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Fluvoxamine for COVID-19: real-time meta analysis of 13 studies
Covid Analysis, December 2022
https://c19early.org/fmeta.html
 
0 0.5 1 1.5+ All studies 31% 13 34,828 Improvement, Studies, Patients Relative Risk Mortality 37% 5 2,432 Ventilation 22% 1 1,497 Hospitalization 28% 8 4,908 Progression 22% 4 2,037 Recovery 85% 2 1,413 Cases 28% 1 9,116 Viral clearance -49% 1 428 RCTs 23% 6 4,178 Peer-reviewed 30% 9 32,347 Prophylaxis 27% 4 29,766 Early 56% 6 2,186 Late 40% 3 2,876 Fluvoxamine for COVID-19 c19early.org/f Dec 2022 Favorsfluvoxamine Favorscontrol
Statistically significant improvement is seen for cases. 8 studies from 8 independent teams in 4 different countries show statistically significant improvements in isolation (4 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 31% [17‑43%] improvement. Results are slightly worse for Randomized Controlled Trials and similar for peer-reviewed studies. Early treatment is more effective than late treatment.
Results are robust — in exclusion sensitivity analysis 7 of 13 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
0 0.5 1 1.5+ All studies 31% 13 34,828 Improvement, Studies, Patients Relative Risk Mortality 37% 5 2,432 Ventilation 22% 1 1,497 Hospitalization 28% 8 4,908 Progression 22% 4 2,037 Recovery 85% 2 1,413 Cases 28% 1 9,116 Viral clearance -49% 1 428 RCTs 23% 6 4,178 Peer-reviewed 30% 9 32,347 Prophylaxis 27% 4 29,766 Early 56% 6 2,186 Late 40% 3 2,876 Fluvoxamine for COVID-19 c19early.org/f Dec 2022 Favorsfluvoxamine Favorscontrol
Treatment recommendations are available from Ontario.
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 may be more effective. Only 15% of fluvoxamine studies show zero events with treatment.
All data to reproduce this paper and sources are in the appendix. Other meta analyses for fluvoxamine can be found in [Lee, Lu, Marcec, Nakhaee], showing significant improvements for hospitalization and severity.
Highlights
Fluvoxamine reduces risk for COVID-19 with very high confidence for pooled analysis and low confidence for mortality, hospitalization, and cases, however increased risk is seen with low confidence for viral clearance.
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+ Lenze (DB RCT) 93% 0.07 [0.01-0.52] progression 0/80 6/72 Improvement, RR [CI] Treatment Control Seftel (QR) 84% 0.16 [0.01-3.29] death/ICU 0/77 2/48 Lenze (DB RCT) 7% 0.93 [0.42-2.06] hosp. 11/272 12/275 Seo (SB RCT) 0% 1.00 [0.15-6.57] progression 2/26 2/26 Bramante (DB RCT) -11% 1.11 [0.33-3.61] death/hosp. 6/329 5/324 OT​1 Pineda 96% 0.04 [0.00-0.40] death 1/594 4/63 Tau​2 = 0.74, I​2 = 53.0%, p = 0.11 Early treatment 56% 0.44 [0.16-1.22] 20/1,378 31/808 56% improvement Reis (DB RCT) 30% 0.70 [0.37-1.26] death 17/741 25/756 Improvement, RR [CI] Treatment Control Calusic (ICU) 42% 0.58 [0.36-0.94] death 30/51 39/51 ICU patients McCarthy (DB RCT) 55% 0.45 [0.04-4.99] hosp. 1/670 2/607 Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Late treatment 40% 0.60 [0.46-0.77] 48/1,462 66/1,414 40% improvement Oskotsky (PSM) -58% 1.58 [0.42-5.93] death 2/11 19/165 Improvement, RR [CI] Treatment Control Fritz 19% 0.81 [0.26-2.22] hosp./ER 4/17 1,896/20,457 Diaz (PSM) 28% 0.72 [0.63-0.81] cases 4,558 (n) 4,558 (n) Trkulja (PSM) 27% 0.73 [0.35-1.55] death Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Prophylaxis 27% 0.73 [0.64-0.82] 6/4,586 1,915/25,180 27% improvement All studies 31% 0.69 [0.57-0.83] 74/7,426 