Evaluation of Anti-Hemagglutinin (Anti-HA) Antibodies as Protection From the Flu in Healthy People: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Evaluation of Anti-Hemagglutinin (Anti-HA) Antibodies as Protection From the Flu in Healthy People (NCT01971255), a non-randomized phase 1, phase 2 study. The primary outcome is Number of Patients With Mild to Moderate Influenza Disease (MMID).

2. Clinical Question

The study addresses whether pre-existing anti-HA antibody levels are associated with protection against influenza disease following exposure to the challenge virus. Because antibody level determined group membership rather than random assignment, any group difference could reflect factors other than antibody level. The record does not describe an adjusted analysis that would address such differences.

The question is narrower than whether a vaccine prevents influenza in the general population. Participants were healthy volunteers in a controlled challenge setting, and the primary endpoint combined symptoms with a positive test. The record describes the study’s objective as examining anti-HA antibodies as a correlate of protection in an H1N1 challenge model. It does not report a result establishing the strength or direction of an association.

3. Trial Design

This completed Phase 1 and Phase 2 study used a non-randomized, single-group challenge design with no masking. Participants were categorized into high- and low-titer groups according to prechallenge HAI antibody measurements, then received the Ca/04/2009/H1N1r challenge virus intranasally. Although the registry describes two titer groups, the study was not a randomized comparison: antibody status was a measured characteristic, not an intervention allocated by chance.

The registered enrollment was 74. The registry lists a start date of 2013-09 and primary completion of 2015-09. Eligible volunteers were healthy people aged 18 to 50 years. The brief description outlines screening, hospital isolation, repeated collection of nasal fluid, symptom questionnaires, and medical monitoring after inoculation. It also describes a stay in an isolation unit for at least 9 days. These are features of the study setting; they do not establish how many participants completed follow-up or contributed to each outcome.

The primary outcome was the number of participants with MMID within 10 days of inoculation. Other listed outcomes included clinical disease severity, duration of shedding, duration of symptoms, number of symptoms, and number of participants with influenza symptoms. The record lists six outcome measures and zero analyses. It does not report the primary outcome counts, a treatment-effect estimate, confidence interval, p-value, or a statistical analysis plan.

The high-titer group description uses a threshold of at least 1:40, while the low-titer group is described as below 1:40. Some group titles render the high threshold differently. The record does not clarify this labeling difference. Since allocation was non-randomized, differences between these groups should be interpreted as associations in this study population rather than as effects of assigning a particular antibody level.

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: The analysis included only those subjects who received the influenza challenge virus and were not found to have a confounding infection (i.e. other respiratory virus infection, urinary tract infection, etc.) In addition this represents the number of people who were high or low titer at the time of challenge with influenza virus not at screening.

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 Patients With Mild to Moderate Influenza Disease (MMID), measured over Within 10 days of inoculation.

The primary endpoint counts participants who met the registry’s definition of mild to moderate influenza disease within 10 days of inoculation. A participant was counted as having MMID only when both components were present: symptoms of influenza and a positive clinical test for influenza. Thus, the endpoint is a binary participant-level outcome: each person either meets the combined definition or does not.

Combining symptoms with a positive test makes the endpoint more specific to clinically apparent, laboratory-supported influenza than either component alone. A positive test without symptoms would not meet the stated definition, and symptoms without a positive test would not meet it either. This is the registry’s operational definition; the record does not provide further detail about the symptom criteria, test type, or adjudication process.

Counting participants with the endpoint gives an interpretable summary of how often this defined disease outcome occurred in each titer group. It does not by itself describe symptom severity, duration of shedding, or duration of illness, which are listed separately among the secondary outcomes. Nor does it measure all possible effects of infection.

The registry record does not report the number of participants meeting MMID in either group. It also does not report an estimate comparing the groups or its uncertainty. Consequently, the endpoint definition can be explained, but the primary outcome’s observed magnitude and precision cannot be assessed from the registry record.

