Evaluating the Safety and Efficacy of Anti-Influenza Intravenous Hyperimmune Immunoglobulin (IVIG) in Adults Hospitalized With Influenza: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Evaluating the Safety and Efficacy of Anti-Influenza Intravenous Hyperimmune Immunoglobulin (IVIG) in Adults Hospitalized With Influenza (NCT02287467), a randomized phase 3 study. The primary outcome is Number of Patients in Each of 6 Clinical Status Categories on Day 7.

2. Clinical Question

The question is about clinical status at a specified time, not just whether a single event occurred. The six categories order outcomes from worse to better, while preserving distinctions among participants whose status falls between the endpoints. That makes the outcome relevant to a trial comparing recovery status among hospitalized participants.

The registry identifies the study as a phase 3 randomized trial and reports enrollment of 329. Its stated purpose was to test the safety and effectiveness of an influenza antibody treatment in people hospitalized for severe flu. The registry record does not report the primary outcome counts by category or an estimated treatment comparison for that outcome. As a result, the clinical question can be described, but the posted information does not establish how the two groups compared on Day 7.

3. Trial Design

The registry describes INSIGHT 006: FLU-IVIG, NCT02287467, as a phase 3 randomized, quadruple-masked, parallel-group study. It lists influenza A and influenza B as the conditions. Enrollment was 329. The study is marked completed; the recorded start was 2015-01 and primary completion was 2018-06-07. The sponsor was the National Institute of Allergy and Infectious Diseases (NIAID).

Participants in the hIVIG arm received a single infusion of intravenous hyperimmune immunoglobulin on Day 0, administered over approximately 2 hours, with standard-of-care treatment for flu. Participants in the placebo arm received a single placebo infusion on the same schedule, also with standard-of-care treatment. Thus, the registry describes a comparison of the study infusion while both groups received standard care.

Random allocation is intended, in general, to support a fair comparison by distributing measured and unmeasured participant characteristics without deliberate selection into treatment groups. Masking is intended, in general, to reduce the chance that knowledge of assignment influences care, outcome assessment, or reporting. The registry labels masking as quadruple but does not specify in the record which roles were masked.

The parallel model means, in general, that participants are assigned to one group rather than crossing between study treatments. The registry record does not report details of the randomization sequence, allocation concealment, eligibility criteria, or the exact roles covered by masking. It also does not report the protocol’s sample-size justification or the planned statistical model for the primary outcome. These details should not be inferred from the design labels 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

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: All infused participants, using multiple imputation to impute outcome for 4 participants with missing data.

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: Participants in the analysis data set.

6. Primary Endpoint

The registry lists the primary outcome as Number of Patients in Each of 6 Clinical Status Categories on Day 7, measured over Assessed on Day 7.

The primary endpoint is the number of participants in each of six clinical status categories on Day 7. The registry describes the categories as an ordinal outcome: they have a natural order from death, the worst status, to discharge from hospital with resumption of normal activities, the best status. The population is all infused participants, with multiple imputation used for the outcome of 4 participants with missing data.

In general, an ordinal endpoint retains more information than a simple event/no-event outcome when the possible statuses have a meaningful order. It can distinguish, for example, participants in intermediate states rather than grouping all outcomes into only “favorable” and “unfavorable” categories. Its usefulness depends on the categories being clinically interpretable and on the analysis respecting their ordered nature.

The endpoint is assessed at a fixed time, Day 7. A fixed-time status measure can describe how participants are doing at that assessment point, but it does not by itself describe the path taken to reach that status or what happens afterward. The registry lists the endpoint and its population but does not report the six category counts, an effect estimate, or a confidence interval. Therefore, the primary endpoint’s observed comparison cannot be characterized from the registry record.

7. Why No Re-analysis Is Shown

8. Statistical Interpretation

9. Statistical Methods Explained

The central statistical feature is an ordered outcome rather than a single binary endpoint. Each participant’s Day 7 status belongs to one of six categories, with the ordering running from death to discharge with normal activities resumed. In general, ordinal outcomes allow distinctions among multiple levels of status while recognizing that the levels have a ranking. This is different from treating every category as unrelated, and different from collapsing the categories into a single yes/no outcome.

What an ordinal comparison can represent

In general, an analysis of an ordinal outcome may compare the distributions across categories or use a model that incorporates their order. A distributional comparison asks whether outcomes tend to fall in better or worse categories between groups, rather than focusing only on one cutoff. The specific statistical method matters: different methods make different assumptions about how the ordering should be represented and how group differences should be summarized. The registry record does not report the planned or performed model for the six-category primary outcome, so the method cannot be identified from the available record.

An ordinal scale is not automatically a numerical scale with equal distances between adjacent categories. In general, the difference between two neighboring clinical states need not be the same as the difference between another neighboring pair. Analysts therefore need to explain the model’s interpretation rather than treat category labels as if they were equally spaced measurements. The registry gives the category ordering and endpoints but does not report the complete category definitions or the primary effect estimate.

