Iota-Carrageenan Nasal Spray in Common Cold: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Iota-Carrageenan Nasal Spray in Common Cold (NCT01944631), a randomized phase 4 study. The primary outcome is Total Symptom Score (TSS) Over Days 2 to 4 (TSS2-4).

2. Clinical Question

The total symptom score combines eight symptoms: headache, muscle ache, chilliness, sore throat, blocked nose, runny nose, cough, and sneezing. Each symptom is scored from 0, for absent, to 3, for severe. The trial’s primary measure averages the total across days 2, 3, and 4. It therefore asks about the overall symptom burden during those early treatment days, rather than a single symptom or a single day.

The registry also lists outcomes concerning systemic and local symptoms, the total symptom score over a 10-day period, duration of the cold, and patients’ overall assessment of efficacy. Those outcomes broaden the picture but do not replace the prespecified primary endpoint. The trial record does not report a more detailed hypothesis or decision rule for interpreting the primary comparison.

3. Trial Design

This was a phase 4, randomized, double-blind, parallel-group trial sponsored by Boehringer Ingelheim. The registry lists 200 participants enrolled and a completed study. It describes two nasal-spray groups: placebo and Iota-Carrageenan, with the latter also named Bisolviral. Both sprays were described as being used four times a day over 4 to 10 days.

Random allocation is intended to make treatment groups comparable, on average, with respect to measured and unmeasured characteristics at the start of a study. Double masking means that the registry identifies the trial as masked for two parties, but it does not specify in the record which roles were masked or how masking was maintained. The parallel design means that participants were assigned to a group and assessed in that group, rather than crossing between treatments.

The primary endpoint analysis included 97 participants in the placebo group and 98 in the Bisolviral group. The endpoint description defines the full analysis set as randomized participants who used at least one dose of trial treatment, provided a baseline total symptom score, and had any post-treatment data for the primary endpoint. The reasons that these analysis counts differ from the enrollment figure are not reported in the registry record.

The study began in 2013-09, reached primary completion in 2014-03, and is listed as completed. The record gives no further detail about recruitment procedures, allocation concealment, masking checks, protocol deviations, or the planned statistical design. Details of the trial’s original sample-size justification and its original power assumptions are not reported in the registry record. The planning calculations described in the sample-size discussion are illustrations based on the observed estimate, not a reconstruction of the original design.

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: Full analysis set (FAS) which included all randomised patients who used at least one dose of trial treatment, who provided a baseline total symptom score (TSS) as well as any post-treatment data for the primary endpoint.

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: Treated set which included all randomised patients who used at least one dose of trial treatment.

6. Primary Endpoint

The registry lists the primary outcome as Total Symptom Score (TSS) Over Days 2 to 4 (TSS2-4), measured over Days 2, 3 and 4.

The primary endpoint is the mean total symptom score over days 2 to 4. The total is formed from eight common cold symptoms: three systemic symptoms—headache, muscle ache, and chilliness—and five local symptoms—sore throat, blocked nose, runny nose, cough, and sneezing. Each symptom receives an ordinal score from 0 to 3, ranging from absent to severe. The daily total ranges from 0 to 24, and the endpoint is the average of the daily totals on days 2, 3, and 4.

A combined score is used to represent overall symptom burden rather than focusing on one complaint. Averaging across the stated days also makes the measure reflect more than a single daily observation. The score remains a constructed scale: its numerical units are not equivalent to a direct count of symptoms, and equal numerical changes need not have identical meaning for every participant.

The posted group estimates are least-squares means, with standard errors reported as the measure of dispersion. The primary comparison is the net mean difference between Bisolviral and placebo, adjusted using the sponsor-reported ANCOVA analysis. A negative difference indicates a lower estimated score in the Bisolviral group, because the comparison is expressed as Bisolviral minus placebo. The registry record does not describe the covariates used in the ANCOVA or provide additional details about how the score was handled in that model.

7. Results Posted in the Registry

• Bisolviral: mean 5.78 versus 6.39 on control; difference -0.61 (95% CI -1.32 to 0.09), standardized effect d = -0.24; z-test p = 0.087.

8. Secondary Efficacy Endpoints

The registry also posts results for secondary outcomes. For each outcome below, the endpoint type, the test applied to the posted summary statistics, the result and its interpretation are given in turn. Secondary endpoints are usually not adjusted for multiple testing, so they support rather than confirm the primary finding.