2,012/27,402 31% improvement 13 fluvoxamine COVID-19 studies c19early.org/f Dec 2022 Tau​2 = 0.02, I​2 = 16.4%, p < 0.0001 Effect extraction pre-specified(most serious outcome, see appendix) 1 OT: comparison with other treatment Favors fluvoxamine Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Lenze (DB RCT) 93% progression Relative Risk [CI] Seftel (QR) 84% death/ICU Lenze (DB RCT) 7% hospitalization Seo (SB RCT) 0% progression Bramante (DB RCT) -11% death/hosp. OT​1 Pineda 96% death Tau​2 = 0.74, I​2 = 53.0%, p = 0.11 Early treatment 56% 56% improvement Reis (DB RCT) 30% death Calusic (ICU) 42% death ICU patients McCarthy (DB RCT) 55% hospitalization Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Late treatment 40% 40% improvement Oskotsky (PSM) -58% death Fritz 19% hosp./ER Diaz (PSM) 28% case Trkulja (PSM) 27% death Tau​2 = 0.00, I​2 = 0.0%, p < 0.0001 Prophylaxis 27% 27% improvement All studies 31% 31% improvement 13 fluvoxamine COVID-19 studies c19early.org/f Dec 2022 Tau​2 = 0.02, I​2 = 16.4%, p < 0.0001 Protocol pre-specified/rotate for details1 OT: comparison with other treatment Favors fluvoxamine 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 fluvoxamine studies.
We analyze all significant studies concerning the use of fluvoxamine 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, for studies within each treatment stage, for individual outcomes, for peer-reviewed studies, for 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.
FIASMAFluvoxamine is a functional inhibitor of acid sphingomyelinase (FIASMA). SARS-CoV-2 activates the ASM/ceramide system which may facilitate viral entry. ASM inhibition may reduce the concentration of ceramides and inhibit viral entry [Carpinteiro, Carpinteiro (B), Hashimoto, Hoertel].
Sigma-1 activationFluvoxamine may reduce clinical deterioration via σ-1 (S1R) receptor activation, which regulates cytokine production [Hashimoto, Hashimoto (B), Sukhatme].
Platelet activationPlatelet activation may contribute to COVID-19 severity. Fluvoxamine inhibits platelet activation [Battinelli, Sukhatme].
Lysosomal traffickingSARS-CoV-2 uses lysosomal trafficking to escape from infected cells. Fluvoxamine is lysosomotropic and interferes with endolysosomal viral trafficking [Hashimoto, Norinder, Sukhatme].
Heme oxygenaseCOVID-19 risk may be related to low intracellular heme oxygenase (HO-1). Fluvoxamine increases HO-1 and HO-1 has cytoprotective and anti-inflammatory properties [Almási, Hooper, Hooper (B)].
Mast cell degranulationFluvoxamine may reduce cytokine storm due to decreased mast cell degranulation [Sukhatme].
MelatoninMelatonin may be beneficial for COVID-19, and fluvoxamine may elevate melatonin levels via CYP1A2 and CYP2C19 inhibition [Anderson, Camp, Hashimoto, Ramos, Sukhatme].
Table 1. Fluvoxamine mechanisms of action. Submit updates.
Table 2 summarizes the results for all stages combined, with different exclusions, and for specific outcomes. Table 3 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, hospitalization, progression, recovery, cases, viral clearance, and peer reviewed studies.
Improvement Studies Patients Authors
All studies31% [17‑43%]13 34,828 133
Peer-reviewed studiesPeer-reviewed30% [22‑37%]9 32,347 79
Randomized Controlled TrialsRCTs23% [-17‑50%]6 4,178 83
Mortality37% [-1‑60%]5 2,432 68
HospitalizationHosp.28% [-3‑49%]8 4,908 97
RCT hospitalizationRCT hosp.22% [-1‑39%]5 4,126 69
Table 2. 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.
