Study Confirming A Human Challenge Model and Investigating The Safety Of VLA1701: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Study Confirming A Human Challenge Model and Investigating The Safety Of VLA1701 (NCT03576183), a randomized phase 2 study. The primary outcome is Percentage of Subjects With Moderate to Severe Diarrhea.

2. Clinical Question

3. Trial Design

This was a randomized, double-blind, parallel-group, placebo-controlled Phase II study. The brief summary describes a single-center vaccination and challenge study intended to confirm a human challenge model using E. coli strain LSN03-016011/A, while also investigating the safety of VLA1701. The registry record lists 34 participants enrolled and a completed study. The listed start date is 2018-06-04 and the primary completion date is 2018-06-26.

Participants were assigned to VLA1701 or placebo. The arm descriptions specify two oral doses about one week apart. The study also used a challenge strain, and the primary population was defined as participants who received two vaccine doses and the challenge dose. The primary outcome was assessed within 120 hours after challenge. The registry describes the primary result as a count of participants with moderate to severe diarrhea, rather than as a continuous measurement.

The posted primary outcome includes 15 participants in each arm, with 7 participants experiencing the outcome in the VLA1701 arm and 11 in the placebo arm. The safety record uses a different at-risk count: 17 in each arm. The registry record does not explain the difference between these denominators, so they should be interpreted according to the outcome each describes rather than combined or treated as interchangeable.

Six secondary outcomes are listed, including disease severity after challenge and adverse-event measures. A disease severity score is reported at 7 days after challenge. The registry record does not report the trial's original sample-size assumptions, detailed allocation procedures, or a formal analysis plan. Those details cannot be inferred from the randomized, double-blind designation alone.

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: Challenge Population = all subjects who received two doses of the vaccine and the challenge dose

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.

Population: Challenge Population = all subjects who received two doses of the vaccine and the challenge dose

6. Primary Endpoint

The registry lists the primary outcome as Percentage of Subjects With Moderate to Severe Diarrhea, measured over 5 days after challenge.

The primary endpoint counts participants with moderate to severe diarrhea within 120 hours of challenge with ETEC strain LSN03-016011/A. The registry defines the analysis population as participants who received two doses of the vaccine and the challenge dose. The outcome is binary at the participant level: each participant either met the stated outcome or did not.

This endpoint is used to summarize a clinically relevant illness outcome following a controlled challenge. In a challenge-model study, an outcome after exposure to the challenge strain can also inform whether the model produces measurable illness under the study conditions. The registry describes the study as designed to confirm a human challenge model and investigate vaccine safety; it does not report additional endpoint-validation criteria or a threshold for deciding that the model was confirmed.

The posted result is 7 participants with moderate to severe diarrhea among 15 in the VLA1701 arm, compared with 11 among 15 in the placebo arm. These counts describe what occurred in the analyzed arms. They do not, by themselves, convey the uncertainty around the comparison, account for possible analysis choices, or establish how well the result would generalize beyond this challenge study. Interpretation therefore depends on the interval estimate, the analysis population, and the limited scale of the trial as well as on the event counts.

7. Results Posted in the Registry

• VLA1701: 46.7% (7/15) versus 73.3% (11/15) on control; risk difference -26.7 percentage points (95% CI -60.4 to 7.1), risk ratio 0.64 (0.34–1.18); unadjusted two-proportion z-test p = 0.136.

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.

Disease Severity Score After Challenge With ETEC Strain LSN03-016011/ A

• 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:
  • VLA1701: mean 2.30 versus 3.70 on control; difference -1.40 (95% CI -2.69 to -0.11), standardized effect d = -0.78; z-test p = 0.033.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the mean is statistically lower for VLA1701 than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

The listed secondary outcomes cover disease severity after challenge and several adverse-event measures. The numerical secondary comparison reported for disease severity is directionally consistent with the primary event-count comparison: the VLA1701 arm has the lower mean score in the posted summary. The safety information records adverse events in both arms, while serious adverse events and deaths are not recorded. Taken together, the secondary information does not present one uniform pattern across efficacy-related and safety-related measures; these outcomes address different questions and use different measurement scales.

