 18% Improvement Relative Risk c19early.org/fm Loucera et al. Famotidine for COVID-19 Prophylaxis Favors famotidine Favors control
[Loucera] Retrospective 15,968 COVID-19 hospitalized patients in Spain, showing lower mortality with existing use of several medications including metformin, HCQ, aspirin, vitamin D, vitamin C, and budesonide.
0 0.5 1 1.5 2+ Case 7% Improvement Relative Risk c19early.org/fm MacFadden et al. Famotidine for COVID-19 Prophylaxis Favors famotidine Favors control
[MacFadden] Retrospective 26,121 cases and 2,369,020 controls ≥65yo in Canada, showing no significant differences in cases with chronic use of famotidine.
0 0.5 1 1.5 2+ Mortality 61% Improvement Relative Risk Death/intubation 50% c19early.org/fm Mather et al. Famotidine for COVID-19 Prophylaxis Favors famotidine Favors control
[Mather] PSM retrospective 878 hospitalized patients in the USA, 83 with existing famotidine use, showing significantly lower mortality with treatment.
0 0.5 1 1.5 2+ Mortality 21% Improvement Relative Risk Mortality (b) 37% c19early.org/fm Mura et al. Famotidine for COVID-19 LATE TREATMENT Favors famotidine Favors control
[Mura] PSM retrospective TriNetX database analysis of 1,379 severe COVID-19 patients requiring respiratory support, showing lower mortality with aspirin (not reaching statistical significance) and famotidine, and improved results from the combination of both.
0 0.5 1 1.5 2+ Mortality 11% Improvement Relative Risk Ventilation 12% ICU admission 10% Hospitalization time 17% Recovery time 10% c19early.org/fm Pahwani et al. Famotidine for COVID-19 RCT LATE TREATMENT Favors famotidine Favors control
[Pahwani] RCT with 89 famotidine and 89 control patients in Pakistan, showing faster recovery but no significant difference in mortality. 40mg oral famotidine daily.
0 0.5 1 1.5 2+ Mortality 27% Improvement Relative Risk c19early.org/fm Razjouyan et al. Famotidine for COVID-19 Prophylaxis Favors famotidine Favors control
[Razjouyan] Retrospective 10,074 veterens in the USA, showing lower mortality with existing famotidine use.
0 0.5 1 1.5 2+ Hospitalization time 33% Improvement Relative Risk Recovery 0% Recovery (b) 50% c19early.org/fm Samimagham et al. Famotidine for COVID-19 RCT LATE Favors famotidine Favors control
[Samimagham] Very small RCT with 20 patients in Iran, showing shorter hospitalization time with famotidine treatment. There was no mortality or ICU admission. Famotidine 160mg four times a day. IRCT20200509047364N2.
0 0.5 1 1.5 2+ Mortality -3% Improvement Relative Risk Death/ICU -3% c19early.org/fm Shoaibi et al. Famotidine for COVID-19 LATE TREATMENT Favors famotidine Favors control
[Shoaibi] Retrospective 1,816 famotidine users and 26,820 non-users hospitalized for COVID-19 in the USA, showing no significant differences with treatment.
0 0.5 1 1.5 2+ Mortality 36% Improvement Relative Risk c19early.org/fm Siraj et al. Famotidine for COVID-19 LATE TREATMENT Favors famotidine Favors control
[Siraj] Retrospective 1,000 COVID+ hospitalized patients in India, showing lower mortality with famotidine and remdesivir in multivariable logistic regression.
0 0.5 1 1.5 2+ Mortality -519% Improvement Relative Risk ICU admission -2390% c19early.org/fm Stolow et al. Famotidine for COVID-19 LATE TREATMENT Favors famotidine Favors control
[Stolow] Retrospective 489 COVID+ hospitalized patients in the USA, showing higher mortality with famotidine treatment.
0 0.5 1 1.5 2+ Mortality 45% Improvement Relative Risk ICU admission 37% Hospitalization time 18% Recovery time 20% c19early.org/fm Taşdemir et al. Famotidine for COVID-19 LATE Favors famotidine Favors pantoprazole
[Taşdemir] Retrospective 179 hospitalized patients in Turkey, 85 treated with famotidine and 94 treated with pantoprazole, showing faster recovery with famotidine in unadjusted results.