Early treatment Late treatment Prophylaxis
All studies56% [-22‑84%] 640% [23‑54%] 327% [18‑36%] 4
Peer-reviewed studiesPeer-reviewed42% [-84‑82%] 440% [23‑53%] 227% [18‑36%] 3
Randomized Controlled TrialsRCTs13% [-63‑54%] 432% [-22‑62%] 2-
Mortality96% [60‑100%] 140% [23‑53%] 212% [-68‑54%] 2
HospitalizationHosp.44% [-6‑70%] 522% [-3‑41%] 2-37% [-233‑44%] 1
RCT hospitalizationRCT hosp.20% [-58‑59%] 322% [-3‑41%] 2-
Table 3. 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.
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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 hospitalization.
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Figure 7. Random effects meta-analysis for progression.
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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. 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.
RCTs help to make study groups more similar, however they are subject to many biases, including age bias, treatment delay bias, severity of illness bias, regulation bias, recruitment bias, trial design bias, followup time bias, selective reporting bias, fraud bias, hidden agenda bias, vested interest bias, publication bias, and publication delay bias [Jadad], all of which have been observed with COVID-19 RCTs.
RCTs have a bias against finding an effect for interventions that are widely available — patients that believe they need the intervention are more likely to decline participation and take the intervention. This is illustrated with the extreme example of an RCT showing no significant differences for use of a parachute when jumping from a plane [Yeh]. RCTs for fluvoxamine are more likely to enroll low-risk participants that do not need treatment to recover, making the results less applicable to clinical practice. This bias is likely to be greater for widely known treatments. Note that this bias does not apply to the typical pharmaceutical trial of a new drug that is otherwise unavailable.
Evidence shows that non-RCT trials can also provide reliable results. [Concato] find that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. [Anglemyer] summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. [Lee (B)] shows 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 could have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see [Deaton, Nichol].
In summary, 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 example, consider trials for an off-patent medication, very high conflict of interest trials may be more likely to be RCTs (and more likely to be large trials that dominate meta analyses).
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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.
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.
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]
Table 4. Early treatment is more effective for baloxavir and influenza.
Figure 16 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 16. 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 17. 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 17. 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 fluvoxamine, 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.
75% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 56% of prospective studies, consistent with a bias toward publishing positive results. The median effect size for retrospective studies is 23% improvement, compared to 42% for prospective studies, suggesting a potential bias towards publishing results showing lower efficacy. Figure 18 shows a scatter plot of results for prospective and retrospective studies.
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Figure 18. 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 19 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 19. 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. Fluvoxamine for COVID-19 lacks this because it is off-patent, has multiple manufacturers, and is very low cost. In contrast, most COVID-19 fluvoxamine 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 fluvoxamine 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 13 studies compare against other treatments, which may reduce the effect seen. Other meta analyses for fluvoxamine can be found in [Lee, Lu, Marcec, Nakhaee], showing significant improvements for hospitalization and severity.
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.
Studies to date show that fluvoxamine is an effective treatment for COVID-19. Statistically significant improvement is seen for cases. 8 studies from 8 independent teams in 4 different countries show statistically significant improvements in isolation (4 for the most serious outcome). Meta analysis using the most serious outcome reported shows 31% [17‑43%] improvement. Results are slightly worse for Randomized Controlled Trials and similar for peer-reviewed studies. Early treatment is more effective than late treatment. Results are robust — in exclusion sensitivity analysis 7 of 13 studies must be excluded to avoid finding statistically significant efficacy in pooled analysis.
Treatment recommendations are available from Ontario.
0 0.5 1 1.5 2+ Death/hospitalization -11% Improvement Relative Risk Progression -16% Hospitalization, day 28 2% Hospitalization, day 14 2% Progression (b) 5% primary c19early.org/f Bramante et al. NCT04510194 COVID-OUT Fluvoxamine RCT EARLY Favors fluvoxamine Favors metformin (p..
COVID-OUT remotely operated RCT, showing no significant difference in outcomes. Results for other treatments are listed separately - metformin, ivermectin.
The "control" group includes patients receiving metformin, which is known to be beneficial for COVID-19 [c19early.org].
Authors note that the dosage used in the trial is lower than that of other trials [twitter.com].
