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Cannabidiol for COVID-19: real-time meta analysis of 6 studies
Covid Analysis, December 2022
https://c19early.org/cbdmeta.html
 
0 0.5 1 1.5+ All studies -45% 6 3,784 Improvement, Studies, Patients Relative Risk Mortality -71% 2 1,831 Ventilation 5% 1 1,831 ICU admission 9% 1 1,831 Hospitalization -557% 1 91 Cases -10% 4 1,862 RCTs -557% 1 91 Symptomatic -152% 4 2,722 Prophylaxis -37% 5 3,693 Late -557% 1 91 Cannabidiol for COVID-19 c19early.org/cbd Dec 2022 Favorscannabidiol Favorscontrol
Meta analysis using the most serious outcome reported shows 45% [-21‑167%] higher risk, without reaching statistical significance.
While non-symptomatic case results show 8% [-14‑25%] improvement, symptomatic results show 152% [48‑329%] higher risk.
0 0.5 1 1.5+ All studies -45% 6 3,784 Improvement, Studies, Patients Relative Risk Mortality -71% 2 1,831 Ventilation 5% 1 1,831 ICU admission 9% 1 1,831 Hospitalization -557% 1 91 Cases -10% 4 1,862 RCTs -557% 1 91 Symptomatic -152% 4 2,722 Prophylaxis -37% 5 3,693 Late -557% 1 91 Cannabidiol for COVID-19 c19early.org/cbd Dec 2022 Favorscannabidiol Favorscontrol
All data to reproduce this paper and sources are in the appendix.
Highlights
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+ Crippa (DB RCT) -557% 6.57 [0.35-124] hosp. 3/49 0/42 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.21 Late treatment -557% 6.57 [0.35-124] 3/49 0/42 -557% improvement Nguyen 50% 0.50 [0.31-0.82] cases 26/531 48/531 Improvement, RR [CI] Treatment Control Huang -181% 2.81 [1.04-7.58] death n/a n/a Merianos -212% 3.12 [1.87-4.97] symp. case 94/416 20/384 Lehrer -24% 1.24 [1.05-1.45] cases n/a n/a Shover (PSM) 2% 0.98 [0.94-1.04] death 3/69 199/1,762 Tau​2 = 0.39, I​2 = 87.8%, p = 0.33 Prophylaxis -37% 1.37 [0.73-2.55] 123/1,016 267/2,677 -37% improvement All studies -45% 1.45 [0.79-2.67] 126/1,065 267/2,719 -45% improvement 6 cannabidiol COVID-19 studies c19early.org/cbd Dec 2022 Tau​2 = 0.39, I​2 = 85.2%, p = 0.24 Effect extraction pre-specified(most serious outcome, see appendix) Favors cannabidiol Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Crippa (DB RCT) -557% hospitalization Relative Risk [CI] Tau​2 = 0.00, I​2 = 0.0%, p = 0.21 Late treatment -557% -557% improvement Nguyen 50% case Huang -181% death Merianos -212% symp. case Lehrer -24% case Shover (PSM) 2% death Tau​2 = 0.39, I​2 = 87.8%, p = 0.33 Prophylaxis -37% -37% improvement All studies -45% -45% improvement 6 cannabidiol COVID-19 studies c19early.org/cbd Dec 2022 Tau​2 = 0.39, I​2 = 85.2%, p = 0.24 Effect extraction pre-specifiedRotate device for details Favors cannabidiol Favors control
B
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D
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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 cannabidiol studies.
We analyze all significant studies concerning the use of cannabidiol 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 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.
An In Vitro study supports the efficacy of cannabidiol [van Breemen].
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, after exclusions, and for specific outcomes. Table 2 shows results by treatment stage. Figure 3, 4, 5, 6, 7, 8, 9, and 10 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, recovery, cases, and non-symptomatic vs. symptomatic results.
Improvement Studies Patients Authors
All studies-45% [-167‑21%]6 3,784 85
Randomized Controlled TrialsRCTs-557% [-12269‑65%]1 91 32
Mortality-71% [-380‑39%]2 1,831 10
Cases-10% [-74‑30%]4 1,862 46
Table 1. Random effects meta-analysis for all stages combined, after exclusions, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval.
Late treatment Prophylaxis
All studies-557% [-12269‑65%] 1-37% [-155‑27%] 5
Randomized Controlled TrialsRCTs-557% [-12269‑65%] 1-
Mortality--71% [-380‑39%] 2
Cases--10% [-74‑30%] 4
Table 2. Random effects meta-analysis results by treatment stage. Results show the percentage improvement with treatment, the 95% confidence interval, and the number of studies for the stage.treatment and the 95% confidence interval.
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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 ICU admission.
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Figure 7. Random effects meta-analysis for hospitalization.
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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 non-symptomatic vs. symptomatic results. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Figure 11 shows a forest plot for random effects meta-analysis of all Randomized Controlled Trials. Currently there is only one RCT.
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Figure 11. 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.
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 3. Early treatment is more effective for baloxavir and influenza.
Figure 12 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 12. 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 13. 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 13. 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. Trials with patented drugs may have a financial conflict of interest that results in positive studies being more likely to be published, or bias towards more positive results. For example with molnupiravir, trials with negative results remain unpublished to date (CTRI/2021/05/033864 and CTRI/2021/08/0354242). For cannabidiol, there is currently not enough data to evaluate publication bias with high confidence.
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 14 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 14. Example funnel plot analysis for simulated perfect trials.
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.
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.
Meta analysis using the most serious outcome reported shows 45% [-21‑167%] higher risk, without reaching statistical significance. While non-symptomatic case results show 8% [-14‑25%] improvement, symptomatic results show 152% [48‑329%] higher risk.
0 0.5 1 1.5 2+ Hospitalization -557% Improvement Relative Risk Recovery time -33% c19early.org/cbd Crippa et al. Cannabidiol for COVID-19 RCT LATE TREATMENT Favors cannabidiol Favors control
[Crippa] RCT 105 patients recruited in an ER in Brazil, 49 treated with CBD, showing no significant differences with treatment. 300mg CBD for 14 days.

