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Famotidine for COVID-19: real-time meta analysis of 30 studies

@CovidAnalysis, July 2024, Version 23V23
 
0 0.5 1 1.5+ All studies 17% 30 114,119 Improvement, Studies, Patients Relative Risk Mortality 18% 21 86,617 Ventilation 4% 3 1,694 ICU admission -2% 5 1,056 Hospitalization 15% 5 528 Recovery 10% 6 890 Cases 12% 4 21,333 Viral clearance 13% 1 151 RCTs 27% 4 461 RCT mortality 15% 2 386 Peer-reviewed 16% 29 114,099 Prophylaxis 16% 12 66,229 Early 48% 1 55 Late 15% 17 47,835 Famotidine for COVID-19 c19early.org July 2024 after exclusions Favorsfamotidine Favorscontrol
Abstract
Statistically significant lower risk is seen for mortality, hospitalization, recovery, and viral clearance. 15 studies from 15 independent teams in 7 countries show significant improvements.
Meta analysis using the most serious outcome reported shows 17% [8‑24%] lower risk. Results are similar for Randomized Controlled Trials, higher quality studies, and peer-reviewed studies. Early treatment is more effective than late treatment.
0 0.5 1 1.5+ All studies 17% 30 114,119 Improvement, Studies, Patients Relative Risk Mortality 18% 21 86,617 Ventilation 4% 3 1,694 ICU admission -2% 5 1,056 Hospitalization 15% 5 528 Recovery 10% 6 890 Cases 12% 4 21,333 Viral clearance 13% 1 151 RCTs 27% 4 461 RCT mortality 15% 2 386 Peer-reviewed 16% 29 114,099 Prophylaxis 16% 12 66,229 Early 48% 1 55 Late 15% 17 47,835 Famotidine for COVID-19 c19early.org July 2024 after exclusions Favorsfamotidine Favorscontrol
1 RCT with 528 patients has not reported results (8 months late)1.
No treatment or intervention is 100% effective. 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.
All data to reproduce this paper and sources are in the appendix.
Evolution of COVID-19 clinical evidence Famotidine p=0.00028 Acetaminophen p=0.00000029 2020 2021 2022 2023 Effective Harmful c19early.org July 2024 meta analysis results (pooled effects) 100% 50% 0% -50%
Famotidine for COVID-19 — Highlights
Famotidine reduces risk with very high confidence for mortality, hospitalization, recovery, and in pooled analysis, and low confidence for viral clearance, however increased risk is seen with low confidence for progression.
26th treatment shown effective with ≥3 clinical studies in October 2021, now with p = 0.00028 from 30 studies, and recognized in 2 countries.
Outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor.
Real-time updates and corrections, transparent analysis with all results in the same format, consistent protocol for 79 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 TOGETHER Together.. (DB RCT) unknown, >8 months late 528 (total) 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% lower risk Shoaibi -3% 1.03 [0.89-1.18] death 1,816 (n) 26,820 (n) Improvement, RR [CI] Treatment Control Zhou (PSM) -84% 1.84 [1.16-2.92] 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 64% 0.36 [0.24-0.50] death 82/638 182/819 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 Özden (ICU) 29% 0.71 [0.45-1.13] death 14/30 19/29 ICU patients Shamsi 75% 0.25 [0.04-1.78] death 1/27 23/156 Mehrizi 19% 0.81 [0.79-0.83] death population-based cohort Tau​2 = 0.07, I​2 = 88.5%, p = 0.047 Late treatment 15% 0.85 [0.72-1.00] 425/6,792 841/41,043 15% lower risk 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) Kim 36% 0.64 [0.51-0.80] cases 105/5,594 480/15,432 Kwon -107% 2.07 [0.96-4.47] progression 204 (n) 204 (n) Tau​2 = 0.03, I​2 = 76.8%, p = 0.013 Prophylaxis 16% 0.84 [0.73-0.96] 229/7,142 2,297/59,087 16% lower risk All studies 17% 0.83 [0.76-0.92] 659/13,961 3,148/100,158 17% lower risk 30 famotidine COVID-19 studies (+1 unreported RCT) c19early.org July 2024 Tau​2 = 0.04, I​2 = 88.4%, p = 0.00028 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 Improvement Relative Risk [CI] TOGETHER Togethe.. (DB RCT) n/a >8 mon late Tau​2 = 0.00, I​2 = 0.0%, p = 0.17 Early treatment 48% 48% lower risk Shoaibi -3% death Zhou (PSM) -84% 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 64% death Pahwani (RCT) 11% death Siraj 36% death Zangeneh (ICU) 39% death ICU patients Chowdhury (RCT) 16% death ICU patients Özden (ICU) 29% death ICU patients Shamsi 75% death Mehrizi 19% death Tau​2 = 0.07, I​2 = 88.5%, p = 0.047 Late treatment 15% 15% lower risk 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 Kim 36% case Kwon -107% progression Tau​2 = 0.03, I​2 = 76.8%, p = 0.013 Prophylaxis 16% 16% lower risk All studies 17% 17% lower risk 30 famotidine C19 studies c19early.org July 2024 Tau​2 = 0.04, I​2 = 88.4%, p = 0.00028 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. Analysis validating pooled outcomes for COVID-19 can be found below. Effect extraction is pre-specified, using the most serious outcome reported. For details see the appendix. B. Timeline of results in famotidine studies. The marked dates indicate the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes, one or more specific outcome, pooled outcomes in RCTs, and one or more specific outcome in RCTs. Efficacy based on RCTs only was delayed by 4.7 months, compared to using all studies. Efficacy based on specific outcomes in RCTs was delayed by 5.8 months, compared to using pooled outcomes in RCTs.