0 0.5 1 1.5 2+ Mortality 70% Improvement Relative Risk c19early.org/fm Wagner et al. Famotidine for COVID-19 LATE TREATMENT Favors famotidine Favors control
[Wagner] Retrospective 2,184 hospitalized patients in the USA, 638 treated with famotidine, showing lower mortality with treatment.
0 0.5 1 1.5 2+ Mortality -11% Improvement Relative Risk c19early.org/fm Wallace et al. Famotidine for COVID-19 Prophylaxis Favors famotidine Favors control
[Wallace] Retrospective 9,532 hospitalized COVID+ veterans in the USA, showing no significant difference in mortality with famotidine use. The study provides results for use before, after, and before+after. Before+after should more accurately represent prophylaxis up to COVID-19 infection (and continued use). Before included use up to 2 years before, and after included use up to 60 days later.
0 0.5 1 1.5 2+ Mortality 51% Improvement Relative Risk Mortality (b) -59% late c19early.org/fm Yeramaneni et al. Famotidine for COVID-19 Prophylaxis Favors famotidine Favors control
[Yeramaneni] Retrospective 7,158 hospitalized COVID-19 patients in the USA, showing higher risk or mortality with in-hospital famotidine use and lower risk with pre-existing use, without statistical significance in both cases.
0 0.5 1 1.5 2+ Mortality 39% Improvement Relative Risk c19early.org/fm Zangeneh et al. Famotidine for COVID-19 ICU Favors famotidine Favors control
[Zangeneh] Retrospective 193 ICU patients in Iran, showing lower mortality with famotidine treatment.
0 0.5 1 1.5 2+ Severe case -81% Improvement Relative Risk c19early.org/fm Zhou et al. Famotidine for COVID-19 LATE TREATMENT Favors famotidine Favors control
[Zhou] Retrospective 4,445 COVID+ patients in China, showing higher risk of combined death/intubation/ICU with famotidine treatment.
We performed ongoing searches of PubMed, medRxiv, ClinicalTrials.gov, The Cochrane Library, Google Scholar, Collabovid, Research Square, ScienceDirect, Oxford University Press, the reference lists of other studies and meta-analyses, and submissions to the site c19early.org. Search terms were famotidine, filtered for papers containing the terms COVID-19 or SARS-CoV-2. Automated searches are performed every few hours with notification of new matches. All studies regarding the use of famotidine for COVID-19 that report a comparison with a control group are included in the main analysis. Sensitivity analysis is performed, excluding studies with major issues, epidemiological studies, and studies with minimal available information. This is a living analysis and is updated regularly.
We extracted effect sizes and associated data from all studies. If studies report multiple kinds of effects then the most serious outcome is used in pooled analysis, while other outcomes are included in the outcome specific analyses. For example, if effects for mortality and cases are both reported, the effect for mortality is used, this may be different to the effect that a study focused on. If symptomatic results are reported at multiple times, we used the latest time, for example if mortality results are provided at 14 days and 28 days, the results at 28 days are used. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms were not used (the next most serious outcome is used — no studies were excluded). For example, in low-risk populations with no mortality, a reduction in mortality with treatment is not possible, however a reduction in hospitalization, for example, is still valuable. Clinical outcome is considered more important than PCR testing status. When basically all patients recover in both treatment and control groups, preference for viral clearance and recovery is given to results mid-recovery where available (after most or all patients have recovered there is no room for an effective treatment to do better). If only individual symptom data is available, the most serious symptom has priority, for example difficulty breathing or low SpO2 is more important than cough. When results provide an odds ratio, we computed the relative risk when possible, or converted to a relative risk according to [Zhang]. Reported confidence intervals and p-values were used when available, using adjusted values when provided. If multiple types of adjustments are reported including propensity score matching (PSM), the PSM results are used. Adjusted primary outcome results have preference over unadjusted results for a more serious outcome when the adjustments significantly alter results. When needed, conversion between reported p-values and confidence intervals followed [Altman, Altman (B)], and Fisher's exact test was used to calculate p-values for event data. If continuity correction for zero values is required, we use the reciprocal of the opposite arm with the sum of the correction factors equal to 1 [Sweeting]. Results are expressed with RR < 1.0 favoring treatment, and using the risk of a negative outcome when applicable (for example, the risk of death rather than the risk of survival). If studies only report relative continuous values such as relative times, the ratio of the time for the treatment group versus the time for the control group is used. Calculations are done in Python (3.10.8) with scipy (1.9.3), pythonmeta (1.26), numpy (1.23.4), statsmodels (0.13.5), and plotly (5.11.0).