Effect estimates and uncertainty

In general, an effect estimate describes the contrast between groups in a form appropriate to the outcome and analysis. A confidence interval describes the uncertainty around an estimate under the assumptions of the analysis; it is not a guarantee that the true effect lies inside the interval. For an ordinal outcome, both the estimate and its interpretation depend on the chosen analysis method. Neither an estimate nor a confidence interval for the primary comparison is reported in the registry record.

A p-value, in general, measures how compatible the observed data are with a specified null hypothesis under a statistical model. It does not measure the size or clinical importance of an effect, and it does not give the probability that the null hypothesis is true. No primary-outcome p-value is reported in the registry record. Without the outcome distribution and analysis details, readers cannot independently assess the comparative result or its precision.

Missing outcome data

The primary outcome population includes all infused participants, and the registry states that multiple imputation was used for 4 participants with missing outcome data. In general, multiple imputation creates more than one plausible completed dataset by replacing missing values with draws based on an imputation model. Analyses are performed across those datasets, and the results are combined to reflect both within-dataset and between-dataset uncertainty.

Multiple imputation does not make missing outcomes observed details. Its validity depends on the imputation model and assumptions about why data are missing, as well as whether relevant predictors and outcome information were included appropriately. The registry record does not report the variables in the model, the assumptions about missingness, the number of imputations, or sensitivity analyses. Those omissions prevent a detailed assessment of how robust the primary comparison is to missing-data assumptions.

Planning calculations

The registry’s planning illustration is described as a calculation based on the observed effect, with a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. It defines the target effect as a stated fraction of the observed effect. Such calculations are planning illustrations, not evidence about the probability that the completed trial detected an effect. They are not observed power and should not be interpreted as a new result of the trial.

For general sample-size planning, the assumed effect, variability, significance level, allocation, and analysis method all influence the required number of participants. If the assumed effect is smaller, more participants are generally required to detect it under otherwise similar assumptions. Here, the individual planning values and the fractions used are not reported in the registry record. The registry also does not report the trial’s own sample-size justification, so the planning illustration should not be treated as documentation of the original design rationale.

Multiplicity and interpretation

The record lists 19 outcome measures and 19 analyses. In general, evaluating several outcomes or several analyses can increase the opportunity for chance findings. A prespecified primary endpoint can help focus interpretation, but does not by itself disclose how secondary analyses or multiple comparisons were handled. The registry record does not report a multiplicity adjustment, hierarchy, or other strategy. It also does not report whether analyses were prespecified in the protocol or modified later.

These method details are important because statistical conclusions rely on more than the outcome label and study design. The primary model, its assumptions, the handling of missing data, and the approach to multiple analyses all affect how an estimate should be understood. Since the primary results and these analysis details are not reported in the registry record, a full statistical appraisal of the treatment comparison is not possible from the record alone.

10. Multiplicity, Missing Data and Analysis Populations

The registry lists 19 outcome measures and 19 analyses, including outcomes assessed at different times and outcomes defined in different ways. When many outcomes or analyses are examined, multiplicity can make isolated results less persuasive if no plan accounts for the number of opportunities to find a chance association. In general, a study may specify a hierarchy of outcomes, adjust statistical tests, or emphasize estimates and uncertainty across analyses. The multiplicity approach is not reported in the registry record.

The primary outcome is a six-category status measure on Day 7. The record states that the primary outcome population includes all infused participants and that multiple imputation was used for 4 participants with missing data. Multiple imputation is a way to account for missing observations by modeling plausible values, but the quality of the result depends on the model and assumptions. The variables used, assumptions about the reasons for missingness, and sensitivity analyses are not reported in the registry record.

The registry does not report the primary outcome category counts or a primary treatment-effect estimate, so it is not possible to evaluate the impact of imputation on the result or compare imputed and observed data. Details of analysis populations for the other outcomes are also not reported in the registry record. The completed status and enrollment figure do not, by themselves, resolve questions about missing outcomes, analysis exclusions, or multiplicity.

11. 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 3% reporting threshold.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: chronic obstructive pulmonary disease (3.3%), atrial fibrillation (1.3%), acute kidney injury (1.3%), respiratory failure (1.3%), acute myocardial infarction (1.3%).

The adverse-event record lists 156 participants at risk for serious adverse events in the hIVIG arm and 152 in the placebo arm. Serious adverse events affected 25 and 26 participants, respectively. For other adverse events, the listed numbers at risk are 156 and 152, and the numbers affected are 13 and 14. The record also lists 6 deaths in the hIVIG arm and 5 in the placebo arm.

These are counts, not adjusted comparisons. The registry record does not report a statistical comparison or uncertainty interval for these figures. The terms listed among the most frequent serious adverse events include chronic obstructive pulmonary disease (3.3%), atrial fibrillation (1.3%), acute kidney injury (1.3%), respiratory failure (1.3%), acute myocardial infarction (1.3%), and influenza (1.3%). The listed terms are not linked to a specific arm in the record.

In general, adverse-event counts describe events recorded under the registry’s reporting definitions; they do not alone establish that an event was caused by a study infusion. These safety figures should be read separately from the six-category primary endpoint. The registry record does not provide the detailed event narratives, timing, or causal assessments needed for further interpretation.

