COVID-19 Study Assessing the Efficacy and Safety of Anti-Spike SARS CoV-2 Monoclonal Antibodies for Prevention of SARS CoV-2 Infection Asymptomatic in Healthy Adults and Adolescents Who Are Household Contacts to an Individual With a Positive SARS-CoV-2 RT-PCR Assay: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for COVID-19 Study Assessing the Efficacy and Safety of Anti-Spike SARS CoV-2 Monoclonal Antibodies for Prevention of SARS CoV-2 Infection Asymptomatic in Healthy Adults and Adolescents Who Are Household Contacts to an Individual With a Positive SARS-CoV-2 RT-PCR Assay (NCT04452318), a randomized phase 3 study. The primary outcome is Cohort A: Percentage of Participants Who Have a Symptomatic RT-qPCR Confirmed SARS-CoV-2 Infection (Broad-term) During the EAP.

2. Clinical Question

The primary outcome was not simply any positive test. It required a positive central laboratory RT-qPCR result from a nasopharyngeal, nasal or saliva sample, together with signs and symptoms whose onset occurred within 14 days of a positive RT-qPCR result during the EAP. The registry gives the timeframe as up to 1 month.

The study also included Cohort A1, Cohort B and Cohort B1 for other stated objectives, including safety and tolerability. The primary efficacy result discussed in the registry is specifically for Cohort A and should not be treated as an estimate for every cohort or for every person described by the broad trial title. The registry record does not report further detail in the primary-population description about the household index case or contact’s exposure circumstances.

3. Trial Design

The registry identifies this as a Phase 3, randomized, double-blind, placebo-controlled, parallel-group study with quadruple masking. The trial evaluated REGN10933 + REGN10987 against placebo. Its title and summary place the research question in household contacts of individuals infected with SARS-CoV-2. The registry lists healthy participants as the condition and reports enrollment of 3303.

The primary efficacy analysis was for Cohort A, not the entire enrollment listed for the study. Its stated population was the seronegative modified full analysis set: randomized participants aged 12 years and older who were laboratory-confirmed negative for SARS-CoV-2 and had negative antibody serostatus at baseline. The primary endpoint concerned symptomatic infection confirmed by RT-qPCR during the EAP, with a registry timeframe of up to 1 month. The registry also states safety and tolerability objectives covering Cohort A and Cohort A1, and efficacy and safety objectives for Cohort B and Cohort B1.

The study status is completed. The registry records a start date of 2020-07-13 and a primary completion date of 2021-10-04. These dates describe the registered study, not necessarily the exact observation period for every participant or outcome. Allocation concealment details beyond the listed masking category, the randomization sequence method, and the original protocol’s sample-size justification are not reported in the registry record.

Because cohort-specific populations and endpoints differ, the primary Cohort A estimate should be read in its own defined population and context. The primary result is not a direct estimate for Cohort B, nor is it a general comparison across all participants enrolled in the trial. Cohort-specific details beyond those stated in the registry record are not reported there.

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.

Before assignment: Of 3375 participants screened, 3298 participants were randomized and 3270 participants were treated. 77 participants were screen failures and 28 participants were randomized but not treated.

Only the first 6 of 7 groups are shown.

Overall Study

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: Seronegative modified full analysis set in Cohort A includes randomized subjects aged 12 years and older who are laboratory confirmed negative for SARS-CoV-2 and negative serostatus for SAS-CoV-2 antibodies at baseline

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.

Only the first 6 of 8 groups are shown.

6. Primary Endpoint

The registry lists the primary outcome as Cohort A: Percentage of Participants Who Have a Symptomatic RT-qPCR Confirmed SARS-CoV-2 Infection (Broad-term) During the EAP, measured over Up to 1 month.

The primary endpoint was the percentage of participants with symptomatic SARS-CoV-2 infection confirmed by RT-qPCR using the registry’s broad-term definition. A case required a positive central laboratory RT-qPCR result from a nasopharyngeal, nasal or saliva sample, associated signs and symptoms, and symptom onset within 14 days of a positive RT-qPCR result during the EAP. The stated timeframe was up to 1 month.