The severity score is a continuous outcome, whereas the primary endpoint counts participants who crossed a specified diarrhea threshold. A lower average score and fewer participants meeting the primary threshold can point in the same direction without being interchangeable measures. The score may reflect gradations in severity, while the primary outcome classifies participants according to the stated threshold. The registry record does not report a threshold or interpretation framework for the score that would permit a direct translation between these endpoints.

The secondary outcomes also include solicited adverse events, any adverse events, serious adverse events, and events described as related to the investigational medicinal product. The record does not provide a complete set of comparative estimates or inferential analyses for every listed measure. The posted safety counts should therefore be read descriptively, and should not be taken as a complete analysis of the safety outcome family.

Unadjusted secondary results carry less weight when several outcomes are examined and the registry record does not report how multiplicity was handled. In general, testing multiple outcomes without adjustment makes isolated small p-values easier to obtain by chance. The record does not identify a prespecified hierarchy that would show which secondary comparison, if any, was intended to support a confirmatory claim. The secondary findings can add context to the primary result, but they do not remove its uncertainty or establish a definitive overall conclusion.

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

• Disease Severity Score After Challenge With ETEC Strain LSN03-016011/ A
• Percentage of Subjects With Solicited Adverse Events
• Percentage of Subjects With Any Adverse Events (AE)
• Number of Subjects With Serious Adverse Events
• Percentage of Subjects With Any IMP (Investigational Medicinal Product) Related Adverse Events
• Percentage of Subjects With IMP-related Serious Adverse Events

9. Statistical Interpretation

10. Statistical Methods Explained

What does the primary comparison estimate?

The primary outcome is binary: participants are counted according to whether they experienced moderate to severe diarrhea within 120 hours after challenge. The registry reports counts and arm denominators, and the comparison is expressed as a risk difference. A risk difference compares the event probability in one arm with the event probability in the other, using the stated arm order. A negative estimate in this comparison indicates a lower observed event probability in the VLA1701 arm than in the placebo arm. This is an unadjusted comparison; the registry record does not report a covariate-adjusted primary analysis.

Other measures for binary outcomes can express the same comparison on different scales. A risk ratio compares the event probabilities as a ratio, while an odds ratio compares event odds. These measures are not interchangeable: they use different scales and can have different numerical magnitudes. For this study, the central interpretation is the direction and uncertainty of the arm comparison, not a claim that one scale is inherently preferable. The registry record does not report a sponsor analysis that supersedes the posted comparison.

What does the p-value say?

The posted primary unadjusted p-value is 0.136. In general, a p-value describes how incompatible the observed data are with a specified null model, assuming that model and the analysis assumptions apply. It is not the probability that the null hypothesis is true, and it does not measure the probability that a result will replicate. A value above a conventional threshold does not prove that the groups are identical. In this study, the p-value should be read together with the event counts and interval estimate, which show that uncertainty remains substantial.

The secondary disease severity comparison has an unadjusted p-value of 0.033. This is a separate comparison on a continuous score, and it does not replace the primary endpoint. The registry record lists multiple secondary outcomes but does not report a multiplicity procedure. When multiple outcomes are examined, the chance of at least one result appearing statistically notable can increase. Without a reported adjustment or prespecified testing hierarchy, an unadjusted secondary p-value is less conclusive than a result interpreted under a clearly specified analysis plan.

How is the disease severity score compared?

The score is reported as a mean and standard deviation for each arm. The posted means are 2.3 for VLA1701 and 3.7 for placebo, and the mean difference is -1.4. A mean difference describes the change in the average score between groups, with its sign depending on the comparison order. The standard deviation describes how spread out individual scores are around the arm mean; it is not the uncertainty interval for the mean difference. The registry record does not provide details about the score's scale range, distribution, or clinically important difference, so those properties cannot be inferred from the summary values.

In general, a continuous-outcome comparison often relies on assumptions about the data or on methods robust to departures from those assumptions. The registry record does not identify the test or model used for the secondary score. The posted estimates can be described, but the analytic method and its assumptions are not reported in the registry record.

