Study to Evaluate the Efficacy of N-acetylcysteine (NAC) in the Treatment of the Common Cold and Cough: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Study to Evaluate the Efficacy of N-acetylcysteine (NAC) in the Treatment of the Common Cold and Cough (NCT02379637), a randomized phase 2 study. The primary outcome is Cough Count (Number of Coughs Will be Measured by a 24-hour Ambulatory Cough Monitoring System for the First 72 Hours).

2. Clinical Question

The study focused on an early measure of cough frequency in adults with a cough associated with a common cold. Coughs were to be measured using a 24-hour ambulatory cough monitoring system during the first 72 hours. The endpoint was analyzed as a continuous measure after log transformation, and the registry identifies the mean as the measure of central tendency.

This question is narrower than whether treatment changes the overall course of a cold or improves every cold symptom. The primary endpoint concerns the recorded cough count over the stated monitoring period. Safety was also listed as a secondary outcome, using the number of patients with adverse events. The registry record does not report additional clinical outcomes or a fuller account of symptom assessment.

3. Trial Design

The registry describes a completed phase 2, randomized, parallel-group, quadruple-masked study comparing N-acetylcysteine with placebo. Its official title characterizes it as a multiple-dose, proof-of-concept study in adults with common cold, or viral upper respiratory tract infection, associated with cough. The enrollment was 70, with 35 participants in each posted arm.

The study began in 2015-01 and had primary completion in 2015-04. The sponsor was Novartis. These registry entries establish the broad setting and design, but they do not provide the full protocol or a detailed account of how treatment and monitoring were carried out. The particular N-acetylcysteine dose, dosing intervals, duration of dosing, and timing of assessments beyond the primary monitoring window are not reported in the registry record.

The primary endpoint was cough count measured by a 24-hour ambulatory cough monitoring system for the first 72 hours. The reported outcome is the log-transformed total cough count, summarized as a mean with standard deviation, for the Full Analysis set. The registry also lists safety, measured as the number of patients with adverse events, as a secondary outcome.

Randomized allocation and masking describe important design features, but the record does not state the randomization method, allocation concealment procedures, or which people were included in the quadruple masking. It also does not report a detailed analysis plan, the original sample-size justification, or how the Full Analysis set was defined. The registry marks the study as completed; that status alone does not establish whether all planned analyses or all protocol procedures were completed as intended.

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.

Before assignment: Screening and randomization are on day 1 Duration of the study = 7 days

Overall Study

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: Full Analysis set

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 Cough Count (Number of Coughs Will be Measured by a 24-hour Ambulatory Cough Monitoring System for the First 72 Hours), measured over 72 hours.

The primary endpoint is the total number of coughs recorded during the first 72 hours using a 24-hour ambulatory cough monitoring system. The posted result is expressed as a log-transformed cough count, rather than as the raw total number of coughs. The registry identifies the endpoint as continuous and reports its mean and standard deviation for each arm in the Full Analysis set.

Continuous measures retain information about the spread of observations and allow the comparison to be expressed as a difference between arm means. Log transformation changes the scale on which the counts are analyzed. It can be useful when count measurements are strongly skewed, since a small number of high counts can otherwise exert substantial influence on the mean. That is a general statistical reason for transforming a measure; the registry record does not state the investigators’ specific rationale for using a log transformation.

The endpoint was selected to capture cough frequency with an ambulatory monitoring system during an early treatment period. This approach differs from relying only on a participant’s recollection of cough frequency, although the registry record does not describe the device’s operating details or any validation procedures. The outcome does not, by itself, describe all cold symptoms, functional impact, or later course. Interpretation should therefore remain tied to the measured quantity and monitoring window rather than being extended to broader outcomes not reported in the registry record.

7. Results Posted in the Registry

• A N-acetylcysteine: mean 6.92 versus 6.51 on control; difference 0.41 (95% CI -0.01 to 0.83), standardized effect d = 0.46; z-test p = 0.055.

8. Statistical Interpretation

9. Statistical Methods Explained

The endpoint is a continuous measure after log transformation, and the registry identifies the mean as its summary measure. A mean describes the arithmetic center of the analyzed values on the scale being used. Because this endpoint is log-transformed, the arm means and their difference refer to that transformed scale. They should not be interpreted as raw cough counts or converted into a raw-count difference without the relevant transformation details and analysis.

What does the standard deviation describe?

The standard deviation describes how spread out individual observations are around the mean within an arm. It is not the standard error of the mean, and it is not the uncertainty interval for the treatment contrast. The posted standard deviations are 0.95 for N-acetylcysteine and 0.83 for placebo. A larger standard deviation indicates more dispersion on the analyzed scale, but it does not by itself determine whether the arm means differ. Precision of the difference also depends on the number of analyzed participants and the variability in both arms.