This endpoint combines a laboratory finding with clinical symptoms. That distinction matters: a positive test without qualifying symptoms would not meet the primary endpoint definition, although the study listed other outcomes concerning infection regardless of symptoms. The percentage format expresses the proportion of the analysis population meeting the case definition, rather than the duration or severity of illness.

General statistical context: binary endpoints such as whether a participant did or did not meet a defined case criterion can be summarized as event proportions and compared between groups. A clearly specified endpoint helps establish which observations count as events and which do not. Here, the broad-term symptom definition, specimen types, and timing rule define the event being compared. The primary endpoint therefore addresses symptomatic, test-confirmed infection under these criteria; it does not by itself describe all infections, all symptoms, or outcomes beyond the stated assessment period.

The registry says the percentage was estimated by logistic regression. The record does not report additional detail about covariates or model specification for the primary analysis. Those details should not be inferred from the endpoint definition alone.

7. Results Posted in the Registry

• Cohort A: R10933 + R10987: 1.5% (11/753) versus 7.8% (59/752) on control; risk difference -6.4 percentage points (95% CI -8.5 to -4.3), risk ratio 0.19 (0.10–0.35); unadjusted two-proportion z-test p = <0.001.

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.

Cohort A and Cohort B: Percentage of Participants With High Viral Load in Nasopharyngeal (NP) Swab Samples During the EAP

• Endpoint type: Binary: the percentage of participants with the event (counts rebuilt from the posted percentages and group sizes).
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • Cohort A: R10933 + R10987: 1.6% (12/745) versus 11.3% (85/749) on control; risk difference -9.7 percentage points (95% CI -12.2 to -7.3), risk ratio 0.14 (0.08–0.26); unadjusted two-proportion z-test p = <0.001.
  • Cohort B: Placebo of R10933 + R10987: 62.4% (63/101) versus 11.3% (85/749) on control; risk difference 51.0 percentage points (95% CI 41.3 to 60.7), risk ratio 5.50 (4.28–7.06); unadjusted two-proportion z-test p = <0.001.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the proportion with the event is statistically lower for Cohort A: R10933 + R10987 than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.
  • The 95% confidence interval excludes no difference, so the proportion with the event is statistically higher for Cohort B: Placebo of R10933 + R10987 than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Cohort A: Number of Weeks of Symptomatic RT-qPCR Confirmed SARS-CoV-2 Infection (Broad Term) During the EAP

• Endpoint type: Continuous: a mean value per participant, compared as a difference in means.
• Test: Unadjusted two-sample z-test for the difference in means, using the posted standard deviations.
• Result:
  • Cohort A: R10933 + R10987: mean 0.02 versus 0.25 on control; difference -0.23 (95% CI -0.31 to -0.15), standardized effect d = -0.28; z-test p = <0.001.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the mean is statistically lower for Cohort A: R10933 + R10987 than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Cohort A and Cohort B: Number of Weeks of High Viral Load in NP Swab Samples During the EAP

• Endpoint type: Continuous: a mean value per participant, compared as a difference in means.
• Test: Unadjusted two-sample z-test for the difference in means, using the posted standard deviations.
• Result:
  • Cohort A: R10933 + R10987: mean 0.02 versus 0.18 on control; difference -0.16 (95% CI -0.20 to -0.12), standardized effect d = -0.39; z-test p = <0.001.
  • Cohort B: Placebo of R10933 + R10987: mean 0.81 versus 0.18 on control; difference 0.63 (95% CI 0.48 to 0.78), standardized effect d = 1.08; z-test p = <0.001.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the mean is statistically lower for Cohort A: R10933 + R10987 than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.
  • The 95% confidence interval excludes no difference, so the mean is statistically higher for Cohort B: Placebo of R10933 + R10987 than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

The posted secondary outcomes extend the primary question by considering high viral load and the duration, in weeks, of symptomatic infection or high viral load. Across the posted measures, the broad pattern in Cohort A is that the antibody group has lower values than placebo for the related infection outcomes. The Cohort B high-viral-load comparisons also show lower values in the antibody group than placebo. These measures offer related but distinct perspectives on laboratory findings and time with an outcome.