Mean of the Sum of 3 Single Systemic Common Cold Symptom Scores Over Days 2 to 4 (SSS2-4)

• Endpoint type: Continuous: a mean value per participant, compared as a difference in means.
• Test: Unadjusted two-sample z-test for the difference in means, using the posted standard errors.
• Result:
  • Bisolviral: mean 1.12 versus 1.30 on control; difference -0.18 (95% CI -0.46 to 0.11), standardized effect d = -0.17; z-test p = 0.227.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Bisolviral and the control. The interval gives the range of effects that remain compatible with the data.

Mean of the Sum of 5 Single Local Common Cold Symptom Scores Mean Over Days 2 to 4 (LSS2-4)

• Endpoint type: Continuous: a mean value per participant, compared as a difference in means.
• Test: Unadjusted two-sample z-test for the difference in means, using the posted standard errors.
• Result:
  • Bisolviral: mean 4.66 versus 5.10 on control; difference -0.44 (95% CI -1.04 to 0.15), standardized effect d = -0.21; z-test p = 0.144.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Bisolviral and the control. The interval gives the range of effects that remain compatible with the data.

Area Under the Curve (AUC) Over the 10-day Period for the TSS (AUC-TSS 1-10)

• Endpoint type: Continuous: a mean value per participant, compared as a difference in means.
• Test: Unadjusted two-sample z-test for the difference in means, using the posted standard errors.
• Result:
  • Bisolviral: mean 41.94 versus 41.21 on control; difference 0.73 (95% CI -5.35 to 6.81), standardized effect d = 0.03; z-test p = 0.815.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Bisolviral and the control. The interval gives the range of effects that remain compatible with the data.

Across the posted secondary symptom outcomes, the mean differences for the systemic symptom score, local symptom score, and 10-day total symptom-score area under the curve are small relative to their uncertainty. The two symptom-subscore estimates point in the same negative direction as the primary estimate, while the area-under-the-curve estimate points in the positive direction. Their reported confidence intervals include zero. Taken together, the secondary results do not create a clear, consistent pattern that changes the interpretation of the primary comparison.

The registry lists other secondary outcomes, including duration of the cold and patients’ overall assessment of efficacy, but their results are not posted among the secondary endpoint results available in the record. Their outcome definitions and findings therefore cannot be assessed; those results are not reported in the registry record.

General explanation: multiple outcomes create multiple opportunities to observe a result that appears unusual by chance. If many outcomes are tested and each is interpreted separately without adjustment, the chance of at least one apparently notable finding can increase. The registry record does not report a multiplicity adjustment or a hierarchy for the secondary outcomes. As a result, unadjusted secondary comparisons carry less weight than the primary result and should be treated as supportive or exploratory rather than as independent confirmatory evidence.

The secondary outcomes measure related but distinct aspects of symptom burden and illness course. Their alignment or disagreement can offer context, but it does not establish a separate treatment effect without consideration of uncertainty, outcome selection, and multiplicity. The posted pattern is best read as additional context that remains inconclusive, not as a substitute for the primary comparison.

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

• Mean of the Sum of 3 Single Systemic Common Cold Symptom Scores Over Days 2 to 4 (SSS2-4)
• Mean of the Sum of 5 Single Local Common Cold Symptom Scores Mean Over Days 2 to 4 (LSS2-4)
• Area Under the Curve (AUC) Over the 10-day Period for the TSS (AUC-TSS 1-10)
• Duration of the Cold
• Patient Overall Assessment of Efficacy

9. Statistical Interpretation

10. Statistical Methods Explained

The registry reports the primary outcome as a continuous measure, summarized by least-squares means and standard errors. The least-squares means are model-based group averages. The standard error describes uncertainty in an estimated mean; it is not the spread of individual participants’ symptom scores. Individual scores can vary more widely than the uncertainty around a group mean.

What does the ANCOVA comparison represent?

The sponsor reports an analysis of covariance, or ANCOVA, and presents a net mean difference. In general, ANCOVA compares group means while accounting for one or more covariates in a statistical model. Adjustment can improve precision or account for baseline differences, depending on the model and study data. The registry record does not identify the covariates, model specification, or missing-data method, so those details cannot be evaluated from this result alone.