Forest plots are computed using PythonMeta [Deng] with the DerSimonian and Laird random effects model (the fixed effect assumption is not plausible in this case) and inverse variance weighting. Mixed-effects meta-regression results are computed with R (4.1.2) using the metafor (3.0-2) and rms (6.2-0) packages, and using the most serious sufficiently powered outcome.
We received no funding, this research is done in our spare time. We have no affiliations with any pharmaceutical companies or political parties.
We have classified studies as early treatment if most patients are not already at a severe stage at the time of treatment (for example based on oxygen status or lung involvement), and treatment started within 5 days of the onset of symptoms. If studies contain a mix of early treatment and late treatment patients, we consider the treatment time of patients contributing most to the events (for example, consider a study where most patients are treated early but late treatment patients are included, and all mortality events were observed with late treatment patients). We note that a shorter time may be preferable. Antivirals are typically only considered effective when used within a shorter timeframe, for example 0-36 or 0-48 hours for oseltamivir, with longer delays not being effective [McLean, Treanor].
A summary of study results is below. Please submit updates and corrections at the bottom of this page.
A summary of study results is below. Please submit updates and corrections at https://c19early.org/fmmeta.html.
Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. For pooled analyses, the first (most serious) outcome is used, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.
[Brennan], 2/10/2022, Double Blind Randomized Controlled Trial, USA, peer-reviewed, 31 authors, study period January 2021 - April 2021, average treatment delay 4.0 days, trial NCT04724720 (history). risk of no recovery, 48.1% lower, RR 0.52, p = 0.23, treatment 5 of 27 (18.5%), control 10 of 28 (35.7%), NNT 5.8, day 28, ITT.
risk of no recovery, 43.2% lower, RR 0.57, p = 0.34, treatment 4 of 19 (21.1%), control 10 of 27 (37.0%), NNT 6.3, day 28, PP.
estimated time to 50% resolution, 28.1% lower, relative time 0.72, p < 0.01, treatment 27, control 28.
Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. For pooled analyses, the first (most serious) outcome is used, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.
[Chowdhury], 8/16/2022, Randomized Controlled Trial, Bangladesh, peer-reviewed, mean age 57.1, 11 authors, trial NCT04504240 (history). risk of death, 16.1% lower, RR 0.84, p = 0.53, treatment 26 of 104 (25.0%), control 31 of 104 (29.8%), NNT 21.
ICU time, 9.3% lower, relative time 0.91, p = 0.33, treatment 78, control 73.
time to improvement, 32.9% lower, relative time 0.67, p < 0.001, treatment mean 9.53 (±5.0) n=78, control mean 14.21 (±5.6) n=73, time to clinical improvement.
recovery time, 7.3% lower, relative time 0.93, p = 0.14, treatment mean 17.9 (±5.4) n=78, control mean 19.3 (±6.3) n=73, time to symptomatic recovery.
hospitalization time, 17.0% lower, relative time 0.83, p = 0.01, treatment 78, control 73.
time to viral-, 13.0% lower, relative time 0.87, p = 0.002, treatment 78, control 73.
[Elhadi], 4/30/2021, prospective, Libya, peer-reviewed, 21 authors, study period 29 May, 2020 - 30 December, 2020, excluded in exclusion analyses: unadjusted results with no group details. risk of death, 7.1% lower, RR 0.93, p = 0.57, treatment 34 of 60 (56.7%), control 247 of 405 (61.0%), NNT 23.
[Kuno], 10/11/2021, retrospective, propensity score matching, USA, peer-reviewed, 4 authors, study period 1 March, 2020 - 30 March, 2021. risk of death, no change, OR 1.00, p = 0.97, treatment 1,593, control 7,972, RR approximated with OR.
