Increasing Influenza and Tdap Vaccination of Pregnant Women: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Increasing Influenza and Tdap Vaccination of Pregnant Women (NCT04444518), a randomized na study. The primary outcome is Rate of Influenza Vaccination.

2. Clinical Question

3. Trial Design

The VAX-MOM study is registered as a randomized, parallel-group trial with no masking. Its listed interventions are VAX-MOM Intervention and Standard of Care. The record gives an enrollment of 12760 and identifies the study status as completed. The recorded start date is 2019-07-01, and the primary completion date is 2022-06-30. Phase is listed as NA.

The trial’s stated aim was practice change in obstetricians’ offices, with an overall goal related to reducing morbidity and mortality from influenza and pertussis infections. That aim provides context, but the reported primary outcome addressed influenza vaccination during pregnancy, not infection, illness, hospitalization, or mortality. The registry lists two outcome measures and no posted secondary outcomes.

For the primary outcome, the reported count and denominator are 2382 of 4609 in the VAX-MOM Intervention arm and 2496 of 5294 in the Standard of Care arm. These are outcome-specific arm figures; the record does not explain how they relate to overall enrollment, or identify whether all enrolled participants contributed to this outcome. The intervention and control arm descriptions contain no further detail. The registry also does not report the randomization procedure, allocation concealment, site-level allocation details, or whether additional procedures were used to limit bias. The comparison is therefore best read as a randomized parallel-arm comparison whose practical implementation and analysis details are not fully described 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.

Overall Study

Analysis population for the primary outcome, as defined in the registry: Pregnant during flu season

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 Rate of Influenza Vaccination, measured over 10/1/2021-6/30/2022 (intervention flu season).

The primary endpoint is the rate of influenza vaccination during pregnancy among people who were pregnant during flu season. Vaccination status was to be obtained from patient electronic health records. The registry gives the measurement timeframe as 10/1/2021-6/30/2022 (intervention flu season). The outcome is recorded as participants, with the posted arm values representing counts of participants with the event and the corresponding outcome denominators.

Vaccination is a binary outcome for this comparison: a participant is counted as having or not having a recorded influenza vaccination during the specified period. A rate allows the proportion of participants with a recorded vaccination to be compared between arms. In this registry record, the reported rates are 0.517 for VAX-MOM Intervention and 0.471 for Standard of Care; the reported risk difference is 0.045.

General explanation: a process measure such as vaccination uptake describes whether a specified action was recorded, rather than directly measuring infection or a health outcome. It can address whether the randomized groups differed in the targeted practice during the defined period. The registry does not report the precise rules for qualifying vaccination records, the handling of vaccinations outside the electronic record, or how participants with incomplete records were classified. It also does not give further details about the denominator definition beyond the posted arm counts.

7. Results Posted in the Registry

• VAX-MOM Intervention: 51.7% (2382/4609) versus 47.1% (2496/5294) on control; risk difference 4.5 percentage points (95% CI 2.6 to 6.5), risk ratio 1.10 (1.05–1.14); unadjusted two-proportion z-test p = <0.001.

8. Statistical Interpretation

9. Statistical Methods Explained

What does the risk difference describe?

General statistical explanation: for a binary outcome, the risk difference compares the proportion with the event in one group with the proportion in another. It is an absolute measure, expressed on the same proportion scale as the underlying rates. Here, the registry reports rates of 0.517 in VAX-MOM Intervention and 0.471 in Standard of Care, with a risk difference of 0.045. The direction is defined by the comparison as intervention versus standard care. A positive estimate indicates that the reported rate is higher in the intervention arm on this scale. The magnitude is interpreted as a difference in proportions, not as a percentage change.

The posted arm counts make the binary endpoint concrete: 2382 of 4609 in the VAX-MOM Intervention arm and 2496 of 5294 in the Standard of Care arm were counted as having the vaccination event. These are the outcome counts and denominators in the registry record. The record does not explain whether any outcome records were excluded or how missing electronic-health-record information was handled, so the counts should not be taken as evidence that all enrolled participants had complete outcome data.

How do the relative measures differ?

