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Vitamin C for COVID-19: real-time meta analysis of 53 studies
Covid Analysis, October 6, 2022, DRAFT
https://c19early.com/cmeta.html
 
0 0.5 1 1.5+ All studies 21% 53 57,710 Improvement, Studies, Patients Relative Risk Mortality 26% 32 35,338 Ventilation 19% 5 530 ICU admission 15% 5 654 Hospitalization 18% 9 3,817 Recovery 27% 8 2,046 Cases -8% 4 15,830 Viral clearance 10% 3 256 RCTs 19% 14 1,129 RCT mortality 17% 8 648 Peer-reviewed 23% 46 38,238 High dose IV 18% 17 1,769 Symptomatic 25% 48 41,754 Prophylaxis 8% 10 36,053 Early 24% 5 571 Late 27% 38 21,086 Vitamin C for COVID-19 c19early.com/c Oct 2022 Favorsvitamin C Favorscontrol after exclusions
Statistically significant improvements are seen for mortality, ICU admission, hospitalization, and recovery. 17 studies from 17 independent teams in 11 different countries show statistically significant improvements in isolation (11 for the most serious outcome).
Meta analysis using the most serious outcome reported shows 21% [12‑29%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Clinical outcomes suggest benefit while viral and case outcomes do not, consistent with an intervention that aids recovery but is not antiviral.
0 0.5 1 1.5+ All studies 21% 53 57,710 Improvement, Studies, Patients Relative Risk Mortality 26% 32 35,338 Ventilation 19% 5 530 ICU admission 15% 5 654 Hospitalization 18% 9 3,817 Recovery 27% 8 2,046 Cases -8% 4 15,830 Viral clearance 10% 3 256 RCTs 19% 14 1,129 RCT mortality 17% 8 648 Peer-reviewed 23% 46 38,238 High dose IV 18% 17 1,769 Symptomatic 25% 48 41,754 Prophylaxis 8% 10 36,053 Early 24% 5 571 Late 27% 38 21,086 Vitamin C for COVID-19 c19early.com/c Oct 2022 Favorsvitamin C Favorscontrol after exclusions
The treatment regimen varies widely across studies and may be high-dose IV vitamin C.
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 2% of vitamin C studies show zero events with treatment. The quality of non-prescription supplements can vary widely [Crawford, Crighton].
All data to reproduce this paper and sources are in the appendix. [Bhowmik] present another meta analysis for vitamin C, showing significant improvements for mortality and severity.
Highlights
Vitamin C reduces risk for COVID-19 with very high confidence for mortality, recovery, and in pooled analysis, high confidence for ICU admission and hospitalization, and very low confidence for ventilation and progression.
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+ Su -135% 2.35 [0.67-8.27] progression n/a n/a Improvement, RR [CI] Treatment Control Thomas (RCT) -204% 3.04 [0.13-72.9] death 1/48 0/50 Zhao (PSM) 72% 0.28 [0.08-0.93] progression 4/55 12/55 Ried (RCT) 31% 0.69 [0.54-0.89] no recov. 69/162 46/75 Usanma Koban 33% 0.67 [0.07-5.38] viral+ 31 (n) 95 (n) Tau​2 = 0.25, I​2 = 44.8%, p = 0.43 Early treatment 24% 0.76 [0.38-1.50] 74/296 58/275 24% improvement Krishnan 31% 0.69 [0.47-0.92] death 40/79 52/73 Improvement, RR [CI] Treatment Control Zhang (RCT) 50% 0.50 [0.20-1.50] death 6/27 11/29 ICU patients Yüksel (ICU) 19% 0.81 [0.66-0.99] death 31/42 40/44 ICU patients Patel 29% 0.71 [0.43-1.14] death 22/96 26/80 Kumari (RCT) 36% 0.64 [0.26-1.55] death 7/75 11/75 Darban (RCT) 33% 0.67 [0.14-3.17] progression 2/10 3/10 ICU patients CT​2 Jang 51% 0.49 [0.23-1.01] no recov. 