The Efficacy of the 2023-2024 Updated COVID-19 Vaccines Against COVID-19 Infection: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for The Efficacy of the 2023-2024 Updated COVID-19 Vaccines Against COVID-19 Infection (NCT06065176), a randomized phase 4 study. The primary outcome is Number of Symptomatic SARS-CoV-2 Infections Between Randomized, Study-vaccinated Participants and the Comparator Group.

2. Clinical Question

The trial also posed a distinct secondary question: whether the number of symptomatic infections differed between participants receiving the protein-subunit vaccine and those receiving the mRNA vaccine. Those arms are not the primary comparison. The primary endpoint combines the two vaccine groups because the registry describes that combined group as the most appropriate representation of the study's primary objective.

The trial record describes a randomized study and lists triple masking. It also describes participants as choosing whether to receive an updated vaccine; those choosing vaccination had a 50/50 chance of receiving either listed vaccine. The registry record does not explain how the decision to enter the vaccinated or non-booster group related to randomization. That distinction matters when interpreting the primary contrast: a randomized comparison between vaccine types is not necessarily the same as random assignment to vaccination versus no vaccination.

The record lists COVID-19, SARS CoV 2 Infection, and upper respiratory conditions among the conditions studied. The endpoint itself is specifically symptomatic SARS-CoV-2 infections. The registry record does not report a more detailed symptom definition or testing case definition, so the clinical question should be understood at the level of the posted endpoint description.

3. Trial Design

This was a phase 4, randomized, triple-masked, parallel-group trial with a posted enrollment of 1188. The registry lists a start date of 2023-11-22, a primary completion date of 2024-09-02, and a completed status. The official title describes a comparison of Novavax and mRNA COVID-19 2023-2024 updated vaccines among adults in the United States.

The study had three groups relevant to the posted primary results: a Novavax vaccine group, a Pfizer mRNA vaccine group, and a non-booster comparison group. The primary analysis combined the two vaccine groups. For the secondary vaccine-type comparison, the posted results separate the protein-subunit and mRNA groups. The registry describes a single dose of each vaccine and gives the formulation and dose volume for each, but those administration details do not establish how treatment assignment was implemented.

Study activities included an online enrollment survey, an in-person enrollment visit, weekly online surveys and COVID-19 tests for 20 weeks, and additional surveys or tests for participants with symptoms or a positive result. The summary also describes surveys in the middle and at the end of the study. The primary endpoint timeframe is 24 weeks after enrollment. The record does not explain the relationship between the weekly activity period and the endpoint timeframe.

Although the record labels the design randomized and triple-masked, it does not report the allocation sequence, concealment procedures, or which participants, investigators, or assessors were masked. It also does not report how participants entered the non-booster group. These details are important for assessing whether the primary groups were comparable at baseline and how much protection the design offers against bias. The trial's original sample-size justification and its prespecified statistical analysis plan are not reported in the registry record.

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: 1188 total participants enrolled.

Overall Study

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: mITT population. Combining both vaccinated arms in a single group most appropriately represents the study's primary objective. The two vaccinated arms are assessed relative to each other in the study's secondary objective.

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 Number of Symptomatic SARS-CoV-2 Infections Between Randomized, Study-vaccinated Participants and the Comparator Group, measured over 24 weeks after enrollment.

The primary endpoint counts participants with symptomatic SARS-CoV-2 infection in the mITT population during the 24 weeks after enrollment. It is a binary participant-level outcome: each participant is counted according to whether a symptomatic infection was recorded. The posted results give 48 events among 278 participants in the non-booster comparator group and 94 among 898 participants in the combined randomized, study-vaccinated group.

This endpoint is useful for expressing the occurrence of symptomatic infection in a way that can be compared between groups. The registry's stated primary objective combines both vaccine groups, while its secondary objective compares the protein-subunit and mRNA vaccine groups with each other. The combined comparison therefore addresses the registry's overall vaccinated-versus-comparator question, whereas the secondary comparison addresses a different question about vaccine type.

Counting participants with an event is not the same as measuring the number of all infection episodes, duration of illness, severity, or time until infection. The posted endpoint title and description refer to the number of participants with symptomatic infections. The registry record does not report a fuller case definition, rules for recurrent infections, or how symptom status was determined. Those details would help readers understand exactly what qualifies as an event and how consistently it was assessed across groups.

