This page separates reported trial information from statistical interpretation. The ClinicalTrials.gov record reports results for the trial, but the ClinicalTrials.gov record does not contain formal statistical analyses, effect estimates, confidence intervals, or p-values for the primary endpoint.
1. Trial at a Glance
SEASON was a randomized, parallel-group, quadruple-masked phase 1/2 study evaluating motavizumab (MEDI-524) and palivizumab 15 mg/kg in children receiving prophylaxis for a second season. The registry identifies safety, tolerability, and immunogenicity as the study's central objectives.
| Feature | SEASON |
|---|---|
| Trial name | SEASON |
| ClinicalTrials.gov identifier | NCT00113490 |
| Brief title | A Study to Evaluate the Safety, Tolerability, and Immunogenicity of Motavizumab (MEDI-524) After Dosing for a Second Season in Children |
| Therapeutic area | Infectious Disease |
| Condition | Motavizumab Administration for a Second Season for RSV Prophylaxis |
| Phase | 1/2 |
| Status | COMPLETED |
| Allocation | RANDOMIZED |
| Design model | PARALLEL |
| Masking | QUADRUPLE |
| Primary purpose | PREVENTION |
| Enrollment | 136 |
| Interventions | Motavizumab (MEDI-524) (biological); palivizumab 15 mg/kg (biological) |
| Lead sponsor | MedImmune LLC |
| Sponsor type | INDUSTRY |
| Start | 2005-05 |
| Primary completion | 2006-02 |
2. Clinical Question
The study addresses whether administration of motavizumab for a second season in children can be evaluated with respect to safety, tolerability, and immunogenicity, with the registered primary endpoint specifically measuring the number of subjects exhibiting anti-motavizumab antibodies.
Population
Children participating in a study of motavizumab administration for a second season for RSV prophylaxis.
Intervention
Motavizumab (MEDI-524), identified in the trial data as a biological intervention.
Comparator
Palivizumab 15 mg/kg, identified in the trial data as a biological intervention.
Primary question
How many subjects exhibit anti-motavizumab antibodies from Day 0 through 120 days after the final dose?
3. Trial Design
Motavizumab
- Motavizumab (MEDI-524)
- Biological intervention
- Serious adverse events: 4 affected subjects among 66 at risk
Palivizumab comparator
- Palivizumab 15 mg/kg
- Biological intervention
- Serious adverse events: 1 affected subject among 70 at risk
The randomized parallel-group structure creates a natural framework for comparing the two intervention groups. Quadruple masking is also important statistically because masking can reduce the risk that knowledge of treatment assignment affects behavior, assessment, reporting, or other aspects of trial conduct. The ClinicalTrials.gov record does not specify the identities of the four masked parties, so no further attribution is made here.
4. Endpoints
| Endpoint | Time frame | Registry definition | Formal analysis posted? |
|---|---|---|---|
| Number of Subjects Exhibiting Anti-motavizumab Antibodies | Day 0 through 120 days post final dose | Serum for measurement of anti-motavizumab antibodies was collected prior to the first, second and, if applicable, fifth doses of study drug, and at the 2 follow-up visits 30 and 90-120 days post final dose. | No statistical analysis reported |
The ClinicalTrials.gov record identifies 1 registered primary endpoint and reports 5 outcome measures overall. Only the registered primary endpoint is described in detail in the ClinicalTrials.gov record, so the page does not infer definitions for the other outcome measures.
Understanding the primary endpoint
The primary endpoint is a subject-level immunogenicity outcome: whether a subject exhibits anti-motavizumab antibodies during the specified observation window. This is different from a continuous laboratory measurement because the registered endpoint is framed as a number of subjects exhibiting antibodies.
For a binary endpoint of this kind, the most direct descriptive summaries are the number and proportion of subjects with the endpoint in each randomized group. A comparative analysis could then quantify the difference between groups using an appropriate method for binary outcomes, but the ClinicalTrials.gov record does not identify such a formal comparison.
5. Planned Analysis
The ClinicalTrials.gov record confirms that results were posted, but they contain no statistical analyses for the registered primary endpoint. Consequently, there is no reported treatment effect estimate, confidence interval, or p-value that can be reproduced from the ClinicalTrials.gov record.
What would normally be analysed?
Because the primary endpoint is the number of subjects exhibiting anti-motavizumab antibodies, the analysis would ordinarily begin with a 2 × 2 comparison of treatment group by antibody status. Depending on the prespecified statistical analysis plan and observed cell counts, possible approaches include a Pearson chi-square test or Fisher's exact test for a categorical comparison.