The posted adjusted estimate is -0.613, with a 95% confidence interval from -1.321 to 0.095. The contrast is reported for Bisolviral relative to placebo, so negative values represent lower estimated scores for Bisolviral. The interval crosses zero, meaning that the data do not pin down the direction of the underlying mean difference with high precision. This does not demonstrate equivalence: an equivalence conclusion would require a stated margin and an analysis designed to test it, neither of which is reported in the registry record.

What is the role of the unadjusted recalculation?

The recalculated comparison uses the posted group summary statistics to produce a simple, unadjusted estimate. It gives a mean difference of -0.613, a 95% confidence interval from -1.316 to 0.090, and an unadjusted p-value of 0.087. Such a calculation can help readers check the general scale and direction of the contrast, but it does not reproduce an adjusted ANCOVA unless its model and assumptions match the sponsor’s analysis. The sponsor’s own analysis takes precedence. The recalculation also relies on an approximate standard deviation derived from the posted standard errors, so it should not be mistaken for access to participant-level data.

How should the confidence interval be read?

General explanation: a 95% confidence interval is a procedure that, under its assumptions, would cover the true parameter in 95% of repeated samples. For a particular result, it is not a 95% probability statement about the fixed true effect. The interval gives a range of effect estimates that are reasonably compatible with the observed data and analysis assumptions. A narrow interval indicates greater precision than a wide interval, but precision is not the same as freedom from bias or model error.

For this outcome, the interval includes zero and spans negative and positive values. Its midpoint is represented by the reported estimate, but readers should interpret the full interval rather than focusing only on the point estimate or p-value. The registry does not report a minimum important difference on the score, so the interval cannot be classified against a clinical-importance threshold based on the record.

Why can symptom scores be difficult to model?

General explanation: a symptom score built from ordinal responses is often analyzed as a continuous outcome when the analysis treats differences in score as meaningful and uses a mean contrast. The score’s construction and distribution matter: floor effects, ceiling effects, skew, or repeated measurements can affect how well a model describes the data. The endpoint ranges from 0 to 24, but the registry does not provide the distribution of individual scores, model diagnostics, or evidence about these assumptions.

The endpoint averages repeated daily totals across days 2, 3, and 4. General explanation: repeated measurements on the same participant are correlated, so an analysis should account for the endpoint’s construction and any within-participant dependence relevant to the model. The registry identifies the summarized endpoint and ANCOVA, but does not give enough detail to reconstruct all aspects of the analysis.

Why does over-dispersion matter?

General explanation: over-dispersion occurs when the observed variability is greater than a statistical model expects. If a model understates variability, standard errors can be too small and confidence intervals too narrow; if it accommodates variability appropriately, uncertainty estimates are more credible. This trial’s registry entry does not report a dispersion diagnostic or a separate over-dispersion adjustment. Since the primary outcome is a mean of a bounded symptom score, the reader would need participant-level data and model details to assess the distribution directly.

The registry reports standard errors for the group least-squares means, but not the underlying individual-level distributions or full model output. These summary values support a description of the reported estimate and its uncertainty; they do not allow every modeling choice to be independently verified.

11. Confidence Intervals and Precision

The sponsor-reported primary 95% confidence interval extends from -1.321 to 0.095 around a mean difference of -0.613. It includes zero, so the result does not identify a single direction of difference with certainty. The point estimate favors a lower symptom score in the Bisolviral group, but the uncertainty includes a small difference in the opposite direction as well as larger negative differences. The registry does not state a clinically important threshold for interpreting the score, which limits conclusions about the practical size of effects compatible with the interval.

The interval is a measure of sampling precision under the stated analysis, not a guarantee that the estimate is unbiased. It does not account automatically for unreported design issues, missing-data assumptions, or any multiplicity concerns. The registry record does not provide enough information to assess those issues fully. The trial’s analysis counts for the primary endpoint are 97 in the placebo group and 98 in the Bisolviral group, while the enrollment figure is 200; the reasons for the difference are not reported in the registry record.

General explanation: an estimate can be directionally suggestive while remaining imprecise. A confidence interval that includes zero does not prove no effect, just as an interval excluding zero would not by itself establish clinical importance. Interpretation depends on the estimate, the width of the interval, the endpoint’s meaning, and any relevant threshold. No such threshold is recorded for this outcome.

The sponsor’s interval and the recalculated unadjusted interval are similar but not identical. The sponsor’s adjusted result is the primary interpretation because it is the analysis reported by the sponsor; the summary-statistic recalculation is a check, not a replacement. Neither interval eliminates uncertainty about the true mean difference.