5/12 6/7 ECMO patients JamaliMo.. (RCT) 0% 1.00 [0.22-4.56] death 3/30 3/30 Gao 86% 0.14 [0.03-0.72] death 1/46 5/30 Hamidi-A.. (RCT) 44% 0.56 [0.20-1.51] death 5/40 9/40 CT​2 Al Sulaiman (PSM) 15% 0.85 [0.61-1.12] death 46/142 59/142 Mulhem -32% 1.32 [1.07-1.62] death 157/794 359/2,425 Gadhiya -1% 1.01 [0.48-1.91] death 19/55 36/226 Hakamifard (RCT) 46% 0.54 [0.14-2.08] ICU 3/38 5/34 CT​2 Elhadi (ICU) -12% 1.12 [0.96-1.31] death 175/277 106/188 ICU patients Suna 21% 0.79 [0.44-1.41] death 17/153 24/170 Pourhoseingholi 13% 0.87 [0.63-1.19] death 54/199 285/2,269 Li (ICU) -11% 1.11 [0.79-1.54] death 7/8 19/24 ICU patients Vishnuram 54% 0.46 [0.24-0.86] death 164/8,634 10/241 Özgünay (ICU) 9% 0.91 [0.63-1.30] death 17/32 75/128 ICU patients Tan 25% 0.75 [0.10-2.98] death/int. 1/46 14/115 CT​2 Zheng (PSM) -157% 2.57 [0.39-16.8] death 12/70 7/327 Simsek 44% 0.56 [0.23-1.35] death 6/58 15/81 Shousha 94% 0.06 [0.01-0.37] death 22/340 31/207 Tehrani (RCT) 87% 0.13 [0.01-2.25] death 0/18 4/26 Majidi (DB RCT) 14% 0.86 [0.74-1.01] death 26/31 67/69 ICU patients Baguma -48% 1.48 [0.41-4.70] death 385 (n) 96 (n) Tu 83% 0.17 [0.08-0.35] death 8/116 26/64 Yang (RCT) 15% 0.85 [0.68-1.06] recov. time 10 (n) 10 (n) LD​3 Gavrielatou (ICU) 58% 0.42 [0.12-1.48] death 2/10 49/103 ICU patients Salehi (ICU) 10% 0.90 [0.65-1.25] death 22/40 52/85 ICU patients Coppock (RCT) 5% 0.95 [0.16-7.84] progression 4/44 2/22 Hess (PSW) 20% 0.80 [0.40-1.60] death 10/25 37/75 Zangeneh (ICU) 4% 0.96 [0.64-1.45] death n/a n/a ICU patients Izzo 41% 0.59 [0.50-0.69] recovery 869 (n) 521 (n) LONG COVID OT​1 CT​2 Fogleman (DB RCT) 4% 0.96 [0.65-1.40] recovery 32 (n) 34 (n) Kumar (DB RCT) 23% 0.77 [0.40-1.47] death 10/30 13/30 ICU patients Özgülteki (ICU) -5% 1.05 [0.81-1.36] death 18/21 18/22 ICU patients Tau​2 = 0.15, I​2 = 82.6%, p = 0.00015 Late treatment 27% 0.73 [0.62-0.86] 922/12,934 1,479/8,152 27% improvement Behera 18% 0.82 [0.45-1.57] cases case control Improvement, RR [CI] Treatment Control Louca 0% 1.00 [0.97-1.04] cases Mahto -26% 1.26 [0.63-2.28] IgG+ 34/140 59/549 Holt -3% 1.03 [0.77-1.39] cases 49/1,580 397/13,647 Abdulateef 19% 0.81 [0.37-1.78] hosp. 8/132 22/295 Aldwihi 36% 0.64 [0.45-0.86] hosp. 142/505 95/233 Mohseni -44% 1.44 [1.22-1.71] cases 34/43 307/560 Nimer 25% 0.75 [0.54-1.04] hosp. 52/651 167/1,497 Shehab 4% 0.96 [0.46-1.99] severe case 14/139 12/114 Loucera 28% 0.72 [0.58-0.88] death 840 (n) 15,128 (n) Tau​2 = 0.05, I​2 = 82.5%, p = 0.35 Prophylaxis 8% 0.92 [0.78-1.09] 333/4,030 1,059/32,023 8% improvement All studies 21% 0.79 [0.71-0.88] 1,329/17,260 2,596/40,450 21% improvement 53 vitamin C COVID-19 studies c19early.com/c Oct 2022 Tau​2 = 0.08, I​2 = 82.8%, p < 0.0001 Effect extraction pre-specified, see appendix 1 OT: comparison with other treatment3 LD: comparison with low dose treatment 2 CT: study uses combined treatment Favors vitamin C Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Su -135% progression Improvement Relative Risk [CI] Thomas (RCT) -204% death Zhao (PSM) 72% progression Ried (RCT) 31% recovery Usanma Koban 33% viral- Tau​2 = 0.25, I​2 = 44.8%, p = 0.43 Early treatment 24% 24% improvement Krishnan 31% death Zhang (RCT) 50% death ICU patients Yüksel (ICU) 19% death ICU patients Patel 29% death Kumari (RCT) 36% death Darban (RCT) 33% progression ICU patients CT​2 Jang 51% recovery ECMO patients JamaliMo.. (RCT) 0% death Gao 86% death Hamidi-A.. (RCT) 44% death CT​2 Al Sulaiman (PSM) 15% death Mulhem -32% death Gadhiya -1% death Hakamifard (RCT) 46% ICU admission CT​2 Elhadi (ICU) -12% death ICU patients Suna 21% death Pourhoseingholi 13% death Li (ICU) -11% death ICU patients Vishnuram 54% death Özgünay (ICU) 9% death ICU patients Tan 25% death/intubation CT​2 Zheng (PSM) -157% death Simsek 44% death Shousha 94% death Tehrani (RCT) 87% death Majidi (DB RCT) 14% death ICU patients Baguma -48% death Tu 83% death Yang (RCT) 15% recovery LD​3 Gavrielatou (ICU) 58% death ICU patients Salehi (ICU) 10% death ICU patients Coppock (RCT) 5% progression Hess (PSW) 20% death Zangeneh (ICU) 4% death ICU patients Izzo 41% recovery LONG COVID OT​1 CT​2 Fogleman (DB RCT) 4% recovery Kumar (DB RCT) 23% death ICU patients Özgülteki (ICU) -5% death ICU patients Tau​2 = 0.15, I​2 = 82.6%, p = 0.00015 Late treatment 27% 27% improvement Behera 18% case Louca 0% case Mahto -26% IgG positive Holt -3% case Abdulateef 19% hospitalization Aldwihi 36% hospitalization Mohseni -44% case Nimer 25% hospitalization Shehab 4% severe case Loucera 28% death Tau​2 = 0.05, I​2 = 82.5%, p = 0.35 Prophylaxis 8% 8% improvement All studies 21% 21% improvement 53 vitamin C COVID-19 studies c19early.com/c Oct 2022 Tau​2 = 0.08, I​2 = 82.8%, p < 0.0001 Effect extraction pre-specifiedRotate device for footnotes/details Favors vitamin C Favors control
Figure 1. A. Random effects meta-analysis. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix. B. Scatter plot showing the distribution of effects reported in studies. C. History of all reported effects (chronological within treatment stages).
Introduction
We analyze all significant studies concerning the use of vitamin C 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.
Preclinical Research
3 In Silico studies support the efficacy of vitamin C [Kumar, Malla, Pandya].
2 In Vitro studies support the efficacy of vitamin C [Goc, Hajdrik].
An In Vivo animal study supports the efficacy of vitamin C [Zuo].
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
Figure 3 shows a visual overview of the results, with details in Table 1 and Table 2. Figure 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, and 15 show forest plots for a random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, cases, viral clearance, high dose IV studies, peer reviewed studies, and non-symptomatic vs. symptomatic results.
0 0.5 1 1.5+ ALL STUDIES MORTALITY VENTILATION ICU ADMISSION HOSPITALIZATION RECOVERY CASES VIRAL CLEARANCE RANDOMIZED CONTROLLED TRIALS RCT MORTALITY PEER-REVIEWED HIGH DOSE IV After Exclusions ALL STUDIES All Prophylaxis Early Late Vitamin C for COVID-19 C19EARLY.COM/C OCT 2022
Figure 3. Overview of results.
