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•Meta analysis using the most serious outcome reported shows
11% [-2‑22%] improvement, without reaching statistical significance.
Results are better 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 may help recovery but is not antiviral.
Early treatment is more effective than late treatment.
•Statistically significant improvements are seen for
mortality and
recovery.
8 studies show statistically significant improvements in isolation (4 for the most serious outcome).
•While many treatments have some level
of efficacy, they do not replace vaccines and other measures to avoid
infection.
Only 4% of vitamin C
studies show zero events in the treatment arm.
•Multiple treatments are typically used
in combination, and other treatments
are significantly more effective.
Treatment varies widely across studies and may be high-dose IV vitamin C.
•Elimination of COVID-19 is a race
against viral evolution. No treatment, vaccine, or intervention is 100%
available and effective for all variants. All practical, effective, and safe
means should be used, including treatments, as supported by Pfizer [Pfizer].
Denying the efficacy of treatments increases the risk of COVID-19 becoming
endemic; and increases mortality, morbidity, and collateral damage.
•All data to reproduce this paper and
sources are in the appendix.
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).
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 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12
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, and peer reviewed studies.
Table 1 summarizes the results by treatment stage.
Figure 3. 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.
Figure 12. Random effects meta-analysis for peer reviewed studies.
Effect extraction is pre-specified, using the most serious outcome reported,
see the appendix for details.
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 13 shows a forest plot for random
effects meta-analysis of all studies after exclusions.
[Gadhiya], substantial unadjusted confounding by indication likely.
[Mohseni], unadjusted results with no group details.
[Mulhem], substantial unadjusted confounding by indication likely, substantial time varying confounding likely due to declining usage over the early stages of the pandemic when overall treatment protocols improved dramatically.
[Suna], substantial time varying confounding likely due to declining usage over the early stages of the pandemic when overall treatment protocols improved dramatically.
[Vishnuram], unadjusted results with no group details, minimal details of groups provided.
[Zhao], substantial time varying confounding likely due to declining usage over the early stages of the pandemic when overall treatment protocols improved dramatically.
[Özgünay], substantial unadjusted confounding by indication likely.
Figure 13. 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.
Figure 14 and 15
show forest plots for a random effects meta-analysis of
all Randomized Controlled Trials and RCT mortality results.
Table 2 summarizes the results.
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 vitamin C 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].