For the between-arm comparison, the reported mean difference is 0.41, with N-acetylcysteine minus placebo as the direction of subtraction. Keeping that order clear matters: reversing the order would reverse the sign while describing the same contrast. The posted result gives a confidence interval and an unadjusted p-value. The interval includes zero, and the p-value is 0.055. These are complementary summaries of uncertainty, not competing verdicts.

What does the confidence interval contribute?

A confidence interval gives a range of values that are compatible with the observed estimate under the statistical procedure and its assumptions. In general, an interval that includes zero for a difference does not rule out no difference on the measured scale. The interval can also include effects in either direction, and its width indicates how precisely the contrast is estimated. Here, the posted interval extends from slightly below zero to a positive value. That supports a cautious reading of the point estimate rather than a claim of a precisely determined difference.

A confidence interval does not assign a probability to the true effect being inside the particular interval after the data have been observed. Its interpretation comes from the long-run properties of the method under its assumptions. The registry record does not identify the confidence level or provide enough analysis detail to evaluate every assumption behind the interval, so interpretation should follow the reported result without adding unreported procedural details.

How should the p-value be read?

The p-value measures how unusual results at least as incompatible with a null model as the observed data would be, if that model and the analysis assumptions were correct. It is not a measure of clinical importance and is not the chance that the result happened by luck. The posted value is unadjusted, which means the record does not present it as corrected for multiple comparisons. Whether such a correction was planned or applied elsewhere is not reported in the registry record.

The sample-size illustration uses a two-sided significance level of 0.05, equal allocation between arms, and a normal approximation. Those are assumptions for the posted planning illustration, not evidence that the trial’s original design used the same assumptions. The trial’s own original sample-size assumptions and justification are not reported in the registry record. A significance level is a planning threshold for a statistical decision procedure; it does not specify the size of an effect that matters clinically.

Why can over-dispersion matter for count outcomes?

As a general statistical concept, count data can vary more than a simple count model assumes. This is called over-dispersion. If the variation is larger than the model allows, standard errors can be too small and confidence intervals can be too narrow, making the data appear more precise than they are. A transformation, a model that accommodates extra variation, or another analysis strategy may be considered depending on the data and study plan. The registry record reports a log-transformed continuous endpoint and a mean comparison, but it does not report a diagnostic assessment for over-dispersion or the investigators’ rationale for the analysis scale. No particular problem with the posted analysis can therefore be inferred from the record alone.

Log transformation also changes how distances on the measurement scale are represented. Differences on a log scale are not interchangeable with differences in raw counts. Interpretation on the original scale generally depends on the transformation definition and on how the results are back-transformed, if that is done. Those details are not reported in the registry record, so the posted estimate should be described on its stated log-transformed scale.

What does the analysis population mean?

The registry names the Full Analysis set as the population for the primary outcome. An analysis population describes which participants contribute to an analysis, but the label alone does not define inclusion rules. The registry record does not state whether the set included every randomized participant, what happened when monitoring data were incomplete, or how deviations from assigned treatment were handled. Those details matter because exclusions or missing measurements can affect both the estimate and its precision.

The registry presents arm summaries and a between-arm comparison, while listing no posted analysis records. The reported summary statistics can be described, but the record does not provide enough detail to reconstruct a complete statistical analysis plan or assess whether the analysis followed one. The comparison should therefore be interpreted as the result posted in the registry, with its stated endpoint, population, and unadjusted p-value, rather than as a fully documented account of every analytic decision.

10. Confidence Intervals and Precision

The mean difference is 0.41 on the log-transformed cough-count scale. Its confidence interval extends from slightly below zero to a positive value, meaning that the interval includes no difference as well as positive differences. The point estimate alone therefore gives an incomplete account: it identifies the center of the estimate, while the interval conveys the uncertainty around that center.

The interval’s span indicates that the estimate is not confined to a narrow range around the point estimate. It permits more than one interpretation of the direction of the underlying contrast. In particular, the data summarized by the registry are compatible with values just below zero and with positive values. This uncertainty does not establish equivalence; the registry record does not report an equivalence design or a margin defining differences small enough to be considered equivalent.

Precision depends on the variability of the endpoint and the amount of information available for the comparison. The posted arm sizes are 35 each, and the standard deviations are 0.95 and 0.83. These summaries help describe the information underlying the estimate, but the interval is the direct summary of uncertainty for the contrast. The registry record does not provide participant-level results, missing-data details, or a full analysis plan that would allow a more complete assessment of precision.

The unadjusted p-value is 0.055. It should not be treated as a substitute for the interval or as a measure of effect size. The combination of a positive point estimate, an interval crossing zero, and the posted p-value calls for a restrained interpretation: the observed data do not pin down the direction and magnitude of the mean difference with high precision. The endpoint’s transformed scale also limits direct translation to raw cough counts.

