Mavrilimumab to Reduce Progression of Acute Respiratory Failure in COVID-19 Pneumonia and Systemic Hyper-inflammation: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Mavrilimumab to Reduce Progression of Acute Respiratory Failure in COVID-19 Pneumonia and Systemic Hyper-inflammation (NCT04399980), a randomized phase 2 study. The primary outcome is Subjects Alive and Off of Oxygen at Day 14.

2. Clinical Question

This question combines two clinically relevant states: being alive and no longer receiving oxygen. A participant had to meet both conditions to count toward the endpoint. The result is therefore a binary outcome at Day 14, rather than a measure of the duration of oxygen use or the timing of recovery. The registry record lists 40 participants enrolled overall. It does not report a more detailed primary analysis population or participant-level endpoint data.

3. Trial Design

The registry describes a randomized, quadruple-masked, parallel-group Phase 2 study with a placebo comparator. The study was prospective and multicenter, according to its brief summary. The sponsor was The Cleveland Clinic. Enrollment was 40 participants, with 21.0 in the intervention arm and 19.0 in the control arm in the primary outcome results. The record lists the study as completed.

The intervention arm received a treatment infusion of mavrilimumab, while the control arm received a placebo infusion. Random allocation is intended to make the groups comparable on average, including with respect to factors that may affect outcomes. Masking is intended to reduce the risk that knowledge of the assigned group influences care, outcome assessment, or reporting. The record labels masking as quadruple, but does not specify which groups of people were masked or how masking was implemented.

The listed study start was 2020-05-20, and the primary completion date was 2021-04-23. The registry record gives the primary endpoint timeframe as Day 14 and lists two secondary outcomes at Day 28: being alive and without respiratory failure, and mortality. It also lists three outcome measures, while the posted results describe the primary and two secondary outcomes. A detailed protocol, the original statistical analysis plan, eligibility criteria, infusion details, and the rationale for the planned enrollment are not reported in the registry record. These omissions limit what can be concluded about how the trial was designed to address specific analytic challenges.

4. Participant Flow and Accounting

The registry reports how participants moved through each stage of the study. All counts below are exactly as posted; missing counts are not inferred from other stages.

Overall Study

5. Baseline Characteristics

The table reproduces the baseline characteristics posted in the registry, which describe the participants analysed at baseline and help show whether the randomized groups were comparable.

6. Primary Endpoint

The registry lists the primary outcome as Subjects Alive and Off of Oxygen at Day 14, measured over Day 14.

The primary endpoint was the number of participants alive and off oxygen at Day 14. The endpoint description also refers to the number and percentage of subjects meeting that condition. It is a binary outcome: each participant either met the combined condition at the specified time or did not. The reported comparison is based on counts in the mavrilimumab and placebo groups.

Combining survival and oxygen status makes the endpoint a measure of both being alive and not requiring oxygen at Day 14. Someone alive but still receiving oxygen would not meet the endpoint; neither would someone who had died. This gives the endpoint a direct, fixed-time interpretation, but it does not distinguish between the different reasons a participant may fail to meet it. Nor does it describe the course of oxygen use, such as how long it continued or whether a participant’s status changed before Day 14.

General statistical context: binary endpoints are often summarized by the number and proportion of participants with the event or condition, then compared between groups. The absolute risk difference expresses the contrast in proportions on the same scale as the outcome. The registry record identifies this endpoint as primary, but does not report the rationale for selecting Day 14, the handling of missing endpoint assessments, or the protocol definition of being off oxygen.

7. Results Posted in the Registry

• Intervention: 57.1% (12/21) versus 47.4% (9/19) on control; risk difference 9.8 percentage points (95% CI -21.1 to 40.6), risk ratio 1.21 (0.66–2.20); unadjusted two-proportion z-test p = 0.536.

8. Secondary Efficacy Endpoints

The registry also posts results for secondary outcomes. For each outcome below, the endpoint type, the test applied to the posted summary statistics, the result and its interpretation are given in turn. Secondary endpoints are usually not adjusted for multiple testing, so they support rather than confirm the primary finding.