General statistical explanation: a risk ratio divides one group’s event rate by the other group’s event rate. A value of 1 represents equal rates; values above or below 1 indicate a relative contrast in the direction determined by the group ordering. The registry reports a risk ratio of 1.096, with a 95% confidence interval from 1.053 to 1.141. An odds ratio compares odds rather than probabilities. It is a different relative measure and may be less intuitive when event rates are not rare. The reported odds ratio is 1.199, with a 95% confidence interval from 1.108 to 1.298. These relative estimates do not replace the absolute risk difference; each gives a different description of the same posted binary outcome comparison.

What does the p-value say?

General statistical explanation: a p-value is calculated under a specified statistical model and a null hypothesis, often that there is no difference between groups. It describes how unusual data at least as incompatible with that null would be if the null and model assumptions held. It is not the probability that the null is true, nor the probability that the observed result arose by chance in an unrestricted sense. The registry reports an unadjusted p-value of p < 0.001 for the comparison. The specific test, its assumptions, and the calculation procedure are not reported in the registry record. The p-value should therefore be read alongside the effect estimate and confidence interval, rather than as a measure of the size or practical importance of the difference.

What role does randomization play?

General statistical explanation: random assignment is intended to make treatment groups comparable, on average, with respect to both measured and unmeasured factors before the intervention. It can support a causal interpretation when allocation is implemented as intended, outcomes are appropriately measured, and follow-up and analysis preserve the randomized comparison. The record identifies the study as randomized and parallel, but does not report the randomization sequence, concealment, unit of allocation, or any adjustments. The intervention was aimed at practice change in obstetricians’ offices, but the record does not specify whether assignment occurred at the practice or participant level. No particular allocation method should be inferred.

The analysis population is also not described. The registry does not state whether the comparison follows an intention-to-treat approach, is restricted to participants with complete outcome records, or uses another population definition. Without those details, the posted arm counts and estimates can be described, but the precise population represented by the analysis cannot be determined from the registry record alone.

What statistical details remain unspecified?

The posted results include an unadjusted comparison, effect estimates, confidence limits, and a p-value. The registry record does not report the statistical test, the confidence-interval method, covariates, handling of clustering, or whether the estimate accounts for the trial’s practice-change setting. It also does not describe missing-data procedures, protocol deviations, or interim analyses. These omissions do not change the values posted, but they limit independent assessment of how the estimates were generated and which assumptions were used.

General statistical explanation: over-dispersion matters when observed variability is larger than a model assumes. In settings with grouping, such as participants associated with shared practices, outcomes can be more alike within a group than across groups. Treating all observations as fully independent can then make uncertainty estimates too narrow. Whether this issue arose, and whether the reported analysis addressed any grouping, is not reported in the registry record. This is a general methodological consideration, not a claim that the posted analysis was over-dispersed or inadequately adjusted.

The record reports two outcome measures, no posted secondary outcomes, and zero posted analyses. It does not report a complete statistical analysis plan, so the number of planned comparisons, any prespecified hierarchy, and the approach to multiple testing are unknown. The comparison is explicitly described as unadjusted. No adjustment for multiple comparisons is reported. These limitations are important when interpreting a single p-value in the context of a trial that may have considered other outcomes or analyses not posted in the registry.

10. Confidence Intervals and Precision

The risk-difference estimate is 0.045, and its 95% confidence interval runs from 0.026 to 0.065. This interval describes the statistical precision of the estimated absolute difference under the analysis used. It does not show where 95% of individual vaccination outcomes fall, and it does not assign a 95% probability to the fixed true difference. In repeated use of a valid method under its assumptions, confidence intervals constructed in this way are designed to cover the underlying parameter at the stated rate.

The confidence limits are both on the positive side of zero. That feature is consistent with the posted unadjusted p-value of p < 0.001 for a test of no difference. The interval still allows a range of possible underlying differences; its limits should not be collapsed into a single certain value. The point estimate is the central summary of this comparison, while the interval communicates uncertainty around it.

The relative estimates have their own intervals: the risk ratio is 1.096 with limits from 1.053 to 1.141, and the odds ratio is 1.199 with limits from 1.108 to 1.298. Their scales differ from the absolute risk difference, so interval widths should be considered on the scale of each measure. The registry does not report the methods used to construct these intervals, whether clustering or covariates were incorporated, or whether they were adjusted for multiple comparisons. Precision therefore describes the posted estimates and intervals, not every possible source of uncertainty about measurement, trial conduct, or analysis.