Treatment timeNumber of studies reporting positive effects Total number of studiesPercentage of studies reporting positive effects Random effects meta-analysis results
Early treatment 3 5 60.0% 24% improvement
RR 0.76 [0.38‑1.50]
p = 0.43
Late treatment 30 38 78.9% 27% improvement
RR 0.73 [0.62‑0.86]
p = 0.00015
Prophylaxis 6 10 60.0% 8% improvement
RR 0.92 [0.78‑1.09]
p = 0.35
All studies 39 53 73.6% 21% improvement
RR 0.79 [0.71‑0.88]
p < 0.0001
Table 1. Results by treatment stage.
Studies Early treatment Late treatment Prophylaxis PatientsAuthors
All studies 5324% [-50‑62%]27% [14‑38%]8% [-9‑22%] 57,710 572
With exclusions 373% [-95‑52%]33% [19‑45%]17% [-3‑33%] 26,881 409
Peer-reviewed 4624% [-50‑62%]31% [16‑43%]5% [-14‑20%] 38,238 484
Randomized Controlled TrialsRCTs 1430% [10‑46%]16% [6‑25%] 1,129 160
Table 2. Results by treatment stage for all studies and with different exclusions.
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Figure 4. Random effects meta-analysis for all studies with pooled effects. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction 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 high dose IV studies. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
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Figure 14. 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 a 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.
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Figure 15. Random effects meta-analysis for non-symptomatic vs. symptomatic results. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
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 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.
[Abdulateef], unadjusted results with no group details.
[Elhadi], unadjusted results with no group details.
[Gadhiya], substantial unadjusted confounding by indication likely.
[Holt], significant unadjusted confounding possible.
[Krishnan], unadjusted results with no group details.
[Li], very late stage, ICU patients.
[Mohseni], unadjusted results with no group details.
[Mulhem], substantial unadjusted confounding by indication likely, substantial confounding by time likely due to declining usage over the early stages of the pandemic when overall treatment protocols improved dramatically.
[Salehi], unadjusted results with no group details.
[Shehab], unadjusted results with no group details.
[Suna], substantial confounding by time likely due to declining usage over the early stages of the pandemic when overall treatment protocols improved dramatically.
[Tu], unadjusted results with no group details.
[Vishnuram], unadjusted results with no group details, minimal details of groups provided.
[Zhao], substantial confounding by time likely due to declining usage over the early stages of the pandemic when overall treatment protocols improved dramatically.
[Zheng], substantial unadjusted confounding by indication likely, immortal time bias may significantly affect results, treatment start times unknown, treatment may not have started at baseline.
[Özgünay], substantial unadjusted confounding by indication likely.
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Figure 16. Random effects meta-analysis for all studies after exclusions. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
Randomized Controlled Trials (RCTs)
Figure 17 shows the distribution of results for Randomized Controlled Trials and other studies, and a chronological history of results. The median effect size for RCTs is 27% improvement, compared to 19% for other studies. Figure 18 and 19 show forest plots for a random effects meta-analysis of all Randomized Controlled Trials and RCT mortality results. Table 3 summarizes the results.
Figure 17. The distribution of results for Randomized Controlled Trials and other studies, and a chronological history of results.
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Figure 18. Random effects meta-analysis for all Randomized Controlled Trials. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
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Figure 19. Random effects meta-analysis for RCT mortality results.
Treatment timeNumber of studies reporting positive effects Total number of studiesPercentage of studies reporting positive effects Random effects meta-analysis results
Randomized Controlled Trials 12 14 85.7% 19% improvement
RR 0.81 [0.73‑0.90]
p = 0.00015
RCT mortality results 6 8 75.0% 17% improvement
RR 0.83 [0.71‑0.96]
p = 0.012
Table 3. Randomized Controlled Trial results.
Heterogeneity
Heterogeneity in COVID-19 studies arises from many factors including:
Treatment delay.
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]. Figure 20 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 20. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 47 treatments. Early treatment is critical.
Patient demographics.
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]).
Effect measured.
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.
Variants.
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].
Regimen.
Effectiveness may depend strongly on the dosage and treatment regimen.
Other treatments.
The use of other treatments may significantly affect outcomes, including anything from supplements, other medications, or other kinds of treatment such as prone positioning.
Medication quality.
