Covid Analysis, December 2022
•Statistically significant improvements are seen for recovery and viral clearance. 27 studies from 27 independent teams in 16 different countries show statistically significant improvements in isolation (12 for the most serious outcome).
•Meta analysis using the most serious outcome reported shows 21% [9‑31%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies.
•Studies to date do not show a significant benefit for mortality. Potential risks of the mechanism of action include the creation of dangerous variants, and mutagenicity, carcinogenicity, teratogenicity, and embryotoxicity [Hadj Hassine, Waters, Zhirnov].
•No treatment, vaccine, or intervention is 100% effective and available. All practical, effective, and safe means should be used based on risk/benefit analysis. Multiple treatments are typically used in combination, and other treatments are more effective. Only 7% of favipiravir studies show zero events with treatment.
Favipiravir reduces risk for COVID-19 with very high confidence for viral clearance and in pooled analysis, high confidence for recovery, and very low confidence for mortality and progression, however increased risk is seen with low confidence for ICU admission. Potential risks include the creation of dangerous variants, carcinogenicity, and genotoxicity.
We show traditional outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor in COVID-19 studies.
We analyze all significant studies concerning the use of favipiravir 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.
An In Vitro study supports the efficacy of favipiravir [Unal].
Preclinical research is an important part of the development of treatments, however results may be very different in clinical trials. Preclinical results are not used in this paper.
Table 1 summarizes the results for all stages combined, with different exclusions, and for specific outcomes. Table 2 shows results by treatment stage. Figure 3, 4, 5, 6, 7, 8, 9, 10, and 11 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, viral clearance, and peer reviewed studies.
|All studies||21% [9‑31%]||55||25,682||824|
|After exclusions||24% [11‑36%]||45||20,956||675|
|Peer-reviewed studiesPeer-reviewed||23% [10‑34%]||52||24,961||784|
|Randomized Controlled TrialsRCTs||26% [11‑39%]||28||5,576||479|
|ICU admissionICU||-27% [-66‑3%]||16||4,458||314|
|RCT mortality||7% [-22‑29%]||11||3,482||164|
|RCT hospitalizationRCT hosp.||16% [-10‑36%]||9||1,128||199|
|Early treatment||Late treatment|
|All studies||30% [-9‑55%] 16||17% [4‑28%] 39|
|After exclusions||30% [-9‑55%] 16||21% [6‑34%] 29|
|Peer-reviewed studiesPeer-reviewed||31% [-10‑56%] 15||19% [5‑31%] 37|
|Randomized Controlled TrialsRCTs||28% [-13‑55%] 10||24% [7‑38%] 18|
|Mortality||44% [-33‑76%] 4||10% [-8‑25%] 25|
|VentilationVent.||-2% [-60‑35%] 1||-4% [-98‑46%] 7|
|ICU admissionICU||-381% [-4086‑45%] 2||-24% [-63‑5%] 14|
|HospitalizationHosp.||6% [-133‑62%] 6||-4% [-32‑18%] 10|
|Viral||11% [-9‑28%] 10||39% [21‑53%] 13|
|RCT mortality||67% [-712‑99%] 1||6% [-23‑29%] 10|
|RCT hospitalizationRCT hosp.||-45% [-198‑29%] 5||25% [9‑39%] 4|
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 favipiravir 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] 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).
To avoid bias in the selection of studies, we analyze all non-retracted studies. Here we show the results after excluding studies with major issues likely to alter results, non-standard studies, and studies where very minimal detail is currently available. Our bias evaluation is based on analysis of each study and identifying when there is a significant chance that limitations will substantially change the outcome of the study. We believe this can be more valuable than checklist-based approaches such as Cochrane GRADE, which may underemphasize serious issues not captured in the checklists, overemphasize issues unlikely to alter outcomes in specific cases (for example, lack of blinding for an objective mortality outcome, or certain specifics of randomization with a very large effect size), or be easily influenced by potential bias. However, they can also be very high quality.
The studies excluded are as below. Figure 16 shows a forest plot for random effects meta-analysis of all studies after exclusions.
[Abdulrahman], very late stage, ICU patients.
[Acar Sevinc], very late stage, ICU patients.
[Al Mutair], very late stage, ICU patients.
[Assiri], unadjusted results with no group details; very late stage, ICU patients.
[Babayigit], substantial unadjusted confounding by indication possible.
[Cilli], unadjusted results with no group details.
[Damayanti], minimal details provided.
[Khamis], trials compares against another treatment showing significant efficacy in trials.
[Kurniyanto], unadjusted results with no group details.
[Tawfik], unadjusted results with minimal group details.