The secondary findings are directionally consistent with the primary Cohort A endpoint, but they are not interchangeable with it. The primary endpoint required symptomatic RT-qPCR-confirmed infection under its broad-term definition. High viral load and weeks of an outcome address other features, with their own analysis populations and definitions. Cohort B results likewise concern a separate cohort and should not be treated as a direct extension of the Cohort A primary estimate.

The registry lists 56 outcome measures and 35 analyses, and 53 secondary outcomes are posted. Multiple outcomes create a multiplicity issue: when many statistical comparisons are examined, the chance of at least one apparently notable result can rise if each is interpreted without accounting for the full set of tests. The registry record does not report whether or how multiplicity was controlled for these secondary analyses.

For that reason, unadjusted secondary results generally carry less confirmatory weight than a clearly identified primary comparison. They can support description and generate questions, but their p-values should not be treated as if each arose from a single prespecified test. The findings should be interpreted alongside the primary result, outcome definitions, cohort, analysis method and the registry’s limited reporting about multiplicity. No inference about unposted secondary results is warranted.

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

• Cohort A and Cohort B: Percentage of Participants With High Viral Load in Nasopharyngeal (NP) Swab Samples During the EAP
• Cohort A: Number of Weeks of Symptomatic RT-qPCR Confirmed SARS-CoV-2 Infection (Broad Term) During the EAP
• Cohort A and Cohort B: Number of Weeks of High Viral Load in NP Swab Samples During the EAP
• Cohort A: Number of Weeks of RT-qPCR Confirmed SARS-CoV-2 Infection (Regardless of Symptoms) During the EAP
• Cohort A: Percentage of Participants Who Have a RT-qPCR Confirmed SARS-CoV-2 Infection (Regardless of Symptoms) During the EAP
• Cohort A: Percentage of Participants in Placebo Group With a RT-qPCR Confirmed SARS-CoV-2 Infection During the EAP With an Index Case Participating in Study R10933-10987-COV-2067 (NCT04425629)
• Cohort A: Percentage of Participants With a Symptomatic RT-qPCR Confirmed SARS-CoV-2 Infection (CDC Definition) During the EAP
• Cohort A: Number of Weeks of Symptomatic RT-qPCR-confirmed SARS-CoV-2 Infection (CDC Definition) During the EAP

9. Statistical Interpretation

10. Statistical Methods Explained

The registry reports logistic regression for the primary endpoint and gives an odds ratio as its effect measure. The posted summary also permits an unadjusted comparison of event proportions. These approaches answer related but different statistical questions, so their estimates should be distinguished rather than treated as duplicate calculations.

What does the primary odds ratio describe?

General statistical explanation: odds compare the chance of an event with the chance of no event. An odds ratio compares those odds between groups. An odds ratio below 1 indicates lower odds in the group named in the comparison than in its reference group; it is not itself a percentage-point difference and is not a risk ratio. When an outcome is not rare, odds ratios can be farther from 1 than corresponding risk ratios. The reported primary odds ratio is 0.17, with a 95% confidence interval from 0.090 to 0.332.

The registry identifies logistic regression as the method for estimating the primary percentage and reports a logistic-regression odds ratio. It does not report the model’s covariates, any stratification factors, or a full specification. The reported estimate should therefore be described as the registry’s logistic-regression result without guessing which adjustments were used.

What does the unadjusted recalculation add?

The recalculated risk difference uses the posted event counts and denominators to compare the unadjusted proportions. It is -0.064, with a 95% confidence interval from -0.085 to -0.043. General statistical explanation: a risk difference is an absolute contrast on the probability scale. Its sign depends on the order of groups in the subtraction; a negative value in this comparison corresponds to a lower event proportion in the antibody group than in placebo. The recalculation is not a replacement for the sponsor’s regression analysis, which remains the registry’s reported analysis.