Introduction
SARS-CoV-2 infection primarily begins in the upper respiratory tract and may progress to the lower respiratory tract, other tissues, and the nervous and cardiovascular systems, which may lead to cytokine storm, pneumonia, ARDS, neurological injury2-9 and cognitive deficits4,9, cardiovascular complications10, organ failure, and death. Minimizing replication as early as possible is recommended.
SARS-CoV-2 infection and replication involves the complex interplay of 50+ host and viral proteins and other factorsA,11-15, providing many therapeutic targets for which many existing compounds have known activity. Scientists have predicted that over 7,000 compounds may reduce COVID-19 risk16, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications.
We analyze all significant controlled studies 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 higher quality studies.
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.
Preclinical Research
An In Vitro study supports the efficacy of famotidine17.
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.
Results
Table 1 summarizes the results for all stages combined, for Randomized Controlled Trials, for peer-reviewed studies, after exclusions, and for specific outcomes. Table 2 shows results by treatment stage. Figure 3 plots individual results by treatment stage. Figure 4, 5, 6, 7, 8, 9, 10, 11, 12, and 13 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, cases, viral clearance, and peer reviewed studies.
Table 1. Random effects meta-analysis for all stages combined, for Randomized Controlled Trials, for peer-reviewed studies, after exclusions, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval. * p<0.05  ** p<0.01  *** p<0.001  **** p<0.0001.
Improvement Studies Patients Authors
All studies17% [8‑24%]
***
30 114,119 242
After exclusions18% [7‑27%]
**
26 113,292 204
Peer-reviewed studiesPeer-reviewed16% [7‑24%]
***
29 114,099 236
Randomized Controlled TrialsRCTs27% [5‑44%]
*
4 461 56
Mortality18% [9‑27%]
***
21 86,617 150
VentilationVent.4% [-18‑21%]3 1,694 15
ICU admissionICU-2% [-75‑41%]5 1,056 37
HospitalizationHosp.15% [7‑22%]
***
5 528 38
Recovery10% [5‑14%]
****
6 890 68
Cases12% [-10‑30%]4 21,333 28
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. * p<0.05  ** p<0.01  *** p<0.001  **** p<0.0001.
Early treatment Late treatment Prophylaxis
All studies48% [-32‑80%]15% [0‑28%]
*
16% [4‑27%]
*
After exclusions48% [-32‑80%]14% [-2‑28%]21% [4‑34%]
*
Peer-reviewed studiesPeer-reviewed48% [-32‑80%]14% [-2‑27%]16% [4‑27%]
*
Randomized Controlled TrialsRCTs48% [-32‑80%]25% [1‑43%]
*
Mortality20% [6‑31%]
**
15% [-4‑29%]
VentilationVent.4% [-18‑21%]
ICU admissionICU-2% [-75‑41%]
HospitalizationHosp.17% [13‑21%]
****
6% [3‑9%]
***
Recovery48% [-32‑80%]10% [5‑14%]
****
37% [-54‑74%]
Cases12% [-10‑30%]
RCT mortality15% [-26‑43%]
RCT hospitalizationRCT hosp.17% [12‑22%]
****
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Figure 3. 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.
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Figure 4. Random effects meta-analysis for all studies. This plot shows pooled effects, see the specific outcome analyses for individual outcomes. Analysis validating pooled outcomes for COVID-19 can be found below. Effect extraction is pre-specified, using the most serious outcome reported. For details see the appendix.
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Figure 5. Random effects meta-analysis for mortality results.
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Figure 6. Random effects meta-analysis for ventilation.
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Figure 7. Random effects meta-analysis for ICU admission.
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Figure 8. Random effects meta-analysis for hospitalization.
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Figure 9. Random effects meta-analysis for progression.
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Figure 10. Random effects meta-analysis for recovery.
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Figure 11. Random effects meta-analysis for cases.
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Figure 12. Random effects meta-analysis for viral clearance.
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Figure 13. Random effects meta-analysis for peer reviewed studies. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details. Analysis validating pooled outcomes for COVID-19 can be found below. Zeraatkar et al. analyze 356 COVID-19 trials, finding no significant evidence that preprint results are inconsistent with peer-reviewed studies. They also show extremely long peer-review delays, with a median of 6 months to journal publication. A six month delay was equivalent to around 1.5 million deaths during the first two years of the pandemic. Authors recommend using preprint evidence, with appropriate checks for potential falsified data, which provides higher certainty much earlier. Davidson et al. also showed no important difference between meta analysis results of preprints and peer-reviewed publications for COVID-19, based on 37 meta analyses including 114 trials.
Randomized Controlled Trials (RCTs)
Figure 14 shows a comparison of results for RCTs and non-RCT studies. Random effects meta analysis of RCTs shows 27% improvement, compared to 16% for other studies. Figure 15, 16, and 17 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 14. Results for RCTs and non-RCT studies.
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Figure 15. Random effects meta-analysis for all Randomized Controlled Trials. This plot shows pooled effects, see the specific outcome analyses for individual outcomes. Analysis validating pooled outcomes for COVID-19 can be found below. Effect extraction is pre-specified, using the most serious outcome reported. For details see the appendix.
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Figure 16. Random effects meta-analysis for RCT mortality results.
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Figure 17. Random effects meta-analysis for RCT hospitalization results.
