Covid Analysis, May 28, 2022, DRAFT
https://c19budesonide.com/meta.html
•Statistically significant improvement is seen for mortality. 5 studies from 5 independent teams in 5 different countries show statistically significant
improvements in isolation (3 for the most serious outcome).
•Meta analysis using the most serious outcome reported shows
39% [23‑52%] improvement. Results are similar for Randomized Controlled Trials and similar for peer-reviewed studies. Early treatment is more effective than late treatment.
•While many treatments have some level
of efficacy, they do not replace vaccines and other measures to avoid
infection.
None of the budesonide studies show zero events in the treatment arm.
Multiple treatments are typically used
in combination, and other treatments
may be more effective.
•No treatment, vaccine, or intervention is 100%
available and effective for all variants. All practical, effective, and safe
means should be used.
Denying the efficacy of treatments increases mortality, morbidity, collateral
damage, and endemic risk.
•All data to reproduce this paper and
sources are in the appendix.
Highlights
Budesonide reduces
risk for COVID-19 with very high confidence for mortality and in pooled analysis, low confidence for ICU admission and cases, and very low confidence for recovery.
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 42
treatments.
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
budesonide
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
[Konduri] investigate a novel formulation of budesonide that may
be more effective for COVID-19.
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, and 11
show forest plots for a random effects meta-analysis of
all studies with pooled effects, mortality results, ventilation, ICU admission, progression, recovery, cases, and peer reviewed studies.
Figure 3. Overview of results.
| Treatment time | Number of studies reporting positive effects | Total number of studies | Percentage of studies reporting positive effects | Random effects meta-analysis results |
| Early treatment | 1 | 1 | 100% |
82% improvement RR 0.18 [0.04‑0.79] p = 0.023 |
| Late treatment | 3 | 5 | 60.0% |
34% improvement RR 0.66 [0.49‑0.89] p = 0.0066 |
| Prophylaxis | 2 | 2 | 100% |
41% improvement RR 0.59 [0.41‑0.85] p = 0.0045 |
| All studies | 6 | 8 | 75.0% |
39% improvement RR 0.61 [0.48‑0.77] p < 0.0001 |
Table 1. Results by treatment stage.
| Studies | Early treatment | Late treatment | Prophylaxis | Patients | Authors | |
| All studies | 8 | 82% [21‑96%] | 34% [11‑51%] | 41% [15‑59%] | 9,951 | 130 |
| Peer-reviewed | 6 | 82% [21‑96%] | 28% [2‑48%] | 49% [10‑72%] | 1,984 | 122 |
| Randomized Controlled TrialsRCTs | 4 | 82% [21‑96%] | 15% [-64‑56%] | 1,856 | 80 |
Table 2. Results by treatment stage for all studies and with different exclusions.
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.
Figure 5. Random effects meta-analysis for mortality results.
Figure 6. Random effects meta-analysis for ventilation.
Figure 7. Random effects meta-analysis for ICU admission.
Figure 8. Random effects meta-analysis for progression.
Figure 9. Random effects meta-analysis for recovery.
Figure 10. Random effects meta-analysis for cases.
Figure 11. Random effects meta-analysis for peer reviewed studies.
[Zeraatkar] analyze 356 COVID-19 trials, finding no
significant evidence that peer-reviewed studies are more trustworthy.
They also show extremely slow review times during 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.
Randomized Controlled Trials (RCTs)
Figure 12 shows the distribution of results for Randomized Controlled Trials and other studies, and
a chronological history of results.
Figure 13 and 14
show forest plots for a random effects meta-analysis of
all Randomized Controlled Trials and RCT mortality results.
Table 3 summarizes the 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 budesonide 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).
Figure 12. The distribution of results for Randomized Controlled Trials and other studies, and
a chronological history of results.
Figure 13. 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.