Control arm results are very different between treatments, for example considering hospitalization/death, this was 1.0% for ivermectin vs. 2.7% for overall control, however it was 1.3% for the ivermectin-specific control. 394 control patients are shared. The rate for the non-shared 261 metformin control patients is 5%, compared to 1.3% for ivermectin control patients. The metformin arm started earlier, however it is unclear why the difference in outcomes is so large.
Results were delayed for 6 months with no explanation, with followup ending Feb 14, 2022.
Multiple outcomes are missing, for example time to recovery (where ACTIV-6 showed superiority of ivermectin).
Adherence was very low, with 77% overall reporting 70+% adherence. Numbers for 100% adherence are not provided.
Treatment was 14 days for metformin and fluvoxamine, but only 3 days for ivermectin.
Trial outcomes were changed on January 20, 2022 [clinicaltrials.gov], and again on March 2, 2022 [clinicaltrials.gov (B)]. COVIDOUT.
Medication delivery varied significantly over the trial. In this presentation [vimeo.com], author indicates that delivery was initially local, later via FedEx, was much slower in August, there were delays due to team bandwidth issues, and they only realized they could use FedEx same day delivery in September.
0 0.5 1 1.5 2+ Mortality 42% Improvement Relative Risk c19early.org/f Calusic et al. Fluvoxamine for COVID-19 ICU Favors fluvoxamine Favors control
[Calusic] Prospective PSM study of 51 COVID-19 ICU patients in Croatia and 51 matched controls, showing significantly lower mortality with treatment.
0 0.5 1 1.5 2+ Case 28% Improvement Relative Risk c19early.org/f Diaz et al. Fluvoxamine for COVID-19 Prophylaxis Favors fluvoxamine Favors control
[Diaz] TriNetX PSM retrospective 82,069 OCD patients, showing lower risk of COVID-19 with fluvoxamine use.
0 0.5 1 1.5 2+ Hospitalization/ER 19% Improvement Relative Risk Hospitalization/ER (b) 12% Hospitalization/ER (c) 12% Hospitalization/ER (d) 10% c19early.org/f Fritz et al. Fluvoxamine for COVID-19 Prophylaxis Favors fluvoxamine Favors control
[Fritz] Retrospective 25,034 COVID+ outpatients showing significantly lower ER/hospitalization with antidepressants and FIASMA antidepressants, and a dose-dependent response.
0 0.5 1 1.5 2+ Hospitalization 7% Improvement Relative Risk c19early.org/f Lenze et al. NCT04668950 STOP COVID 2 Fluvoxamine RCT EARLY Favors fluvoxamine Favors control
[Lenze] Presentation noting that STOP COVID 2 was terminated early for futility with only 30/551 cases of detioration and no significant treatment effect. The main results are not available yet, however partial results presented suggest that early treatment was more effective. Hospitalization results are from [medrxiv.org].
0 0.5 1 1.5 2+ Progression 93% Improvement Relative Risk Hospitalization 82% c19early.org/f Lenze et al. NCT04342663 Fluvoxamine RCT EARLY TREATMENT Favors fluvoxamine Favors control
[Lenze (B)] RCT 152 outpatients, 80 treated with fluvoxamine showing lower progression with treatment (0 of 80 versus 6 of 72 control). STOP COVID trial. NCT04342663.
0 0.5 1 1.5 2+ Hospitalization 55% Improvement Relative Risk Progression, C19 pneu.. 68% Progression -10% Recovery -4% post-hoc primary Clinical progression, day 7 -28% Clinical progression, day.. -17% Clinical progression.. (b) -46% c19early.org/f McCarthy et al. NCT04885530 ACTIV-6 Fluvoxamine RCT LATE Favors fluvoxamine Favors control
RCT low-risk outpatients with very late treatment (median 5 days, 20% ≥8 days) in the USA, showing no significant differences with low-dose fluvoxamine treatment.
Many of the issues noted for the ivermectin arm [Naggie] also apply to this arm, for example none of the pre-specified outcomes have been reported [fnih.org]. In the ivermectin paper the abstract refers to the main post-hoc outcome as the "main outcome", this wording has been updated to "primary outcome" for fluvoxamine.
eFigure 4 shows efficacy continuously declining over time, which may be due to lower severity as the virus evolves. Treatment delay was up to 13 days (eFigure 1). Severe dyspnea at baseline was more common for fluvoxamine (6/586 vs. 3/533, eTable 1).