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. Non-prescription supplements may show very wide variations in quality [Crawford, Crighton].
Meta analysis.
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 the treatment may be very effective when used earlier.
In general, by combining heterogeneous studies, as all meta analyses do, we run the risk of obscuring an effect by including studies where the treatment is less effective, not effective, or harmful.
When including studies where a treatment is less effective we expect the estimated effect size to be lower than that for the optimal case. We do not a priori expect that pooling all studies will create a positive result for an effective treatment. 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 pooled results for all studies, we also present individual outcome and treatment time analyses, which are more relevant for specific use cases.
Discussion
Publication bias.
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].
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.
31% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 33% of prospective studies, showing similar results. The median effect size for retrospective studies is 19% improvement, compared to 19% for prospective studies, showing similar results. Figure 21 shows a scatter plot of results for prospective and retrospective studies.
Figure 21. Prospective vs. retrospective studies.
Funnel plot 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 22 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 22. Example funnel plot analysis for simulated perfect trials.
Conflicts of interest.
Pharmaceutical drug trials often have conflicts of interest whereby sponsors or trial staff have a financial interest in the outcome being positive. Vitamin C for COVID-19 lacks this because it is an inexpensive and widely available supplement. In contrast, most COVID-19 vitamin C 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 vitamin C trials represent the optimal conditions for efficacy.
Early/late vs. mild/moderate/severe.
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.
Notes.
1 of the 53 studies compare against other treatments, which may reduce the effect seen. 5 of 53 studies combine treatments. The results of vitamin C alone may differ. 3 of 14 RCTs use combined treatment. [Bhowmik] present another meta analysis for vitamin C, showing significant improvements for mortality and severity.
Conclusion
Vitamin C is an effective treatment for COVID-19. Statistically significant improvements are seen for mortality, ICU admission, hospitalization, and recovery. 17 studies from 17 independent teams in 11 different countries show statistically significant improvements in isolation (11 for the most serious outcome). Meta analysis using the most serious outcome reported shows 21% [12‑29%] improvement. Results are similar for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies. Clinical outcomes suggest benefit while viral and case outcomes do not, consistent with an intervention that aids recovery but is not antiviral.
The treatment regimen varies widely across studies and may be high-dose IV vitamin C.
Study Notes
0 0.5 1 1.5 2+ Hospitalization 19% Improvement Relative Risk c19early.com/c Abdulateef et al. Vitamin C for COVID-19 Prophylaxis Favors vitamin C Favors control
[Abdulateef] Survey of 428 recovered COVID-19 patients in Iraq, showing fewer hospital visits for patients on prophylactic vitamin C or D. Hospitalization was lower for those on vitamin C, D, or zinc, without statistical significance.
0 0.5 1 1.5 2+ Mortality 15% Improvement Relative Risk c19early.com/c Al Sulaiman et al. Vitamin C for COVID-19 LATE Favors vitamin C Favors control
[Al Sulaiman] Retrospective 158 critically ill patients receiving vitamin C and propensity matched controls, showing mortality OR 0.77 [0.48-1.23], and statistically significantly lower thrombosis, OR 0.42 [0.18-0.94]. 1000mg of vitamin C was given daily.
0 0.5 1 1.5 2+ Hospitalization 36% Improvement Relative Risk c19early.com/c Aldwihi et al. Vitamin C for COVID-19 Prophylaxis Favors vitamin C Favors control
[Aldwihi] Retrospective survey-based analysis of 738 COVID-19 patients in Saudi Arabia, showing lower hospitalization with vitamin C, turmeric, zinc, and nigella sativa, and higher hospitalization with vitamin D. For vitamin D, most patients continued prophylactic use. For vitamin C, the majority of patients continued prophylactic use. For nigella sativa, the majority of patients started use during infection. Authors do not specify the fraction of prophylactic use for turmeric and zinc.
0 0.5 1 1.5 2+ Mortality -48% Improvement Relative Risk c19early.com/c Baguma et al. Vitamin C for COVID-19 LATE TREATMENT Favors vitamin C Favors control
[Baguma] Retrospective COVID+ hospitalized patients in Uganda, 385 patients receiving vitamin C treatment, showing higher mortality with treatment, without statistical significance.