For an effect estimate, investigators could report the difference in proportions, a risk ratio, or an odds ratio, each accompanied by a confidence interval. The appropriate estimand would depend on the protocol and statistical analysis plan.
Here, p would represent the proportion of subjects exhibiting the registered antibody endpoint. The actual proportions are not provided in the ClinicalTrials.gov record.
Why a p-value cannot be reported
A p-value requires the observed data and a specified null hypothesis together with a statistical test or model. Since the registry-reported statistical-analysis array is empty, reporting a numerical p-value would require information not present in the trial data and would therefore violate the source restriction for this page.
6. Statistical Methodology
Randomization
The study used randomized allocation. Randomization is the principal design mechanism that allows treatment groups to be compared without relying solely on adjustment for measured baseline characteristics. In expectation, randomization balances both measured and unmeasured prognostic factors across groups, although any particular randomized sample can still exhibit numerical differences.
Parallel-group comparison
The design model was PARALLEL, meaning participants were assigned to one of two intervention groups rather than being intentionally exposed to both interventions in a crossover sequence. For a parallel design, the primary comparison is naturally framed between outcomes observed in the randomized groups over the relevant follow-up period.
Quadruple masking
The trial was QUADRUPLE-masked. Masking is especially relevant when outcomes or treatment-related observations could be influenced by knowledge of assignment. The registry data establish that quadruple masking was used, but do not identify which parties were masked.
Binary immunogenicity endpoint
The primary endpoint is expressed as the number of subjects exhibiting anti-motavizumab antibodies. Statistically, this creates a binary endpoint at the subject level: a subject either meets the registered antibody criterion or does not meet it during the specified endpoint window.
The four cells would contain the numbers of antibody-positive and antibody-negative subjects in each intervention group. The ClinicalTrials.gov record does not provide those four cell counts.
Confidence intervals
A confidence interval would quantify statistical uncertainty around a treatment-effect estimate. For a binary endpoint, the interval could be attached to an absolute difference, risk ratio, odds ratio, or another prespecified estimand. The choice should be established before examining the observed results because different effect measures answer different questions.
Multiplicity
The ClinicalTrials.gov record identifies one registered primary endpoint and five posted outcome measures, but they do not describe a multiplicity-adjustment strategy. It would therefore be inappropriate to assume a particular familywise error procedure or to infer how the other outcome measures were positioned within a formal testing hierarchy.
Missing data and analysis population
The ClinicalTrials.gov record does not specify an analysis population, missing-data strategy, imputation method, or rules for handling subjects without evaluable antibody measurements. Those details can materially affect a binary immunogenicity analysis and should be taken from the protocol or statistical analysis plan when available rather than inferred from the registry record.
7. Statistical Methods Explained
Why is randomization important for this study?
Randomization determines treatment assignment without making the observed antibody outcome the basis for assignment. This provides the structural basis for comparing the two groups. It does not guarantee identical groups, and it does not by itself supply an effect estimate or statistical significance.
Why is the primary endpoint treated as a binary outcome?
The registry wording asks for the number of subjects exhibiting anti-motavizumab antibodies. That formulation is fundamentally different from analysing an antibody concentration as a continuous measurement. A binary endpoint focuses on whether the prespecified antibody criterion is met.
What would a risk difference mean?
If the trial reported proportions, a risk difference would be the proportion of subjects meeting the antibody endpoint in one group minus the corresponding proportion in the other group. It would describe an absolute difference in the probability of the observed endpoint, rather than a relative comparison.
Why might Fisher's exact test be considered?
For a 2 × 2 table, Fisher's exact test can be useful when expected cell counts are small. The ClinicalTrials.gov record does not provide the antibody-positive and antibody-negative counts, so it is not possible to determine whether that method would actually have been necessary or used.
Why is the confidence interval important?
A point estimate alone does not communicate how precisely the treatment effect has been estimated. A confidence interval adds information about statistical uncertainty. It does not describe the range of individual patient outcomes and should not be confused with a prediction interval.
Why can safety and immunogenicity require different summaries?
Safety outcomes and antibody outcomes answer different questions. The primary endpoint concerns immunogenicity, whereas serious adverse events describe an important safety dimension. A treatment-group difference in one domain does not automatically establish a corresponding difference in another.
8. Results
The ClinicalTrials.gov record indicates that results were posted, but no formal statistical analyses are included in the posted results.
Primary Endpoint: Anti-motavizumab Antibodies
Registered primary outcome
Time frame: Day 0 through 120 days post final dose
No formal comparative estimate, confidence interval, or p-value is reported in the ClinicalTrials.gov record.