Why does over-dispersion matter in general?

Over-dispersion is a general concern when the observed variability in count data is greater than a model assumes. If a model understates variability, its standard errors may be too small and intervals too narrow, making evidence appear more precise than it is. For the primary endpoint, participants are counted as having or not having an event, and the registry reports no dispersion estimate. The record does not identify a model that assesses over-dispersion, so it would be inappropriate to claim that it was tested or ruled out. This general issue is a reason to distinguish a descriptive count comparison from a fully documented model-based analysis.

What is not established by the analysis record?

The registry record does not report a formal statistical analysis plan, the test used for the primary comparison, covariate adjustments, interim analyses, or the original sample-size justification. It also does not report an analysis population flow or explain why the safety at-risk count differs from the primary outcome denominator. Randomization and double-blinding describe important design features, but they do not supply these missing analytic details. Consequently, the posted comparisons can be interpreted on their stated scales, while the exact analysis procedures and any unreported sensitivity analyses remain unknown.

11. Confidence Intervals and Precision

Precision concerns how narrowly the data constrain the underlying group difference. The primary risk-difference interval spans values on both sides of no difference. That means the posted data are compatible with no difference as well as with differences in either direction. The interval is broad relative to the observed comparison, so the primary result does not provide a sharply determined estimate. The unadjusted p-value of 0.136 is consistent with that uncertainty; neither figure establishes equality between the arms.

In general, a confidence interval is a range generated by a method that, over repeated studies under its assumptions, would contain the target parameter at the stated confidence level at the method's nominal rate. It is not a range containing a stated probability that the particular parameter lies inside it. Its width reflects factors including the amount of information and the variability in the observations. In a small study with binary outcomes, a few event counts can have substantial influence on the estimated comparison and its interval.

The disease severity mean-difference interval is reported as excluding zero, and the unadjusted p-value is 0.033. That interval therefore supports a nonzero difference under its calculation, but its width still matters for judging how precisely the magnitude is estimated. The registry record does not report a clinically important threshold for the score, so statistical separation from zero does not establish whether the estimated difference is important in practical or clinical terms.

Intervals are also conditional on the analysis and its assumptions. The record does not report the full analysis plan or enough detail to assess every assumption. The confidence intervals should therefore be interpreted as uncertainty estimates for the posted comparisons, not as guarantees about results in other populations, challenge settings, or future studies. The small arm counts and the absence of reported analysis details limit how narrowly the findings can be generalized.

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 planning figures are participants per arm for 80% power and participants per arm for 90% power. They are not total events. The planning illustration assumes a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. It uses the stated fraction of the observed effect as the effect to detect. The registry record's original design assumptions and original sample-size justification are not reported in the registry record.

For the primary outcome, planning for 100% of the observed effect gives 52 participants per arm for 80% power and 69 participants per arm for 90% power. Planning for 75% of the observed effect gives 90 participants per arm for 80% power and 120 participants per arm for 90% power. Planning for 50% of the observed effect gives 196 participants per arm for 80% power and 261 participants per arm for 90% power.

In plain terms, 80% power describes the chance, under the planning assumptions and if the chosen effect were the true effect, of detecting that effect using the stated testing approach. The 90% column describes a higher target chance under the same kind of assumptions. Those targets are not guarantees for a future study, and they do not describe the probability that a particular result is correct. Smaller effects require more participants to detect with the same target power, which is why the planning counts increase as the assumed fraction of the observed effect becomes smaller.

These are planning illustrations based on the observed effect, not observed power and not evidence about the trial's original design. Because the observed effect is uncertain, using it as a planning reference also carries uncertainty. The registry record does not report the trial's own assumptions for choosing its enrollment target.

