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Covid Analysis, November 18, 2021
https://c19ly.com/meta.html
•Meta analysis using the most serious outcome reported shows
65% [36‑81%] improvement.
Results are slightly worse for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies.
Early treatment is more effective than late treatment.
•Statistically significant improvement is seen for
hospitalization.
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 11% of bamlanivimab
studies show zero events in the treatment arm.
•Multiple treatments are typically used
in combination, and other treatments
may be more effective.
The US EUA for bamlanivimab as a sole therapy was revoked due to
the increase of resistant variants. Bamlanivimab-etesevimab is authorized
only in specific locations, based on the distribution of variants.
•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.
| Studies | Early treatment | Late treatment | Prophylaxis | Patients | Authors | |
| All studies | 9 | 79% [59‑89%] | 18% [-377‑86%] | 57% [33‑72%] | 19,400 | 94 |
| With exclusions | 7 | 76% [50‑88%] | 18% [-377‑86%] | 57% [33‑72%] | 9,136 | 78 |
| Peer-reviewed | 6 | 78% [54‑89%] | -100% [-483‑31%] | 17,198 | 70 | |
| Randomized Controlled TrialsRCTs | 5 | 75% [46‑88%] | -100% [-483‑31%] | 57% [33‑72%] | 2,653 | 42 |
| Percentage improvement with bamlanivimab treatment | ||||||
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
bamlanivimab 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.
Results
Figure 3, 4, 5, 6, and 7
show forest plots for a random effects meta-analysis of
all studies with pooled effects, mortality results, ICU admission, hospitalization, and peer reviewed studies.
Table 1 summarizes the results by treatment stage.
| 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 | 6 | 6 | 100% |
79% improvement RR 0.21 [0.11‑0.41] p < 0.0001 |
| Late treatment | 1 | 2 | 50.0% |
18% improvement RR 0.82 [0.14‑4.77] p = 0.84 |
| Prophylaxis | 1 | 1 | 100% |
57% improvement RR 0.43 [0.28‑0.67] p = 0.00021 |
| All studies | 8 | 9 | 88.9% |
65% improvement RR 0.35 [0.19‑0.64] p = 0.00082 |
Table 1. 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 4. Random effects meta-analysis for mortality results.
Figure 5. Random effects meta-analysis for ICU admission.
Figure 6. Random effects meta-analysis for hospitalization.
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 8 shows a forest plot for random
effects meta-analysis of all studies after exclusions.
[Cooper], unadjusted results with no group details.
[Rubin], significant unadjusted confounding possible.
Figure 8. 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 9 and 10
show forest plots for a random effects meta-analysis of
all Randomized Controlled Trials and RCT mortality results.
Table 2 summarizes the results.
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].
Figure 11. Randomized Controlled Trials. The
distribution of results for RCTs and other studies.
Figure 9. 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 10. Random effects meta-analysis for RCT mortality results.
Effect extraction is pre-specified, using the most serious outcome reported,
see the appendix for details.
| 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 | 4 | 5 | 80.0% |
55% improvement RR 0.45 [0.21‑0.97] p = 0.04 |
| RCT mortality results | 1 | 2 | 50.0% |
47% improvement RR 0.53 [0.02‑12.14] p = 0.7 |
Table 2. 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]. Other
medications might be beneficial for late stage complications, while early use
may not be effective or may even be harmful. Figure 12 shows
an example where efficacy declines as a function of treatment delay.
Figure 12. Effectiveness may depend critically
on treatment delay.
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 thousands of
different variants of SARS-CoV-2 and efficacy may depend critically on the
distribution of variants encountered by the patients in a study.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. 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, several trials with negative results remain unpublished to
date (NCT04575584, CTRI/2021/05/033864, and CTRI/2021/08/0354242).
For bamlanivimab, 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.
The median effect size for
retrospective studies is 73% improvement,
compared to 71% for prospective
studies, consistent with a positive publication bias.
100% of retrospective studies
report a statistically significant positive effect, compared to
80% of prospective studies, consistent with a bias toward publishing positive results.
Figure 13 shows a scatter plot of
results for prospective and retrospective studies.
Figure 13. Prospective vs. retrospective studies.
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
Bamlanivimab is an effective treatment for COVID-19.
Meta analysis using the most serious outcome reported shows
65% [36‑81%] improvement.
Results are slightly worse for Randomized Controlled Trials, similar after exclusions, and similar for peer-reviewed studies.
Early treatment is more effective than late treatment.
Statistically significant improvement is seen for
hospitalization.
8 studies show statistically significant improvements in isolation (4 for the most serious outcome).
Study Notes
[ACTIV-3/TICO] Late stage RCT of LY-CoV555 added to remdesivir, showing non-statistically significant higher mortality with the addition of LY-CoV555.