0 0.5 1 1.5 2+ Case 18% Improvement Relative Risk Case (b) 29% c19early.com/c Behera et al. Vitamin C for COVID-19 Prophylaxis Favors vitamin C Favors control
[Behera] Retrospective matched case-control prophylaxis study for HCQ, ivermectin, and vitamin C with 372 healthcare workers, showing lower COVID-19 incidence for all treatments, with statistical significance reached for ivermectin.

HCQ OR 0.56, p = 0.29
Ivermectin OR 0.27, p < 0.001
Vitamin C OR 0.82, p = 0.58
0 0.5 1 1.5 2+ Progression 5% Improvement Relative Risk Improvement 50% Discharge 22% c19early.com/c Coppock et al. Vitamin C for COVID-19 RCT LATE TREATMENT Favors vitamin C Favors control
[Coppock] RCT with 66 very late stage (8 days from symptom onset) hospitalized patients, 44 treated with vitamin C and 22 control patients, showing no significant differences with treatment.
0 0.5 1 1.5 2+ Progression 33% Improvement Relative Risk ICU time 6% c19early.com/c Darban et al. Vitamin C for COVID-19 RCT ICU PATIENTS Favors vitamin C Favors control
[Darban] Small RCT in Iran with 20 ICU patients, 10 treated with high-dose vitamin C, melatonin, and zinc, not showing significant differences. IRCT20151228025732N52.
0 0.5 1 1.5 2+ Mortality -12% Improvement Relative Risk c19early.com/c Elhadi et al. Vitamin C for COVID-19 ICU PATIENTS Favors vitamin C Favors control
[Elhadi] Prospective study of 465 COVID-19 ICU patients in Libya showing no significant differences with treatment.
0 0.5 1 1.5 2+ Recovery 4% Improvement Relative Risk c19early.com/c Fogleman et al. NCT04530539 Vitamin C RCT LATE TREATMENT Favors vitamin C Favors control
[Fogleman] Early terminated low-risk patient RCT with 32 low-dose vitamin C, 32 melatonin, and 34 placebo patients, showing faster resolution of symptoms with melatonin in spline regression analysis, and no significant difference for vitamin C. All patients recovered with no serious outcomes reported. Baseline symptoms scores were higher in the melatonin and vitamin C arms (median 27 and 24 vs. 18 for placebo).
0 0.5 1 1.5 2+ Mortality -1% Improvement Relative Risk c19early.com/c Gadhiya et al. Vitamin C for COVID-19 LATE TREATMENT Favors vitamin C Favors control
[Gadhiya] Retrospective 283 patients in the USA showing higher mortality with all treatments (not statistically significant). Confounding by indication is likely. In the supplementary appendix, authors note that the treatments were usually given for patients that required oxygen therapy. Oxygen therapy and ICU admission (possibly, the paper includes ICU admission for model 2 in some places but not others) were the only variables indicating severity used in adjustments.
0 0.5 1 1.5 2+ Mortality 86% Improvement Relative Risk c19early.com/c Gao et al. Vitamin C for COVID-19 LATE TREATMENT Favors vitamin C Favors control
[Gao] Retrospective 76 COVID-19 patients, 46 treated with intravenous high-dose vitamin C, showing lower mortality and improved oxygen requirements with treatment. Dosage was 6g intravenous infusion per 12hr on the first day, and 6g once for the following 4 days.
0 0.5 1 1.5 2+ Mortality 58% Improvement Relative Risk c19early.com/c Gavrielatou et al. Vitamin C for COVID-19 ICU Favors vitamin C Favors control
[Gavrielatou] Retrospective 113 consecutive mechanically ventilated COVID+ ICU patients in Greece, 10 receiving high dose IV vitamin C, showing lower mortality with treatment, without statistical significance (p=0.11).

The associated meta analysis includes only 11 studies, while there are currently 53 studies, 32 with mortality results. Authors only include critical patients, however not all studies with critical patients are included, for example [Hamidi-Alamdari, Majidi, Yüksel, Özgünay]. The meta analysis also uses unadjusted results, while PSM, Cox proportional hazards, or KM results are reported by [Al Sulaiman, Gao, Zhang (B), Zheng]. For [Zhang (B)] authors use 28 day mortality, while the study reports longer term in-hospital mortality.