Number of Subjects Alive and Without Respiratory Failure at Day 28

• Endpoint type: Binary: the number of participants with the event, compared as proportions.
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • Intervention: 95.2% (20/21) versus 78.9% (15/19) on control; risk difference 16.3 percentage points (95% CI -4.2 to 36.8), risk ratio 1.21 (0.94–1.55); unadjusted two-proportion z-test p = 0.120.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Intervention and the control. The interval gives the range of effects that remain compatible with the data.

Mortality at Day 28

• Endpoint type: Binary: the number of participants with the event, compared as proportions.
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • Intervention: 4.8% (1/21) versus 15.8% (3/19) on control; risk difference -11.0 percentage points (95% CI -29.8 to 7.7), risk ratio 0.30 (0.03–2.66); unadjusted two-proportion z-test p = 0.246.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Intervention and the control. The interval gives the range of effects that remain compatible with the data.

The two secondary outcomes examine status at Day 28, after the Day 14 primary endpoint. One concerns being alive and without respiratory failure; the other concerns mortality. Taken together, they address related features of the clinical course but use different outcome definitions from the primary endpoint. Day 28 respiratory status is not identical to being off oxygen at Day 14, and mortality is a separate endpoint rather than a combined recovery measure.

The overall pattern should be read in relation to the primary result, not as a replacement for it. The primary endpoint estimates an uncertain group contrast, and the secondary results likewise do not provide a basis for a definitive conclusion on their own. The posted summaries can help describe whether the later outcomes point in a similar or different direction, but the small enrollment and uncertainty around comparisons remain important context. The secondary endpoints are not independent confirmations simply because they are measured separately; outcomes within the same participants may be related.

Unadjusted secondary results carry less weight than a prespecified primary analysis for several reasons. Multiple outcomes create multiple opportunities to observe a result that appears notable by chance, especially when no adjustment strategy is reported. Secondary endpoints may also be intended to provide supporting or exploratory information, rather than to serve as the main test of the study’s question. The registry record does not report a multiplicity plan, detailed analyses, or adjustment of secondary comparisons. Their results should therefore be treated as descriptive and interpreted cautiously, with attention to their definitions and uncertainty rather than as standalone evidence.

These outcomes can still be useful for understanding the range of questions the trial recorded. However, without a reported analysis plan and clear information on missing assessments, it is difficult to know how strongly to weigh them relative to the primary endpoint. The registry record does not report additional secondary analyses beyond the posted outcome summaries.

Secondary outcomes registered for this trial, for reference (only those above were recalculated):

• Number of Subjects Alive and Without Respiratory Failure at Day 28
• Mortality at Day 28

9. Statistical Interpretation

10. Statistical Methods Explained

These results concern a binary endpoint and binary secondary outcomes: participants either met the stated condition at the specified time or they did not. The registry reports counts by arm and an unadjusted risk difference for each comparison. General statistical explanations below describe how these measures are commonly interpreted; they do not establish which full procedures were specified in the trial’s protocol.

What does the risk difference describe?

For a binary outcome, the risk in a group is the proportion of participants who meet the outcome definition. A risk difference subtracts the control-group risk from the intervention-group risk. A positive difference means the observed proportion is higher in the intervention group; a negative difference means it is lower. A value of zero corresponds to equal observed proportions. The primary posted risk difference is 0.098. It is an absolute contrast, not a relative measure such as a risk ratio. It should be interpreted alongside its confidence interval and the underlying counts, rather than in isolation.

General statistical context: a risk difference is often useful because it stays on the outcome’s probability scale and directly represents the absolute separation between groups. Relative measures can tell a different story in scale and can be less intuitive when the underlying event frequency is not considered. No single measure provides a complete description, so the endpoint counts and uncertainty estimates are important context.

What does a confidence interval mean?

A confidence interval describes uncertainty around an estimated quantity under the assumptions of the method used to calculate it. In repeated use of a procedure, a stated confidence level refers to how often intervals constructed in that way would cover the true value. It is not a range that assigns a probability to the true value after the data have been observed. The primary interval, -0.211 to 0.406, includes zero and also includes differences in either direction. That breadth shows that the observed data do not pin down the risk difference precisely.

General statistical context: interval width is influenced by the amount of information available, including the number of participants and the frequency of the outcome. Smaller studies and sparse binary outcomes commonly yield less precise estimates. A narrow interval would indicate greater precision for the parameter under the model; it would not by itself establish that the endpoint is clinically important or that the analysis is free from bias.