Serum for measurement of anti-motavizumab antibodies was collected prior to the first, second and, if applicable, fifth doses of study drug, and at the two follow-up visits 30 and 90-120 days post final dose.
The endpoint is designed to identify subjects exhibiting anti-motavizumab antibodies over the specified observation period. It does not, by itself, quantify the magnitude of an antibody response, establish clinical protection, or establish a causal difference between the two treatment groups.
Because the ClinicalTrials.gov record does not provide the antibody-positive counts by arm, there is no valid effect estimate to interpret here. A confidence interval would ordinarily describe the precision of a prespecified comparative measure, while a p-value would address a null hypothesis under a specified statistical test. Neither can be reported without the underlying result and analysis information.
The timing of specimen collection is also part of the endpoint definition. Antibody status observed at a particular collection point should not automatically be interpreted as persistent immunogenicity outside the registered Day 0 through 120-day post-final-dose window.
9. Safety Results
The ClinicalTrials.gov record reports serious adverse events by randomized arm. These figures are presented exactly as provided and are not supplemented with additional safety outcomes from external sources.
| Safety measure | Motavizumab (MEDI-524) 15 mg/kg | Palivizumab 15 mg/kg |
|---|---|---|
| Serious adverse events, affected / at risk | 4 / 66 | 1 / 70 |
The affected/at-risk counts show that serious adverse events were reported in 4 of 66 subjects at risk in the motavizumab group and 1 of 70 subjects at risk in the palivizumab group. Because the ClinicalTrials.gov record provides only these counts, they should not be expanded into a broader safety profile or interpreted as a formal hypothesis test.
10. Reading the Serious-Adverse-Event Counts Correctly
What the counts show
There were 4 affected subjects among 66 at risk in the motavizumab group and 1 affected subject among 70 at risk in the palivizumab group.
What the counts do not show
They do not establish causality, severity beyond the serious-adverse-event classification, exposure-adjusted incidence, or statistical significance.
Why denominators matter
Reporting affected subjects together with the number at risk provides the basic denominator needed to describe the frequency of the reported safety outcome.
Why formal analysis matters
A comparison requires a prespecified estimand and appropriate statistical method. The ClinicalTrials.gov record does not report that analysis.
11. Immunogenicity: The Statistical Question
Immunogenicity is central to the trial because the registered primary endpoint concerns anti-motavizumab antibodies. From a statistical perspective, the key question is not simply whether antibodies were observed, but how the prespecified antibody endpoint was distributed across the randomized intervention groups during the registered follow-up window.
A complete analysis would ideally distinguish the descriptive frequency of antibody-positive subjects from the comparative treatment effect. For example, an absolute difference would answer how much the proportions differed, while a relative measure would describe the ratio of those proportions. These measures can lead to different interpretations when event frequencies are low.
The denominator must follow the prespecified analysis population and evaluability rules. Those rules are not reported in the ClinicalTrials.gov record for this trial.
The collection schedule is also important. Samples were collected before specified doses and at two post-treatment follow-up visits. A longitudinal laboratory dataset could therefore contain repeated measurements per subject, but the registered primary endpoint itself is expressed as a number of subjects exhibiting antibodies over the defined time frame. The appropriate analysis depends on the exact endpoint construction and statistical analysis plan.
12. Blinding and Bias Control
SEASON used quadruple masking. Masking is a design feature intended to reduce the influence of treatment knowledge on trial conduct and assessment. Its value is particularly clear when outcomes involve judgments, reporting behavior, clinical management, or laboratory procedures that could potentially be influenced by knowledge of assignment.
For an antibody endpoint, masking can help maintain consistent specimen collection and study procedures, although laboratory-based measurements also depend on the assay definition and handling rules. The ClinicalTrials.gov record does not identify which parties were masked or provide assay-operational details beyond the timing of serum collection.
13. Randomization and the Comparator
The randomized design places motavizumab and palivizumab in a direct parallel-group comparison. This is statistically important because the comparator provides a reference distribution against which the intervention group's immunogenicity and safety outcomes can be interpreted.
Without randomization, a difference in antibody status or serious adverse events could reflect systematic differences between the groups rather than the intervention itself. Randomization reduces this concern at the design stage, although it does not eliminate sampling variability or other sources of bias that can arise after assignment.
Why the comparator matters
A single-arm immunogenicity rate could describe how often an antibody outcome occurred after motavizumab exposure, but it would not provide the same randomized comparative framework as this two-arm study. The palivizumab group therefore supplies an important reference group for interpreting observed outcomes.