0 0.5 1 1.5 2+ ICU admission 46% Improvement Relative Risk Hospitalization time 1% c19early.com/c Hakamifard et al. Vitamin C for COVID-19 RCT LATE Favors vitamin C Favors control
[Hakamifard] RCT with 38 patients treated with vitamin C and vitamin E, and 34 control patients, showing lower ICU admission with treatment, but not statistically significant.
0 0.5 1 1.5 2+ Mortality 44% Improvement Relative Risk Hospitalization time 38% c19early.com/c Hamidi-Alamdari et al. NCT04370288 Vitamin C RCT LATE Favors vitamin C Favors control
[Hamidi-Alamdari] RCT 80 hospitalized patients with severe COVID-19, 40 treated with methylene blue + vitamin C + N-acetylcysteine, showing lower mortality, shorter hospitalization, and significantly improved SpO2 and respiratory distress with treatment. NCT04370288.
0 0.5 1 1.5 2+ Mortality 20% Improvement Relative Risk Ventilation 40% Ventilation (b) 50% ICU admission 27% ICU admission (b) 30% c19early.com/c Hess et al. Vitamin C for COVID-19 LATE TREATMENT Favors vitamin C Favors control
[Hess] Retrospective 100 severe condition hospitalized patients in the USA, 25 treated with high dose IV vitamin C, showing lower mechanical ventilation and cardiac arrest, and increased length of survival with treatment. 3g IV vitamin C every 6h for 7 days.
0 0.5 1 1.5 2+ Case -3% Improvement Relative Risk c19early.com/c Holt et al. NCT04330599 COVIDENCE UK Vitamin C Prophylaxis Favors vitamin C Favors control
[Holt] Prospective survey-based study with 15,227 people in the UK, showing lower risk of COVID-19 cases with vitamin A, vitamin D, zinc, selenium, probiotics, and inhaled corticosteroids; and higher risk with metformin and vitamin C. Statistical significance was not reached for any of these. Except for vitamin D, the results for treatments we follow were only adjusted for age, sex, duration of participation, and test frequency. NCT04330599. COVIDENCE UK.
0 0.5 1 1.5 2+ Recovery 41% Improvement Relative Risk Recovery (b) 68% c19early.com/c Izzo et al. Vitamin C for COVID-19 LONG COVID Favors vitamin C Favors Vitamin B1, ..
[Izzo] Long COVID trial comparing L-arginine + vitamin C with multivitamin treatment (vitamin B1, B2, B6, B12, nicotinamide, folic acid, pantothenic acid), showing significant improvement in symptoms with L-arginine + vitamin C treatment.
0 0.5 1 1.5 2+ Mortality 0% Improvement Relative Risk Ventilation -25% Hospitalization time -31% c19early.com/c JamaliMoghadamSiahkali et al. Vitamin C RCT LATE Favors vitamin C Favors control
[JamaliMoghadamSiahkali] Small late stage RCT for the addition of vitamin C to HCQ and lopinavir/ritonavir, with 30 treatment and 30 control patients, finding a significant reduction in temperature and a significant improvement in oxygenation after 3 days in the vitamin C group. However, hospitalization time was longer and there was no significant difference in mortality.
0 0.5 1 1.5 2+ Recovery 51% Improvement Relative Risk c19early.com/c Jang et al. Vitamin C for COVID-19 ECMO PATIENTS Favors vitamin C Favors control
[Jang] Retrospective 19 COVID-19 ECMO patients in South Korea, showing a higher rate of weaning from ECMO with vitamin C treatment, without statistical significance. Authors perform multivariate analysis but do not provide full results, only reporting p > 0.05.
0 0.5 1 1.5 2+ Mortality 31% Improvement Relative Risk c19early.com/c Krishnan et al. Vitamin C for COVID-19 LATE TREATMENT Favors vitamin C Favors control
[Krishnan] Retrospective 152 mechanically ventilated patients in the USA showing unadjusted lower mortality with vitamin C, vitamin D, HCQ, and zinc treatment, statistically significant only for vitamin C.