How should the p-value be read?

The primary unadjusted p-value is 0.536. General statistical context: a p-value measures compatibility between the observed data and a specified null model, using a chosen test statistic and assumptions. A large p-value does not demonstrate that the null hypothesis is true, nor does it show that the groups are practically equivalent. A small p-value does not measure the probability that a result is due to chance, the probability that a treatment has an effect, or the magnitude of an effect. For this trial, the interval and point estimate should be read alongside the p-value.

The record does not report the sponsor’s detailed test procedure or a complete statistical analysis plan. The comparison statistics are unadjusted summaries based on the reported counts. Thus, they do not account for covariates or other factors through a reported adjusted model. Whether adjustment was planned or conducted outside the posted results is not reported in the registry record.

Why do counts and analysis populations matter?

The arm-level outcome counts are the basic information behind the binary comparisons. The primary results report 12.0 participants meeting the endpoint among 21.0 in the intervention group and 9.0 among 19.0 in the control group. The registry record does not report an analysis population definition, such as whether all randomized participants were included under an intention-to-treat approach. It also does not report missing outcome counts or the handling of missing observations. Without those details, it is not possible to assess whether the denominators reflect every randomized participant or how incomplete measurements affected the posted estimates.

General statistical context: missingness can influence a binary outcome comparison when the chance of an unavailable assessment is related to group assignment or outcome status. Analysts may use different approaches to address missing outcomes, and their assumptions can affect results. The specific handling cannot be inferred from the counts alone.

What does over-dispersion mean for these results?

Over-dispersion is relevant when data are modelled as counts with more variability than a chosen statistical model expects. General statistical context: ignoring over-dispersion can make uncertainty estimates too small and confidence intervals too narrow. The posted outcomes are participant counts for binary conditions at fixed times, and the registry does not report a count-regression model or an over-dispersion parameter. No claim about an over-dispersion adjustment can therefore be made from the registry record.

More generally, randomization supports a causal comparison when the trial is conducted and analyzed as intended, but it does not remove uncertainty in a small sample. It also does not substitute for knowing how outcomes were defined, how missing data were handled, or what analysis population was used. Those details are not reported in the registry record, so interpretation should remain anchored to the posted group counts and the uncertainty around their contrasts.

11. Confidence Intervals and Precision

The primary risk difference is 0.098, and its 95% confidence interval extends from -0.211 to 0.406. Because zero lies within the interval, the data are compatible with no difference as well as with differences in either direction. The interval is wide relative to the point estimate, indicating limited precision for the size and direction of the underlying contrast. It does not establish that the groups are equivalent or that a meaningful difference is absent.

General statistical context: confidence intervals show a range of parameter values compatible with the data under the method’s assumptions. Their width reflects uncertainty, not the probability that any particular value is correct. Intervals also do not account for every possible source of error, such as limitations in outcome measurement, missing data, or bias in trial conduct.

The trial enrolled 40 participants, with 21.0 in the intervention arm and 19.0 in the control arm for the primary outcome. With this amount of information, individual outcomes can have substantial influence on group proportions. The posted data do not include participant-level information that would allow further assessment of outcome patterns or subgroups. Nor does the registry record report a more detailed uncertainty analysis or adjusted estimate.

The p-value of 0.536 is consistent with the interval’s inclusion of zero, but the interval offers a clearer view of the range of estimates compatible with the data. Precision should be considered separately from the direction of the point estimate and from clinical importance. The registry record does not specify a threshold for a clinically important difference, so the observed estimate cannot be judged against such a threshold from the posted information alone.

12. Planning a Similar Trial: Sample Size

This is a planning illustration, not a re-evaluation of the original trial. It asks: if a new study wanted to detect a difference like the one observed, or a smaller one, how large would it need to be? It is deliberately not “observed power” of the finished trial, which is not a meaningful quantity.

The sample-size figures are planning illustrations based on the observed effect, not the trial’s original design assumptions. They use a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. Each planning scenario uses the stated fraction of the observed effect as the effect to detect. The trial’s own sample-size justification and original design assumptions are not reported in the registry record.

For these binary outcomes, the figures are participants per arm, not total events. The 80% power column gives the estimated number of participants per arm for a design that would have an 80% chance of rejecting the null hypothesis under the planning assumptions if the specified effect were the true effect. The 90% power column applies the same idea with a higher target probability of detecting that effect. These are planning quantities, not guarantees about what a future study would find.