RCTs help to make study groups more similar and can provide a higher level of evidence, however they are subject to many biases20, and analysis of double-blind RCTs has identified extreme levels of bias21. For COVID-19, the overhead may delay treatment, dramatically compromising efficacy; they may encourage monotherapy for simplicity at the cost of efficacy which may rely on combined or synergistic effects; the participants that sign up may not reflect real world usage or the population that benefits most in terms of age, comorbidities, severity of illness, or other factors; standard of care may be compromised and unable to evolve quickly based on emerging research for new diseases; errors may be made in randomization and medication delivery; and investigators may have hidden agendas or vested interests influencing design, operation, analysis, reporting, and the potential for fraud. All of these biases have been observed with COVID-19 RCTs. There is no guarantee that a specific RCT provides a higher level of evidence.
RCTs are expensive and many RCTs are funded by pharmaceutical companies or interests closely aligned with pharmaceutical companies. For COVID-19, this creates an incentive to show efficacy for patented commercial products, and an incentive to show a lack of efficacy for inexpensive treatments. The bias is expected to be significant, for example Als-Nielsen et al. analyzed 370 RCTs from Cochrane reviews, showing that trials funded by for-profit organizations were 5 times more likely to recommend the experimental drug compared with those funded by nonprofit organizations. For COVID-19, some major philanthropic organizations are largely funded by investments with extreme conflicts of interest for and against specific COVID-19 interventions.
High quality RCTs for novel acute diseases are more challenging, with increased ethical issues due to the urgency of treatment, increased risk due to enrollment delays, and more difficult design with a rapidly evolving evidence base. For COVID-19, the most common site of initial infection is the upper respiratory tract. Immediate treatment is likely to be most successful and may prevent or slow progression to other parts of the body. For a non-prophylaxis RCT, it makes sense to provide treatment in advance and instruct patients to use it immediately on symptoms, just as some governments have done by providing medication kits in advance. Unfortunately, no RCTs have been done in this way. Every treatment RCT to date involves delayed treatment. Among the 79 treatments we have analyzed, 63% of RCTs involve very late treatment 5+ days after onset. No non-prophylaxis COVID-19 RCTs match the potential real-world use of early treatments. They may more accurately represent results for treatments that require visiting a medical facility, e.g., those requiring intravenous administration.
Evidence shows that non-RCT studies can also provide reliable results. Concato et al. found that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. Anglemyer et al. summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. Lee et al. showed that only 14% of the guidelines of the Infectious Diseases Society of America were based on RCTs. Evaluation of studies relies on an understanding of the study and potential biases. Limitations in an RCT can outweigh the benefits, for example excessive dosages, excessive treatment delays, or Internet survey bias may have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see26,27.
Currently, 47 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of these, 30 have been confirmed in RCTs, with a mean delay of 7.0 months. When considering only low cost treatments, 25 have been confirmed with a delay of 8.4 months. For the 17 unconfirmed treatments, 3 have zero RCTs to date. The point estimates for the remaining 14 are all consistent with the overall results (benefit or harm), with 11 showing >20%. The only treatments showing >10% efficacy for all studies, but <10% for RCTs are sotrovimab and aspirin.
We need to evaluate each trial on its own merits. RCTs for a given medication and disease may be more reliable, however they may also be less reliable. For off-patent medications, very high conflict of interest trials may be more likely to be RCTs, and more likely to be large trials that dominate meta analyses.
Unreported RCTs
1 famotidine RCT has not reported results1. The trial reports total actual enrollment of 528 patients. The result is delayed over 8 months.
Exclusions
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 can be easily influenced by potential bias, may ignore or underemphasize serious issues not captured in the checklists, and may overemphasize issues unlikely to alter outcomes in specific cases (for example certain specifics of randomization with a very large effect size and well-matched baseline characteristics).
The studies excluded are as below. Figure 18 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.
Shamsi, unadjusted results with no group details.
Taşdemir, excessive unadjusted differences between groups.
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Figure 18. Random effects meta-analysis for all studies after exclusions. This plot shows pooled effects, see the specific outcome analyses for individual outcomes. Analysis validating pooled outcomes for COVID-19 can be found below. Effect extraction is pre-specified, using the most serious outcome reported. For details see the appendix.
Heterogeneity
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 hours32,33. Baloxavir studies for influenza also show that treatment delay is critical — Ikematsu et al. report an 86% reduction in cases for post-exposure prophylaxis, Hayden et al. 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 et al. 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 cases34
<24 hours-33 hours symptoms35
24-48 hours-13 hours symptoms35
Inpatients-2.5 hours to improvement36
Figure 19 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 79 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
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Figure 19. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 79 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, for example as in López-Medina et al.
Efficacy may depend critically on the distribution of SARS-CoV-2 variants encountered by patients. Risk varies significantly across variants38, for example the Gamma variant shows significantly different characteristics39-42. Different mechanisms of action may be more or less effective depending on variants, for example the degree to which TMPRSS2 contributes to viral entry can differ across variants43,44.
Effectiveness may depend strongly on the dosage and treatment regimen.
The use of other treatments may significantly affect outcomes, including supplements, other medications, or other interventions such as prone positioning. Treatments may be synergistic45-55, therefore efficacy may depend strongly on combined treatments.
The quality of medications may vary significantly between manufacturers and production batches, which may significantly affect efficacy and safety. Williams et al. analyze ivermectin from 11 different sources, showing highly variable antiparasitic efficacy across different manufacturers. Xu et al. analyze a treatment from two different manufacturers, showing 9 different impurities, with significantly different concentrations for each manufacturer.
Across all studies there is a strong association between different outcomes, for example improved recovery is strongly associated with lower mortality. However, efficacy may differ depending on the effect measured, for example a treatment may be more effective against secondary complications and have minimal effect on viral clearance.
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. 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.
Pooled Effects
This section validates the use of pooled effects for COVID-19, which enables earlier detection of efficacy, however note that pooled effects are no longer required for famotidine as of October 2021. Efficacy is now known for famotidine based on specific outcomes for all studies and when restricted to RCTs. Efficacy based on specific outcomes in RCTs was delayed by 5.8 months, compared to using pooled outcomes in RCTs.