[Mura], 3/31/2021, retrospective, database analysis, multiple countries, peer-reviewed, 6 authors. risk of death, 20.9% lower, RR 0.79, p = 0.02, treatment 563, control 563, odds ratio converted to relative risk, famotidine only, control prevalence approximated with treatment prevalence, propensity score matching.
risk of death, 37.3% lower, RR 0.63, p = 0.001, treatment 305, control 305, odds ratio converted to relative risk, famotidine and aspirin, control prevalence approximated with treatment prevalence, propensity score matching.
[Pahwani], 2/20/2022, Randomized Controlled Trial, Pakistan, peer-reviewed, mean age 52.0, 8 authors, study period December 2020 - September 2021. risk of death, 11.1% lower, RR 0.89, p = 1.00, treatment 8 of 89 (9.0%), control 9 of 89 (10.1%), NNT 89.
risk of mechanical ventilation, 12.5% lower, RR 0.88, p = 0.73, treatment 21 of 89 (23.6%), control 24 of 89 (27.0%), NNT 30.
risk of ICU admission, 10.0% lower, RR 0.90, p = 0.86, treatment 18 of 89 (20.2%), control 20 of 89 (22.5%), NNT 44.
hospitalization time, 16.5% lower, relative time 0.83, p < 0.001, treatment mean 8.6 (±1.6) n=89, control mean 10.3 (±2.2) n=89.
recovery time, 9.6% lower, relative time 0.90, p = 0.001, treatment mean 8.5 (±1.7) n=89, control mean 9.4 (±1.9) n=89.
[Samimagham], 4/27/2021, Single Blind Randomized Controlled Trial, placebo-controlled, Iran, preprint, 6 authors. hospitalization time, 33.3% lower, relative time 0.67, p = 0.04, treatment 10, control 10.
risk of no recovery, no change, RR 1.00, p = 1.00, treatment 5 of 10 (50.0%), control 5 of 10 (50.0%), >50% CT lung involvment.
risk of no recovery, 50.0% lower, RR 0.50, p = 0.37, treatment 3 of 10 (30.0%), control 6 of 10 (60.0%), NNT 3.3, no improvement in cough.
[Shoaibi], 9/24/2020, retrospective, database analysis, USA, peer-reviewed, 5 authors. risk of death, 3.0% higher, RR 1.03, p = 0.67, treatment 1,816, control 26,820.
risk of death/ICU, 3.0% higher, RR 1.03, p = 0.62, treatment 1,816, control 26,820.
[Siraj], 2/28/2022, retrospective, India, peer-reviewed, median age 56.0, 13 authors, study period March 2020 - December 2020. risk of death, 36.2% lower, RR 0.64, p = 0.002, treatment 183 of 711 (25.7%), control 122 of 289 (42.2%), NNT 6.1, adjusted per study, inverted to make RR<1 favor treatment, odds ratio converted to relative risk, multivariable.
[Stolow], 10/31/2021, retrospective, USA, peer-reviewed, 9 authors. risk of death, 518.9% higher, OR 6.19, p < 0.001, treatment 137, control 352, RR approximated with OR.
risk of ICU admission, 2389.6% higher, OR 24.90, p < 0.001, treatment 137, control 352, RR approximated with OR.
[Taşdemir], 7/12/2021, retrospective, Turkey, peer-reviewed, 7 authors, this trial compares with another treatment - results may be better when compared to placebo, excluded in exclusion analyses: excessive unadjusted differences between groups. risk of death, 44.7% lower, RR 0.55, p = 0.29, treatment 5 of 85 (5.9%), control 10 of 94 (10.6%), NNT 21.
risk of ICU admission, 36.8% lower, RR 0.63, p = 0.36, treatment 8 of 85 (9.4%), control 14 of 94 (14.9%), NNT 18.
hospitalization time, 18.1% lower, relative time 0.82, p = 0.003, treatment 85, control 94.
recovery time, 20.0% lower, relative time 0.80, p = 0.04, treatment 85, control 94, duration of fever.
[Wagner], 10/31/2021, retrospective, USA, peer-reviewed, 5 authors, study period March 2020 - March 2021. risk of death, 70.0% lower, OR 0.30, p < 0.001, treatment 638, control 819, adjusted per study, multivariable, RR approximated with OR.
[Yeramaneni], 2/28/2021, retrospective, USA, peer-reviewed, 6 authors, study period 11 February, 2020 - 8 May, 2020. risk of death, 59.0% higher, OR 1.59, p = 0.09, treatment 410, control 746, adjusted per study, hospital use, multivariable, RR approximated with OR, late treatment result.