Heterogeneity in COVID-19 studies arises from many factors including:
[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.
|Post exposure prophylaxis||86% fewer cases [Ikematsu]|
|<24 hours||-33 hours symptoms [Hayden]|
|24-48 hours||-13 hours symptoms [Hayden]|
|Inpatients||-2.5 hours to improvement [Kumar]|
Figure 17 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 47 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
[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].
[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.
Figure 18. For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, etc. An antiviral tested with a low-risk population may report zero mortality in both arms, however a reduction in severity and improved viral clearance may translate into lower mortality among a high-risk population, and including these results in pooled analysis allows faster detection of efficacy. Trials with high-risk patients may also be restricted due to ethical concerns for treatments that are known or expected to be effective.
Pooled analysis enables using more of the available information. While there is much more information available, for example dose-response relationships, the advantage of the method used here is simplicity and transparency. Note that pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral replication or early stage disease could show no efficacy in pooled analysis if most studies only examine viral clearance. While we present pooled results, we also present individual outcome analyses, which may be more informative for specific use cases.
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.
[Boulware, Meeus, Meneguesso].
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.
36% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 60% of prospective studies, consistent with a bias toward publishing negative results. The median effect size for retrospective studies is 33% improvement, compared to 14% for prospective studies, suggesting a potential bias towards publishing results showing higher efficacy. Figure 19 shows a scatter plot of results for prospective and retrospective studies.
Figure 20 plot A shows a funnel plot for a simulation of 80 perfect trials, with random group sizes, and each patient's outcome randomly sampled (10% control event probability, and a 30% effect size for treatment). Analysis shows no asymmetry (p > 0.05). In plot B, we add a single typical variation in COVID-19 treatment trials — treatment delay. Consider that efficacy varies from 90% for treatment within 24 hours, reducing to 10% when treatment is delayed 3 days. In plot B, each trial's treatment delay is randomly selected. Analysis now shows highly significant asymmetry, p < 0.0001, with six variants of Egger's test all showing p < 0.05 [Egger, Harbord, Macaskill, Moreno, Peters, Rothstein, Rücker, Stanley]. Note that these tests fail even though treatment delay is uniformly distributed. In reality treatment delay is more complex — each trial has a different distribution of delays across patients, and the distribution across trials may be biased (e.g., late treatment trials may be more common). Similarly, many other variations in trials may produce asymmetry, including dose, administration, duration of treatment, differences in SOC, comorbidities, age, variants, and bias in design, implementation, analysis, and reporting.
[Hung, Lan], showing significant improvements for viral clearance, improvement, and hospital discharge.
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.
Favipiravir is an effective treatment for COVID-19. Statistically significant improvements are seen for recovery and viral clearance. 27 studies from 27 independent teams in 16 different countries show statistically significant improvements in isolation (12 for the most serious outcome). Meta analysis using the most serious outcome reported shows 21% [9‑31%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies.
[Abdulrahman] Retrospective 1,699 ICU patients in Saudi Arabia, 193 treated with favipiravir, showing no significant difference in mortality.
[Acar Sevinc] Retrospective 100 ICU patients in Turkey, showing improved survival with favipiravir vs. lopinavir/ritonavir.
[Adhikari] Preliminary report for an RCT in Nepal with 38 favipiravir patients and 32 control patients, showing no significant differences. There were no serious side effects.
[Aghajani] Retrospective 991 hospitalized patients in Iran focusing on aspirin use but also showing results for HCQ, remdesivir, and favipiravir.
[Al Mutair] Retrospective 269 favipiravir ICU patients in Saudi Arabia and 269 matched controls receiving different treatments, showing no significant difference.
[Al-Muhsen] Prospective observational study of 598 hospitalized patients in Saudi Arabia, showing higher risk of mortality and longer hospitalization time with favipiravir.
[Alamer] Retrospective 234 favipiravir and 223 control patients in Saudi Arabia, showing shorter time to discharge and lower progression to ventilation, but no significant difference in mortality.
[Alattar] PSM retrospective with 1,493 patients, showing significantly improved viral clearance with favipiravir. There were no significant differences in clinical improvement or mortality. Mortality was lower (2.1% vs 3.1%), without statistical significance with the small number of events.
[Almoosa] Retrospective 226 COVID-19 pneumonia patients, 110 treated with favipiravir, showing higher mortality (p=0.1) and ICU admission (p=0.02) with treatment in multivariate analysis.
[Alotaibi] Retrospective hospitalized patients in Saudi Arabia, showing lower mortality with favipiravir compared to HCQ, not quite reaching statistical significance. Authors do not indicate the factors behind which therapy was chosen. May be subject to significant confounding by indication and confounding by time.
[AlQahtani] RCT with 54 favipiravir, 51 HCQ, and 52 SOC hospitalized patients in Bahrain, showing no significant differences. Viral clearance improved with both treatments, but did not reach statistical significance with the small sample size.