The rows use different fractions of the observed effect. Planning for 75% of the observed effect means targeting a smaller difference than planning for the full observed effect; planning for 50% means targeting a still smaller difference. Smaller effects are generally harder to detect, so they require more participants under otherwise similar assumptions. That pattern is reflected in the increasing per-arm requirements across the scenarios. The calculations are not observed power: they do not describe the probability that this completed trial detected an effect after its results were known.

These estimates are illustrative rather than a substitute for a prospective design calculation. They inherit uncertainty from the observed effect, which is itself imprecise in a small trial. The planning method also uses assumptions that may not match a future study’s design or analysis. No adjustment for attrition or other design features is specified in the planning assumptions.

13. How the Sample Size Is Derived

Power is the probability that a study of a given size rejects the null hypothesis when a specific true effect exists. It depends on four things: the effect to detect, the variability of the outcome, the significance level (here a two-sided α of 0.05, so z_1−α/2 = 1.960) and the desired power (z_1−β = 0.842 for 80% and 1.282 for 90%).

Formula

n is the number of participants in each arm, p₀ and p₁ are the control and treatment proportions, and p̄ is their average.

Worked calculation for this trial

1. Control proportion p₀ = 0.474; treatment proportion p₁ = 0.571; average p̄ = 0.523.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.7064 = 1.3845.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.7030 = 0.5917.
4. n = (1.3845 + 0.5917)² / (0.0977)² = 408.7, rounded up to 409 participants per arm for 80% power.

The smaller-effect rows in the table above repeat the same calculation with the effect multiplied by 0.75 and 0.5 (for ratios, applied to the logarithm of the ratio).

R code

alpha <- 0.05
z_a   <- qnorm(1 - alpha / 2)          # two-sided
p0 <- 0.473684210526316                          # control proportion
p1 <- 0.571428571428571                          # treatment proportion
n_per_arm <- function(power, frac = 1) {
  q1 <- p0 + (p1 - p0) * frac               # smaller effect = fraction of the observed difference
  pbar <- (p0 + q1) / 2
  z_b <- qnorm(power)
  ceiling(round((z_a * sqrt(2 * pbar * (1 - pbar)) + z_b * sqrt(p0 * (1 - p0) + q1 * (1 - q1)))^2 / (q1 - p0)^2, 6))
}
for (f in c(1, 0.75, 0.5))
  cat(sprintf("%3.0f%% of observed effect: n per arm = %s (80%% power), %s (90%% power)\n", 100 * f,
              format(n_per_arm(0.80, f), big.mark = ","), format(n_per_arm(0.90, f), big.mark = ",")))

# Check against R's built-in function (unrounded n per arm)
cat("power.prop.test:", round(power.prop.test(p1 = p0, p2 = p1, power = 0.80)$n, 1), "\n")

# Simulation: how often does the z-test reject at the 80%-power sample size?
set.seed(4399980)
n    <- n_per_arm(0.80)
reps <- 20000
x0 <- rbinom(reps, n, p0); x1 <- rbinom(reps, n, p1)
ph <- (x0 + x1) / (2 * n)
z  <- ((x1 - x0) / n) / sqrt(ph * (1 - ph) * 2 / n)
cat(sprintf("Simulated power at n = %s per arm: %.3f (%d replicates)\n", format(n, big.mark = ","), mean(abs(z) > z_a, na.rm = TRUE), reps))

Output from running the code

100% of observed effect: n per arm = 409 (80% power), 547 (90% power)
 75% of observed effect: n per arm = 729 (80% power), 976 (90% power)
 50% of observed effect: n per arm = 1,642 (80% power), 2,198 (90% power)
power.prop.test: 408.7 
Simulated power at n = 409 per arm: 0.799 (20000 replicates)

The simulation draws many pairs of event counts at the planned sample size and counts how often the two-proportion z-test rejects the null. It should land close to the target power; at very small required sample sizes it can fall below it, because the normal approximation is rough there.