For COVID-19, delay in clinical results translates into additional death and morbidity, as well as additional economic and societal damage. Combining the results of studies reporting different outcomes is required. There may be no mortality in a trial with low-risk patients, however a reduction in severity or improved viral clearance may translate into lower mortality in a high-risk population. Different studies may report lower severity, improved recovery, and lower mortality, and the significance may be very high when combining the results. "The studies reported different outcomes" is not a good reason for disregarding results.
We present both specific outcome and pooled analyses. In order to combine the results of studies reporting different outcomes we use the most serious outcome reported in each study, based on the thesis that improvement in the most serious outcome provides comparable measures of efficacy for a treatment. A critical advantage of this approach is simplicity and transparency. There are many other ways to combine evidence for different outcomes, along with additional evidence such as dose-response relationships, however these increase complexity.
Another way to view pooled analysis is that we are using more of the available information. Logically we should, and do, use additional information. For example dose-response and treatment delay-response relationships provide significant additional evidence of efficacy that is considered when reviewing the evidence for a treatment.
Trials with high-risk patients may be restricted due to ethics for treatments that are known or expected to be effective, and they increase difficulty for recruiting. Using less severe outcomes as a proxy for more serious outcomes allows faster collection of evidence.
For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, which follows from a reduction in PCR positivity. We can directly test this for COVID-19.
Analysis of the the association between different outcomes across studies from all 79 treatments we cover confirms the validity of pooled outcome analysis for COVID-19. Figure 20 shows that lower hospitalization is very strongly associated with lower mortality (p < 0.000000000001). Similarly, Figure 21 shows that improved recovery is very strongly associated with lower mortality (p < 0.000000000001). Considering the extremes, Singh et al. show an association between viral clearance and hospitalization or death, with p = 0.003 after excluding one large outlier from a mutagenic treatment, and based on 44 RCTs including 52,384 patients. Figure 22 shows that improved viral clearance is strongly associated with fewer serious outcomes. The association is very similar to Singh et al., with higher confidence due to the larger number of studies. As with Singh et al., the confidence increases when excluding the outlier treatment, from p = 0.0000011 to p = 0.0000000036.
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Figure 20. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
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Figure 21. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
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Figure 20. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Currently, 47 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 91% of these have been confirmed with one or more specific outcomes, with a mean delay of 5.2 months. When restricting to RCTs only, 54% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 6.4 months. Figure 23 shows when treatments were found effective during the pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
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Figure 23. 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.
Pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral clearance may show no efficacy if most studies only examine viral clearance. In practice, it is rare for a non-antiviral treatment to report viral clearance and to not report clinical outcomes; and in practice other sources of heterogeneity such as difference in treatment delay is more likely to hide efficacy.
Analysis validates the use of pooled effects and shows significantly faster detection of efficacy on average. However, as with all meta analyses, it is important to review the different studies included. We also present 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 results59-62. 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.
Figure 24 shows a scatter plot of results for prospective and retrospective studies. 44% 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 21% improvement, compared to 16% for prospective studies, showing similar results.
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Figure 24. Prospective vs. retrospective studies. The diamonds show the results of random effects meta-analysis.
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 25 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 < 0.0563-70. 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 25. 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.
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 for 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.
Some analyses classify treatment based on early or late administration, as done here, while others distinguish between mild, moderate, and severe cases. 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.
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 with conflicts of interest 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 alone45-55. 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 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.
1 of the 30 studies compare against other treatments, which may reduce the effect seen.
SARS-CoV-2 infection and replication involves a complex interplay of 50+ host and viral proteins and other factors11-15, providing many therapeutic targets. Over 7,000 compounds have been predicted to reduce COVID-19 risk16, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications. Figure 26 shows an overview of the results for famotidine in the context of multiple COVID-19 treatments, and Figure 27 shows a plot of efficacy vs. cost for COVID-19 treatments.
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Figure 26. Scatter plot showing results within the context of multiple COVID-19 treatments. Diamonds shows the results of random effects meta-analysis. 0.6% of 7,000+ proposed treatments show efficacy71.
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Figure 27. Efficacy vs. cost for COVID-19 treatments.
Famotidine is an effective treatment for COVID-19. Statistically significant lower risk is seen for mortality, hospitalization, recovery, and viral clearance. 15 studies from 15 independent teams in 7 countries show significant improvements. Meta analysis using the most serious outcome reported shows 17% [8‑24%] lower risk. Results are similar for Randomized Controlled Trials, higher quality studies, and peer-reviewed studies. Early treatment is more effective than late treatment.
Symp. case 22% Improvement Relative Risk Recovery time 37% Famotidine for COVID-19  Balouch et al.  Prophylaxis Is prophylaxis with famotidine beneficial for COVID-19? Retrospective 307 patients in the USA Fewer symptomatic cases (p=0.49) and faster recovery (p=0.32), not sig. c19early.org Balouch et al., J. Voice, January 2021 Favorsfamotidine Favorscontrol 0 0.5 1 1.5 2+
Balouch: Survey of 307 patients in the USA, showing no significant difference in COVID-19 cases with famotidine use.
Recovery 48% Improvement Relative Risk Recovery (b) 43% Estimated time to 50% r.. 28% Famotidine  Brennan et al.  EARLY TREATMENT  DB RCT Is early treatment with famotidine beneficial for COVID-19? Double-blind RCT 55 patients in the USA (January - April 2021) Improved recovery with famotidine (not stat. sig., p=0.23) c19early.org Brennan et al., Gut, February 2022 Favorsfamotidine Favorscontrol 0 0.5 1 1.5 2+
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.