7. Why No Re-analysis Is Shown

8. Statistical Interpretation

9. Statistical Methods Explained

The registry identifies the primary outcome as a participant count, not a continuous measurement or a time-to-event endpoint. General statistical explanation: for a binary outcome, a basic group summary reports how many participants meet the definition and how many are evaluated. A comparison can then express how the observed outcome differs between groups, with an uncertainty interval. No such primary outcome summaries or comparison estimates are reported in the registry record.

What does non-randomized allocation imply?

Randomization is a design method intended to make comparison groups similar, on average, with respect to both measured and unmeasured characteristics. In this study, group membership followed prechallenge HAI titer rather than random assignment. The groups may therefore differ in other ways that relate to influenza disease. A simple difference between group outcomes, if available, would describe an association and would not automatically isolate the effect of antibody level.

Adjustment can sometimes address measured differences by including relevant characteristics in a statistical model. It cannot guarantee removal of bias from unmeasured or poorly measured factors. The registry record reports zero analyses and does not describe an adjusted model, covariates, or a statistical plan. Whether adjustment was performed or intended is not reported in the registry record.

How should a confidence interval be read?

General statistical explanation: a confidence interval conveys uncertainty around an estimate. It is not a guarantee that the true value lies within a particular interval, and it does not remove limitations in study design or measurement. For a binary outcome, the interval depends on the number of observed events, the number of participants evaluated, and the chosen comparison and model. Without reported group counts, an estimate, and an interval, neither the magnitude nor the precision of the primary comparison can be judged.

A p-value, if reported, would address how compatible the data are with a specified statistical null model under the assumptions of the analysis. It would not measure the probability that the null hypothesis is true, nor would it establish causality. The registry record does not report a p-value or the inferential method used for the primary outcome.

Why does the analysis population matter?

The record says the primary analysis included participants who received the challenge virus and were not found to have a confounding infection. It also refers to participants’ high or low titer at the time of challenge. Restricting analysis based on post-enrollment events can change the population represented by the result, and exclusions can affect comparability between groups. The record does not report the final analysis counts, the number excluded, or the handling of missing outcome data.

The brief description indicates repeated symptom questionnaires and daily nasal-fluid collection. Repeated observations can provide information on symptom course or viral shedding, but the primary endpoint is a participant count within a stated time window. The registry does not specify how repeated observations were converted into endpoint status, how incomplete observations were handled, or whether outcome assessment was blinded.

What can planning calculations tell a reader?

General statistical explanation: a sample-size planning illustration asks how many participants per arm might be required under stated assumptions to detect a target effect with a chosen error rate and power. Such an illustration is conditional on its assumed effect and design; it is not an observed result and does not show that a completed study had a particular probability of finding significance. The registry’s planning description says illustrations are based on the observed effect, assume equal allocation, a two-sided significance level of 0.05, and use a normal approximation. It also distinguishes participant-per-arm outputs from total-event outputs for time-to-event calculations. No numerical planning estimates are reported in the registry record, and the record does not establish that these illustrations were part of the trial’s original design.

For count outcomes, over-dispersion is a general concern when observed variability exceeds the assumptions of a simple count model. It can make uncertainty estimates too narrow if not accommodated. The primary endpoint is described as the number of participants meeting a binary definition, not as a recurrent-event count. The registry does not report a count model or any model diagnostics; no claim about over-dispersion in this trial can therefore be made.

10. Exact Estimates for Each Arm

No two-arm comparison is shown for this trial, but each arm's result can still be summarized with exact interval estimates computed from the posted counts. The Clopper-Pearson interval is exact and conservative; the Wilson interval is a common closer-to-nominal alternative; the Bayesian credible interval uses a uniform Beta(1, 1) prior. For small groups the three can differ noticeably.