For discussion see [liebertpub.com].
0 0.5 1 1.5 2+ Mortality -181% Improvement Relative Risk Case 19% c19early.org/cbd Huang et al. Cannabidiol for COVID-19 Prophylaxis Favors cannabidiol Favors control
[Huang] UK Biobank retrospective with 13,099 cannabis users, showing a lower risk of COVID-19 infection, however regular users had a significantly higher risk of mortality.
0 0.5 1 1.5 2+ Case -24% Improvement Relative Risk c19early.org/cbd Lehrer et al. Cannabidiol for COVID-19 Prophylaxis Favors cannabidiol Favors control
[Lehrer] UK Biobank retrospective showing a higher risk of COVID-19 cases with a history of cannabis use.
0 0.5 1 1.5 2+ Symptomatic case -212% Improvement Relative Risk Symptomatic case (b) -71% Case -3% c19early.org/cbd Merianos et al. Cannabidiol for COVID-19 Prophylaxis Favors cannabidiol Favors control
[Merianos] Retrospective 800 e-cigarette users in the USA, showing higher risk of COVID-19 diagnosis and symptoms with cannabis use.
0 0.5 1 1.5 2+ Case 50% Improvement Relative Risk Case (b) 33% c19early.org/cbd Nguyen et al. Cannabidiol for COVID-19 Prophylaxis Favors cannabidiol Favors control
[Nguyen] Retrospective 1,212 patients in the USA with a history of seizure-related conditions, showing patients treated with CBD100 had significantly lower incidence of COVID-19 cases compared to a matched control group.

In Vitro study showing CBD inhibits SARS-CoV-2 with Vero E6 and Calu-3 cells. Mouse study showing CBD significantly inhibited viral replication in the lung and nasal turbinate.

Authors note that CBD does not inhibit ACE2 expression or the main viral proteases, inhibition occurs after viral entry. Authors stress several limitations for use at this time, including purity, quality, and the formulation of products, and potential lung damage based on administration method.