Severe case -34% Improvement Relative Risk Famotidine for COVID-19  Cheung et al.  Prophylaxis Is prophylaxis with famotidine beneficial for COVID-19? Retrospective 952 patients in China No significant difference in severe cases c19early.org Cheung et al., Gastroenterology, April 2021 Favorsfamotidine Favorscontrol 0 0.5 1 1.5 2+
Cheung: Retrospective 952 COVID-19 patients in Hong Kong, showing no significant difference in severe disease with famotidine use.
Mortality 16% Improvement Relative Risk ICU time 9% Time to improvement 33% Recovery time 7% Hospitalization time 17% Time to viral- 13% Famotidine  Chowdhury et al.  ICU PATIENTS  RCT Is very late treatment with famotidine beneficial for COVID-19? RCT 208 patients in Bangladesh (August 2020 - April 2021) Faster improvement (p<0.0001) and shorter hospitalization (p=0.013) c19early.org Chowdhury et al., World J. Clinical Ca.., Aug 2022 Favorsfamotidine Favorscontrol 0 0.5 1 1.5 2+
Chowdhury: RCT 208 ICU patients in Bangladesh, showing improved recovery with famotidine. Famotidine 40mg (<60kg) or 60mg every 8 hours.
Mortality 7% Improvement Relative Risk Famotidine for COVID-19  Elhadi et al.  ICU PATIENTS Is very late treatment with famotidine beneficial for COVID-19? Prospective study of 465 patients in Libya (May - December 2020) No significant difference in mortality c19early.org Elhadi et al., PLOS ONE, April 2021 Favorsfamotidine Favorscontrol 0 0.5 1 1.5 2+
Elhadi: Prospective study of 465 COVID-19 ICU patients in Libya showing no significant differences with treatment.
Death/intubation 57% Improvement Relative Risk Famotidine for COVID-19  Freedberg et al.  Prophylaxis Is prophylaxis with famotidine beneficial for COVID-19? PSM retrospective 1,620 patients in the USA Lower death/intubation with famotidine (p=0.019) c19early.org Freedberg et al., Gastroenterology, May 2020 Favorsfamotidine Favorscontrol 0 0.5 1 1.5 2+
Freedberg: PSM retrospective 1,620 hospitalized patients in the USA, 84 with existing famotidine use, showing lower risk of combined death/intubation with treatment.
Mortality 0% Improvement Relative Risk Hospitalization 6% Case -12% Famotidine for COVID-19  Fung et al.  Prophylaxis Is prophylaxis with famotidine beneficial for COVID-19? Retrospective study in the USA Lower hospitalization (p=0.00016) and more cases (p<0.0001) c19early.org Fung et al., PLoS ONE, October 2021 Favorsfamotidine Favorscontrol 0 0.5 1 1.5 2+
Fung: Retrospective database analysis of 374,229 patients in the USA, showing higher cases, lower hospitalizations, and no change in mortality with famotidine use.
Case 36% Improvement Relative Risk Famotidine for COVID-19  Kim et al.  Prophylaxis Does famotidine reduce COVID-19 infections? Retrospective 21,026 patients in South Korea (Jan - Jun 2020) Fewer cases with famotidine (p=0.000093) c19early.org Kim et al., J. Korean Medical Science, Mar 2023 Favorsfamotidine Favorscontrol 0 0.5 1 1.5 2+
Kim: PSM retrospective in South Korea, showing lower risk of COVID-19 cases with H2RA (including famotidine) and PPA use, but no significant difference in severe outcomes (few events, results provided for the combined groups only).
Mortality 0% Improvement Relative Risk Famotidine for COVID-19  Kuno et al.  LATE TREATMENT Is late treatment with famotidine beneficial for COVID-19? PSM retrospective 9,565 patients in the USA (Mar 2020 - Mar 2021) No significant difference in mortality c19early.org Kuno et al., J. Medical Virology, October 2021 Favorsfamotidine Favorscontrol 0 0.5 1 1.5 2+
Kuno: PSM retrospective 9,565 COVID-19 hospitalized patients in the USA, 1,593 receiving famotidine, showing no significant difference in mortality.
Progression -107% Improvement Relative Risk Progression (b) -256% Oxygen therapy -109% Famotidine for COVID-19  Kwon et al.  Prophylaxis Is prophylaxis with famotidine beneficial for COVID-19? Retrospective 6,556 patients in South Korea (Jul - Dec 2020) Higher progression (p=0.063) and higher oxygen therapy (p=0.069), not sig. c19early.org Kwon et al., Heliyon, May 2023 Favorsfamotidine Favorscontrol 0 0.5 1 1.5 2+
Kwon: PSM retrospective 6,556 COVID-19 patients in South Korea, showing higher risk of poor outcomes with famotidine vs. other H2-blocker use.
Mortality 18% Improvement Relative Risk Famotidine for COVID-19  Loucera et al.  Prophylaxis Is prophylaxis with famotidine beneficial for COVID-19? Retrospective 15,968 patients in Spain (January - November 2020) Lower mortality with famotidine (not stat. sig., p=0.25) c19early.org Loucera et al., Virology J., August 2022 Favorsfamotidine Favorscontrol 0 0.5 1 1.5 2+
Loucera: Retrospective 15,968 COVID-19 hospitalized patients in Spain, showing lower mortality with existing use of several medications including metformin, HCQ, azithromycin, aspirin, vitamin D, vitamin C, and budesonide. Since only hospitalized patients are included, results do not reflect different probabilities of hospitalization across treatments.