11. 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 planning estimates are participants per arm, not total events. They illustrate the size of a future equal-allocation study under the stated planning assumptions, using the observed effect as the starting point. The assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. For these estimates, the effect to detect is set at a stated fraction of the observed effect. The trial’s own original design assumptions and sample-size justification are not reported in the registry record.

At 100% of the observed effect, the illustration gives 75 participants per arm for 80% power and 100 participants per arm for 90% power. At 75% of the observed effect, the corresponding figures are 133 and 177 participants per arm. At 50% of the observed effect, they are 298 and 398 participants per arm.

The 80% power column means that, if the planning assumptions and target effect were correct, the design would have an 80% chance of meeting the specified statistical criterion. The 90% column uses a higher target chance of detecting the specified effect under those assumptions. These are planning probabilities, not guarantees about a particular study’s result. Smaller effects require more participants to detect under otherwise comparable assumptions, which is why the displayed estimates rise as the target fraction of the observed effect falls.

These figures are a planning illustration, not observed power. They do not show the probability that the completed study detected an effect, and they are not a retrospective assessment of whether its result is true. They also depend on the observed effect used as the planning reference, which is uncertain. The original study’s own sample-size calculation and the assumptions behind it are not reported in the registry record.

12. 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 = 0.410; pooled standard deviation σ = 0.892.
2. n = 2 × (1.96 + 0.842)² × 0.892² / 0.410² = 74.3, rounded up to 75 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 <- 0.41                      # observed difference in means
sigma <- 0.892020179143947                     # 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(2379637)
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 = 75 (80% power), 100 (90% power)
 75% of observed effect: n per arm = 133 (80% power), 177 (90% power)
 50% of observed effect: n per arm = 298 (80% power), 398 (90% power)
power.t.test: 75.3 
Simulated power at n = 75 per arm: 0.803 (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.

13. Multiplicity, Missing Data and Analysis Populations

The registry lists the cough-count measure as the primary outcome and safety, measured as the number of patients with adverse events, as a secondary outcome. It also reports two outcome measures, but the record does not provide a complete hierarchy of all analyses or state how many statistical comparisons were planned. The comparison p-value is described as unadjusted. Whether the investigators used a multiplicity procedure, designated a single primary contrast in a protocol, or made adjustments in an analysis not represented in the registry is not reported in the registry record.

Multiplicity is a general statistical concern when several outcomes, time points, subgroups, or treatment contrasts are tested. The more opportunities there are to find a result meeting a chosen threshold, the greater the chance of at least one such result under a setting where none of the tested effects is real. Adjustments or a prespecified testing hierarchy can address that concern, but the record does not document a procedure for this study. The posted p-value should therefore be read as unadjusted, without assuming that it reflects a multiplicity-adjusted analysis.

The primary population is named as the Full Analysis set. The rules defining that population are not reported in the registry record. The record also does not explain whether participants with missing cough-monitoring measurements were excluded, imputed, or handled using another method. The amount and pattern of missing endpoint data, reasons for missingness, and any sensitivity analyses are not reported. Missing-data choices can influence an estimate if the availability of data is related to measured outcomes or treatment assignment, so these omissions limit appraisal of the result.

The registry includes arm-level sample sizes and summary statistics, but does not report the number randomized who lacked usable primary endpoint data or provide participant-level data. It is therefore not possible from the registry record to determine how closely the analysis population matched the randomized population or to evaluate the consequences of missingness. The record also does not report interim analyses, subgroup analyses, or a detailed statistical analysis plan. Their presence or absence should not be inferred from the registry’s summary fields.

14. 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 3% reporting threshold.

Most frequent non-serious adverse event terms

The posted serious adverse event figures are zero patients in the N-acetylcysteine arm and zero in the placebo arm, with 35 at risk in each arm. The registry lists two patients with other adverse events in each arm, also with 35 at risk in each. These counts are descriptive summaries for the groups and outcomes recorded in the registry.

Zero reported serious adverse events in these groups does not establish that serious events cannot occur, or that the groups have identical safety profiles. The trial enrollment was limited, and a registry count does not provide a complete characterization of event types, timing, severity, or causal attribution. The record does not provide the serious-event terms, and deaths are not reported in the registry record.

The other-adverse-event figures should not be confused with event counts: the record describes the number of patients with adverse events. It does not supply a detailed breakdown of those events. Safety interpretation is therefore limited to the reported counts and at-risk group sizes, rather than a broader conclusion about safety.

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

The posted primary result describes a difference in mean log-transformed cough count during the first 72 hours. The mean was 6.92 for N-acetylcysteine and 6.51 for placebo, with a reported mean difference of 0.41 in the N-acetylcysteine-minus-placebo direction. The interval crosses zero and the unadjusted p-value is 0.055. Together, these statistics show a positive point estimate but leave uncertainty about the underlying difference.