In general, a participant-level binary endpoint can be easy to interpret, but its meaning depends on the event definition, follow-up, and completeness of outcome ascertainment. The trial summary describes repeated surveys and tests, but the registry record does not report how missing tests or incomplete follow-up were handled in the primary analysis.

7. Results Posted in the Registry

• Randomized Group: 10.5% (94/898) versus 17.3% (48/278) on control; risk difference -6.8 percentage points (95% CI -11.7 to -1.9), risk ratio 0.61 (0.44–0.84); unadjusted two-proportion z-test p = 0.002.

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 Symptomatic SARS-CoV-2 Infections Between the Protein Subunit and mRNA Vaccine Groups.

• 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:
  • mRNA: 9.3% (42/454) versus 11.7% (52/444) on control; risk difference -2.5 percentage points (95% CI -6.5 to 1.5), risk ratio 0.79 (0.54–1.16); unadjusted two-proportion z-test p = 0.229.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between mRNA and the control. The interval gives the range of effects that remain compatible with the data.

The posted secondary endpoint concerns symptomatic infections in the protein-subunit and mRNA vaccine groups. Its overall pattern points in the same broad direction as the primary result in that the mRNA group has a lower observed event proportion than the protein-subunit group. However, the secondary comparison is substantially less conclusive: the posted interval includes no difference, and the unadjusted result does not establish a vaccine-type difference.

This secondary analysis is related to, but does not test, the same question as the primary analysis. The primary comparison combines vaccine groups and compares them with participants who did not receive a study vaccine. The secondary comparison contrasts the two vaccine types among vaccinated participants. A result from one comparison cannot simply be substituted for the other. The secondary findings therefore neither confirm the primary contrast nor refute it.

The registry lists two outcome measures, while one secondary endpoint is posted in the record. The record also lists no reported analyses for that secondary outcome. The absence of posted analyses does not mean no analysis was conducted; it means the registry record does not provide that information. Readers should distinguish the numerical comparison that is displayed from a complete account of the planned and performed analyses.

Unadjusted secondary results carry less weight because multiple outcomes or comparisons can increase the chance of apparently notable results arising by chance. In general, a prespecified multiplicity strategy can control or contextualize that concern. The registry record does not report whether such a strategy was used, which outcomes were prioritized beyond the stated primary endpoint, or whether the secondary question was powered as a formal confirmatory test. The secondary result is best read as limited evidence about the vaccine-type comparison, with uncertainty that remains unresolved.

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

• Number of Symptomatic SARS-CoV-2 Infections Between the Protein Subunit and mRNA Vaccine Groups.

9. Statistical Interpretation

10. Statistical Methods Explained

What is being compared? The primary endpoint is a count of participants with a symptomatic infection, analyzed as a binary outcome. The primary comparison combines both study-vaccinated groups and contrasts them with the non-booster comparator. The secondary comparison separates the protein-subunit and mRNA groups. Keeping these questions distinct is important: the primary result does not estimate the difference between vaccine types, and the secondary result does not reproduce the primary vaccinated-versus-comparator comparison.

What does a risk difference mean? In general, a risk difference subtracts one group's event proportion from the other group's event proportion. A negative value in the posted primary comparison corresponds to a lower event proportion in the randomized, study-vaccinated group than in the comparator group, given the comparison's stated direction. Its confidence interval describes a range of effect values compatible with the data and the method used to construct the interval. It is not a guarantee that the true value lies within that range.

How do relative measures differ? The results also report a risk ratio and an odds ratio. In general, the risk ratio compares event proportions directly, while the odds ratio compares event odds. Both are relative scales; they can make a result look different from an absolute risk difference because they express the contrast in different units. They are derived from the same group data and should be interpreted alongside, not instead of, the risk difference and confidence interval. The registry record does not report an adjusted model for these comparisons.

What does the p-value contribute? The primary p-value is reported as unadjusted. In general, a p-value measures how incompatible the observed data are with a specified no-difference model, under the assumptions of the analysis. It is not the probability that the null hypothesis is true, does not measure the size or importance of an effect, and does not account for unreported sources of bias. A small p-value cannot repair problems with group formation, outcome measurement, or missing data. A larger p-value does not prove that groups are equivalent.

Why consider the confidence interval as well? The interval gives information about both the direction and precision of the estimated comparison. For the primary result, the posted interval excludes the no-difference value, in keeping with the unadjusted p-value. For the secondary result, the interval includes that value. In general, an interval that includes no difference signals that the data do not distinguish the estimated contrast from no difference at the interval's stated confidence level. It can also include effect sizes in either direction, so the full interval matters more than a binary significant/not-significant label.