14. Multiplicity, Missing Data and Analysis Populations

The registry lists a primary endpoint and two secondary outcomes. Testing or interpreting several outcomes raises the issue of multiplicity: as the number of comparisons grows, the chance of at least one apparently unusual result can increase. General statistical context: a prespecified testing hierarchy, adjustment procedure, or explicit exploratory interpretation can help readers understand how multiple comparisons were intended to be handled. The registry record does not report a multiplicity strategy or a statistical analysis plan specifying how the primary and secondary outcomes were to be interpreted together.

The secondary results are posted with unadjusted comparisons. No multiplicity-adjusted p-values or confidence intervals are reported. This does not mean that an adjustment was necessarily absent from every analysis conducted by the investigators; rather, such an approach is not reported in the registry record. The posted secondary results should not be treated as if they were individually protected against the chance findings that can arise when multiple outcomes are examined.

Missing data are another important issue for fixed-time binary endpoints. A participant’s Day 14 status may be unavailable, and the way such cases are classified or excluded can affect both the numerator and denominator. The registry record does not report the number of missing assessments, reasons for missingness, or the method used to handle missing outcomes. It also does not report sensitivity analyses exploring alternative assumptions about missing data.

The analysis population is not reported in the registry record. In particular, the posted information does not specify whether the analysis included all randomized participants, only participants with observed outcomes, or another defined group. The primary outcome counts and arm denominators are available, but the analysis framework behind them is not fully described. The record also lists no reported analyses for the secondary outcomes beyond the posted comparisons. Without details on multiplicity, missingness, and analysis populations, the precision figures cannot capture all uncertainty about how analytic choices may affect interpretation.

15. Safety Results

The registry reports serious adverse events as affected participants divided by participants at risk.

Deaths

Other (non-serious) adverse events

Participants with at least one non-serious event at or above the registry's 0% reporting threshold.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: hypoxia - worsening (15.8%), acute respiratory distress syndrome (14.3%), pneumonia (9.5%), chronic respiratory failure (5.3%), elevated wbc (5.3%).

The registry’s serious adverse event summary reports 5 of 21 participants at risk in the intervention group and 4 of 19 in the control group with serious adverse events. The adverse event summary also reports 1 death in the intervention group and 4 in the control group. These figures are descriptive counts within the recorded groups; they do not by themselves establish that treatment caused or prevented an event.

The most frequent serious adverse event terms listed include Hypoxia - Worsening (15.8%), Acute Respiratory Distress Syndrome (14.3%), and Pneumonia (9.5%). Other listed terms are Chronic Respiratory Failure (5.3%), Elevated WBC (5.3%), and Nausea (4.8%). The registry record does not provide additional detail about event timing, severity grading, attribution to study treatment, or whether participants could contribute more than one event term. These percentages should therefore be read as part of the posted adverse event summary, not as a complete account of individual event histories.

General statistical context: safety counts are often difficult to compare precisely in small studies, particularly when events are uncommon and participants may experience multiple events. The group counts are useful for describing what was recorded, but are not a standalone basis for estimating a causal safety effect.

16. What the Result Does, and Does Not, Mean

The posted primary results show that 12.0 of 21.0 participants in the intervention group and 9.0 of 19.0 in the control group were alive and off oxygen at Day 14. The estimated unadjusted risk difference was 0.098, with a 95% confidence interval from -0.211 to 0.406. This interval includes zero and a broad range of differences in either direction. The results therefore leave substantial uncertainty about the size and direction of the group contrast.

These data do not establish that mavrilimumab is effective or ineffective, and they do not demonstrate that the treatments are equivalent. A non-significant p-value does not prove there is no effect. A positive point estimate does not, by itself, establish a reliable effect. The registry results describe the participants and outcomes recorded in this trial; they do not resolve what would be observed in a larger or differently conducted study.

The Day 28 secondary outcomes add information about later respiratory status and mortality, but they should not be treated as independent confirmation of the primary finding. The registry record does not report a plan for handling multiple outcomes or detailed sponsor analyses. It also does not report how missing data were handled, how the analysis population was defined, or a threshold for a clinically important difference. Those omissions constrain the conclusions that can be drawn from the posted summaries.

General statistical context: the certainty of a result depends on more than whether a p-value crosses a conventional threshold. The magnitude of the estimate, its precision, the endpoint definition, the design, and the completeness of the analysis all matter. Here, the small enrollment and broad primary interval are central features of the evidence. The results are best understood as an uncertain estimate from a completed Phase 2 trial, not as a definitive answer about treatment effects.