The recalculated p-value is p < 0.001. The sponsor’s reported p-value is p < 0.001. These values arise from different analysis approaches and should be reported with their respective methods. A p-value describes how compatible data are with a specified null model under the assumptions of the test; it does not give the probability that the null hypothesis is true, measure the size of an effect, or show the probability that results will repeat.

Why use a confidence interval?

General statistical explanation: a confidence interval gives a range of estimates compatible with the data and the method used, at the stated confidence level. It communicates uncertainty more directly than a single point estimate. Its width depends on factors such as the amount of information and variability. The interval does not ensure that the true effect lies within it, and it does not represent a range of individual participant outcomes.

The registry’s primary 95% confidence interval applies to its odds-ratio analysis. The recalculated interval applies to the unadjusted risk difference. Since these intervals concern different effect measures and methods, comparing their endpoints as if they were the same quantity would be misleading.

Why are binary outcomes treated differently from duration outcomes?

The primary outcome is binary at the participant level: the participant either met the endpoint definition or did not. Secondary outcomes include percentages with high viral load and measures of weeks. The registry reports a stratified Wilcoxon rank sum test for the weeks outcomes, rather than a logistic regression. General statistical explanation: rank-based tests compare the ordering of observations and can be useful when a distribution does not fit the assumptions of a simple normal-theory comparison. A test result does not, by itself, supply a mean difference or confidence interval unless the analysis reports one.

The registry also posts group means and standard deviations for certain weeks outcomes, from which an unadjusted mean difference was recalculated. The sponsor’s reported analysis for these measures is a stratified Wilcoxon rank sum test. The recalculated mean difference is a simple summary-scale comparison and does not reproduce that rank-based test. The tests target different aspects of the data and may have different assumptions.

General statistical context: duration data can be skewed or concentrated near zero, and a mean can be influenced by larger observations. Rank methods do not require the same normal-distribution assumption as a t-test, but their interpretation depends on the distributions being compared and on the test’s implementation. The registry record does not report enough detail to assess every modeling assumption or the exact stratification procedure.

How should significance and analysis populations be understood?

Statistical significance is not a measure of clinical importance. The effect estimate, interval, endpoint definition, study population and assessment window all matter. The primary population is specifically described as seronegative and laboratory-negative at baseline, and the endpoint requires symptomatic infection confirmed by RT-qPCR. Results should not be generalized automatically to populations or endpoints that differ from those conditions.

The record reports randomized participants in the primary population definition, but the detailed procedures for missing observations, exclusions after randomization, and analysis-set construction are not reported in the registry record. The interpretation should not assume a particular imputation method, complete-case rule, or treatment of participants without endpoint data.

11. Confidence Intervals and Precision

Precision is conveyed by the range of estimates that remains compatible with the data and the analysis method, as represented by a confidence interval. The sponsor’s primary odds-ratio estimate is 0.17, and its 95% confidence interval runs from 0.090 to 0.332. The interval lies below 1, the null value for an odds ratio. This indicates that the reported analysis is compatible with lower odds in the antibody group than in placebo, while still allowing a range of possible effect sizes.

The unadjusted recalculated risk difference is -0.064, with a 95% confidence interval from -0.085 to -0.043. This interval is on the absolute difference scale, not the odds-ratio scale. It therefore describes uncertainty about a different estimand. General statistical explanation: a confidence interval’s width reflects sampling uncertainty under the analysis method; it is not a guarantee about the true value and does not describe the spread of individual outcomes.

The registered primary result and the recalculated result are both statistically precise enough to distinguish the observed comparison from their respective null values under the stated analyses. That does not remove uncertainty about the exact magnitude, nor does it resolve limitations in generalizability, endpoint scope or reporting of missing-data procedures. The confidence interval also does not account for every possible source of uncertainty, such as unreported model choices or departures from assumptions.