Figure 14. Random effects meta-analysis for RCT mortality results.
| Treatment time | Number of studies reporting positive effects | Total number of studies | Percentage of studies reporting positive effects | Random effects meta-analysis results |
| Randomized Controlled Trials | 2 | 4 | 50.0% |
38% improvement RR 0.62 [0.29‑1.33] p = 0.22 |
| RCT mortality results | 1 | 3 | 33.3% |
15% improvement RR 0.85 [0.44‑1.64] p = 0.63 |
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 15 shows a mixed-effects meta-regression for efficacy
as a function of treatment delay in COVID-19 studies from 42 treatments, showing
that efficacy declines rapidly with treatment delay. Early treatment is
critical for COVID-19.
Figure 15. Meta-regression
showing efficacy as a function of treatment delay in COVID-19 studies from 42 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.
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.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.
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].
For budesonide, there is currently not
enough data to evaluate publication bias with high confidence.
One method to evaluate bias is to compare prospective vs.
retrospective studies. Prospective studies are more likely to be published
regardless of the result, while retrospective studies are more likely to
exhibit bias. For example, researchers may perform preliminary analysis with
minimal effort and the results may influence their decision to continue.
Retrospective studies also provide more opportunities for the specifics of
data extraction and adjustments to influence results.
100% of retrospective studies
report positive effects, compared to
60% of prospective studies, consistent with a bias toward publishing positive results.
The median effect size for
retrospective studies is 49% improvement,
compared to 32% for prospective
studies, suggesting a potential bias towards publishing results showing higher efficacy.
Figure 16 shows a scatter plot of
results for prospective and retrospective studies.
Figure 16. 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 17 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 17. 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. Budesonide for COVID-19
lacks this because it is
off-patent, has multiple manufacturers, and is very low cost.
In contrast, most COVID-19 budesonide 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 budesonide 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.Conclusion
Budesonide is
an effective treatment for COVID-19.
Statistically significant improvement is seen for mortality. 5 studies from 5 independent teams in 5 different countries show statistically significant
improvements in isolation (3 for the most serious outcome).
Meta analysis using the most serious outcome reported shows
39% [23‑52%] improvement. Results are similar for Randomized Controlled Trials and similar for peer-reviewed studies. Early treatment is more effective than late treatment.
Study Notes
[Agustí]
Small early-terminated RCT with 40 inhaled budesonide and 49 control patients, showing no significant differences. 400µg/12h via Pulmicort Turbuhaler. TACTIC. NCT04355637.
[Al Sulaiman]
Combined retrospective (Mar-Jun 2020) and prospective (until Mar 2021) study of 954 COVID+ ICU patients in Saudi Arabia, 68 treated with ICS (80% budesonide or budesonide/formoterol, 20% fluticasone/salmeterol), showing lower mortality with treatment, statistically significant for 30-day but not in-hospital mortality.
[Alsultan]
Small RCT 49 severe condition hospitalized patients in Syria, showing lower mortality with colchicine and shorter hospitalization time with both colchicine and budesonide (all of these not statistically significant).
[Lee (B)]
Retrospective 44,968 patients in South Korea, 7,019 on inhaled corticosteroids, showing no statistically significant differences in COVID-19 cases.
[Monserrat Villatoro]
PSM retrospective 3,712 hospitalized patients in Spain, showing lower mortality with existing use of azithromycin, bemiparine, budesonide-formoterol fumarate, cefuroxime, colchicine, enoxaparin, ipratropium bromide, loratadine, mepyramine theophylline acetate, oral rehydration salts, and salbutamol sulphate, and higher mortality with acetylsalicylic acid, digoxin, folic acid, mirtazapine, linagliptin, enalapril, atorvastatin, and allopurinol.
[Ramakrishnan]
RCT with 73 budesonide patients and 73 control patients, showing significantly lower combined risk of an ER visit or hospitalization, and lower risk of no recovery at day 14.
[Ramlall]
Retrospective 948 intubated patients, 33 treated with budesonide, showing lower mortality with treatment.
[Yu]
Interim results from the PRINCIPLE trial, 1,073 treated with budesonide starting a median of 6 days after symptom onset, showing lower hospitalization/death, and faster recovery with treatment. ISRCTN86534580.
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 c19budesonide.com. Search terms were budesonide, 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 budesonide 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
(3.9.12) with
scipy (1.8.0), pythonmeta (1.26), numpy (1.22.2), statsmodels (0.14.0), and plotly (5.6.0).