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 = 0.733; treatment proportion p₁ = 0.467; average p̄ = 0.600.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.6928 = 1.3579.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.6667 = 0.5611.
4. n = (1.3579 + 0.5611)² / (-0.2667)² = 51.8, rounded up to 52 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 <- 0.733333333333333                          # control proportion
p1 <- 0.466666666666667                          # 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(3576183)
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 = 52 (80% power), 69 (90% power)
 75% of observed effect: n per arm = 90 (80% power), 120 (90% power)
 50% of observed effect: n per arm = 196 (80% power), 261 (90% power)
power.prop.test: 51.8 
Simulated power at n = 52 per arm: 0.822 (20000 replicates)

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

14. Multiplicity, Missing Data and Analysis Populations

The registry record lists six secondary outcomes and reports a primary outcome, but does not report a multiplicity adjustment, a testing hierarchy, or a complete statistical analysis plan. Multiplicity arises when more than one outcome or comparison is examined. In general, as the number of statistical tests increases, the chance of at least one apparently notable result under a collection of null hypotheses can also increase. Adjustment methods or a prespecified hierarchy can address this issue, but no such method is reported in the registry record.

The unadjusted p-value for the secondary disease severity score should therefore be treated as exploratory rather than as a result that independently confirms the primary finding. The primary endpoint remains the central comparison identified in the record. The secondary score provides related information, but the record does not state that it was intended to substitute for the primary endpoint or to control the overall error rate. The registry record reports no analyses for the other secondary outcomes, although some safety counts are posted.

Missing-data handling is also not reported in the registry record. The outcome description defines a challenge population based on receipt of two doses and the challenge dose, but the record does not give participant flow, reasons for exclusions, or methods for handling missing endpoint observations. It is not possible to determine from the posted information whether all randomized participants were included in each analysis or how missing measurements were treated. No missing observations or exclusions should be inferred from the difference between the enrollment, primary, and safety counts.

The analysis population is described for the primary endpoint, but the registry does not provide enough detail to evaluate adherence to that definition or to compare it with other possible analysis populations. The safety record reports 17 at risk in each arm, whereas the primary result reports 15 in each arm. These denominators belong to different outcome entries, and the record does not explain the difference. Without participant flow and analysis details, the reason should not be guessed.

Interim analyses, stopping rules, and the original sample-size justification are not reported in the registry record. Their absence from the record does not establish that a particular procedure was or was not used. It means that those aspects of the study's statistical conduct cannot be assessed from the posted information.

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 non-serious adverse event terms

The safety record lists 17 participants at risk in each arm. No serious adverse events are recorded in either arm, and no deaths are recorded in either arm. The record also lists other adverse events for 10 participants in the VLA1701 arm and 13 participants in the placebo arm. These are descriptive counts; they do not establish that the groups differ in overall safety, and the listed other events are not necessarily related to the investigational medicinal product.

In general, a zero count means that no event of that category was recorded in the observed group during the period represented by the record. It does not mean the event is impossible or that its underlying risk is zero. With limited numbers at risk, uncommon events may not appear even when they remain possible. The registry record does not provide serious-event terms, and it does not report enough detail to interpret event timing, duration, severity grading, or relatedness for the other adverse events.

The safety denominator differs from the primary outcome denominator. The registry record does not explain this difference, so the safety counts should be interpreted using their own stated at-risk numbers and not merged with the primary analysis counts.

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

The posted primary result describes fewer observed moderate to severe diarrhea events in the VLA1701 arm than in the placebo arm after challenge. The uncertainty interval includes no difference, and the unadjusted p-value is 0.136. The result therefore does not precisely establish a difference between arms, but it also does not establish that the arms are identical. The observed counts are a description of this study's challenge population and should be interpreted with their uncertainty.

The disease severity score provides a secondary result in the same direction, with a lower reported mean in the VLA1701 arm and an unadjusted interval that excludes zero. Because this is a secondary outcome and the registry record does not report multiplicity control, it should not be treated as definitive confirmation of the primary comparison. The score and the primary endpoint measure different features of illness, and the record gives no clinical interpretation threshold for the score.

The safety entries record no serious adverse events or deaths and record other adverse events in both arms. These observations are reassuring only in the narrow descriptive sense that no serious events were recorded among those listed as at risk; they cannot establish that serious events cannot occur or precisely compare safety. The record does not provide a complete analysis of adverse-event relatedness or event characteristics.