For secondary outcomes, the registry reports different methods and in some cases only a p-value rather than a model-based effect interval. Precision should therefore be assessed from the estimate and interval actually reported for each outcome, not inferred from a p-value alone. When no interval is reported, the registry record does not supply that additional measure of uncertainty.

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 illustrations are planning calculations based on the observed effect, not the original trial design. For non-time-to-event comparisons, the displayed quantities are participants per arm; the time-to-event convention of total events does not apply to these listed endpoints. The calculation assumptions are a two-sided significance level of 0.05, equal allocation between two arms, and a normal approximation. The effect to detect is a stated fraction of the observed effect.

The 80% power column gives the illustrative number of participants per arm associated with an 80% chance of detecting the specified effect under those planning assumptions, if that effect and the assumptions hold. The 90% power column gives the corresponding illustration for a 90% chance. These are planning probabilities under a statistical model, not guarantees for a future study. This is not observed power.

Rows for 100%, 75% and 50% of the observed effect represent progressively smaller target effects. Smaller effects generally require more participants to detect, all else equal, which is why the planning illustrations increase as the assumed fraction of the observed effect decreases. The values are tied to the particular observed summary effect and should not be treated as a recommendation or as a reconstruction of the trial’s enrollment plan.

The registry record does not report the original design’s sample-size assumptions or justification. The planning illustration therefore cannot establish whether the original enrollment target was chosen using these assumptions, and it should not be used to infer the trial’s original power calculation.

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 = 0.078; treatment proportion p₁ = 0.015; average p̄ = 0.047.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.2979 = 0.5838.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.2944 = 0.2478.
4. n = (0.5838 + 0.2478)² / (-0.0638)² = 169.7, rounded up to 170 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 <- 0.0784574468085106                          # control proportion
p1 <- 0.0146082337317397                          # 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(4452318)
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 = 170 (80% power), 227 (90% power)
 75% of observed effect: n per arm = 352 (80% power), 471 (90% power)
 50% of observed effect: n per arm = 902 (80% power), 1,206 (90% power)
power.prop.test: 169.7 
Simulated power at n = 170 per arm: 0.843 (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. Analyses Reported by the Sponsor

The registry’s own primary analysis is a logistic regression comparing Cohort A’s REGN10933 + REGN10987 group with placebo. It reports an odds ratio of 0.17, a 95% confidence interval from 0.090 to 0.332, and p < 0.001. This is the sponsor-reported analysis and is the main model-based result to use when describing the registry’s primary comparison.

The recalculated result uses the posted event counts and denominators to estimate an unadjusted risk difference of -0.064, with a 95% confidence interval from -0.085 to -0.043 and p < 0.001. It is a straightforward comparison of proportions, not a reproduction of the logistic regression. The two estimates are on different scales: an odds ratio compares odds, while a risk difference compares event proportions directly.

The registry’s sponsor analysis and recalculation both indicate the same direction for the primary comparison, but they should not be merged into one estimate. The recalculation is a check using posted summaries; it does not include any model adjustments that may have been used by the sponsor. The registry record does not give enough detail to reconstruct the full regression model. The record does not flag a discrepancy between the sponsor analysis and the recalculation, so no discrepancy qualification applies.

For secondary outcomes, sponsor analyses also include stratified Wilcoxon rank sum tests. Those are not the same as recalculated mean differences or unadjusted comparisons. The analysis method associated with each estimate should remain clear when interpreting or citing the result.

15. Multiplicity, Missing Data and Analysis Populations

The registry lists multiple outcome measures and analyses, including the primary symptomatic infection endpoint and secondary outcomes concerning infection, viral load and weeks with outcomes. When many tests are considered, multiplicity matters because repeated testing can increase the chance of observing nominally small p-values even if none of the underlying effects is real. General statistical explanation: multiplicity procedures can control error across a family of comparisons, but the appropriate family and procedure depend on the analysis plan.