Case 7% Improvement Relative Risk Famotidine for COVID-19  MacFadden et al.  Prophylaxis Does famotidine reduce COVID-19 infections? Retrospective study in Canada (January - December 2020) No significant difference in cases c19early.org MacFadden et al., Open Forum Infectiou.., Mar 2022 Favorsfamotidine Favorscontrol 0 0.5 1 1.5 2+
MacFadden: Retrospective 26,121 cases and 2,369,020 controls ≥65yo in Canada, showing no significant difference in cases with chronic use of famotidine.
Mortality 61% Improvement Relative Risk Death/intubation 50% Famotidine for COVID-19  Mather et al.  Prophylaxis Is prophylaxis with famotidine beneficial for COVID-19? PSM retrospective 772 patients in the USA Lower mortality (p=0.004) and death/intubation (p=0.003) c19early.org Mather et al., American J. Gastroenter.., Aug 2020 Favorsfamotidine Favorscontrol 0 0.5 1 1.5 2+
Mather: PSM retrospective 878 hospitalized patients in the USA, 83 with existing famotidine use, showing significantly lower mortality with treatment.
Mortality 19% Improvement Relative Risk Famotidine  Mehrizi et al.  LATE TREATMENT Is late treatment with famotidine beneficial for COVID-19? Retrospective 917,198 patients in Iran (February 2020 - March 2022) Lower mortality with famotidine (p<0.000001) c19early.org Mehrizi et al., Frontiers in Public He.., Dec 2023 Favorsfamotidine Favorscontrol 0 0.5 1 1.5 2+
Mehrizi: Retrospective study of 917,198 hospitalized COVID-19 cases covered by the Iran Health Insurance Organization over 26 months showing that antithrombotics, corticosteroids, and antivirals reduced mortality while diuretics, antibiotics, and antidiabetics increased it. Confounding makes some results very unreliable. For example, diuretics like furosemide are often used to treat fluid overload, which is more likely in ICU or advanced disease requiring aggressive fluid resuscitation. Hospitalization length has increased risk of significant confounding, for example longer hospitalization increases the chance of receiving a medication, and death may result in shorter hospitalization. Mortality results may be more reliable.

Confounding by indication is likely to be significant for many medications. Authors adjustments have very limited severity information (admission type refers to ward vs. ER department on initial arrival). We can estimate the impact of confounding from typical usage patterns, the prescription frequency, and attenuation or increase of risk for ICU vs. all patients.

Mortality 21% Improvement Relative Risk Mortality (b) 37% Famotidine for COVID-19  Mura et al.  LATE TREATMENT Is late treatment with famotidine beneficial for COVID-19? PSM retrospective 1,126 patients in multiple countries Lower mortality with famotidine (p=0.017) c19early.org Mura et al., Signal Transduction and T.., Mar 2021 Favorsfamotidine Favorscontrol 0 0.5 1 1.5 2+
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.
Mortality 11% Improvement Relative Risk Ventilation 12% ICU admission 10% Hospitalization time 17% Recovery time 10% Famotidine  Pahwani et al.  LATE TREATMENT  RCT Is late treatment with famotidine beneficial for COVID-19? RCT 178 patients in Pakistan (December 2020 - September 2021) Shorter hospitalization (p<0.0001) and faster recovery (p=0.0011) c19early.org Pahwani et al., Cureus, February 2022 Favorsfamotidine Favorscontrol 0 0.5 1 1.5 2+
Pahwani: RCT with 89 famotidine and 89 control patients in Pakistan, showing faster recovery but no significant difference in mortality. 40mg oral famotidine daily.
Mortality 27% Improvement Relative Risk Famotidine for COVID-19  Razjouyan et al.  Prophylaxis Is prophylaxis with famotidine beneficial for COVID-19? Retrospective 10,074 patients in the USA Lower mortality with famotidine (p=0.006) c19early.org Razjouyan et al., Nicotine & Tobacco R.., Oct 2021 Favorsfamotidine Favorscontrol 0 0.5 1 1.5 2+
Razjouyan: Retrospective 10,074 hospitalized veterans with COVID-19 in the USA, showing lower mortality with existing famotidine use.
Hospitalization time 33% Improvement Relative Risk Recovery 0% Recovery (b) 50% Famotidine  Samimagham et al.  LATE TREATMENT  RCT Is late treatment with famotidine beneficial for COVID-19? RCT 20 patients in Iran Shorter hospitalization with famotidine (p=0.04) c19early.org Samimagham et al., Research Square, Apr 2021 Favorsfamotidine Favorscontrol 0 0.5 1 1.5 2+
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.
Mortality 75% Improvement Relative Risk Famotidine for COVID-19  Shamsi et al.  LATE TREATMENT Is late treatment with famotidine beneficial for COVID-19? Retrospective 183 patients in Iran (March 2020 - August 2021) Lower mortality with famotidine (not stat. sig., p=0.21) c19early.org Shamsi et al., Canadian J. Infectious .., Jul 2023 Favorsfamotidine Favorscontrol 0 0.5 1 1.5 2+
Shamsi: Retrospective 183 hospitalized pediatric COVID-19 patients in Iran, showing no significant difference in mortality with famotidine in unadjusted results.
Mortality -3% Improvement Relative Risk Death/ICU -3% Famotidine  Shoaibi et al.  LATE TREATMENT Is late treatment with famotidine beneficial for COVID-19? Retrospective 28,636 patients in the USA No significant difference in outcomes seen c19early.org Shoaibi et al., American J. Gastroente.., Sep 2020 Favorsfamotidine Favorscontrol 0 0.5 1 1.5 2+
Shoaibi: Retrospective 1,816 famotidine users and 26,820 non-users hospitalized for COVID-19 in the USA, showing no significant differences with treatment.