[Bariola] Retrospective 234 patients receiving bamlanivimab and 234 matched controls, showing lower hospitalization and mortality with treatment. Greater benefit was seen with administration within 4 days of their positive COVID-19 test.
[Chen] Interim analysis of the BLAZE-1 phase 2 trial of outpatients showing lower hospitalization or ER visits (1.6% versus 6.3%), and improvements in symptoms and viral load compared to placebo.
NCT04427501
[Cooper] Retrospective 2,879 patients and matched controls in the USA, showing significantly lower mortality and hospitalization with bamlanivimab, bamlanivimab-etesevimab, and casirivimab-imdevimab. There was significantly lower hospitalization with casirivimab-imdevimab compared to bamlanivimab or bamlanivimab-etesevimab. PSM and multivariate analysis is only provided for all treatments combined.
[Gottlieb] RCT for LY-CoV555 monotherapy and LY-CoV555/LY-CoV016 combination therapy with 592 patients showing lower hospitalization/ER visits with treatment.
For viral load at day 11, a statistically significant reduction was found with combination therapy but not monotherapy.
[Lilly (B)] Press release on the BLAZE-2 trial at nursing homes showing significantly lower symptomatic COVID-19 with treatment.
[Lilly] Results from the BLAZE-1 study of combined bamlanivimab/etesevimab, showing significantly lower mortality and combined mortality/hospitalization with treatment.
[Rubin] Retrospective database analysis of 1257 PCR+ outpatients with age ≥65, BMI≥35, 191 receiving bamlanivimab via lottery. Authors note that the alpha variant was most common during the study period, and that efficacy against other variants can be much lower. Authors note confounding due to prioritization in the lottery and differential reporting in the database. The adjusted mortality result is in the abstract, with no details or mention in the paper, and we note that the change after adjustments is large: RR 0.56 -> OR 0.03.
[Webb] Retrospective 479 patients treated with bamlanivimab showing lower mortality, hospital admission, and emergency department visits with treatment. Authors falsely state that "no other COVID-19 therapies for ambulatory patients have proven effective".
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 c19ly.com. Search terms were bamlanivimab, filtered for papers containing the terms COVID-19, SARS-CoV-2, or coronavirus. Automated searches are performed
every few hours with notification of new matches.
All studies regarding the use of bamlanivimab for COVID-19 that report
a comparison with a control group are included in the main analysis.
Sensitivity analysis is performed, excluding studies with major issues,
epidemiological studies, and studies with minimal available information. 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 calculations for that study. 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.
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 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 (3.9.7)
with
scipy (1.7.1), pythonmeta (1.23), numpy (1.21.2), statsmodels (0.13.0), and plotly (5.3.1).
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.
None
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, 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://c19ly.com/meta.html.
Effect extraction follows pre-specified rules as detailed above
and gives priority to more serious outcomes. Only the first (most serious)
outcome is used in calculations, which may differ from the effect a paper
focuses on.
| [Chen], 10/28/2020, Randomized Controlled Trial, USA, North America, peer-reviewed, 12 authors. | risk of hospitalization, 74.3% lower, RR 0.26, p = 0.02, treatment 5 of 309 (1.6%), control 9 of 143 (6.3%). |
| [Cooper], 10/8/2021, retrospective, USA, North America, peer-reviewed, 9 authors, excluded in exclusion analyses: unadjusted results with no group details. | risk of death, 45.3% lower, RR 0.55, p = 1.00, treatment 1 of 473 (0.2%), control 33 of 8,534 (0.4%), unadjusted, bamlanivimab-etesevimab. |
| risk of ICU admission, 57.5% lower, RR 0.42, p = 0.33, treatment 2 of 473 (0.4%), control 85 of 8,534 (1.0%), unadjusted, bamlanivimab-etesevimab. | |
| risk of hospitalization, 5.0% lower, RR 0.95, p = 0.86, treatment 37 of 473 (7.8%), control 703 of 8,534 (8.2%), unadjusted, bamlanivimab-etesevimab. | |
| risk of death, 17.2% higher, RR 1.17, p = 0.59, treatment 11 of 2,427 (0.5%), control 33 of 8,534 (0.4%), unadjusted, bamlanivimab. | |
| risk of ICU admission, 9.0% lower, RR 0.91, p = 0.81, treatment 22 of 2,427 (0.9%), control 85 of 8,534 (1.0%), unadjusted, bamlanivimab. | |