Authors recommend clinical trials, but do not mention the existing RCT by Crippa et al.
0 0.5 1 1.5 2+ Mortality 2% Improvement Relative Risk Ventilation 5% ICU admission 9% Oxygen therapy 3% c19early.org/cbd Shover et al. Cannabidiol for COVID-19 Prophylaxis Favors cannabidiol Favors control
[Shover] Retrospective 1,831 hospitalized COVID-19 patients in the USA, showing lower mechanical ventilation and ICU admission, but no significant difference in mortality.
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 cannabidiol, 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 cannabidiol 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/cbdmeta.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.
[Crippa], 10/7/2021, Double Blind Randomized Controlled Trial, placebo-controlled, Brazil, peer-reviewed, 32 authors, study period 7 July, 2020 - 16 October, 2020. risk of hospitalization, 557.1% higher, RR 6.57, p = 0.25, treatment 3 of 49 (6.1%), control 0 of 42 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm).
recovery time, 33.3% higher, relative time 1.33, p = 0.20, treatment 49, control 42.
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.
[Huang], 3/8/2022, retrospective, United Kingdom, peer-reviewed, 3 authors. risk of death, 181.0% higher, HR 2.81, p = 0.04, regular users, Cox proportional hazards.
risk of case, 19.0% lower, OR 0.81, p < 0.001, adjusted per study, multivariable, RR approximated with OR.
[Lehrer], 6/22/2022, retrospective, United Kingdom, peer-reviewed, mean age 57.0, 3 authors, study period 16 March, 2020 - 26 April, 2020. risk of case, 23.8% higher, OR 1.24, p = 0.009, RR approximated with OR.
[Merianos], 3/31/2022, retrospective, USA, peer-reviewed, survey, 6 authors. risk of symptomatic case, 211.9% higher, RR 3.12, p < 0.001, treatment 94 of 416 (22.6%), control 20 of 384 (5.2%), odds ratio converted to relative risk, COVID-19 symptoms.
risk of symptomatic case, 70.6% higher, RR 1.71, p = 0.008, treatment 77 of 416 (18.5%), control 38 of 384 (9.9%), odds ratio converted to relative risk, COVID-19 diagnosis.
risk of case, 3.4% higher, RR 1.03, p = 0.33, treatment 367 of 416 (88.2%), control 317 of 384 (82.6%), odds ratio converted to relative risk, COVID-19 test.
[Nguyen], 1/20/2022, retrospective, USA, peer-reviewed, 34 authors. risk of case, 49.6% lower, RR 0.50, p = 0.006, treatment 26 of 531 (4.9%), control 48 of 531 (9.0%), NNT 24, odds ratio converted to relative risk, active CBD100 users.
risk of case, 32.9% lower, RR 0.67, p = 0.009, treatment 75 of 1,212 (6.2%), control 108 of 1,212 (8.9%), NNT 37, odds ratio converted to relative risk, all CBD100 users.
[Shover], 8/5/2022, retrospective, USA, peer-reviewed, 7 authors, study period 12 February, 2020 - 27 February, 2021. risk of death, 1.8% lower, RR 0.98, p = 0.56, treatment 3 of 69 (4.3%), control 199 of 1,762 (11.3%), odds ratio converted to relative risk, propensity score matching.
risk of mechanical ventilation, 5.1% lower, RR 0.95, p = 0.02, treatment 3 of 69 (4.3%), control 292 of 1,762 (16.6%), NNT 8.2, odds ratio converted to relative risk, propensity score matching.
risk of ICU admission, 8.6% lower, RR 0.91, p = 0.02, treatment 8 of 69 (11.6%), control 543 of 1,762 (30.8%), NNT 5.2, odds ratio converted to relative risk, propensity score matching.
risk of oxygen therapy, 2.6% lower, RR 0.97, p = 0.27, treatment 35 of 69 (50.7%), control 1,417 of 1,762 (80.4%), NNT 3.4, odds ratio converted to relative risk, propensity score weighting.
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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