Whether a multiplicity adjustment or hierarchy was used for the secondary outcomes is not reported in the registry record. The registry record also does not report a complete statistical analysis plan, details of interim analyses, or the sample-size justification. These omissions limit the ability to judge which secondary tests were confirmatory, how their interpretation was prioritized, or whether results were adjusted for multiple testing. No interim-analysis procedure should be assumed.

The primary population is described as randomized participants aged 12 years and older in Cohort A who were laboratory-confirmed negative for SARS-CoV-2 and antibody-negative at baseline. However, detailed information about the number of participants with missing primary endpoint data, reasons for missingness, exclusions from the analysis set, and methods for handling missing observations is not reported in the registry record. The record does not state whether missing values were imputed or whether a particular missing-data assumption was used.

These are reporting limitations, not proof that a specific method was absent from the full trial documentation. They mean that the posted record alone is insufficient to reconstruct every analytic decision. Results should therefore be interpreted using the endpoint and population definitions that are reported, without assuming an unreported multiplicity correction, missing-data strategy, covariate set or interim procedure.

16. 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 5% reporting threshold.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: covid-19 (1.2%), covid-19 pneumonia (1.2%), road traffic accident (0.6%), pancreatitis acute (0.6%), appendicitis (0.1%).

The registry posts serious adverse event counts separately for Cohort A, Cohort A1, Cohort B and undetermined groups. For Cohort A, the placebo group had 23 serious adverse events among 1428 at risk, and the REGN10933 + REGN10987 group had 24 among 1439 at risk. The posted death counts were 2 and 3, respectively. These counts are descriptive registry results; they do not establish that events were caused by study treatment or that groups are directly comparable for every safety question.

The record also lists the most frequent serious-event terms and their percentages: COVID-19 and COVID-19 pneumonia at 1.2% each, road traffic accident and acute pancreatitis at 0.6% each, appendicitis and suicidal ideation at 0.1% each. These are registry-listed terms; the posted summary does not provide enough detail to attribute individual events to treatment or determine their clinical context.

Other adverse events were also recorded, but their counts should not be interpreted as a treatment effect without considering definitions, observation, and analysis methods. Serious adverse event summaries are not a substitute for a full safety analysis. Follow-up details, event adjudication procedures and causal assessments are not reported in the registry record.

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

The registry’s primary comparison shows a lower percentage meeting the defined symptomatic RT-qPCR-confirmed infection endpoint in Cohort A’s REGN10933 + REGN10987 group than in the placebo group during the stated assessment period. The sponsor’s logistic-regression estimate and the unadjusted recalculation both point in that direction. Their intervals communicate uncertainty about the magnitude, and their different effect measures should remain distinct.

The result does not mean that every participant in the antibody group avoided infection, or that every participant in the placebo group experienced the endpoint. Group-level percentages summarize the observed analysis populations. They do not predict an individual participant’s outcome. Nor does the primary endpoint encompass all SARS-CoV-2 infections: it required a positive central laboratory RT-qPCR result together with associated symptoms and a specified symptom-onset window.

The findings are specific to Cohort A’s seronegative modified full analysis set, the registered intervention and placebo comparison, and the timeframe of up to 1 month. They should not automatically be generalized to other populations, cohorts, interventions, follow-up periods or endpoint definitions. Cohort B and the secondary outcomes address different questions.

Statistical evidence is not the same as proof of a mechanism or a complete account of benefits and harms. The posted primary result does not answer every safety question, describe every outcome that could matter, or resolve limitations in reporting about missing data and multiplicity. Serious adverse event counts are descriptive and do not establish causation. These findings are an interpretation of the registry record, not medical advice or a recommendation about treatment.

18. Why This Trial Matters Statistically

This trial is statistically instructive because it places a clearly defined binary endpoint beside both a model-based comparison and an unadjusted recalculation. The sponsor’s logistic regression reports an odds ratio, while the recalculation expresses the comparison as a risk difference. Understanding that those effect measures answer related but nonidentical questions helps prevent a common interpretive error: treating a relative odds comparison as though it were an absolute percentage-point change.