| risk of hospitalization, 28.0% lower, RR 0.72, p < 0.001, treatment 144 of 2,427 (5.9%), control 703 of 8,534 (8.2%), unadjusted, bamlanivimab. | |
| [Gottlieb], 1/21/2021, Randomized Controlled Trial, USA, North America, peer-reviewed, 27 authors. | risk of hospitalization/ER, 70.6% lower, RR 0.29, p = 0.05, treatment 4 of 101 (4.0%), control 7 of 52 (13.5%), LY-CoV555 all dosages. |
| risk of hospitalization/ER, 79.9% lower, RR 0.20, p = 0.13, treatment 1 of 37 (2.7%), control 7 of 52 (13.5%), LY-CoV555 700mg. | |
| risk of hospitalization/ER, 75.2% lower, RR 0.25, p = 0.25, treatment 1 of 30 (3.3%), control 7 of 52 (13.5%), LY-CoV555 2800mg. | |
| risk of hospitalization/ER, 56.3% lower, RR 0.44, p = 0.31, treatment 2 of 34 (5.9%), control 7 of 52 (13.5%), LY-CoV555 7000mg. | |
| risk of hospitalization/ER, 91.8% lower, RR 0.08, p = 0.04, treatment 0 of 31 (0.0%), control 7 of 52 (13.5%), relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), LY-CoV555/LY-CoV016. | |
| [Lilly], 3/10/2021, Double Blind Randomized Controlled Trial, USA, North America, preprint, 1 author. | risk of death, 92.3% lower, RR 0.08, p = 0.01, treatment 0 of 511 (0.0%), control 4 of 258 (1.6%), relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of combined hospitalization/death, 86.5% lower, RR 0.13, p < 0.001, treatment 4 of 511 (0.8%), control 15 of 258 (5.8%). | |
| [Rubin], 11/3/2021, retrospective, USA, North America, peer-reviewed, 7 authors, excluded in exclusion analyses: significant unadjusted confounding possible, conflicts of interest: research funding from the drug patent holder, consulting for the pharmaceutical industry. | risk of death, 97.0% lower, RR 0.03, p < 0.01, treatment 1 of 191 (0.5%), control 10 of 1,066 (0.9%), odds ratio converted to relative risk. |
| risk of hospitalization, 65.3% lower, RR 0.35, p = 0.04, treatment 16 of 191 (8.4%), control 121 of 1,065 (11.4%), odds ratio converted to relative risk, IPTW weighted logistic regression. | |
| [Webb], 6/23/2021, retrospective, USA, North America, peer-reviewed, 14 authors. | risk of death, 79.7% lower, RR 0.20, p = 0.09, treatment 1 of 479 (0.2%), control 57 of 5,536 (1.0%). |
| risk of hospitalization, 52.7% lower, RR 0.47, p < 0.001, treatment 22 of 479 (4.6%), control 538 of 5,536 (9.7%). |
Effect extraction follows pre-specified rules as detailed above
and gives priority to more serious outcomes. Only the first (most serious)
outcome is used in calculations, which may differ from the effect a paper
focuses on.
| [ACTIV-3/TICO], 12/22/2020, Randomized Controlled Trial, USA, North America, peer-reviewed, 1 author. | risk of death, 100% higher, RR 2.00, p = 0.22, treatment 9 of 163 (5.5%), control 5 of 151 (3.3%), adjusted per study, proportional hazards regression. |
| [Bariola], 3/30/2021, retrospective, USA, North America, preprint, 22 authors. | risk of death, 66.8% lower, RR 0.33, p = 0.05, treatment 4 of 234 (1.7%), control 12 of 234 (5.1%), odds ratio converted to relative risk. |
| risk of hospitalization, 60.7% lower, RR 0.39, p = 0.001, treatment 15 of 234 (6.4%), control 39 of 234 (16.7%), odds ratio converted to relative risk. |
Effect extraction follows pre-specified rules as detailed above
and gives priority to more serious outcomes. Only the first (most serious)
outcome is used in calculations, which may differ from the effect a paper
focuses on.
| [Lilly (B)], 1/21/2021, Randomized Controlled Trial, USA, North America, preprint, 1 author. | risk of symptomatic case, 57.0% lower, RR 0.43, p < 0.001, treatment 483, control 482, group sizes estimated because they were not supplied. |
| risk of symptomatic case, 80.0% lower, RR 0.20, p < 0.001, treatment 150, control 149, nursing home residents, group sizes estimated because they were not supplied. |
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https://jamanetwork.com/journals/jama/fullarticle/192425.
Please send us corrections, updates, or comments. Vaccines and treatments are both extremely valuable and complementary. All
practical, effective, and safe means should be used. Elimination of COVID-19
is a race against viral evolution. No treatment, vaccine, or intervention is
100% available and effective for all current and future variants. Denying the
efficacy of any method increases the risk of COVID-19 becoming endemic; and
increases mortality, morbidity, and collateral damage. 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. Treatment protocols for physicians are
available from the FLCCC.