11. 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 own design calculation. For this binary outcome, the figures are participants per arm, not total events. The illustration assumes a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. It considers planning for 100%, 75%, and 50% of the observed effect. The observed risk difference used as the reference is 0.045.

The 80% power column describes the participant count per arm that the illustration estimates would be needed to detect the stated effect fraction with 80% power under those assumptions. The 90% power column gives the corresponding per-arm count for 90% power. In plain terms, power is the chance of detecting an effect of the specified size under the assumed model if that effect is the true one. The larger power target generally calls for more participants than the lower power target when the other assumptions are held fixed.

The rows for smaller fractions of the observed effect require more participants per arm. This is because smaller differences are harder to distinguish from sampling variability. The curve and figures are planning aids for hypothetical future designs; they are not observed power for the completed trial. The registry does not report the original sample-size justification or the assumptions used in the trial’s own design.

12. 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 = 0.471; treatment proportion p₁ = 0.517; average p̄ = 0.494.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.7071 = 1.3858.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.7063 = 0.5945.
4. n = (1.3858 + 0.5945)² / (0.0453)² = 1,907.8, rounded up to 1,908 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 <- 0.471477143936532                          # control proportion
p1 <- 0.516814927316121                          # 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(4444518)
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 = 1,908 (80% power), 2,554 (90% power)
 75% of observed effect: n per arm = 3,392 (80% power), 4,540 (90% power)
 50% of observed effect: n per arm = 7,627 (80% power), 10,210 (90% power)
power.prop.test: 1907.8 
Simulated power at n = 1,908 per arm: 0.802 (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.

13. Multiplicity, Missing Data and Analysis Populations

The registry lists two outcome measures, but it reports no posted secondary outcomes and zero posted analyses. The primary outcome is the rate of influenza vaccination during pregnancy. The available result is an unadjusted comparison, with an unadjusted p-value. The registry record does not report whether the study had other planned outcomes, subgroup analyses, interim analyses, or additional comparisons that were not posted.

Multiplicity arises when multiple hypotheses are tested or multiple outcomes are examined. When many tests are conducted, the chance of at least one apparently unusual result can increase if each test is interpreted without accounting for the number of tests. General statistical approaches can address this issue through prespecified testing sequences or adjusted thresholds, but no such strategy is reported in the registry record. The number of posted outcomes alone does not establish how many tests were planned or performed.

Missing-data handling is also not reported. The primary outcome was to be obtained from electronic health records, but the record does not explain how incomplete records, unavailable vaccination information, or participants without a recorded status were handled. It does not state whether missing outcomes were excluded, imputed, or classified in another way. The posted denominators therefore cannot be interpreted as proof that outcome data were complete for every enrolled participant.

The analysis population is not identified as intention-to-treat, per-protocol, or another defined population. The registry does not report whether participants were analyzed according to their randomized assignment, how protocol deviations were treated, or whether the outcome analysis included only a subset. It also does not describe covariate adjustment, clustering, or sensitivity analyses. These points matter because they determine what population the estimates represent and how robust they are to incomplete data or departures from the planned procedures. None of these unreported details can be inferred from the arm-level counts or the p-value.

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

The posted result describes a difference in recorded influenza vaccination rates during pregnancy between the two randomized arms in the stated intervention flu season. The registry reports a rate of 0.517 for VAX-MOM Intervention and 0.471 for Standard of Care. The estimated risk difference is 0.045, with a 95% confidence interval from 0.026 to 0.065. The posted unadjusted p-value is p < 0.001. These figures show the direction and statistical precision of the reported comparison; they do not explain the processes that produced it.

The outcome is vaccination status recorded in electronic health records. It is not a direct measure of influenza infection, severity of illness, hospital admission, death, or other clinical outcomes. Although the study summary describes broader public-health aims related to influenza and pertussis, the posted primary result concerns influenza vaccination. No pertussis-vaccination result or infection outcome is posted as a secondary result in the registry record.

A statistically clear difference does not, by itself, establish how well the intervention was delivered, whether the difference would persist in other settings or seasons, or whether it translated into changes in health outcomes. The intervention is described as a behavioral strategy intended to encourage practice change, but the registry does not provide enough detail to identify the component or components associated with the comparison. The record also does not report implementation measures, adherence, or effects by subgroup.

