APT™ T3X on the COVID-19 Contamination Rate: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for APT™ T3X on the COVID-19 Contamination Rate (NCT04716426), a randomized na study. The primary outcome is COVID-19 Contamination Rate..

2. Clinical Question

3. Trial Design

The registry record describes a randomized, quadruple-masked, parallel-group trial comparing tetracycline hydrochloride 3% (APT™ T3X) with placebo. The record lists the study as completed, with an enrollment of 100. The posted results report 50 participants in the tetracycline arm and 50 in the placebo arm. The record identifies the sponsor as the University of Nove de Julho and lists the condition as COVID-19.

The intervention was a topical formulation used intranasally. The arm description specifies 4 drops applied inside the nasal channels and nostrils once a day every day for 21 days, except that health professionals were instructed to use 4 drops twice a day every day for 21 days. The placebo description also specifies drops once a day for 21 days, but its exception for health professionals refers to using APT™ T3X. The record does not explain this wording or how it was implemented in the placebo group.

The study start is listed as 2021-01-28, and primary completion as 2021-03-25. The primary outcome was assessed at 22 days after randomization. These dates and the endpoint timeframe describe the registry record; they do not establish the exact assessment schedule for each participant.

The record lists phase as NA. It does not report the detailed randomization procedure, allocation concealment, masking procedures, eligibility criteria, or the original sample-size justification. The fourfold masking label identifies the recorded masking category, but the identities of the masked groups or the practical procedures used to maintain masking are not reported in the registry record.

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:

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 COVID-19 Contamination Rate., measured over 22 days after randomization..

The primary endpoint is the COVID-19 contamination rate, defined in the registry as the number of people infected with COVID-19 over the course of the study in each group. It is reported as a count of participants, with the assessment timeframe listed as 22 days after randomization. The posted results report 2 participants with the outcome in the tetracycline hydrochloride 3% arm and 5 in the placebo arm, with 50 participants in each arm.

A participant count is a binary-outcome summary: each participant is classified according to whether the event was recorded or not. Comparing the proportions in the two groups expresses the frequency of the event relative to the number of participants assessed. This is useful for a trial question framed around whether participants became infected during a specified follow-up period. A fixed assessment timeframe also gives the endpoint a defined window of observation, although the record does not describe the detailed timing or method of infection ascertainment.

As a general statistical point, a binary endpoint does not capture differences in the timing, duration, or severity of an event unless those features are separately measured. The registry lists other outcomes, including adverse events and contamination with other viruses or bacteria, but those are not part of the stated primary endpoint. The available record does not report a detailed operational definition of infection or the analysis population used for this outcome.

7. Results Posted in the Registry

• Tetracycline Hydrochloride 3%: 4.0% (2/50) versus 10.0% (5/50) on control; risk difference -6.0 percentage points (95% CI -15.9 to 3.9), risk ratio 0.40 (0.08–1.97); unadjusted two-proportion z-test p = 0.240.

8. Statistical Interpretation

9. Statistical Methods Explained

The primary outcome is binary: each participant either had a recorded COVID-19 infection during the outcome window or did not. For this kind of outcome, the event count and the number assessed in each arm describe the basic data. The registry reports 2 events among 50 participants in the tetracycline hydrochloride 3% arm and 5 among 50 in the placebo arm. A comparison can then express the separation between groups in absolute terms, as a risk difference, or in relative terms, as a risk ratio or odds ratio.

What does the risk difference mean?

The risk difference subtracts one group’s event risk from the other’s, according to a stated comparison order. In the posted comparison, the estimate is -0.06 for tetracycline hydrochloride 3% relative to placebo. A negative estimate indicates a lower observed event risk in the intervention group under that ordering. The risk difference is an absolute measure: it describes the separation between the groups on the event-risk scale rather than expressing one group as a multiple of the other.

Absolute measures are often directly interpretable as differences in event frequency. Their uncertainty still matters: an estimate from a limited set of observations can vary from one study to another, even if the underlying group risks are unchanged. The confidence interval provides a range of values compatible with the observed data under the interval method and its assumptions.

How do relative measures differ?

The risk ratio compares the event risk in one group with the risk in the other. The posted risk ratio is 0.4, with a confidence interval from 0.081 to 1.966. A ratio at the no-association value represents equal risks; the interval spans that value. The odds ratio compares the odds of an event rather than the event risks themselves. The posted odds ratio is 0.375, with a confidence interval from 0.069 to 2.031, which also spans its no-association value.

Relative measures can communicate proportional contrasts, but may be less intuitive when events are uncommon. They can also appear numerically quite different from an absolute difference because they use a different scale. None of these measures should be read without the event counts and uncertainty. Here, all the listed measures come from the same posted group results, and their intervals indicate considerable uncertainty.

What does the p-value say?

The unadjusted p-value is 0.24. As a general explanation, a p-value is calculated under a specified null model, commonly a model of no group difference, and summarizes how unusual results at least as discrepant as the observed results would be if that model and the analysis assumptions held. It does not give the probability that the null model is true. It is not an effect-size measure, and it does not show whether a difference is practically important.