12. Planning a Similar Trial: Sample Size

This is a planning illustration, not a re-evaluation of the original trial. It asks: if a new study wanted to detect a difference like the one observed, or a smaller one, how large would it need to be? It is deliberately not “observed power” of the finished trial, which is not a meaningful quantity.

The sample-size figures are planning illustrations, not the original trial design calculations. For these continuous outcomes, the figures are participants per arm, not total participants or total events. The planning assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. Each row uses the stated fraction of the observed effect as the effect to detect. The trial’s original sample-size assumptions and justification are not reported in the registry record.

The 80% power column indicates the illustrative number of participants per arm for a design with an 80% chance of detecting the specified effect, if that effect and the planning assumptions were correct. The 90% power column gives the corresponding planning target for a 90% chance. These are design probabilities under assumptions, not probabilities that the trial’s result is true or that a particular finding will occur.

For the primary endpoint, the planning illustration lists 262 participants per arm for 80% power and 351 participants per arm for 90% power when planning for 100% of the observed effect. Planning for 75% of the observed effect gives 466 participants per arm for 80% power and 623 participants per arm for 90% power. Planning for 50% gives 1,047 participants per arm for 80% power and 1,402 participants per arm for 90% power. The pattern illustrates that smaller target effects require more participants to detect, all else being equal.

These calculations use the observed effect as their starting point, so they are sensitive to the uncertainty in that estimate. They are not observed power and should not be read as a measure of whether the completed study “had enough power” after seeing its result. They are planning scenarios under stated assumptions, not evidence about the trial’s original planning process.

13. How the Sample Size Is Derived

Power is the probability that a study of a given size rejects the null hypothesis when a specific true effect exists. It depends on four things: the effect to detect, the variability of the outcome, the significance level (here a two-sided α of 0.05, so z_1−α/2 = 1.960) and the desired power (z_1−β = 0.842 for 80% and 1.282 for 90%).

Formula

n is the number of participants in each arm, Δ is the difference in means to detect and σ is the common standard deviation.

Worked calculation for this trial

1. Observed difference Δ = -0.613; pooled standard deviation σ = 2.503.
2. n = 2 × (1.96 + 0.842)² × 2.503² / -0.613² = 261.7, rounded up to 262 participants per arm for 80% power.

The smaller-effect rows in the table above repeat the same calculation with the effect multiplied by 0.75 and 0.5 (for ratios, applied to the logarithm of the ratio).

R code

alpha <- 0.05
z_a   <- qnorm(1 - alpha / 2)          # two-sided
delta <- -0.613                      # observed difference in means
sigma <- 2.50309916297559                     # pooled standard deviation
n_per_arm <- function(power, frac = 1) {
  ceiling(round(2 * (z_a + qnorm(power))^2 * sigma^2 / (delta * frac)^2, 6))
}
for (f in c(1, 0.75, 0.5))
  cat(sprintf("%3.0f%% of observed effect: n per arm = %s (80%% power), %s (90%% power)\n", 100 * f,
              format(n_per_arm(0.80, f), big.mark = ","), format(n_per_arm(0.90, f), big.mark = ",")))

# Check against R's built-in function (t-based, so very slightly larger)
cat("power.t.test:", round(power.t.test(delta = delta, sd = sigma, power = 0.80)$n, 1), "\n")

# Simulation: exact two-sample t-test using simulated means and variances
set.seed(1944631)
n    <- n_per_arm(0.80)
reps <- 20000
m_diff <- rnorm(reps, delta, sigma * sqrt(2 / n))             # difference in sample means
s2     <- sigma^2 * rchisq(reps, 2 * (n - 1)) / (2 * (n - 1))   # pooled sample variance
tstat  <- m_diff / sqrt(s2 * 2 / n)
cat(sprintf("Simulated power at n = %s per arm: %.3f (%d replicates)\n", format(n, big.mark = ","), mean(abs(tstat) > qt(0.975, 2 * (n - 1))), reps))

Output from running the code

100% of observed effect: n per arm = 262 (80% power), 351 (90% power)
 75% of observed effect: n per arm = 466 (80% power), 623 (90% power)
 50% of observed effect: n per arm = 1,047 (80% power), 1,402 (90% power)
power.t.test: 262.7 
Simulated power at n = 262 per arm: 0.795 (20000 replicates)

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

14. Analyses Reported by the Sponsor

The sponsor reports an ANCOVA estimate of -0.613 for the primary mean difference, with a 95% confidence interval from -1.321 to 0.095 and a p-value of 0.089. The unadjusted recalculation from the posted group summaries gives the same point estimate to the reported precision, a confidence interval from -1.316 to 0.090, and an unadjusted p-value of 0.087. The estimates are close, while the small differences in interval limits and p-values reflect that the sponsor’s analysis and the recalculation are not the same analysis.

