Peanut Oral Immunotherapy in Children: IMPACT Follow Up Study: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Peanut Oral Immunotherapy in Children: IMPACT Follow Up Study (NCT03345160), a na phase 2 study. The primary outcome is Number of Participants With Treatment-related Adverse Events (AEs).

2. Clinical Question

In practical terms, the record describes an open-label treatment opportunity that followed the double-blind, placebo-controlled phase and initial follow-up of an earlier study. The earlier study context explains why participants previously assigned placebo were eligible for this follow-up, but it does not create a control group within the present record. The clinical question is therefore descriptive: what adverse events and challenge outcomes were recorded during this open-label treatment experience? Because there is no concurrent comparison group, the record cannot directly estimate how outcomes would have differed without treatment or under another treatment. The registry does not report a separate numerical result for the primary outcome or the peanut challenge outcome.

3. Trial Design

This is a Phase 2, open-label, single-group follow-up study sponsored by Johns Hopkins University. Its official title is Open Label Peanut Oral Immunotherapy in Children: IMPACT Follow Up Study. The intervention is peanut flour, described as open-label peanut OIT for participants who had previously received placebo in an earlier peanut OIT study. The registry records enrollment of 7 and classifies allocation as not applicable and masking as none.

The study began on 2018-04-27, reached primary completion on 2020-10-07, and is listed as completed. The primary outcome timeframe is up to 52 weeks. The registry describes the study as following the earlier IMPACT study, which was double-blind and placebo-controlled in children 12-48 months of age. Participants who received placebo in that earlier study had been promised an opportunity to receive open-label treatment after the double-blind phase and initial follow-up. Those features belong to the prior study context; they do not make this follow-up record randomized or controlled.

The single-group structure matters when interpreting all outcomes. Without a concurrent comparator, observed event counts and challenge outcomes describe participants receiving the open-label intervention, rather than a treatment contrast. No detailed crossover protocol was specified when the earlier protocol was submitted; further protocol details for this follow-up are not reported in the registry record. The registry lists two outcome measures, but does not report a separate numerical primary endpoint estimate or a numerical result for the secondary outcome of amount of peanut protein tolerated at the end-of-treatment challenge. It lists no analyses.

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

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 Participants With Treatment-related Adverse Events (AEs), measured over up to 52 weeks.

The primary endpoint is the number of participants with treatment-related adverse events. Its description says that symptoms were collected as adverse events using CTCAE v4.0, and gives a timeframe of up to 52 weeks. This is a participant-count endpoint: it concerns how many participants had an event, not the number of separate symptoms or episodes. The registry does not state in the primary outcome description how treatment relatedness was determined or provide a separate numerical result for this endpoint.

In general, adverse-event endpoints are used to describe the safety experience observed during a study. A participant count can communicate whether any participants were recorded as having an event, but it does not by itself describe event severity, timing, duration, recurrence, or cause. Those details require additional definitions or outcome data. The registry's adverse-event record separately lists serious and other adverse-event counts; those figures should not be assumed to be a complete numerical report of the primary treatment-related endpoint.

The listed secondary outcome is the amount of peanut protein tolerated at the end-of-treatment peanut challenge. This could describe a measured challenge outcome, but the result value and its summary method are not reported in the registry record. Thus the endpoint definitions indicate what the study intended to assess, while the available record does not provide a separate numerical result for either listed outcome.

7. Why No Re-analysis Is Shown

8. Statistical Interpretation

9. Statistical Methods Explained

The methods needed to interpret this record begin with its design. This is a single-group, open-label study, not a randomized comparison. The registry lists allocation as not applicable and masking as none. Participants received peanut flour as open-label peanut OIT after having received placebo in a prior peanut OIT study. Outcomes therefore describe the treatment group's experience; the record does not provide a contemporaneous group against which to estimate a treatment contrast.

What does a participant-count outcome represent?

The primary outcome is a count of participants with treatment-related adverse events. In general, a participant-count outcome records whether participants meet a specified event definition during a defined observation period. It differs from an event-count outcome, which counts repeated episodes, and from a rate, which relates events to time observed. The stated timeframe is up to 52 weeks. The registry description says symptoms were collected as adverse events using CTCAE v4.0. It does not report separate details on event attribution, grading, or handling of repeated events for this primary outcome.

In general, a clear count interpretation depends on a defined denominator and consistent follow-up. The adverse-event record lists participants at risk for serious and other events, along with the number affected in each category. These counts are descriptive. Without further analysis details, they do not communicate the duration of each participant's observation or whether all participants had the same opportunity for events to be observed. The record does not report additional follow-up or censoring rules.

Why does the lack of a comparator matter?