14. Analysis Population and Missing Data
The ClinicalTrials.gov record does not specify whether the primary endpoint was analysed in an intention-to-treat population, a per-protocol population, an evaluable immunogenicity population, or another defined analysis set. They also do not specify how missing serum samples or unevaluable antibody measurements were handled.
This matters because a subject-level antibody endpoint depends on whether a valid assessment was available. If subjects with missing measurements are excluded, the denominator can differ from the randomized population. If missingness is handled using a prespecified rule, the resulting estimate can differ again.
15. Multiplicity and the Five Posted Outcomes
The registry data report 5 outcome measures but identify only 1 registered primary endpoint. This distinction is important. A trial can collect several outcomes while designating only one as the primary endpoint.
The ClinicalTrials.gov record does not describe a multiplicity strategy for the five outcome measures. Therefore, the page does not assume that all five outcomes were tested under a common familywise type I error budget, nor does it treat any unreported comparison as confirmatory.
| Registry information | What can be concluded |
|---|---|
| 1 registered primary endpoint | The anti-motavizumab antibody endpoint is the registered primary endpoint in the ClinicalTrials.gov record. |
| 5 outcome measures posted | The trial reports five outcome measures overall, but the ClinicalTrials.gov record does not provide their complete definitions or statistical hierarchy. |
| No statistical analyses reported | No formal comparative p-values, confidence intervals, or effect estimates can be reproduced from the ClinicalTrials.gov record. |
16. Non-Inferiority, Crossover, Factorial, Interim, and Bayesian Methods
The ClinicalTrials.gov record does not support descriptions of several design features that are sometimes important in clinical-trial statistical analysis.
| Topic | Evidence in the ClinicalTrials.gov record |
|---|---|
| Non-inferiority margin | Not reported in the ClinicalTrials.gov record. |
| Crossover | Not reported in the ClinicalTrials.gov record. |
| Factorial design | Not reported; the recorded design model is PARALLEL. |
| Interim analysis | Not reported in the ClinicalTrials.gov record. |
| Missing-data / imputation method | Not reported in the ClinicalTrials.gov record. |
| Stratification factors | Not reported in the ClinicalTrials.gov record. |
| Bayesian methods | Not reported in the ClinicalTrials.gov record. |
This distinction is intentional. The absence of a method in the ClinicalTrials.gov recordset is not evidence that investigators did or did not use it; it means only that the method is not available in the source material provided for this page.
17. What a Complete Statistical Result Would Contain
For an educational statistical analysis of the primary endpoint, a complete comparative result would normally contain several linked pieces of information:
- Analysis population: the subjects included in the antibody analysis.
- Event definition: the exact laboratory criterion used to classify a subject as exhibiting anti-motavizumab antibodies.
- Counts: antibody-positive and antibody-negative subjects by treatment group.
- Effect measure: an absolute difference, relative risk, odds ratio, or other prespecified estimand.
- Confidence interval: an interval describing uncertainty around the effect estimate.
- Hypothesis test: a prespecified statistical test and corresponding p-value, if formal testing was part of the analysis.
- Missing-data handling: rules for subjects without evaluable measurements.
The ClinicalTrials.gov record provides the endpoint definition and collection schedule, but not the remaining numerical analysis components.
18. Serious Adverse Events: A Separate Statistical Domain
The reported serious-adverse-event counts illustrate why efficacy or immunogenicity and safety should be analysed separately. The primary endpoint concerns anti-motavizumab antibodies, whereas serious adverse events concern safety outcomes.
| Arm | Affected | At risk | Role in interpretation |
|---|---|---|---|
| Motavizumab (MEDI-524) 15 mg/kg | 4 | 66 | Descriptive serious-adverse-event result |
| Palivizumab 15 mg/kg | 1 | 70 | Comparator safety result |
The denominator difference also illustrates a basic clinical-trial reporting principle: event counts should be interpreted alongside the population at risk. However, because the ClinicalTrials.gov record does not state the safety analysis population beyond the affected/at-risk figures, the page does not infer whether these denominators correspond exactly to randomized, treated, or otherwise defined safety populations.