[Assiri] Retrospective 118 ICU patients in Saudi Arabia showing no significant differences in unadjusted results with zinc, vitamin D, and favipiravir treatment.
[Atipornwanich] RCT 200 moderate/severe patients in Thailand, showing significantly lower progression with favipiravir vs. oseltamivir. NCT04303299.
[Babayigit] Retrospective 1,472 hospitalized patients in Turkey, showing a higher ICU admission and ventilation with favipiravir. Results may be subject to confounding by indication.
[Behboodikhah] Retrospective 2,174 hospitalized patients showing significantly shorter length of stay with favipiravir treatment.
[Bosaeed] RCT with 112 favipiravir and 119 control patients showing no significant differences in outcomes. Viral clearance and clinical recovery for patients treated within 48 hours was better than those treated later. NCT04464408.
[Bruminhent] Retrospective 514 patients in Thailand, showing higher risk of progression with favipiravir treatment.
[Cai] Comparison of 35 FPV patients and 35 LPV/RTV patients, showing significant improvements in chest CT and faster viral clearance with FPV.
[Chandiwana] Very high COI low-risk patient RCT in South Africa, showing no significant differences with favipiravir plus nitazoxanide. There were no deaths and no COVID-19 hospitalizations for favipiravir plus nitazoxanide. More patients were seropositive at baseline in the treatment arm (28% vs 22%). Favipiravir 1600mg 12-hourly for 1 day, then 600mg 12-hourly for 6 days. Nitazoxanide 1000mg 12-hourly for 7 days.
[Chen] Very late stage (9 days from symptom onset) RCT with 116 favipiravir patients and 120 arbidol patients in China, showing no significant difference in clinical recovery (relief of fever and cough, respiratory frequency ≤24 times/min, and oxygen saturation ≥98%), however the time to resolution of fever and cough was significantly lower with favipiravir. ChiCTR2000030254.
[Chuah] RCT 500 hospitalized patients in Malaysia, showing no significant differences with favipiravir treatment.
[Cilli] Retrospective 46 idiopathic pulmonary fibrosis patients with COVID-19 in Turkey, showing lower mortality with favipiravir in unadjusted results, without statistical significance.
[Damayanti] Retrospective 192 hospitalized patients in Indonesia, 96 patients treated with favipiravir, showing improved recovery with treatment. Only the abstract is currently available.
[Finberg] Small very late treatment RCT in the USA, with 25 favipiravir and 25 control patients, showing faster viral clearance with treatment. The benefit was only seen in patients <8 days from symptom onset. There were no significant differences in clinical outcomes. The death in the favipiravir group occurred after discharge and was believed to be unrelated to COVID-19 or favipiravir.
[Golan] RCT low-risk (1 death in the control arm) patients in the USA, showing no significant differences with favipiravir. A majority of trial outcomes were modified after completion: [clinicaltrials.gov]. 44% of patients had no detectable viral load at baseline in the viral shedding sub-study. The primary outcome required 4 days of sustained clinical recovery and occurred after a median of 7 days, suggesting there was limited room for improvement in the population studied. The percentages for viral clearance at day 10 do not match any number of the reported group sizes. Authors write "of the six RCTs conducted", however there has been at least 24 other RCTs at the time of publication [c19favipiravir.com]. 1800mg bid day 1, 800mg bid days 2-10.
[Hafez] Retrospective hospitalized patients in the United Arab Emirates, showing no significant difference in viral clearance with different combinations of HCQ, AZ, favipiravir, and lopinavir/ritonavir.
[Hassaniazad] RCT comparing favipiravir and lopinavir/ritonavir, showing no significant differences. All patients received interferon-beta. Favipiravir 1600mg bid for the first day and 600mg bid for the following 4 days.
[Holubar] Small RCT 116 mITT patients in the USA, 59 treated with favipiravir, showing no significant differences with treatment.
[Ivashchenko] Intermin results for a small RCT with 40 favipiravir and 20 control patients showing faster viral clearance with favipiravir. There is limited data in this report to evaluate the results. The report indicates that 75% of the control group received HCQ/CQ.
[Khamis] Small 89 patient RCT comparing favipiravir and inhaled interferon with HCQ for moderate to severe COVID-19 pneumonia, not finding significant differences. There was no control group.
[Kulzhanova] Retrospective 40 favipiravir patients in Kazakhstan and 40 controls, showing faster recovery and viral clearance with treatment.
[Kurniyanto] Retrospective 477 hospitalized patients in Indonesia, showing lower mortality with favipiravir in unadjusted results, not reaching statistical significance.
[Lou] Small late stage RCT with 10 favipiravir, 10 baloxavir marboxil, and 10 control patients in China, showing no significant differences. ChiCTR 2000029544.