The term “unadjusted” indicates that the posted value is not adjusted for multiplicity. The registry record does not report whether another adjustment was planned or used, nor does it give the exact testing procedure. The listed p-value should therefore be read as the posted unadjusted comparison, not as evidence about unreported analyses.

Why does uncertainty matter with event counts?

With binary data, each participant contributes information about whether an event occurred within the defined period. When event counts are limited, a small change in the observations can have a noticeable effect on the estimated group contrast. General statistical reasoning therefore treats the point estimate and its interval together: the point estimate gives the observed contrast, while the interval communicates how uncertain that estimate is under the method used.

The record reports no dispersion measure for this endpoint, which is expected for a count of participants rather than a continuous measurement. It also does not give detailed information about missing observations, exclusions, or the analysis population. Those details matter because an estimate based on all randomized participants can differ from one based only on participants with outcome information, particularly if missingness is related to outcome or group assignment.

The posted comparison is an unadjusted group comparison. The registry record does not describe covariate adjustment, stratification, a model specification, or an analysis plan. No particular method beyond the posted summary statistics should be inferred. General explanations of risk differences, ratios, p-values, and confidence intervals describe how these quantities are commonly interpreted; they do not establish which computational procedure the study team used.

10. Confidence Intervals and Precision

The risk difference estimate is -0.06, with a 95% confidence interval from -0.159 to 0.039. The interval ranges from a negative value, consistent with a lower event risk in the tetracycline group under the posted comparison order, to a positive value, consistent with a higher event risk in that group. Because zero lies within the interval, the reported results are compatible with no difference as well as with differences in either direction.

A confidence interval is a measure of statistical uncertainty, not a range that contains a fixed percentage of individual outcomes. In general, a 95% confidence interval is produced by a procedure that would cover the target parameter in 95% of repeated applications under its assumptions. It does not mean that there is a 95% probability that the true value lies inside this particular interval.

The relative-effect intervals are also wide: the risk ratio is 0.4, with a confidence interval from 0.081 to 1.966, and the odds ratio is 0.375, with a confidence interval from 0.069 to 2.031. Each interval includes the relative no-association value. Together with the risk-difference interval, these summaries show that the estimated contrast is not precise enough to pin down a narrow range of plausible effects.

Precision depends on the amount of information in the data and on the variability of the outcome. For a binary outcome, the number of events is particularly relevant to the stability of group comparisons. The registry reports the group counts but does not provide the calculation method used for the intervals, so their exact construction is not reported in the registry record. The confidence intervals should be read as the posted uncertainty summaries, not as guarantees about future results.

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 illustrations express required participants per arm for 80% power and participants per arm for 90% power. They are not totals across both arms and are not total-event counts. They use the observed effect as a reference and show planning for 100% of that observed effect, 75% of the observed effect, and 50% of the observed effect. At each effect assumption, the 90% power column gives a larger required number per arm than the 80% power column.

As a general explanation, power is the probability that a study’s statistical test will meet its chosen significance criterion if a specified effect is present and the design assumptions hold. The 80% and 90% columns represent different target probabilities of detecting the assumed effect. Higher target power generally requires more participants when the other planning assumptions are held constant. Smaller effects require more participants to distinguish them from no difference with comparable power.

The planning assumptions listed for these illustrations are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. The effect to detect is the stated fraction of the observed effect. The registry record does not report the trial’s own original design assumptions or sample-size justification, so the illustrations should not be taken as the actual basis for enrollment. They are planning calculations derived from the observed effect, not observed power.

The displayed requirements are 283 participants per arm for 80% power and 378 participants per arm for 90% power when planning for 100% of the observed effect; 554 participants per arm and 740 participants per arm for the 75% effect assumption; and 1,356 participants per arm and 1,814 participants per arm for the 50% effect assumption. These figures illustrate how the planning requirement grows as the target effect becomes smaller and as the desired power increases.

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, p₀ and p₁ are the control and treatment proportions, and p̄ is their average.

Worked calculation for this trial

1. Control proportion p₀ = 0.100; treatment proportion p₁ = 0.040; average p̄ = 0.070.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.3608 = 0.7072.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.3583 = 0.3016.
4. n = (0.7072 + 0.3016)² / (-0.0600)² = 282.7, rounded up to 283 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
p0 <- 0.1                          # control proportion
p1 <- 0.04                          # treatment proportion
n_per_arm <- function(power, frac = 1) {
  q1 <- p0 + (p1 - p0) * frac               # smaller effect = fraction of the observed difference
  pbar <- (p0 + q1) / 2
  z_b <- qnorm(power)
  ceiling(round((z_a * sqrt(2 * pbar * (1 - pbar)) + z_b * sqrt(p0 * (1 - p0) + q1 * (1 - q1)))^2 / (q1 - p0)^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 (unrounded n per arm)
cat("power.prop.test:", round(power.prop.test(p1 = p0, p2 = p1, power = 0.80)$n, 1), "\n")