19. Why This Trial Matters Statistically
SEASON is a useful teaching case because it combines several fundamental clinical-trial concepts in a relatively focused design: randomized allocation, a parallel comparator, quadruple masking, a binary immunogenicity endpoint, longitudinal specimen collection, and safety event reporting.
| Concept | How it appears in SEASON |
|---|---|
| Randomization | The allocation is recorded as RANDOMIZED. |
| Parallel-group design | The design model is PARALLEL with 2 arms. |
| Masking | The trial is recorded as QUADRUPLE-masked. |
| Binary endpoint thinking | The primary endpoint counts subjects exhibiting anti-motavizumab antibodies. |
| Repeated specimen collection | Serum was collected before specified doses and at two follow-up visits. |
| Comparative safety | Serious adverse events are reported by intervention arm with affected/at-risk counts. |
| Missing-data considerations | The ClinicalTrials.gov record does not specify the rules for subjects without evaluable antibody measurements. |
| Statistical reporting discipline | Results are posted, but no formal statistical analyses are reported in the ClinicalTrials.gov record. |
20. Clinical Interpretation vs Statistical Interpretation
Statistical interpretation
The trial's design supports a randomized comparison between motavizumab and palivizumab, but the ClinicalTrials.gov record does not provide a formal primary-endpoint comparison.
Clinical interpretation
The registered primary endpoint concerns anti-motavizumab antibodies, while serious adverse events provide a separate safety description. The available the ClinicalTrials.gov record should therefore be read as endpoint-specific rather than as a single overall treatment judgment.
21. Important Limitations and Interpretation Issues
- No formal statistical analyses reported: the statistical-analysis array is empty, so no primary-endpoint effect estimate, confidence interval, or p-value can be reported from the ClinicalTrials.gov record.
- Incomplete outcome definitions: five outcome measures are posted, but only the registered primary endpoint is fully described in the ClinicalTrials.gov record.
- Unknown analysis population: the ClinicalTrials.gov record does not specify the analysis population used for the antibody endpoint.
- Unknown missing-data rules: the record does not specify how missing or unevaluable serum measurements were handled.
- Safety denominator interpretation: serious-adverse-event results are reported as affected/at-risk counts, but the ClinicalTrials.gov record does not define the underlying safety population.
- No multiplicity strategy reported: the presence of five posted outcomes does not establish how multiple statistical comparisons, if any, were handled.
- No subgroup analysis reported: the dataset does not provide subgroup estimates or interaction tests.
- No longitudinal statistical model reported: although serum collection occurred at multiple time points, the ClinicalTrials.gov record does not specify whether antibody status was analysed longitudinally or collapsed into a subject-level endpoint.
- No causal interpretation from descriptive counts alone: numerical differences in reported safety counts do not by themselves establish a statistically significant treatment effect or causality.
22. Statistical Methods Explained in Practical Terms
Randomization establishes the comparison structure. It means that treatment assignment is not simply a decision made after observing a patient's antibody or safety outcome. This is fundamental to interpreting between-group differences causally.
Quadruple masking is a trial-design safeguard. Its statistical importance is indirect: reducing opportunities for knowledge of assignment to influence conduct or assessment can reduce bias in the data that are ultimately analysed.
The primary endpoint is naturally expressed as a count of subjects exhibiting antibodies. A statistical analysis therefore needs to establish who qualifies as antibody-positive, who is evaluable, and which denominator defines the analysis population.
An effect estimate describes the size and direction of a treatment-group difference. A p-value addresses compatibility with a specified null hypothesis under a specified statistical model or test. Neither quantity is reported in the ClinicalTrials.gov record.
A confidence interval adds uncertainty information to an effect estimate. A narrow interval generally represents greater statistical precision than a wide interval, but precision should always be considered alongside the estimand, analysis population, and study design.
23. Related Tutorials
Learn more about the methods used in this trial:
24. Related Statistical Calculators
25. Sources
- ClinicalTrials.gov: SEASON — NCT00113490.
- Linked publication: PubMed record associated with the ClinicalTrials.gov record, PMID 19258920.
Continue through Clinical Biostats
Use the trial's statistical concepts as a starting point for deeper tutorials and practical analysis tools.
26. Record Summary
SEASON provides a focused example of how clinical-trial statistical interpretation begins with study design and endpoint definition before moving to formal inference. The trial was randomized, parallel, and quadruple-masked, with 136 enrolled subjects and two biological interventions: motavizumab (MEDI-524) and palivizumab 15 mg/kg. Its registered primary endpoint was the number of subjects exhibiting anti-motavizumab antibodies from Day 0 through 120 days post final dose. The ClinicalTrials.gov record also report serious adverse events as 4 of 66 subjects at risk for motavizumab and 1 of 70 for palivizumab.
The most important statistical limitation is equally clear: although results were posted, the ClinicalTrials.gov record contains no formal statistical analyses. Instead, it explains how the registered endpoint would normally be analysed and distinguishes descriptive safety information from formal comparative inference.