What is over-dispersion? Over-dispersion is a general issue in count data: variability may be greater than a statistical model expects. It can make standard errors too small if the model does not account for the extra variability. Here, the posted outcome is a count of participants with an event, and the record does not describe a count model or report a dispersion estimate. The concept is useful for count outcomes, but the registry record does not establish that over-dispersion was present or that a particular correction was used.

What is known about the analysis population? The primary endpoint description names an mITT population and says that combining the vaccinated arms most appropriately represents the primary objective. The registry record does not define mITT, list its inclusion or exclusion criteria, or describe departures from assigned treatment. The posted group sizes and event counts describe the reported comparisons, but do not by themselves explain how participants were selected for each analysis. The analysis population used for the secondary comparison is likewise not described in detail.

What is known about masking and randomization? The design is labeled randomized and triple-masked. In general, random allocation can reduce confounding by distributing measured and unmeasured baseline factors across groups, while masking can reduce bias in care, reporting, or outcome assessment. Those protections depend on implementation. The registry record does not report sequence generation, allocation concealment, the identities of masked parties, or how the non-booster group was formed. The study summary says participants could opt not to receive a vaccine and still take part, so the relationship between that choice and the randomized design needs clarification in a full protocol or analysis report.

What is not reported? The registry record does not report the trial's original sample-size justification, full statistical analysis plan, missing-data procedures, adjusted analyses, or interim analyses. It also does not provide a detailed definition of symptomatic infection or the method used to handle repeat infections. These omissions limit independent assessment of whether the posted analyses follow the planned methods and how robust the estimates are to alternative assumptions.

11. Confidence Intervals and Precision

The confidence interval is central to understanding how precisely the primary comparison is estimated. In general, a confidence interval summarizes the range of values compatible with the observed data under the chosen statistical method. A narrower interval represents greater precision; a wider interval leaves more uncertainty about the size of the contrast. The interval is not a prediction range for individual participants and does not account automatically for design or measurement biases.

For the primary risk difference, the posted confidence interval lies below the no-difference value. This is consistent with the reported unadjusted p-value of 0.002. The interval still allows a range of possible effect sizes, so the point estimate should not be read as the uniquely established effect. The results table presents the exact estimate and interval; the estimate should be read with the event counts, group sizes, and the limitations of the analysis population in view.

For the secondary comparison between protein-subunit and mRNA groups, the confidence interval crosses the no-difference value. The posted p-value is 0.229. This interval is compatible with no difference as well as with differences in either direction, so it does not precisely settle the vaccine-type comparison. The result is not evidence that the vaccines are equivalent; equivalence would require a defined margin and a suitable analysis, neither of which is reported in the registry record.

Precision is affected by the number of participants and events, but also by study design, outcome ascertainment, and missingness. The trial's enrollment figure does not alone determine the precision of each comparison: the primary and secondary analyses use different groups and event counts. The registry record does not report an interval method, adjustments, or sensitivity analyses that would help assess how much the intervals depend on modeling choices.

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 section is a planning illustration based on the observed effect, not the trial's original design calculation. For these binary outcomes, the table gives participants per arm, not total events. The stated assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. Each row uses a stated fraction of the observed effect: 100%, 75%, or 50% of that effect.

The 80% power column gives the estimated participants per arm for a hypothetical study to have an 80% chance of detecting the effect specified in that row, under the listed assumptions. The 90% power column gives the corresponding per-arm planning figure for a 90% chance. These are conditional planning quantities: they depend on the assumed effect and the analysis assumptions, and they are not guarantees that a future study will detect an effect.

As the assumed effect becomes smaller, the table shows that more participants per arm are needed. This is because a smaller separation between groups is harder to distinguish from random variation. The primary comparison's planning illustrations are based on the observed primary effect; the secondary comparison has its own illustrations based on its observed effect. The two sets should not be treated as interchangeable, because the underlying comparisons differ.

The listed figures are not observed power for this completed trial. They do not show the probability that the completed study detected its result, nor do they establish that the study was designed with those sample sizes or assumptions. The trial's own original sample-size justification is not reported in the registry record. A planning curve, if shown alongside the table, should be read as a visual display of these same assumed relationships rather than as another analysis of trial outcomes.