Mortality 36% Improvement Relative Risk Famotidine for COVID-19  Siraj et al.  LATE TREATMENT Is late treatment with famotidine beneficial for COVID-19? Retrospective 1,000 patients in India (March - December 2020) Lower mortality with famotidine (p=0.0016) c19early.org Siraj et al., Indian J. Clinical Pract.., Feb 2022 Favorsfamotidine Favorscontrol 0 0.5 1 1.5 2+
Siraj: Retrospective 1,000 COVID+ hospitalized patients in India, showing lower mortality with famotidine and remdesivir in multivariable logistic regression.
Mortality -519% Improvement Relative Risk ICU admission -2390% Famotidine for COVID-19  Stolow et al.  LATE TREATMENT Is late treatment with famotidine beneficial for COVID-19? Retrospective 489 patients in the USA Higher mortality (p=0.001) and ICU admission (p=0.001) c19early.org Stolow et al., American J. Gastroenter.., Oct 2021 Favorsfamotidine Favorscontrol 0 0.5 1 1.5 2+
Stolow: Retrospective 489 COVID+ hospitalized patients in the USA, showing higher mortality with famotidine treatment.
Mortality 45% Improvement Relative Risk ICU admission 37% Hospitalization time 18% Recovery time 20% Famotidine  Taşdemir et al.  LATE TREATMENT Is late treatment with famotidine beneficial for COVID-19? Retrospective 179 patients in Turkey Study compares with pantoprazole, results vs. placebo may differ Shorter hospitalization (p=0.003) and faster recovery (p=0.04) c19early.org Taşdemir et al., Konuralp Tıp Dergisi, Jul 2021 Favorsfamotidine Favorspantoprazole 0 0.5 1 1.5 2+
Taşdemir: Retrospective 179 hospitalized patients in Turkey, 85 treated with famotidine and 94 treated with pantoprazole, showing faster recovery with famotidine in unadjusted results.
Together Trial: 528 patient famotidine early treatment RCT with results not reported over 8 months after completion.
Mortality 64% Improvement Relative Risk Ventilation 6% Famotidine for COVID-19  Wagner et al.  LATE TREATMENT Is late treatment with famotidine beneficial for COVID-19? Retrospective 1,457 patients in the USA (March 2020 - March 2021) Lower mortality with famotidine (p<0.000001) c19early.org Wagner et al., JGH Open, October 2021 Favorsfamotidine Favorscontrol 0 0.5 1 1.5 2+
Wagner: Retrospective 2,184 hospitalized patients in the USA, 638 treated with famotidine, showing lower mortality with treatment.
Mortality -11% Improvement Relative Risk Famotidine for COVID-19  Wallace et al.  Prophylaxis Is prophylaxis with famotidine beneficial for COVID-19? Retrospective 7,944 patients in the USA No significant difference in mortality c19early.org Wallace et al., BMJ Open, December 2021 Favorsfamotidine Favorscontrol 0 0.5 1 1.5 2+
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.
Mortality, day 30 51% Improvement Relative Risk Mortality -59% late Famotidine  Yeramaneni et al.  Prophylaxis Is prophylaxis with famotidine beneficial for COVID-19? Retrospective 7,158 patients in the USA (February - May 2020) Lower mortality with famotidine (not stat. sig., p=0.22) c19early.org Yeramaneni et al., Gastroenterology, Feb 2021 Favorsfamotidine Favorscontrol 0 0.5 1 1.5 2+
Yeramaneni: Retrospective 7,158 hospitalized COVID-19 patients in the USA, showing higher risk or mortality with in-hospital famotidine use, but lower risk when there was pre-existing at-home use, without statistical significance in both cases.
Mortality 39% Improvement Relative Risk Famotidine for COVID-19  Zangeneh et al.  ICU PATIENTS Is very late treatment with famotidine beneficial for COVID-19? Retrospective study in Iran Lower mortality with famotidine (p=0.014) c19early.org Zangeneh et al., Obesity Medicine, May 2022 Favorsfamotidine Favorscontrol 0 0.5 1 1.5 2+
Zangeneh: Retrospective 193 ICU patients in Iran, showing lower mortality with famotidine treatment.
Severe case -84% Improvement Relative Risk Famotidine for COVID-19  Zhou et al.  LATE TREATMENT Is late treatment with famotidine beneficial for COVID-19? PSM retrospective 3,114 patients in China (January - August 2020) Higher severe cases with famotidine (p=0.0001) c19early.org Zhou et al., Gut, December 2020 Favorsfamotidine Favorscontrol 0 0.5 1 1.5 2+
Zhou: Retrospective 4,445 COVID+ patients in China, showing higher risk of combined death/intubation/ICU with famotidine and with PPIs.
Mortality 29% Improvement Relative Risk Ventilation -1% Ventilation time -33% ICU time 26% Famotidine for COVID-19  Özden et al.  ICU PATIENTS Is very late treatment with famotidine beneficial for COVID-19? Retrospective 59 patients in Turkey (September 2020 - February 2021) Lower mortality (p=0.19) and shorter ICU admission (p=0.6), not sig. c19early.org Özden et al., Boğazi̇çi̇ Tip Dergi̇si̇, Feb 2023 Favorsfamotidine Favorscontrol 0 0.5 1 1.5 2+
Özden: Retrospective 59 ICU patients in Turkey, showing no significant difference in 30-day mortality or invasive mechanical ventilation with 160mg/day famotidine treatment. However, the famotidine group had lower fibrinogen and procalcitonin, suggesting possible benefits for coagulation, inflammation, and secondary infections. Limitations include the small sample size, lack of randomization, and other confounding treatments.