In general, a randomized controlled comparison can help separate differences associated with treatment from differences due to other factors, because random allocation creates groups intended to be comparable. That logic does not apply to this single-group record. Its open-label status also means treatment and outcome assessment were not masked, as recorded. The counts can describe what was observed in participants who received the intervention, but they cannot isolate the intervention's causal effect or establish what would have occurred under a different treatment or without treatment.

The earlier IMPACT study was described as double-blind and placebo-controlled, but those design properties belong to the earlier study. In this follow-up, the placebo recipients were offered open-label treatment after the earlier study's double-blind phase and initial follow-up. The prior-study context does not supply a control group for the outcomes in this record.

What can confidence intervals and tests add?

In general, a confidence interval expresses uncertainty around an estimated quantity under a statistical model. It can show that a result is compatible with a range of values rather than being an exact population value. A hypothesis test assesses how compatible observed data are with a specified null model, subject to its assumptions. Neither type of analysis removes design limitations, and neither creates a valid concurrent comparison when the study has no control group.

The registry lists no analyses and does not report a numerical result for the primary endpoint or the secondary peanut-challenge outcome. It therefore does not provide an endpoint estimate with a confidence interval or a reported significance test for these outcomes. No inferential conclusion should be inferred from the descriptive adverse-event counts alone.

How should planning calculations be understood?

In general, sample-size planning illustrations describe the number of participants that a hypothetical future study might require under stated assumptions. They are not observed outcomes, and they do not indicate the probability that this completed study detected an effect. The planning assumptions recorded for this study describe a two-sided significance level of 0.05, equal allocation between two arms, a normal approximation, and effects defined as fractions of an observed effect. Those assumptions do not match the recorded single-group design. They should therefore be read as planning conventions rather than as the actual design or analysis of this follow-up study. No sample-size justification for the trial is reported in the registry record.

In general, over-dispersion occurs when outcome variability exceeds what a simple model assumes. It can make standard errors too small if ignored, but the endpoint data and model information needed to assess over-dispersion are not reported in the registry record. No model specification, adjustment strategy, or uncertainty analysis is listed. These absences limit reconstruction of an analysis, rather than demonstrating that a particular method was or was not used outside the registry entry.

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("Peanut Flour: Open Label Peanut OIT")
n      <- c(7)
events <- c(7)
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  Peanut Flour: Open Label Peanut OIT  7  7  100.0  59.0  100.0  64.6  100.0  63.1  99.7

11. Multiplicity, Missing Data and Analysis Populations

The registry lists two outcome measures: the number of participants with treatment-related adverse events as the primary outcome, and the amount of peanut protein tolerated at the end-of-treatment peanut challenge as a secondary outcome. It lists no analyses and provides no separate numerical endpoint estimate for either measure. A multiplicity adjustment strategy, hierarchy of hypotheses, or distinction between confirmatory and exploratory analyses is not reported in the registry record.

In general, testing multiple outcomes can increase the chance of observing at least one apparently unusual result when each is assessed separately. Methods such as prespecifying a primary outcome or adjusting for multiple tests can address that issue, but the record does not say which, if any, approach was used. With no reported outcome tests, there is no reported multiplicity-adjusted or unadjusted inferential result to interpret.

Missing-data handling is also not reported in the registry record. The entry does not describe whether outcome values were missing, how missing observations were handled, or whether any sensitivity analyses were conducted. The number of participants enrolled is listed, but the record does not provide an analysis population definition or a participant flow accounting for inclusion in each outcome analysis. It would therefore be inappropriate to assume that every enrolled participant contributed to every outcome or that a particular imputation method was used.

Information about protocol deviations, analysis populations, and the statistical plan is not reported in the registry record. Those omissions matter because outcome definitions, follow-up completeness, and analysis choices affect what an estimate represents. The adverse-event counts remain useful as registry descriptions, but their precision and relationship to the primary endpoint cannot be fully assessed without those details.

12. 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 0% reporting threshold.

Most frequent non-serious adverse event terms

The registry adverse-event record lists 0 serious adverse events among 7 participants at risk, 7 participants affected among 7 at risk for other adverse events, and 0 deaths. These are counts, not comparative rates, and they describe the single open-label treatment group. The categories should be kept separate: the record does not establish that the other adverse-event count is equivalent to the primary endpoint's count of participants with treatment-related adverse events.

In general, a count of zero in a small study means no events were recorded in that study group; it does not show that the underlying risk is zero. The counts also do not show the number of separate events, their severity, timing, duration, or treatment attribution. The registry does not report a further breakdown of the other adverse-event category or a separate numerical result for the primary endpoint. These figures therefore support a limited description of events recorded during the study, not a complete safety comparison or a precise estimate of risk.

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

The record supports a narrow descriptive reading. It identifies an open-label peanut OIT follow-up in a single group, with enrollment of 7, and records adverse-event counts for serious and other categories. It also identifies a primary outcome focused on participants with treatment-related adverse events and a secondary end-of-treatment peanut challenge outcome. The registry lists 0 serious adverse events, 7 participants affected by other adverse events, and 0 deaths in the adverse-event record.