Authors state that the median treatment delay (5 days) is at the upper limit of the "recommend (sic) start of antiviral medicines (≤5 days)". There is no reference and it's unclear who recommends such late treatment.
Fluvoxamine 50mg twice daily.
0 0.5 1 1.5 2+ Mortality -58% Improvement Relative Risk Mortality (b) 26% c19early.org/f Oskotsky et al. Fluvoxamine for COVID-19 Prophylaxis Favors fluvoxamine Favors control
[Oskotsky] Retrospective database analysis of 83,584 patients in the USA, showing lower mortality with existing fluoxetine use in PSM analysis. There were 11 fluvoxamine patients, showing non-statistically significant higher mortality.
0 0.5 1 1.5 2+ Mortality 96% Improvement Relative Risk Oxygen therapy 77% Hospitalization 54% Hospitalization time -36% c19early.org/f Pineda et al. Fluvoxamine for COVID-19 EARLY TREATMENT Favors fluvoxamine Favors control
[Pineda] Prospective study of 657 COVID+ outpatients in Honduras, 594 accepting fluvoxamine treatment, showing significantly lower mortality and hospitalization with treatment.
0 0.5 1 1.5 2+ Mortality 30% Improvement Relative Risk Mortality (b) 91% Ventilation 22% Hospitalization 22% Extended ER observation.. 32% primary Extended ER observa.. (b) 31% Extended ER observa.. (c) 66% Viral clearance -49% c19early.org/f Reis et al. NCT04727424 TOGETHER Fluvoxamine RCT LATE Favors fluvoxamine Favors control
[Reis] Together Trial showing significantly lower hospitalization/extended ER visits with fluvoxamine treatment. Adherence was only 73.2%. Symptom onset was unspecified or >= 4 days for 57% of patients. The schedule of study activities specifies treatment administration only one day after randomization, adding an additional day delay. Overall mortality is high for the patient population. Results may be impacted by late treatment, poor SOC, and may be specific to local variants [science.sciencemag.org, thelancet.com]. Per-protocol analysis shows significantly improved results in this trial, however this may be subject to bias - the probability of adherence may be related to the probability of the outcome.

Regarding the combined hospitalization/extended ER observation outcome, authors have noted that at the study sites, extended medical observation was essentially equivalent to being hospitalized. “These were not standard emergency rooms but instead were COVID-19 emergency centers that were set up due to hospitals being overloaded,” Reiersen noted in an email to The Scientist. “A stay in these centers >6 hours was an indication that the patient was receiving care equivalent to hospitalization.”

Authors state "this study is only the second study to show an important treatment benefit for a repurposed drug in the early treatment population", however the actual number is at least 66 based on our database at the time of publication, using a conservative definition of at least 10% benefit (with statistical significance).

The total dose used is less than half of that in Lenze et al. There is an unusual amount of missing data - age is unknown for 6.5% of patients according to the sub-group analysis. Both age <=50 and >50 show better results on the primary outcome than the overall result. The number of placebo patients changed significantly between the preprint and journal version. The number of treatment patients with viral clearance results reduced significantly between the preprint and journal version. Also see [twitter.com (B)]. NCT04727424.

Authors do not specify if the placebo looks identical to the film-coated Luvox tablets. Reportedly there is no registration of manufacturing for matching tablets by Abbott in Brazil, and no import license for identical placebo tablets abroad. This would be an additional reason for blinding failure if the placebo tablets are not identical in appearance.

For other issues with this trial see: [twitter.com (C), twitter.com (D), twitter.com (E)].

Many of the issues in the companion ivermectin trial may also apply to this trial [c19ivermectin.com], notably the potential for significant use of an effective treatment in the placebo group [doyourownresearch.substack.com], which would reduce the efficacy seen.