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 https://c19budesonide.com/meta.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.
| [Ramakrishnan], 2/8/2021, Randomized Controlled Trial, United Kingdom, Europe, peer-reviewed, 24 authors, average treatment delay 3.0 days. | risk of hospitalization/ER, 81.8% lower, RR 0.18, p = 0.02, treatment 2 of 73 (2.7%), control 11 of 73 (15.1%), NNT 8.1, ITT. |
| risk of hospitalization/ER, 90.1% lower, RR 0.10, p = 0.004, treatment 1 of 70 (1.4%), control 10 of 69 (14.5%), NNT 7.7, PP. | |
| risk of no recovery, 67.1% lower, RR 0.33, p = 0.003, treatment 7 of 70 (10.0%), control 21 of 69 (30.4%), NNT 4.9, PP, day 14. |
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.
| [Agustí], 2/10/2022, Randomized Controlled Trial, Spain, Europe, peer-reviewed, 21 authors, trial NCT04355637. | risk of death, 22.5% higher, RR 1.23, p = 1.00, treatment 1 of 40 (2.5%), control 1 of 49 (2.0%), day 90. |
| risk of progression, 38.7% lower, RR 0.61, p = 0.69, treatment 2 of 40 (5.0%), control 4 of 49 (8.2%), NNT 32. | |
| [Al Sulaiman], 11/10/2021, prospective, Saudi Arabia, Middle East, peer-reviewed, 80% of treatment patients used budesonide, mean age 61.4, 24 authors, study period 1 March, 2020 - 31 March, 2021. | risk of death, 32.0% lower, HR 0.68, p = 0.13, treatment 30 of 64 (46.9%), control 31 of 64 (48.4%), adjusted per study, in-hospital mortality, propensity score matching, multivariable, Cox proportional hazards. |
| risk of death, 47.0% lower, HR 0.53, p = 0.03, treatment 25 of 65 (38.5%), control 29 of 65 (44.6%), adjusted per study, propensity score matching, multivariable, Cox proportional hazards, day 30. | |
| [Alsultan], 12/31/2021, Randomized Controlled Trial, Syria, Middle East, peer-reviewed, 11 authors. | risk of death, 7.1% higher, RR 1.07, p = 1.00, treatment 5 of 14 (35.7%), control 7 of 21 (33.3%). |
| [Ramlall], 10/18/2020, retrospective, USA, North America, preprint, 3 authors. | risk of death, 71.0% lower, HR 0.29, p = 0.01, treatment 33, control 915, Cox proportional hazards. |
| [Yu], 4/12/2021, Randomized Controlled Trial, United Kingdom, Europe, peer-reviewed, 24 authors, average treatment delay 6.0 days. | risk of death, 39.1% lower, RR 0.61, p = 0.45, treatment 6 of 787 (0.8%), control 10 of 799 (1.3%), NNT 204. |
| risk of mechanical ventilation, 6.0% lower, RR 0.94, p = 1.00, treatment 13 of 776 (1.7%), control 14 of 784 (1.8%), NNT 905. | |
| risk of ICU admission, 52.0% lower, RR 0.48, p = 0.07, treatment 10 of 771 (1.3%), control 21 of 779 (2.7%), NNT 71. | |
| risk of death/hospitalization, 25.0% lower, RR 0.75, p = 0.96, treatment 72 of 787 (9.1%), control 116 of 1,069 (10.9%), NNT 59, adjusted per study, day 28. | |
| recovery time, 17.4% lower, relative time 0.83, p = 0.001, treatment 787, control 1,069, adjusted per study. |
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.
| [Lee (B)], 9/9/2021, retrospective, South Korea, Asia, preprint, 5 authors. | risk of case, 32.6% lower, RR 0.67, p = 0.10, treatment 19 of 1,674 (1.1%), control 95 of 5,345 (1.8%), NNT 156, adjusted per study, odds ratio converted to relative risk, multivariate. |
| [Monserrat Villatoro], 1/8/2022, retrospective, propensity score matching, Spain, Europe, peer-reviewed, 18 authors. | risk of death, 49.0% lower, OR 0.51, p = 0.01, RR approximated with OR. |
Supplementary Data
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