The sponsor’s result is the governing reported analysis. The recalculation is an approximate, unadjusted comparison based on summary data; it cannot reproduce the sponsor’s covariate-adjusted model or verify its assumptions. In both analyses, the interval includes zero, so the broad interpretation is consistent: the estimated difference is negative, but the result remains uncertain about whether the underlying mean difference is below or above zero.

The registry does not report detailed ANCOVA covariates or model diagnostics, so the adjusted analysis cannot be fully reconstructed from the record. No discrepancy flag is indicated between the sponsor’s result and the recalculation.

15. Multiplicity, Missing Data and Analysis Populations

The registry identifies a primary endpoint and several secondary outcomes. It does not report whether the statistical plan controlled the family-wise error rate or used another multiplicity procedure. General explanation: when researchers test several outcomes, the chance of obtaining at least one apparently positive result by chance can rise if each is assessed independently. A prespecified hierarchy or adjustment can address that issue. Without a reported plan, secondary p-values should not be treated as if they were individually confirmatory.

The registry record also does not describe the handling of missing endpoint data, the amount of missingness, imputation procedures, sensitivity analyses, or reasons for participants’ missing data. The primary endpoint population is defined as randomized participants who used at least one dose, had a baseline total symptom score, and contributed any post-treatment data for that endpoint. This definition clarifies who was included, but it does not show how missing values were handled or establish that missingness had no effect on the estimate.

The record describes a full analysis set rather than stating that every randomized participant was included in the primary analysis. It reports 97 placebo and 98 Bisolviral participants for that outcome. The reasons that these counts differ from the 200 enrolled are not reported in the registry record. The record does not provide a separate per-protocol analysis or a detailed accounting of participants excluded from the endpoint set.

Details of the original statistical analysis plan, covariate selection for ANCOVA, assumptions about the outcome distribution, interim analyses, and any protocol-defined decision thresholds are not reported in the registry record. These omissions limit assessment of whether the posted analysis fully follows a prespecified plan and how robust the result is to alternative assumptions. They do not by themselves show that the analysis was inappropriate; they identify what cannot be checked from the registry entry.

16. 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 5% reporting threshold.

The registry reports serious adverse events in 0 of 100 participants at risk in the placebo group and 0 of 100 participants at risk in the Bisolviral group. It also reports other adverse events in 0 of 100 participants at risk in each group. No serious adverse event terms are listed. Death figures are not reported in the registry record.

These entries describe what was recorded for the listed risk groups; they do not establish that no adverse events of any kind occurred. The registry’s serious and other adverse-event summaries are separate categories, and the record does not provide a fuller narrative of collection methods, event definitions, follow-up completeness, or event severity beyond these figures. The adverse-event risk counts are not the same as the primary endpoint analysis counts, and the registry does not explain the difference.

General explanation: an observed count of zero does not prove that an event is impossible or that there is no risk. Safety interpretation depends on the number followed, the length and completeness of follow-up, the event definitions, and how events were collected and coded. The available figures should therefore be read as registry-reported counts, not as a comprehensive safety assessment.

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

The primary result estimates a lower average total symptom score for Bisolviral than for placebo over days 2 to 4, with a mean difference of -0.613. The sponsor-reported 95% confidence interval, -1.321 to 0.095, includes zero. The posted result therefore does not establish a clear difference between groups, and the uncertainty leaves more than one direction of effect compatible with the data and model.

This does not establish that the treatments are equivalent or that their effects are exactly the same. Equivalence requires a prespecified margin and an appropriate analysis, and neither is reported in the registry record. Nor does a negative point estimate alone establish a meaningful benefit: the record does not state a threshold for a clinically important change on the total symptom scale.

The endpoint summarizes eight symptoms averaged across days 2, 3, and 4. It does not directly answer whether the groups differed in every individual symptom, how long the cold lasted, or how participants judged efficacy. The registry lists some of these as secondary outcomes, but not all their results are posted. The primary result should not be extended to outcomes that were not reported.