R code and output

arm    <- c("High Titer (HAI > 1:40)", "Low Titer (HAI < 1:40)")
n      <- c(25, 40)
events <- c(6, 29)
for (i in seq_along(n)) {
  ci <- binom.test(events[i], n[i])$conf.int          # Clopper-Pearson exact interval
  cr <- qbeta(c(0.025, 0.975), 1 + events[i], 1 + n[i] - events[i])   # Bayesian, uniform Beta(1,1) prior
  wi <- (function(x, n) { p <- x / n; z <- qnorm(0.975); d <- 1 + z^2 / n
    c((p + z^2 / (2 * n) - z * sqrt(p * (1 - p) / n + z^2 / (4 * n^2))) / d, (p + z^2 / (2 * n) + z * sqrt(p * (1 - p) / n + z^2 / (4 * n^2))) / d) })(events[i], n[i])
  cat(sprintf("ARM|%s|%d|%d|%.1f|%.1f|%.1f|%.1f|%.1f|%.1f|%.1f\n", arm[i], n[i], events[i], 100 * events[i] / n[i],
              100 * ci[1], 100 * ci[2], 100 * wi[1], 100 * wi[2], 100 * cr[1], 100 * cr[2]))
}

ARM  High Titer (HAI > 1:40)  25  6  24.0  9.4  45.1  11.5  43.4  11.6  43.6
ARM  Low Titer (HAI < 1:40)  40  29  72.5  56.1  85.4  57.2  83.9  57.1  83.9

11. Multiplicity, Missing Data and Analysis Populations

The record lists six outcome measures: the primary MMID count and secondary measures covering clinical disease severity, duration of shedding, duration of symptoms, number of symptoms, and participants with influenza symptoms. Examining several outcomes can raise a multiplicity issue: when many statistical tests are conducted, the chance of at least one apparently unusual result can increase. General approaches include prespecifying a primary comparison, adjusting significance thresholds, or treating secondary results as exploratory. The registry record does not report a multiplicity strategy or statistical testing hierarchy.

The registry lists zero analyses. It does not report outcome-specific results, group denominators, effect estimates, confidence intervals, p-values, or the statistical methods used to analyze the outcomes. Whether outcomes were analyzed but not entered into the registry is not reported in the registry record. No inference about multiplicity adjustment can be made from the outcome list alone.

The primary population description excludes participants with a confounding infection and includes those who received the challenge virus. The record does not give the number meeting those criteria, the number in each titer group in the analysis, or the number with missing outcome information. It does not describe imputation, complete-case analysis, or another missing-data method. How incomplete follow-up affected the analysis is therefore not reported in the registry record.

General statistical explanation: missing outcomes can undermine a comparison when the chance of missingness differs between groups or is related to participants’ health status. Reporting analysis denominators and reasons for exclusion helps readers assess that risk. Those details are not reported in the registry record.

12. Safety Results

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

Other (non-serious) adverse events

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

Most frequent non-serious adverse event terms

The adverse-event record separates serious adverse events from other adverse events and reports participants at risk and affected in each category. For the high-titer group, 25 were listed as at risk for serious adverse events and 0 as affected; 25 were at risk for other adverse events and 10 were affected. For the low-titer group, 40 were listed as at risk for serious adverse events and 0 as affected; 40 were at risk for other adverse events and 4 were affected.

These are registry counts, not a randomized safety comparison. Because group membership was based on antibody titer, differences in reported counts cannot be attributed to titer status from these figures alone. The record does not report a statistical comparison of adverse events, event timing, severity details for the other events, or whether each event was judged related to the challenge virus.

Deaths are recorded as not reported for both groups. A missing death count should not be interpreted as a reported zero. The serious-event counts also do not establish that no other clinically relevant harms occurred; they describe only the categories and counts entered in the registry record.

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

The registry record establishes the study question, challenge design, titer-based groups, primary endpoint definition, and some adverse-event counts. It does not report the primary outcome counts or a statistical comparison of MMID between the high- and low-titer groups. As a result, the record cannot show whether the observed disease outcome differed between groups, how large any difference was, or how precisely it was estimated.

Even if a group difference were available, this non-randomized comparison would need cautious interpretation. Titer group was not assigned at random, and the record does not report an adjusted analysis. A difference would be an association in the study population, not by itself proof that changing anti-HA antibody level would change disease risk. The challenge model and healthy volunteer eligibility criteria also limit the setting to which results could be generalized.