[Zangeneh], 5/13/2022, retrospective, Iran, peer-reviewed, 3 authors. risk of death, 39.0% lower, HR 0.61, p = 0.01, Cox proportional hazards.
[Zhou], 12/4/2020, retrospective, propensity score matching, China, peer-reviewed, 7 authors, study period 1 January, 2020 - 22 August, 2020. risk of severe case, 81.0% higher, HR 1.81, p < 0.001, treatment 72 of 519 (13.9%), control 198 of 2,595 (7.6%), death/intubation/ICU, propensity score matching, Cox proportional hazards.
Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. For pooled analyses, the first (most serious) outcome is used, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.
[Balouch], 1/20/2021, retrospective, USA, peer-reviewed, 5 authors. risk of symptomatic case, 22.0% lower, RR 0.78, p = 0.49, treatment 18 of 80 (22.5%), control 49 of 227 (21.6%), adjusted per study, odds ratio converted to relative risk, multivariable.
recovery time, 36.9% lower, relative time 0.63, p = 0.32, treatment 80, control 227.
[Cheung], 4/30/2021, retrospective, China, peer-reviewed, 3 authors. risk of severe case, 34.0% higher, OR 1.34, p = 0.72, treatment 23, control 929, adjusted per study, multivariable, RR approximated with OR.
[Freedberg], 5/21/2020, retrospective, propensity score matching, USA, peer-reviewed, 15 authors. risk of death/intubation, 57.0% lower, RR 0.43, p = 0.02, treatment 8 of 84 (9.5%), control 332 of 1,536 (21.6%), NNT 8.3, propensity score matching.
[Fung], 10/1/2021, retrospective, population-based cohort, USA, peer-reviewed, 6 authors, excluded in exclusion analyses: not fully adjusting for the different baseline risk of systemic autoimmune patients. risk of death, no change, HR 1.00, p = 1.00, vs. never used.
risk of hospitalization, 6.0% lower, HR 0.94, p < 0.001, vs. never used.
risk of case, 12.0% higher, HR 1.12, p < 0.001, vs. never used.
[Loucera], 8/16/2022, retrospective, Spain, preprint, 8 authors, study period January 2020 - November 2020. risk of death, 17.5% lower, HR 0.82, p = 0.25, treatment 207, control 15,761, Cox proportional hazards, day 30.
[MacFadden], 3/29/2022, retrospective, Canada, peer-reviewed, 9 authors, study period 15 January, 2020 - 31 December, 2020. risk of case, 7.0% lower, OR 0.93, p = 0.16, RR approximated with OR.
[Mather], 8/26/2020, retrospective, USA, peer-reviewed, 3 authors. risk of death, 61.4% lower, HR 0.39, p = 0.004, treatment 83, control 689, propensity score matching, Cox proportional hazards.
risk of death/intubation, 50.5% lower, HR 0.49, p = 0.003, treatment 83, control 689, propensity score matching, Cox proportional hazards.
[Razjouyan], 10/25/2021, retrospective, USA, peer-reviewed, 7 authors. risk of death, 27.0% lower, OR 0.73, p = 0.006, treatment 93, control 9,981, adjusted per study, RR approximated with OR.
[Wallace], 12/31/2021, retrospective, database analysis, USA, peer-reviewed, 6 authors. risk of death, 11.0% higher, RR 1.11, p = 0.33, treatment 98 of 423 (23.2%), control 1,436 of 7,521 (19.1%), adjusted per study, odds ratio converted to relative risk, multivariable.
[Yeramaneni], 2/28/2021, retrospective, USA, peer-reviewed, 6 authors, study period 11 February, 2020 - 8 May, 2020. risk of death, 51.0% lower, OR 0.49, p = 0.22, treatment 351, control 6,807, adjusted per study, home use, multivariable, RR approximated with OR.
Please send us corrections, updates, or comments. Vaccines and treatments are complementary. All practical, effective, and safe means should be used based on risk/benefit analysis. No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. We do not provide medical advice. Before taking any medication, consult a qualified physician who can provide personalized advice and details of risks and benefits based on your medical history and situation. FLCCC and WCH provide treatment protocols.
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