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 = 0.173; treatment proportion p₁ = 0.105; average p̄ = 0.139.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.4888 = 0.9579.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.4864 = 0.4094.
4. n = (0.9579 + 0.4094)² / (-0.0680)² = 404.5, rounded up to 405 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 <- 0.172661870503597                          # control proportion
p1 <- 0.10467706013363                          # 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(6065176)
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 = 405 (80% power), 541 (90% power)
 75% of observed effect: n per arm = 757 (80% power), 1,013 (90% power)
 50% of observed effect: n per arm = 1,785 (80% power), 2,389 (90% power)
power.prop.test: 404.5 
Simulated power at n = 405 per arm: 0.806 (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 record identifies a primary endpoint and lists a secondary endpoint, but it does not report a multiplicity adjustment or a hierarchy for interpreting multiple outcomes. The posted p-values are unadjusted. When several outcomes or comparisons are examined, the chance of at least one result appearing notable by chance can rise. In general, prespecifying a testing order or applying an adjustment can help manage that issue. Without a reported strategy, the secondary result should be interpreted cautiously and the primary endpoint should retain its designated role.

The registry describes two outcome measures and one posted secondary endpoint. It reports zero analyses for the primary endpoint and no reported analyses for the posted secondary outcome, despite including numerical results. The record does not explain this reporting structure, identify every analysis performed, or state whether the posted comparisons were specified before data were examined. The trial's statistical analysis plan is not reported in the registry record.

The primary population is called mITT, but its membership rules are not described. The record does not report whether participants were analyzed according to assignment, receipt of vaccine, completion of follow-up, or another rule. It also does not state how participants with incomplete surveys, missed tests, withdrawal, or other missing endpoint information were handled. Missingness can affect an estimate if participants with unavailable outcomes differ systematically from those with observed outcomes. The registry summary describes regular testing and surveys, but it does not provide completeness or adherence results for those activities.

The record does not report interim analyses, stopping rules, imputation methods, or sensitivity analyses. These are not assumptions that can be inferred from a completed status or from the displayed results. Without these details, readers cannot determine whether the analysis accounted for all planned looks at the data, whether missing outcomes were influential, or whether the reported population and methods followed a prespecified plan. A full protocol or statistical analysis report would be needed to resolve these questions.

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 5% reporting threshold.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: infections due to complications of surgery (0.4%), emergency cesarean section due to premature labor (0.2%), serious injuries due to auto accident (0.2%), hospitalization due to mental health crisis (0.2%).

The safety section lists serious adverse events and deaths separately for the Novavax, Pfizer, and non-boosted groups. The posted serious adverse event counts are 1 in the Novavax group, 5 in the Pfizer group, and 0 in the non-boosted comparison group. The listed death counts are 0 for each group. These are reported counts, not estimates of comparative risk adjusted for follow-up or participant characteristics.

The registry also lists serious-event terms with percentages: infections due to complications of surgery at 0.4%, emergency Cesarean section due to premature labor at 0.2%, serious injuries due to auto accident at 0.2%, and hospitalization due to mental health crisis at 0.2%. These entries describe the posted event terms and percentages. The record does not provide enough detail to determine whether an event was judged related to vaccination, how event terms were coded, or whether the listed terms cover all serious events.

Safety counts should be read in the context of the groups at risk and the reporting period, as well as the study's methods for collecting and classifying events. The registry gives group-specific numbers at risk, but does not report a detailed safety analysis plan, exposure-adjusted rates, or causal attribution. The counts alone cannot establish that an event was caused by a vaccine or that groups had equivalent safety outcomes.

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

The posted primary results show a difference in symptomatic SARS-CoV-2 infections between the combined study-vaccinated group and the non-booster comparator group. The risk-difference interval lies below no difference, and the unadjusted p-value is 0.002. This describes the observed comparison in the registry record; it is not, by itself, proof that vaccination caused the entire difference. The record does not explain fully how participants entered the comparator group or how incomplete outcome data were handled.

The secondary comparison does not establish a difference between protein-subunit and mRNA vaccine groups. Its estimate points toward a lower event proportion in the mRNA group, but the interval includes no difference and the p-value is unadjusted. It should not be interpreted as proof that the vaccine types are the same, either. A lack of a conclusive difference is not evidence of equivalence unless an equivalence question, margin, and analysis are specified.