We perform ongoing searches of PubMed, medRxiv, Europe PMC, ClinicalTrials.gov, The Cochrane Library, Google Scholar, Research Square, ScienceDirect, Oxford University Press, the reference lists of other studies and meta-analyses, and submissions to the site c19early.org. Search terms are famotidine and COVID-19 or SARS-CoV-2. Automated searches are performed twice daily, with all matches reviewed for inclusion. 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 have preference. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms are not used, the next most serious outcome with one or more events is used. 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 outcomes are considered more important than viral test 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 little or no room for an effective treatment to do better, however faster recovery is valuable. 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 compute the relative risk when possible, or convert to a relative risk according to97. Reported confidence intervals and p-values were used when available, using adjusted values when provided. If multiple types of adjustments are reported propensity score matching and multivariable regression has preference over propensity score matching or weighting, which has preference over multivariable regression. Adjusted 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 1100. 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 (3.12.4) with scipy (1.14.0), pythonmeta (1.26), numpy (1.26.4), statsmodels (0.14.2), and plotly (5.22.0).
Forest plots are computed using PythonMeta101 with the DerSimonian and Laird random effects model (the fixed effect assumption is not plausible in this case) and inverse variance weighting. Results are presented with 95% confidence intervals. Heterogeneity among studies was assessed using the I2 statistic. Mixed-effects meta-regression results are computed with R (4.4.0) using the metafor (4.6-0) and rms (6.8-0) packages, and using the most serious sufficiently powered outcome. For all statistical tests, a p-value less than 0.05 was considered statistically significant. Grobid 0.8.0 is used to parse PDF documents.
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 effective32,33.
We received no funding, this research is done in our spare time. We have no affiliations with any pharmaceutical companies or political parties.
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.
Together Trial, 11/1/2023, Double Blind Randomized Controlled Trial, placebo-controlled, trial NCT04727424 (history) (TOGETHER). 528 patient RCT with results unknown and over 8 months late.
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, study period 1 August, 2020 - 15 April, 2021, 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.
Mehrizi, 12/18/2023, retrospective, Iran, peer-reviewed, 10 authors, study period 1 February, 2020 - 20 March, 2022. risk of death, 19.0% lower, OR 0.81, p < 0.001, 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.
Shamsi, 7/17/2023, retrospective, Iran, peer-reviewed, 4 authors, study period 1 March, 2020 - 1 August, 2021, excluded in exclusion analyses: unadjusted results with no group details. risk of death, 74.9% lower, RR 0.25, p = 0.21, treatment 1 of 27 (3.7%), control 23 of 156 (14.7%), NNT 9.1.
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 1 March, 2020 - 1 March, 2021. risk of death, 64.5% lower, RR 0.36, p < 0.001, treatment 82 of 638 (12.9%), control 182 of 819 (22.2%), adjusted per study, odds ratio converted to relative risk, multivariable.
risk of mechanical ventilation, 6.4% lower, RR 0.94, p = 0.77, treatment 48 of 638 (7.5%), control 75 of 819 (9.2%), 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, 59.0% higher, OR 1.59, p = 0.09, treatment 410, control 746, adjusted per study, hospital use only, 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, 84.0% higher, HR 1.84, p < 0.001, treatment 72 of 519 (13.9%), control 198 of 2,595 (7.6%), adjusted per study, death/intubation/ICU, propensity score matching, multivariable, Cox proportional hazards.
Özden, 2/28/2023, retrospective, Turkey, peer-reviewed, mean age 65.3, 2 authors, study period September 2020 - February 2021, trial NCT05122208 (history). risk of death, 28.8% lower, RR 0.71, p = 0.19, treatment 14 of 30 (46.7%), control 19 of 29 (65.5%), NNT 5.3.
risk of mechanical ventilation, 1.1% higher, RR 1.01, p = 1.00, treatment 23 of 30 (76.7%), control 22 of 29 (75.9%).
ventilation time, 33.3% higher, relative time 1.33, p = 0.28, treatment 30, control 29.
ICU time, 25.5% lower, relative time 0.74, p = 0.60, treatment 30, control 29.
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.
Kim, 3/21/2023, retrospective, South Korea, peer-reviewed, 8 authors, study period 1 January, 2020 - 4 June, 2020. risk of case, 36.3% lower, RR 0.64, p < 0.001, treatment 105 of 5,594 (1.9%), control 480 of 15,432 (3.1%), NNT 81, adjusted per study, odds ratio converted to relative risk, multivariable, model 3.
Kwon, 5/31/2023, retrospective, South Korea, peer-reviewed, 8 authors, study period 1 July, 2020 - 31 December, 2020. risk of progression, 107.0% higher, OR 2.07, p = 0.06, treatment 204, control 204, adjusted per study, ICU, mechanical ventilation, or death, famotidine vs. other H2-blocker use, multivariable, RR approximated with OR.
risk of progression, 256.0% higher, OR 3.56, p = 0.04, treatment 204, control 204, adjusted per study, high oxygen, ICU, mechanical ventilation, or death, famotidine vs. other H2-blocker use, multivariable, RR approximated with OR.
risk of oxygen therapy, 109.0% higher, OR 2.09, p = 0.07, treatment 204, control 204, adjusted per study, famotidine vs. other H2-blocker use, multivariable, RR approximated with OR.
Loucera, 8/16/2022, retrospective, Spain, peer-reviewed, 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, with home use, multivariable, day 30, RR approximated with OR.
Please send us corrections, updates, or comments. c19early involves the extraction of 100,000+ datapoints from thousands of papers. Community updates help ensure high accuracy. Treatments and other interventions are complementary. All practical, effective, and safe means should be used based on risk/benefit analysis. No treatment 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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