The endpoint requires both symptoms and a positive influenza test, so it represents the registry’s particular definition of mild to moderate influenza disease rather than infection alone or every possible measure of illness. Other listed outcomes address symptom burden and duration, but their results are not reported in the registry record.

The adverse-event counts describe what was entered in the registry and are distinct from the primary disease endpoint. Zero affected participants are listed in the serious-event category for each group, while other adverse events are also recorded. The record does not provide a causal safety comparison, and death counts are not reported.

Overall, the registry record documents a completed challenge study intended to evaluate anti-HA antibodies as a correlate of protection. It does not provide the outcome results needed to determine whether the study observed an association or how uncertain that association might be.

14. Why This Trial Matters Statistically

This study is statistically notable because it links a measured prechallenge immune marker to a defined outcome after controlled exposure. A challenge model can make the exposure context more structured than ordinary community observations, while the antibody-based grouping retains the limitations of an observational comparison. The distinction between controlled exposure and randomized assignment is important: controlling exposure does not make the titer groups exchangeable in all other respects.

The primary endpoint also illustrates the value and consequence of an explicit operational definition. Requiring symptoms and a positive test focuses the outcome on clinically apparent, test-supported illness. At the same time, conclusions depend on the specific symptom and testing rules, which are not fully detailed in the registry record.

For biostatistical interpretation, the registry’s absence of outcome analyses is consequential. A study description and endpoint definition cannot substitute for observed counts, denominators, and uncertainty estimates. Without them, readers cannot assess the size, precision, or statistical compatibility of a group association. The missing analysis population details also prevent evaluation of exclusions and incomplete follow-up.

The record’s planning description is another useful distinction: sample-size illustrations based on an observed effect are planning exercises, not evidence of observed power or of the trial’s original design justification. The record does not report an original sample-size rationale. In combination, these features show why design, analysis reporting, and outcome availability must be considered separately when interpreting a clinical trial registry entry.

15. 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 principal limitation for statistical interpretation is that the registry record does not report the primary outcome counts, group-specific denominators, effect estimate, confidence interval, or p-value. It lists zero analyses. Therefore, neither the observed association nor its precision can be assessed from the record.

The study is non-randomized, and the titer groups may differ in characteristics other than antibody level. No adjusted analysis or covariate set is reported. The record also does not provide a complete account of the primary analysis population, exclusions, missing outcomes, or handling of incomplete follow-up. These omissions make it difficult to assess selection and missing-data concerns.

The high-titer arm description gives a threshold of at least 1:40, while some group-title wording differs in how the boundary is expressed. The record does not clarify the labeling discrepancy. It also does not report details of the clinical test, symptom criteria, or the procedure used to classify endpoint status.

The registered population was healthy volunteers aged 18 to 50 years in an H1N1 challenge model. This setting is not the same as naturally acquired influenza in a broader population. The record does not report enough outcome detail to evaluate generalizability, and the safety entries do not include a comparative analysis or reported death counts.

16. Related Tutorials

17. Related Statistical Calculators

18. Sources

• ClinicalTrials.gov: Evaluation of Anti-Hemagglutinin (Anti-HA) Antibodies as Protection From the Flu in Healthy People — NCT01971255.
• Registry record last updated 2017-04-24; status last verified 2017-03.
• Results first posted 2017-04-24.
• 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.

19. Record Summary

This completed Phase 1 and Phase 2 study evaluated anti-HA antibodies as a correlate of protection in an H1N1 human challenge model. Healthy volunteers aged 18 to 50 years were grouped by prechallenge HAI titer and received the Ca/04/2009/H1N1r challenge virus intranasally. Allocation was non-randomized, masking was absent, and the study model was listed as single group.

The primary endpoint counted participants with symptoms and a positive clinical test for influenza within 10 days of inoculation. The registry lists 74 enrolled participants and six outcome measures, but zero analyses. Primary outcome results, group analysis counts, and inferential estimates are not reported in the registry record. Serious adverse events were recorded as affecting zero participants in each group; other adverse events were also recorded. Death counts are not reported.