0 0.5 1 1.5 2+ Mortality 23% Improvement Relative Risk Ventilation 21% c19early.com/c Kumar et al. CTRI/2020/11/029230 Vitamin C RCT ICU Favors vitamin C Favors control
[Kumar (B)] RCT 60 ICU patients in India, showing no significant difference in outcomes with vitamin C. Mortality was lower in the vitamin C arm despite having more severe cases at baseline (87% vs. 67%). 1 gram intravenous vitamin C 8 hourly for four days.
0 0.5 1 1.5 2+ Mortality 36% Improvement Relative Risk Ventilation 20% Recovery time 26% Hospitalization time 24% c19early.com/c Kumari et al. Vitamin C for COVID-19 RCT LATE TREATMENT Favors vitamin C Favors control
[Kumari] RCT 150 hospitalized patients in Pakistan showing 26% faster recovery, p < 0.0001. 36% lower mortality, not statistically significant due to the small number of events. Dosage was 50 mg/kg/day of intravenous vitamin C.
0 0.5 1 1.5 2+ Mortality -11% Improvement Relative Risk c19early.com/c Li et al. Vitamin C for COVID-19 ICU PATIENTS Favors vitamin C Favors control
[Li] PSM retrospective 8 ICU patients treated with vitamin C and 24 matched controls, showing no significant difference. Authors note that "it is possible for the delayed timing of IV vitamin C to have blunted the beneficial effects as these patients may have already progressed to the late fibroproliferative phase or ARDS". IV vitamin C 1.5 grams every 6 hours.
0 0.5 1 1.5 2+ Case 0% Improvement Relative Risk c19early.com/c Louca et al. Vitamin C for COVID-19 Prophylaxis Favors vitamin C Favors control
[Louca] Survey analysis of dietary supplements showing no significant difference in PCR+ cases with vitamin C usage in the UK, however significant reductions were found in the US and Sweden. These results are for PCR+ cases only, they do not reflect potential benefits for reducing the severity of cases. A number of biases could affect the results, for example users of the app may not be representative of the general population, and people experiencing symptoms may be more likely to install and use the app.
0 0.5 1 1.5 2+ Mortality 28% Improvement Relative Risk c19early.com/c Loucera et al. Vitamin C for COVID-19 Prophylaxis Favors vitamin C Favors control
[Loucera] Retrospective 15,968 COVID-19 hospitalized patients in Spain, showing lower mortality with existing use of several medications including metformin, HCQ, aspirin, vitamin D, vitamin C, and budesonide.
0 0.5 1 1.5 2+ IgG positive -26% Improvement Relative Risk c19early.com/c Mahto et al. Vitamin C for COVID-19 Prophylaxis Favors vitamin C Favors control
[Mahto] Retrospective 689 healthcare workers in India, showing no significant difference in IgG positivity with vitamin C prophylaxis.
0 0.5 1 1.5 2+ Mortality 14% Improvement Relative Risk c19early.com/c Majidi et al. Vitamin C for COVID-19 RCT ICU PATIENTS Favors vitamin C Favors control
[Majidi] RCT 100 ICU patients in Iran, 31 treated with vitamin C, showing lower mortality with treatment. IRCT20151226025699N5.
0 0.5 1 1.5 2+ Case -44% Improvement Relative Risk c19early.com/c Mohseni et al. Vitamin C for COVID-19 Prophylaxis Favors vitamin C Favors control
[Mohseni] Retrospective 603 patients in Iran, 34 taking vitamin C supplements, showing increased risk of COVID-19 cases in unadjusted results. IR.SHOUSHTAR.REC.1399.015.
0 0.5 1 1.5 2+ Mortality -32% Improvement Relative Risk c19early.com/c Mulhem et al. Vitamin C for COVID-19 LATE TREATMENT Favors vitamin C Favors control
[Mulhem] Retrospective database analysis of 3,219 hospitalized patients in the USA. Very different results in the time period analysis (Table S2), and results significantly different to other studies for the same medications (e.g., heparin OR 3.06 [2.44-3.83]) suggest significant confounding by indication and confounding by time.
0 0.5 1 1.5 2+ Hospitalization 25% Improvement Relative Risk Severe case 17% c19early.com/c Nimer et al. Vitamin C for COVID-19 Prophylaxis Favors vitamin C