Those observations do not establish a causal treatment effect. The study has no concurrent comparator, and masking is listed as none. A single-group observation cannot determine how outcomes would have differed without treatment or under another treatment. The record also does not report a numerical result for the primary endpoint as defined, or the amount of peanut protein tolerated at the end-of-treatment challenge. Thus the adverse-event counts should not be treated as a substitute for missing outcome results or as evidence of a comparative benefit.

In general, estimates based on a small group are imprecise, especially for uncommon events. The zero serious-event count is an observation in this study, not proof that serious events cannot occur. The count for other adverse events does not indicate how often events occurred within each participant or how they were assessed. These limits apply even if the registry record is complete as an administrative record.

The study has value as a description of an open-label treatment follow-up promised to participants who had previously received placebo in the earlier IMPACT study. That context helps explain the study's purpose, but it does not change the evidential limits of its single-group design. Interpretation should remain tied to the population, outcomes, follow-up timeframe, and event categories explicitly recorded, while recognizing that important analysis details are not reported in the registry record.

14. Why This Trial Matters Statistically

This study illustrates an important distinction between a treatment opportunity and a comparative trial. Participants who had previously received placebo in an earlier peanut OIT study were offered open-label treatment in this follow-up. That design addresses a different question from a randomized comparison: it records outcomes among participants receiving treatment but does not directly estimate how those outcomes compare with a control condition.

The distinction is especially important for safety reporting. A zero count for serious adverse events can be a relevant descriptive observation, while still offering limited precision about uncommon events. Likewise, an observed count in the other adverse-event category is meaningful as a record of affected participants, but it does not by itself establish event severity, cause, or treatment attribution. Statistical interpretation depends not only on counts but also on definitions, denominators, follow-up, and design.

The registry also makes visible a gap between endpoint definitions and reported results. It identifies a primary adverse-event endpoint and a secondary peanut challenge outcome, yet lists no analyses and does not report a separate numerical result for either outcome. This highlights why readers should distinguish study objectives from analyzable, reported endpoint estimates. A stated outcome is not itself evidence about its result.

Finally, planning assumptions should not be mistaken for the study's actual design. The record describes planning conventions that use two arms, whereas the follow-up is classified as single-group. Recognizing that mismatch prevents hypothetical planning calculations from being interpreted as observed power or as an analysis of the trial. These lessons apply broadly when registry design, endpoint definitions, and reported results are read together.

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.

Several limitations are specific to this record. The study is open-label and single-group, with no concurrent comparator. This prevents a like-for-like treatment comparison and limits causal interpretation. The prior placebo-controlled study supplies context for the follow-up, but its design cannot be carried over to the present record.

Enrollment is listed as 7, which limits how precisely event counts can characterize outcomes beyond this group. The registry does not report a separate numerical result for the primary treatment-related adverse-event endpoint or for the secondary peanut challenge outcome. Although adverse-event counts are listed, the relationship between those categories and the primary endpoint definition is not fully specified.

The record does not report detailed treatment dosing, participant ages at entry to this follow-up, missing-data handling, analysis populations, protocol deviations, or a statistical analysis plan. It also does not report the basis for the trial's sample-size planning. As a result, the record does not support reconstruction of outcome analyses or assessment of whether all participants contributed comparable follow-up to each outcome.

Finally, the adverse-event summary does not provide a detailed event breakdown, timing, severity distribution, recurrence, or attribution rules. The stated timeframe for the primary outcome is up to 52 weeks, but additional information about observation periods is not reported in the registry record. These limitations constrain interpretation of both safety and the unreported challenge outcome.

16. Related Tutorials

17. Related Statistical Calculators

18. Sources

• ClinicalTrials.gov: Peanut Oral Immunotherapy in Children: IMPACT Follow Up Study — NCT03345160.
• Registry record last updated 2021-10-29; status last verified 2021-10.
• Results first posted 2021-10-29.
• 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

The registry describes a completed Phase 2, open-label, single-group follow-up study sponsored by Johns Hopkins University. Peanut flour was used for open-label peanut OIT in participants who had previously received placebo in a prior peanut OIT study. Enrollment was 7; allocation was listed as not applicable, and masking as none. The study began on 2018-04-27 and reached primary completion on 2020-10-07.

The primary outcome was the number of participants with treatment-related adverse events, with symptoms collected using CTCAE v4.0 over up to 52 weeks. The secondary outcome was the amount of peanut protein tolerated at the end-of-treatment challenge. The adverse-event record lists 0 serious events among 7 at risk, 7 affected by other adverse events among 7 at risk, and 0 deaths. It lists no analyses and does not report separate numerical results for the primary or secondary outcome. The single-group design supports descriptive interpretation, not a controlled treatment comparison.