0 0.5 1 1.5 2+ Death/ICU 84% Improvement Relative Risk Hospitalization 94% Recovery 99% c19early.org/f Seftel et al. Fluvoxamine for COVID-19 EARLY TREATMENT Favors fluvoxamine Favors control
[Seftel] Prospective quasi-randomized (patient choice) study with 125 outpatients, 77 treated with fluvoxamine, showing lower death/ICU admission (0 of 77 vs. 2 of 48), lower hospitalization (0 of 77 vs. 6 of 48), and faster recovery with treatment. Note that 12 treatment patients were added but are not reflected in the table in the paper (because the numbers had been previously published and the IRB did not allow updating the table).
0 0.5 1 1.5 2+ Progression 0% Improvement Relative Risk Progression (b) 34% Time to progression 13% c19early.org/f Seo et al. Fluvoxamine for COVID-19 RCT EARLY TREATMENT Favors fluvoxamine Favors control
[Seo] Early terminated RCT with 52 COVID+ patients in South Korea, showing no significant difference in progression with fluvoxamine treatment. There were only 2 events in each arm, and only one event for fluvoxamine in PP analysis. The trial was terminated early because the treatment center closed. 100mg fluvoxamine bid for 10 days.
0 0.5 1 1.5 2+ Mortality 27% Improvement Relative Risk Hospitalization -37% c19early.org/f Trkulja et al. Fluvoxamine for COVID-19 Prophylaxis Favors fluvoxamine Favors control
[Trkulja] Retrospective COVID+ patients in Croatia, showing no significant difference in outcomes with fluvoxamine prophylaxis.
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 fluvoxamine, 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 fluvoxamine for COVID-19 that report a comparison with a control group are included in the main analysis. 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/fmeta.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.
[Bramante], 8/18/2022, Double Blind Randomized Controlled Trial, placebo-controlled, USA, peer-reviewed, 3 authors, average treatment delay 5.0 days, this trial compares with another treatment - results may be better when compared to placebo, trial NCT04510194 (history) (COVID-OUT). risk of death/hospitalization, 10.8% higher, RR 1.11, p = 0.88, treatment 6 of 329 (1.8%), control 5 of 324 (1.5%), odds ratio converted to relative risk.
risk of progression, 16.1% higher, RR 1.16, p = 0.68, treatment 18 of 329 (5.5%), control 15 of 324 (4.6%), odds ratio converted to relative risk, combined ER, hospitalization, death.
risk of hospitalization, 1.5% lower, RR 0.98, p = 1.00, treatment 5 of 329 (1.5%), control 5 of 324 (1.5%), NNT 4264, Figure S8, day 28.
risk of hospitalization, 1.5% lower, RR 0.98, p = 1.00, treatment 5 of 329 (1.5%), control 5 of 324 (1.5%), NNT 4264, Figure S7, day 14.
risk of progression, 4.6% lower, RR 0.95, p = 0.75, treatment 79 of 329 (24.0%), control 80 of 321 (24.9%), NNT 110, odds ratio converted to relative risk, combined hypoxemia, ER, hospitalization, death, primary outcome.
[Lenze], 8/20/2021, Double Blind Randomized Controlled Trial, USA, preprint, median age 47.0 (treatment) 48.0 (control), 1 author, average treatment delay 5.0 days, trial NCT04668950 (history) (STOP COVID 2). risk of hospitalization, 7.3% lower, RR 0.93, p = 1.00, treatment 11 of 272 (4.0%), control 12 of 275 (4.4%), NNT 313.
[Lenze (B)], 11/12/2020, Double Blind Randomized Controlled Trial, USA, peer-reviewed, 11 authors, average treatment delay 4.0 days, trial NCT04342663 (history). risk of progression, 92.7% lower, RR 0.07, p = 0.009, treatment 0 of 80 (0.0%), control 6 of 72 (8.3%), NNT 12, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), clinical deterioration over 15 days.
risk of hospitalization, 82.0% lower, RR 0.18, p = 0.009, treatment 1 of 80 (1.2%), control 5 of 72 (6.9%), NNT 18, COVID-19 hospitalization within 15 days, see supplemental appendix for details.