This was a Phase II human challenge study designed to examine a challenge model and investigate safety. Its results do not, by themselves, answer questions about outcomes in other settings or populations. The registry record does not report enough information to assess all analysis decisions, missing-data handling, or multiplicity procedures. The findings should therefore be understood as limited evidence from the posted study, not as medical advice or a conclusive judgment about treatment effect.

17. Why This Trial Matters Statistically

This trial illustrates why the direction of an observed comparison and its precision are separate questions. The primary event counts are lower in the VLA1701 arm, but the interval spans no difference and the unadjusted p-value is 0.136. Reading only the counts would omit the uncertainty; reading only the p-value could wrongly suggest that no difference has been established. Both the estimate and its uncertainty are needed to describe what the study shows.

It also illustrates how related endpoints can tell complementary but non-identical stories. The primary outcome classifies participants according to a diarrhea-severity threshold, while the secondary score summarizes disease severity on a continuous scale. Their posted directions agree, but the secondary result is not a substitute for the primary result. The different scales and the lack of reported multiplicity procedures matter when assessing the strength of the combined evidence.

The challenge-study setting makes the definition of the analysis population particularly important. The registry specifies participants who received two vaccine doses and the challenge dose, while the outcome entries use different denominators for primary and safety results. The record does not explain the difference. This highlights the value of transparent participant flow and explicit analysis-population reporting when interpreting clinical results.

Finally, the planning illustration shows how sample-size requirements depend on the effect selected for planning. Smaller assumed effects require more participants for the same target power. These calculations are not a reconstruction of the original study design and are not observed power. Separating planning illustrations from the trial's own design assumptions prevents a common interpretive error and keeps the conclusions aligned with what the registry record actually documents.

18. Limitations

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

The study is a pilot challenge trial with 15 participants per arm in the primary outcome record. The resulting event counts and estimates are imprecise, limiting the strength of conclusions from the primary comparison. The challenge setting and the defined population also constrain interpretation: the posted result concerns participants who received two vaccine doses and a specific challenge dose, not all possible clinical settings.

The registry record does not report the original sample-size justification, formal statistical analysis plan, interim analyses, missing-data methods, or complete participant flow. It also does not explain why the safety at-risk counts differ from the primary outcome denominators. These gaps limit assessment of how the posted populations were formed and how robust the results are to alternative analysis choices.

The primary interval includes no difference, while the secondary disease severity result has an unadjusted interval excluding zero. A multiplicity procedure or testing hierarchy is not reported, and the score's clinically important threshold is not reported. The secondary finding should therefore be interpreted cautiously, particularly because several secondary outcomes are listed and complete comparative analyses are not reported for all of them.

The safety record reports no serious adverse events or deaths, but the number at risk is limited and the record does not give serious-event terms or detailed event characteristics. Absence of recorded serious events cannot establish absence of risk. The posted information also does not provide enough detail to determine the duration of safety follow-up or fully evaluate adverse-event relatedness. These limitations are specific to what the registry record documents and should not be resolved by assumptions about the study protocol.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: Study Confirming A Human Challenge Model and Investigating The Safety Of VLA1701 — NCT03576183.
• Registry record last updated 2021-04-23; status last verified 2021-03.
• Results first posted 2021-04-23.
• 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 record describes a completed, randomized, double-blind, placebo-controlled Phase II study of VLA1701 using an ETEC human challenge model. The primary endpoint was moderate to severe diarrhea within 120 hours after challenge. The posted counts were 7 of 15 in the VLA1701 arm and 11 of 15 in the placebo arm; the unadjusted p-value was 0.136, and the uncertainty interval includes no difference.

The secondary disease severity score had reported means of 2.3 for VLA1701 and 3.7 for placebo, with a mean difference of -1.4 and an unadjusted p-value of 0.033. The record lists no serious adverse events or deaths and records other adverse events in both arms. Multiplicity handling, missing-data methods, the original sample-size assumptions, and the reason for different primary and safety denominators are not reported in the registry record.