The results do not establish protection against every SARS-CoV-2 infection, severe disease, or outcomes beyond the stated symptomatic endpoint and follow-up timeframe. The registry record does not report a detailed infection definition or results for all possible clinical outcomes. Nor does it report adjusted analyses that could clarify how measured participant characteristics relate to the comparison.

The trial's randomized and triple-masked labels are relevant design features, but implementation details are not reported. The study summary says participants could decide not to receive an updated vaccine while continuing study activities. Until the relationship between that choice and allocation to the comparator is clear, the primary contrast should be understood as a posted group comparison rather than assumed to be a fully randomized vaccine-versus-no-vaccine contrast.

In short, the data show a primary group difference in the posted analysis and leave uncertainty around both its causal interpretation and the vaccine-type comparison. The tables and intervals communicate the numerical findings; the registry record does not include enough methodological detail to remove these interpretive limits.

17. Why This Trial Matters Statistically

This trial is statistically informative because it separates two questions that are easy to conflate: the contrast between a combined vaccinated group and a non-booster comparator, and the contrast between two vaccine types. The primary endpoint is defined at the participant level, and the registry explicitly states why it combines the vaccinated arms for that objective. The secondary comparison then addresses vaccine type rather than repeating the primary question.

The record also illustrates why a point estimate should be read alongside its uncertainty. The primary comparison's interval excludes no difference, whereas the secondary interval includes it. The unadjusted p-values alone do not capture the difference in precision or the distinct questions being tested. Looking at event counts, group sizes, effect measures, and intervals together gives a more complete account than relying on a threshold label.

There are also important reporting gaps. Randomization and triple masking are listed, but the record does not describe how either was implemented or how participants entered the non-booster group. The mITT population is named without a full definition, and missing-data procedures and multiplicity handling are not reported. These omissions show how design labels and numerical results, though useful, may not be enough for independent assessment of bias and robustness.

Finally, the sample-size illustrations demonstrate how planning depends on the effect assumed: smaller target effects require larger per-arm samples under the stated calculation assumptions. Those illustrations are not evidence about the original design or the completed trial's observed power. Keeping planning calculations separate from trial results is essential for a statistically sound reading of the record.

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.

Several limitations specific to this trial are apparent in the registry record. The study summary says participants could choose not to receive a 2023-2024 updated vaccine and could still participate, while the record also labels the study randomized. It does not explain whether the non-booster group was randomized, self-selected, or formed through another process. That gap limits assessment of comparability in the primary vaccinated-versus-comparator analysis.

The record names triple masking but does not identify who was masked or describe how masking was maintained. It does not report the randomization sequence, concealment, or allocation procedures. The mITT population is not fully defined, and the record does not say how protocol deviations or incomplete follow-up affected inclusion in the analyses.

Outcome ascertainment involved weekly surveys and tests for 20 weeks, with the primary timeframe stated as 24 weeks after enrollment. The record does not explain this timing difference, define symptomatic infection in detail, or describe how repeat infections and missing tests were treated. Without completeness information, readers cannot assess whether event detection was similar across groups.

The posted p-values are unadjusted, and no multiplicity strategy, full analysis plan, or interim-analysis information is reported. The registry does not provide adjusted analyses or sensitivity checks for missing data. It also does not report the original sample-size justification. These limitations do not negate the posted findings, but they restrict how confidently readers can attribute the primary group difference to the intervention or draw conclusions from the secondary comparison.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: The Efficacy of the 2023-2024 Updated COVID-19 Vaccines Against COVID-19 Infection — NCT06065176.
• Registry record last updated 2026-01-28; status last verified 2025-11.
• Results first posted 2025-11-21.
• 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

The registry describes a completed phase 4 randomized, triple-masked, parallel-group trial with enrollment of 1188. It compared a combined group receiving either a 2023-2024 updated Novavax or Pfizer mRNA vaccine with a non-booster comparator for the primary endpoint of symptomatic SARS-CoV-2 infections during the 24 weeks after enrollment. A secondary comparison examined the protein-subunit and mRNA vaccine groups.

The primary posted comparison shows fewer participants with symptomatic infection in the combined vaccinated group than in the comparator group, with a risk-difference confidence interval below no difference and an unadjusted p-value of 0.002. The secondary comparison does not establish a difference between vaccine types. Interpretation is limited by unreported details about comparator-group formation, masking, the mITT definition, missing outcomes, multiplicity, and the original sample-size rationale.