The trial is registered as randomized, which is relevant to how an arm comparison may be interpreted. But the allocation procedure, analysis population, missing-data approach, and other analysis details are not reported. As a result, the appropriate scope is the posted arm-level outcome and estimates, rather than a broader claim about all pregnant people, clinical outcomes, or the mechanism of any difference. The result is evidence about this registered comparison and outcome as reported, with important questions about implementation and analysis remaining unanswered.

15. Why This Trial Matters Statistically

This trial addresses a statistical question about a behavior measured in a clinical setting: whether vaccination rates differed between a randomized intervention arm and standard care. The primary endpoint is binary and reported with both arm-level counts and rates. That makes it possible to consider the result in absolute terms through the risk difference and in relative terms through the risk ratio and odds ratio. Comparing these measures helps distinguish the size of an absolute rate contrast from a relative comparison.

The setting also raises issues that matter in biostatistical interpretation. The intervention aimed at practice change in obstetricians’ offices, while the registry does not state whether assignment occurred by participant or practice, nor whether analysis accounted for any grouping. Shared practice environments can affect outcome similarity, so the allocation unit and uncertainty method are relevant design details. Their absence in the registry record is a reason to avoid assuming that an individual-level analysis was used or that clustering was handled in a particular way.

The result also illustrates the distinction between evidence for a difference in a process measure and evidence for a clinical outcome. The registry’s primary result concerns electronic-health-record vaccination status, not infection or morbidity. A precise comparison on one endpoint should not be extended to outcomes that were not posted. Finally, the planning illustration makes clear that required sample size depends on the effect a future study is designed to detect: smaller target effects require more participants. Together, these points make the trial useful for discussing effect scales, uncertainty, endpoint scope, design transparency, and the limits of interpreting a registry result.

16. 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 registry record provides limited detail on how the intervention and comparator were implemented. Both arm descriptions are blank beyond their names, and the interventions are categorized as behavioral. The study summary states that the trial aimed to change practice in obstetricians’ offices, but does not identify the specific activities, how often they were delivered, who delivered them, or whether implementation varied among offices. This limits interpretation of what the comparison represents in practice.

Several design details are also unreported: the randomization method, allocation concealment, the unit of assignment, and whether the analysis accounted for practice-level grouping. No baseline characteristics are posted in the record. The overall enrollment is 12760, while the primary outcome has arm-specific denominators of 4609 and 5294; the registry does not explain the relationship between those figures or the reasons not all enrolled participants appear in the outcome denominators.

The primary endpoint relies on electronic health records. The record does not report validation procedures, handling of records from outside the health system, missing-status rules, or the precise definition of the denominator. The statistical test and confidence-interval method are likewise not identified. No adjustment strategy, sensitivity analysis, or analysis population is described.

Finally, the posted result addresses influenza vaccination during one specified flu-season period. The registry does not report secondary outcomes, including pertussis vaccination or health outcomes, and does not provide further results on implementation or subgroup differences. These limits constrain what can be concluded from the registry record, without changing the values reported for the primary comparison.

17. Related Tutorials

18. Related Statistical Calculators

19. Sources

• ClinicalTrials.gov: Increasing Influenza and Tdap Vaccination of Pregnant Women — NCT04444518.
• Registry record last updated 2025-01-10; status last verified 2024-11.
• Results first posted 2025-01-10.
• 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.

20. Record Summary

The VAX-MOM study is registered as a completed, randomized, parallel-group trial with no masking. It lists VAX-MOM Intervention and Standard of Care and has an enrollment of 12760. Its primary outcome is the rate of influenza vaccination during pregnancy, based on electronic health records during 10/1/2021-6/30/2022.

The posted primary comparison reports 2382 of 4609 participants with the event in the intervention arm and 2496 of 5294 in standard care. The rates are 0.517 and 0.471; the risk difference is 0.045, with a 95% confidence interval from 0.026 to 0.065 and an unadjusted p-value of p < 0.001. The registry lists two outcome measures, no posted secondary outcomes, and zero posted analyses. Several design, analysis, and missing-data details are not reported in the registry record.