It also illustrates why endpoints need careful definition. The primary outcome was not simply a positive test; it required symptoms associated with a central laboratory RT-qPCR result and a specified timing relation. That definition determines which participants count as events and sets the scope of the conclusion. Other outcomes, such as high viral load or weeks with an outcome, add context but answer different questions.

The record also makes visible a distinction between primary and secondary evidence. A large collection of outcomes can enrich interpretation, but without reported multiplicity procedures the secondary p-values need more cautious treatment. Transparent reporting of analysis populations, model details, missing-data methods and multiplicity plans is important for evaluating how firmly results support claims.

Finally, planning calculations based on an observed effect are not the same as the original design rationale or observed power. Keeping these concepts separate prevents retrospective calculations from being mistaken for prospective design choices. Together, these issues make the study a useful example of reading estimates, intervals, tests and registry reporting as parts of a statistical argument rather than as isolated numbers.

19. 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.
• Participant counts were reconstructed from posted percentages and arm sizes, so small rounding differences are possible.

The primary interpretation is limited to Cohort A’s described seronegative modified full analysis set and to the symptomatic, RT-qPCR-confirmed endpoint. The registry’s broad title mentions healthy adults and adolescents who were household contacts, but the primary-population description is more specific: randomized participants aged 12 years and older who tested negative for SARS-CoV-2 and had negative antibody serostatus at baseline. The result should not be generalized automatically beyond that population.

The endpoint was assessed during the EAP, with a registry timeframe of up to 1 month. It required symptoms linked to a positive test under a stated timing rule, so it does not represent all infection regardless of symptoms or outcomes over longer periods. The registry record does not provide further endpoint detail beyond the stated definition and timeframe.

The registry does not report the complete primary regression specification, including its covariates, and does not provide enough information to reconstruct every analysis decision. Missing-data handling, detailed analysis-set construction, multiplicity procedures and interim analyses are not reported in the registry record. The original sample-size justification is also not reported. These omissions constrain independent assessment of the complete analytic plan.

The posted secondary outcomes span several measures and cohorts. Their interpretation is limited by differing definitions and analysis methods, and by the lack of reported multiplicity handling. Secondary findings should not be pooled conceptually with the primary result as though they were the same endpoint.

Safety summaries are counts and listed event terms. The registry record does not establish causality for those events or supply all details needed for a comprehensive safety assessment. The posted results are therefore informative within their stated scope but do not answer every question about longer-term outcomes, other populations or the full balance of outcomes.

20. Related Tutorials

21. Related Statistical Calculators

22. Sources

• ClinicalTrials.gov: COVID-19 Study Assessing the Efficacy and Safety of Anti-Spike SARS CoV-2 Monoclonal Antibodies for Prevention of SARS CoV-2 Infection Asymptomatic in Healthy Adults and Adolescents Who Are Household Contacts to an Individual With a Positive SARS-CoV-2 RT-PCR Assay — NCT04452318.
• Registry record last updated 2023-05-15; status last verified 2023-04.
• Results first posted 2023-05-15.
• 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.

23. Record Summary

NCT04452318 is a completed Phase 3 randomized, double-blind, placebo-controlled, parallel-group study of REGN10933 + REGN10987 versus placebo. The registry lists enrollment of 3303 and a study start of 2020-07-13, with primary completion on 2021-10-04.

The primary result concerns Cohort A’s seronegative modified full analysis set and the percentage with symptomatic, broad-term RT-qPCR-confirmed SARS-CoV-2 infection during the EAP, with a timeframe of up to 1 month. The sponsor reported a logistic-regression odds ratio of 0.17 (95% CI 0.090 to 0.332; p < 0.001). The unadjusted recalculation gives a risk difference of -0.064 (95% CI -0.085 to -0.043; p < 0.001).

Secondary outcomes concern viral load and outcome duration across cohorts. The registry record does not report several details needed to reconstruct the full analysis plan, including multiplicity handling, missing-data procedures and the original sample-size justification.