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

The registry record establishes that this was a randomized, quadruple-masked comparison of hIVIG and placebo, with standard-of-care flu treatment in both arms. It identifies a clinically ordered primary outcome assessed on Day 7 and describes the intended population and missing-data approach. Those details explain what the trial set out to compare, but they do not show which group had better clinical status.

The primary outcome results are not reported in the registry record. No category counts, comparative effect estimate, confidence interval, or p-value for that endpoint is available in the record. It is therefore not possible to characterize the size, direction, or precision of a primary-outcome difference. Missing results should not be interpreted as evidence of no difference, nor do the design details establish that a difference occurred.

The adverse-event record contains descriptive participant counts and a list of serious event terms with percentages. Without an arm-specific analysis, uncertainty estimates, and detailed event information, those entries do not establish a causal safety comparison. They also do not substitute for the primary efficacy outcome. The registry lists secondary outcomes such as hospital discharge, mortality, viral load change, and death or re-hospitalization, but their names alone do not provide their results.

In general, clinical interpretation requires looking at the estimated contrast, its uncertainty, the analysis population, and the outcome definition together. The registry record does not report enough primary outcome analysis to make that assessment. The trial’s completed status indicates that its registry status is complete; it does not imply that all outcome results are present in the registry record or that any particular treatment conclusion follows.

13. Why This Trial Matters Statistically

This trial illustrates a common statistical challenge in clinical research: the primary outcome is not a simple event count but a ranked set of clinical states. Such outcomes can preserve distinctions among levels of status, but their analysis and interpretation depend on how the ordering is modeled. A clear definition of the categories, a prespecified analysis, and an interpretable effect estimate are important for readers assessing what a group comparison means.

The trial also illustrates why outcome reporting matters as much as design labels. Randomization and masking describe features intended to support an unbiased comparison, but they cannot replace the group outcome distributions or the analysis of those outcomes. In this record, the primary endpoint is identified, but its category counts and comparative estimate are not reported. Statistical conclusions about the primary question therefore cannot be drawn from the registry record.

Missing data and multiple analyses are further reasons to report methods in detail. The record notes multiple imputation for 4 participants on the primary outcome and lists 19 outcome measures and 19 analyses. Without the imputation assumptions and multiplicity strategy, readers cannot fully assess how these choices were addressed. These are general considerations for interpreting clinical trials, not evidence that a particular method was or was not appropriate in this study.

The safety entries demonstrate another distinction: descriptive adverse-event counts are not the same as a statistical comparison of treatment groups. Counts, event definitions, follow-up, and uncertainty all matter when interpreting safety. Taken together, the registry record offers useful information about the intended comparison and endpoint, while the absent primary analysis limits what can be concluded statistically.

14. 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 main trial-specific limitation for interpreting the posted information is that the six-category Day 7 primary outcome results are not reported in the registry record. The category counts by arm, the primary effect estimate, and its uncertainty are absent. Without them, the comparative clinical status cannot be assessed from this record.

The record states that multiple imputation was used for 4 participants with missing primary outcome data, but does not describe the imputation model, assumptions, or sensitivity analyses. It also does not report the statistical model used for the ordinal outcome. These omissions make it difficult to evaluate whether the analysis reflects the outcome’s ordering appropriately and how sensitive the result might be to missing-data assumptions.

Although 19 outcome measures and 19 analyses are listed, the registry record does not describe a multiplicity strategy or identify how the analyses relate to any prespecified hierarchy. The sample-size information is described as a planning illustration based on an observed effect, but the record does not report the trial’s own design justification or the illustration’s specific values. These registry entries limit interpretation of the analysis plan, not the validity of the trial itself.

The safety record provides counts and selected serious adverse-event terms, but does not report comparative uncertainty or link the listed terms to an arm. Further details about event timing and causal assessment are not reported in the registry record. These limitations concern what can be evaluated from the registry record and should not be taken as evidence that the information is absent from every other source.

15. Related Tutorials

16. Related Statistical Calculators

17. Sources

• ClinicalTrials.gov: Evaluating the Safety and Efficacy of Anti-Influenza Intravenous Hyperimmune Immunoglobulin (IVIG) in Adults Hospitalized With Influenza — NCT02287467.
• Registry record last updated 2019-11-14; status last verified 2019-11.
• Results first posted 2019-11-14.
• 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.

18. Record Summary

NCT02287467, titled “Evaluating the Safety and Efficacy of Anti-Influenza Intravenous Hyperimmune Immunoglobulin (IVIG) in Adults Hospitalized With Influenza,” is listed as a completed phase 3 randomized, quadruple-masked, parallel-group trial with enrollment of 329. The recorded start was 2015-01, and primary completion was 2018-06-07. The sponsor was the National Institute of Allergy and Infectious Diseases (NIAID).

The study compared a single Day 0 hIVIG infusion with placebo; both groups also received standard-of-care flu treatment. The primary endpoint was the distribution across six clinical status categories on Day 7, from death to discharge with normal activities resumed. The registry states that multiple imputation was used for 4 participants with missing primary outcome data. The primary category counts and treatment comparison are not reported in the registry record.