17. Why This Trial Matters Statistically

This trial illustrates how randomized design and limited enrollment can coexist: randomization supports a fair group comparison in principle, while a small number of participants still leaves estimates imprecise. The primary outcome is a clearly timed binary measure combining survival and oxygen status. Its interpretation is direct, but it compresses several possible clinical courses into a single yes-or-no classification at Day 14.

The primary estimate and its confidence interval demonstrate why a point estimate should not be read alone. The observed contrast is positive, yet the interval spans zero and values on both sides. That uncertainty is especially important when the trial has 40 participants and the endpoint count is based on relatively few observations. The result is not evidence of equivalence, and it is not a precise estimate of the possible difference.

The registry record also provides a useful example of the importance of transparent analysis details. Randomization and masking are recorded, as are the arm counts and several outcome summaries. But the statistical analysis plan, original sample-size justification, analysis population, missing-data handling, and multiplicity approach are not reported in the registry record. Those details are essential for understanding how the posted results were intended to be interpreted and how robust they may be to analytic choices.

Finally, the secondary outcomes and serious adverse event summaries demonstrate the value and limits of descriptive reporting. They broaden the view of outcomes recorded, but they do not erase uncertainty in the primary comparison or establish causality from counts alone. Statistical interpretation requires attention to design, endpoint meaning, precision, and what remains undocumented—not just whether a single comparison appears favorable or unfavorable.

18. Limitations

• The posted summary statistics are the only data these results rest on; patient-level data, full protocols and statistical analysis plans are not available.
• Recalculated results are simple, unadjusted calculations from posted summaries. They do not replace the sponsor's prespecified analysis.

The trial was small, with 40 participants enrolled and 21.0 in the intervention group and 19.0 in the control group for the primary outcome. That limits the precision of the estimated group difference and makes the results potentially sensitive to a small number of participant outcomes. The primary confidence interval is broad and includes zero, so the posted estimate does not resolve the direction or size of the difference.

Several design and analysis details are not reported in the registry record. These include the detailed eligibility criteria, participant characteristics, mavrilimumab dose and schedule, original sample-size justification, and full statistical analysis plan. Although the record identifies the study as randomized, quadruple-masked, parallel, and multicenter, it does not describe the randomization procedures or which parties were masked.

The registry record does not report the analysis population, missing outcome counts, or how missing data were handled. It also does not report a multiplicity strategy for the primary and secondary outcomes or sponsor-reported detailed analyses. As a result, the arm-level summaries cannot be evaluated against a clearly documented analysis plan, and the possibility that analytic choices affect interpretation cannot be assessed from the record alone.

The endpoints are measured at fixed time points and do not describe the full trajectory of respiratory status. The primary endpoint combines survival and oxygen status, so failure to meet it can reflect different underlying circumstances. The registry record does not report a threshold for clinical importance, making it difficult to judge the practical meaning of the estimated risk difference from the posted results alone.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: Mavrilimumab to Reduce Progression of Acute Respiratory Failure in COVID-19 Pneumonia and Systemic Hyper-inflammation — NCT04399980.
• Registry record last updated 2021-05-13; status last verified 2021-05.
• Results first posted 2021-05-13.
• Data retrieved from ClinicalTrials.gov on 2026-11-09.
• Group sizes, means, event counts and sponsor-reported p-values are reproduced as posted. Effect estimates, confidence intervals and p-values not attributed to the sponsor are independently calculated from the posted summary statistics.

22. Record Summary

NCT04399980 is a completed randomized, quadruple-masked, parallel-group Phase 2 study sponsored by The Cleveland Clinic. It enrolled 40 participants and compared a mavrilimumab treatment infusion with a placebo infusion in severe COVID-19 pneumonia and systemic hyper-inflammation. The listed study start was 2020-05-20, and the primary completion date was 2021-04-23.

The primary outcome was the number of participants alive and off oxygen at Day 14. The registry also lists Day 28 outcomes for being alive and without respiratory failure and for mortality, along with serious adverse event summaries. The primary comparison is imprecise, with a confidence interval that includes zero. Detailed statistical plans, missing-data handling, analysis populations, and multiplicity procedures are not reported in the registry record.