General explanation: failure to demonstrate a statistically clear difference is not proof of no effect. It may reflect an effect near zero, uncertainty in the estimate, or limits in the data and analysis. Conversely, a statistically clear difference would not automatically establish practical importance. Here, both the estimate and its interval matter, and the registry does not provide a clinical-importance threshold for judging the size of the possible differences.

The safety figures record zero serious adverse events and zero other adverse events in the stated risk groups, but these counts alone cannot establish comprehensive safety. The most defensible reading is limited to the outcomes and data actually posted: an uncertain primary symptom-score contrast, secondary findings that do not resolve that uncertainty, and sparse registry-level adverse-event summaries.

18. Why This Trial Matters Statistically

This trial illustrates why a treatment comparison should be interpreted through both its estimated effect and its uncertainty. The primary point estimate is negative, but the confidence interval includes zero. Looking only at the direction of the estimate could overstate what the data establish; looking only at whether a threshold is crossed could hide the range of effects still compatible with the result.

It also shows the distinction between the sponsor-reported adjusted analysis and a recalculation from group summaries. The recalculation is useful as a transparent check of the broad result, but it cannot replace the reported ANCOVA or recover its details. Similar estimates do not make the statistical procedures interchangeable.

The secondary outcomes highlight multiplicity and selective interpretation. Several related measures can describe different aspects of a condition, but multiple unadjusted comparisons should not be treated as a collection of independent confirmations. A coherent analysis plan, outcome hierarchy, and clear reporting of missing data would help readers judge how much weight those results deserve.

Finally, the record demonstrates the limits of registry summaries. It reports the primary endpoint definition, group estimates, an adjusted comparison, and basic adverse-event counts, while leaving important details of modeling, missing data, and the original sample-size rationale unreported. Statistical interpretation depends not only on a point estimate, but also on design, analysis choices, precision, and transparent reporting.

19. 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 posted primary result is based on a full analysis set that requires treatment use, baseline measurement, and post-treatment data. The record does not give a participant flow or explain exclusions from that set, so the effect of those eligibility conditions on the comparison cannot be assessed. The reason for the difference between the enrollment count and the primary endpoint analysis counts is not reported in the registry record.

The ANCOVA is not described in enough detail to reproduce it: covariates, model diagnostics, and missing-data handling are not reported. The recalculation is unadjusted and approximate, and therefore cannot address those limitations. The registry also does not state the original sample-size assumptions or justification, so the trial’s planned sensitivity to effects of particular sizes cannot be judged from the record.

Several secondary outcomes are listed, but only some have posted results. The registry does not report a multiplicity approach, and results for duration of the cold and patient overall assessment are not reported in the registry record. This limits the ability to compare the primary symptom-score result with other measures of illness or participant assessment.

The primary score combines ordinal symptom ratings into an average. The registry does not provide individual-level distributions or evidence about the score’s behavior, such as floor or ceiling effects. No threshold for a clinically important score difference is reported. These gaps make it difficult to assess the practical meaning of the estimate independently of its statistical uncertainty.

Safety reporting is also limited to the listed event counts. The record does not provide detailed collection procedures, follow-up completeness, event descriptions beyond the serious-event terms listing, or death counts. These omissions constrain interpretation of the safety summary and should not be read as proof that unreported events did not occur.

20. Related Tutorials

21. Related Statistical Calculators

22. Sources

• ClinicalTrials.gov: Iota-Carrageenan Nasal Spray in Common Cold — NCT01944631.
• Registry record last updated 2015-04-03; status last verified 2015-03.
• Results first posted 2015-04-03.
• 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.

23. Record Summary

The registry describes a completed, randomized, double-blind, parallel-group phase 4 trial of Iota-Carrageenan nasal spray, named Bisolviral in the trial arm, versus placebo for common cold symptoms. Enrollment was 200. Both sprays were described as being used four times daily over 4 to 10 days.

The primary endpoint was the average total symptom score over days 2 to 4, based on eight symptoms scored from 0 to 3. The sponsor-reported ANCOVA mean difference was -0.613, with a 95% confidence interval from -1.321 to 0.095. The interval includes zero, leaving uncertainty about the direction and size of the average difference.

Some secondary symptom outcomes were posted, while results for other listed secondary outcomes are not reported in the registry record. The record gives zero serious and zero other adverse events among 100 at-risk participants in each group; deaths are not reported in the registry record. Details of multiplicity, missing-data handling, and the original sample-size justification are not reported in the registry record.