This result does not establish that the treatments are equivalent, because no equivalence margin or equivalence design is reported. Nor does it establish that a difference is present on the primary endpoint: the confidence interval includes zero. Statistical uncertainty is not the same as evidence that the true difference is exactly zero. The estimate should be read with its interval and the limitations of the registry’s analysis detail in mind.

The result is specific to the analyzed log-transformed cough count, the Full Analysis set, and the first 72 hours of monitoring. It does not directly state how many raw coughs separated the groups, and the record does not give the transformation details needed to translate the estimate back to the original count scale. It also does not answer questions about cold symptoms other than cough, outcomes after the monitoring window, or effects in populations not represented by the adult study population.

The study’s safety summary records zero serious adverse events in each arm and two patients with other adverse events in each arm. These are limited counts, not a complete evaluation of all potential safety outcomes. The registry record does not report event details or deaths. The findings should therefore be described as the results recorded for this study, without extending them into a general claim about treatment benefit or lack of benefit, or a broad safety conclusion.

16. Why This Trial Matters Statistically

This trial illustrates why a treatment comparison should not be reduced to a single p-value. The posted mean difference is positive, but the confidence interval includes zero and the unadjusted p-value is 0.055. Reading the estimate, interval, and p-value together makes the uncertainty visible and avoids treating a threshold as a substitute for the size and precision of the result.

It also highlights the importance of the measurement scale. The endpoint is a log-transformed cough count, so its mean difference is not a raw-count difference. Statistical summaries are meaningful only when interpreted on the scale that was analyzed. For clinical researchers, this is a reminder to distinguish a transformed endpoint from the original measurement and to check whether the registry provides enough information for translation back to the original scale.

The record also shows how design labels and analysis details answer different questions. Randomization, parallel groups, placebo control, and quadruple masking describe core design features. They do not replace a report of allocation procedures, the analysis population rules, missing-data handling, or the prespecified statistical plan. Those details are needed to judge the transparency and robustness of an analysis.

Finally, the sample-size illustration makes clear that planning depends on the effect one aims to detect. Smaller target effects require larger participant counts under the stated assumptions. Because the illustration is not the original design justification, it cannot establish why the completed study enrolled its participants or whether its design was adequate for the investigators’ original objectives. Keeping planning illustrations separate from observed results is an important part of statistical interpretation.

17. 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 registry record gives a concise account of the study but leaves important details unreported. It does not specify the N-acetylcysteine dose, dosing schedule, treatment duration, randomization procedure, allocation concealment, or the roles included in quadruple masking. The record also does not give participant characteristics or fuller eligibility criteria, limiting assessment of how the enrolled population may compare with other adult populations with cough associated with a common cold.

The endpoint is described as a log-transformed total cough count measured during the first 72 hours, but the precise transformation and any back-transformation procedure are not reported. The record does not explain the choice of analysis model, report model diagnostics, or provide participant-level results. Those omissions limit evaluation of the distributional assumptions and translation of the mean difference to the original count scale.

The Full Analysis set is named, but its definition is not reported. The registry does not describe missing endpoint data, exclusions, protocol deviations, or sensitivity analyses. It also does not provide a detailed statistical analysis plan, multiplicity strategy, interim-analysis information, or the trial’s original sample-size justification. These items cannot be inferred from the posted summaries.

The safety summary contains counts of patients with serious and other adverse events, but it does not provide event terms or a detailed account of timing and attribution. Deaths are not reported in the registry record. The posted safety counts therefore offer only a limited description of recorded events and should not be extended into a comprehensive assessment of safety.

18. Related Tutorials

19. Related Statistical Calculators

20. Sources

• ClinicalTrials.gov: Study to Evaluate the Efficacy of N-acetylcysteine (NAC) in the Treatment of the Common Cold and Cough — NCT02379637.
• Registry record last updated 2016-07-11; status last verified 2016-05.
• Results first posted 2016-07-11.
• 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.

21. Record Summary

The registry record describes a completed phase 2, randomized, parallel-group, quadruple-masked, placebo-controlled study of N-acetylcysteine in adults with common cold associated with cough. Enrollment was 70, with 35 participants in each arm. The study began in 2015-01 and had primary completion in 2015-04.

The primary outcome was the mean log-transformed total cough count measured by ambulatory monitoring during the first 72 hours in the Full Analysis set. The posted mean difference, N-acetylcysteine minus placebo, was 0.41; the confidence interval crossed zero and the unadjusted p-value was 0.055. The record also reports zero patients with serious adverse events in each arm and two patients with other adverse events in each arm.

The registry does not report the original sample-size justification, detailed analysis plan, analysis-population definition, or missing-data methods. The posted statistics therefore support a focused description of the measured endpoint, with uncertainty, rather than broader conclusions beyond the outcomes and details recorded.