[Pineda], 10/4/2022, prospective, Honduras, preprint, mean age 47.9, 24 authors, study period November 2020 - January 2022. risk of death, 95.6% lower, RR 0.04, p = 0.005, treatment 1 of 594 (0.2%), control 4 of 63 (6.3%), NNT 16, adjusted per study, inverted to make RR<1 favor treatment, odds ratio converted to relative risk, multivariable.
risk of oxygen therapy, 76.8% lower, RR 0.23, p < 0.001, treatment 21 of 594 (3.5%), control 12 of 63 (19.0%), NNT 6.4, adjusted per study, inverted to make RR<1 favor treatment, odds ratio converted to relative risk, multivariable.
risk of hospitalization, 53.7% lower, RR 0.46, p = 0.04, treatment 30 of 594 (5.1%), control 10 of 63 (15.9%), NNT 9.2, adjusted per study, inverted to make RR<1 favor treatment, odds ratio converted to relative risk, multivariable.
hospitalization time, 35.5% higher, relative time 1.36, p = 0.33, treatment 30, control 10.
[Seftel], 2/1/2021, prospective quasi-randomized (patient choice), USA, peer-reviewed, 2 authors. risk of death/ICU, 83.9% lower, RR 0.16, p = 0.15, treatment 0 of 77 (0.0%), control 2 of 48 (4.2%), NNT 24, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of hospitalization, 94.0% lower, RR 0.06, p = 0.003, treatment 0 of 77 (0.0%), control 6 of 48 (12.5%), NNT 8.0, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of no recovery, 98.7% lower, RR 0.01, p < 0.001, treatment 0 of 77 (0.0%), control 29 of 48 (60.4%), NNT 1.7, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
[Seo], 3/3/2022, Single Blind Randomized Controlled Trial, placebo-controlled, South Korea, peer-reviewed, median age 53.5, 14 authors, study period 15 January, 2021 - 19 February, 2021. risk of progression, no change, RR 1.00, p = 1.00, treatment 2 of 26 (7.7%), control 2 of 26 (7.7%).
risk of progression, 34.2% lower, RR 0.66, p = 1.00, treatment 1 of 19 (5.3%), control 2 of 25 (8.0%), NNT 37, PP.
time to progression, 13.3% lower, relative time 0.87, p = 0.16, treatment mean 6.5 (±0.7) n=26, control mean 7.5 (±3.5) n=26.
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.
[Calusic], 11/1/2021, prospective, propensity score matching, Croatia, peer-reviewed, 7 authors, study period 1 April, 2021 - 31 May, 2021. risk of death, 42.0% lower, HR 0.58, p = 0.03, treatment 30 of 51 (58.8%), control 39 of 51 (76.5%), NNT 5.7, adjusted per study, propensity score matching.
[McCarthy], 10/18/2022, Double Blind Randomized Controlled Trial, placebo-controlled, USA, preprint, mean age 48.5, 27 authors, study period 6 August, 2021 - 27 May, 2022, average treatment delay 5.0 days, trial NCT04885530 (history) (ACTIV-6). risk of hospitalization, 55.0% lower, RR 0.45, p = 0.53, treatment 1 of 670 (0.1%), control 2 of 607 (0.3%), NNT 555, day 28.
risk of progression, 67.6% lower, RR 0.32, p = 0.48, treatment 0 of 615 (0.0%), control 1 of 565 (0.2%), NNT 565, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), C19 pneumonia, eTable 2.
risk of progression, 10.0% higher, RR 1.10, p = 0.75, treatment 26 of 670 (3.9%), control 23 of 607 (3.8%), adjusted per study, urgent or emergency care visits, hospitalizations, or death.
risk of no recovery, 4.2% higher, HR 1.04, p = 0.45, treatment 674, control 614, inverted to make HR<1 favor treatment, post-hoc primary outcome.
clinical progression, 28.0% higher, OR 1.28, p = 0.34, treatment 670, control 607, mid-recovery, day 7, RR approximated with OR.
clinical progression, 17.0% higher, OR 1.17, p = 0.68, treatment 670, control 607, day 14, RR approximated with OR.
clinical progression, 46.0% higher, OR 1.46, p = 0.16, treatment 670, control 607, day 28, RR approximated with OR.
[Reis], 8/23/2021, Double Blind Randomized Controlled Trial, Brazil, peer-reviewed, 27 authors, study period 20 January, 2021 - 5 August, 2021, trial NCT04727424 (history) (TOGETHER). risk of death, 30.3% lower, RR 0.70, p = 0.24, treatment 17 of 741 (2.3%), control 25 of 756 (3.3%), NNT 99, odds ratio converted to relative risk, ITT.
risk of death, 90.8% lower, RR 0.09, p = 0.02, treatment 1 of 548 (0.2%), control 12 of 618 (1.9%), NNT 57, odds ratio converted to relative risk, per protocol.
risk of mechanical ventilation, 22.2% lower, RR 0.78, p = 0.33, treatment 26 of 741 (3.5%), control 34 of 756 (4.5%), NNT 101, odds ratio converted to relative risk, ITT.
risk of hospitalization, 21.6% lower, RR 0.78, p = 0.10, treatment 75 of 741 (10.1%), control 97 of 756 (12.8%), NNT 37, odds ratio converted to relative risk, ITT.
extended ER observation or hospitalization, 32.0% lower, RR 0.68, p = 0.004, treatment 79 of 741 (10.7%), control 119 of 756 (15.7%), NNT 20, ITT, primary outcome.
extended ER observation or hospitalization, 31.0% lower, RR 0.69, p = 0.006, treatment 78 of 740 (10.5%), control 115 of 752 (15.3%), NNT 21, mITT.
extended ER observation or hospitalization, 66.0% lower, RR 0.34, p < 0.001, treatment 541, control 609, per protocol.
risk of no viral clearance, 49.3% higher, RR 1.49, p = 0.09, treatment 167 of 207 (80.7%), control 163 of 221 (73.8%), adjusted per study, inverted to make RR<1 favor treatment.
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.
[Diaz], 10/6/2022, retrospective, USA, peer-reviewed, 2 authors. risk of case, 28.0% lower, OR 0.72, p < 0.001, treatment 4,558, control 4,558, propensity score matching, RR approximated with OR.
[Fritz], 8/22/2022, retrospective, USA, peer-reviewed, 5 authors, study period 1 March, 2020 - 16 May, 2021. risk of hospitalization/ER, 19.4% lower, RR 0.81, p = 0.69, treatment 4 of 17 (23.5%), control 1,896 of 20,457 (9.3%), adjusted per study, odds ratio converted to relative risk, fluvoxamine, multivariable.
risk of hospitalization/ER, 11.9% lower, RR 0.88, p = 0.03, treatment 707 of 3,414 (20.7%), control 1,896 of 20,457 (9.3%), adjusted per study, odds ratio converted to relative risk, FIASMA, multivariable.
risk of hospitalization/ER, 11.9% lower, RR 0.88, p = 0.04, treatment 559 of 2,744 (20.4%), control 1,896 of 20,457 (9.3%), adjusted per study, odds ratio converted to relative risk, SSRI, multivariable.
risk of hospitalization/ER, 10.1% lower, RR 0.90, p = 0.04, treatment 971 of 4,577 (21.2%), control 1,896 of 20,457 (9.3%), adjusted per study, odds ratio converted to relative risk, all antidepressants, multivariable.
[Oskotsky], 11/15/2021, retrospective, propensity score matching, USA, peer-reviewed, 8 authors. risk of death, 57.9% higher, RR 1.58, p = 0.62, treatment 2 of 11 (18.2%), control 19 of 165 (11.5%), fluvoxamine.
risk of death, 26.0% lower, RR 0.74, p = 0.04, treatment 48 of 481 (10.0%), control 956 of 7,215 (13.3%), NNT 31, fluoxetine.
[Trkulja], 11/7/2022, retrospective, Croatia, preprint, 2 authors. risk of death, 27.0% lower, RR 0.73, p = 0.41, treatment 749, control 31,336, cohort A vs. B, propensity score matching.
risk of hospitalization, 37.0% higher, RR 1.37, p = 0.50, treatment 749, control 31,336, cohort A vs. B, COVID-related, propensity score matching.
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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