# Simulation: how often does the z-test reject at the 80%-power sample size?
set.seed(4716426)
n    <- n_per_arm(0.80)
reps <- 20000
x0 <- rbinom(reps, n, p0); x1 <- rbinom(reps, n, p1)
ph <- (x0 + x1) / (2 * n)
z  <- ((x1 - x0) / n) / sqrt(ph * (1 - ph) * 2 / n)
cat(sprintf("Simulated power at n = %s per arm: %.3f (%d replicates)\n", format(n, big.mark = ","), mean(abs(z) > z_a, na.rm = TRUE), reps))

Output from running the code

100% of observed effect: n per arm = 283 (80% power), 378 (90% power)
 75% of observed effect: n per arm = 554 (80% power), 740 (90% power)
 50% of observed effect: n per arm = 1,356 (80% power), 1,814 (90% power)
power.prop.test: 282.7 
Simulated power at n = 283 per arm: 0.817 (20000 replicates)

The simulation draws many pairs of event counts at the planned sample size and counts how often the two-proportion z-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 COVID-19 contamination rate as the primary outcome and identifies four secondary outcomes: the number of participants with adverse events, the average number of adverse events, days over which an adverse event was reported, and other virus or bacteria contamination rate. It reports five outcome measures overall and four posted secondary outcomes. Considering more than one outcome or analysis can create a multiplicity issue: when many statistical tests are conducted, the chance of at least one apparently notable result can increase even if none of the underlying effects is real.

The registry record does not report a multiplicity strategy, such as prioritizing outcomes for formal testing, adjusting significance levels, or treating secondary comparisons as exploratory. It also does not report an analysis hierarchy or whether any secondary-outcome results were prespecified for confirmatory interpretation. The primary comparison is posted with an unadjusted p-value of 0.24; no adjustment should be inferred from the record.

Information about missing outcome data and analysis populations is also not reported in the registry record. In particular, the record does not describe whether analyses followed an intention-to-treat principle, whether any participants were excluded after randomization, how unavailable infection assessments were handled, or whether imputation was used. These details can affect how closely an estimate represents the randomized comparison.

As a general statistical matter, missingness is not only a question of how much data are absent. It also matters why data are missing and whether that reason is associated with treatment assignment or the outcome. Without the study’s handling rules and analysis-population definitions, readers cannot assess those features from the posted summary. The record also does not describe interim analyses or stopping rules; those matters are not reported in the registry record.

14. Safety Results

The registry reports serious adverse events as affected participants divided by participants at risk.

Deaths

Other (non-serious) adverse events

Participants with at least one non-serious event at or above the registry's 0% reporting threshold.

Most frequent non-serious adverse event terms

The serious adverse event figures report 0 participants affected in the tetracycline hydrochloride 3% group and 0 in the placebo group, with 50 participants at risk in each. The record also reports 0 deaths in both groups. These are observed counts in the posted safety summary; they do not show that serious events or deaths are impossible, nor do they by themselves establish comparative safety.

For other adverse events, the record reports 17 affected participants in the tetracycline group and 23 in the placebo group, with 50 at risk in each. These are counts of participants affected, not counts of all individual event occurrences. The registry separately lists average number of adverse events and days over which an adverse event was reported as secondary outcomes, but their results are not included in the posted safety figures.

The record does not report detailed event definitions, severity categories for non-serious events, timing, relatedness assessments, or the method for collecting adverse events. Without those details, differences in counts should be interpreted descriptively. A zero count in the serious-event category is an observation in this trial record, not proof that an intervention has no serious risks.

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

The posted primary results show fewer participants with recorded COVID-19 infection in the tetracycline hydrochloride 3% arm than in the placebo arm: 2 compared with 5. The estimated risk difference is -0.06, and its 95% confidence interval ranges from -0.159 to 0.039. The unadjusted p-value is 0.24. The numerical direction of the point estimate favors fewer recorded infections in the tetracycline arm, but the uncertainty interval includes no difference and values in the opposite direction.

These results do not establish that the groups had the same underlying risk, because a non-significant p-value is not proof of equivalence. They also do not establish a reliable reduction in infections: the interval is compatible with a range of effects, including no difference. The confidence interval and p-value address statistical uncertainty under their assumptions; they do not determine whether any particular possible effect would be important in practice.

The outcome applies to the registry’s stated measurement of participants infected over the course of the study, assessed at 22 days after randomization. It does not describe longer-term outcomes, infection severity, or effects on transmission unless those were measured elsewhere. The record does not provide the detailed infection ascertainment procedure, eligibility criteria, or analysis population, limiting assessment of how the reported endpoint was defined and who contributed to it.

The result also cannot, on its own, answer questions about generalizability to populations outside the enrolled group or about use under different conditions. The registry describes a particular intranasal regimen and includes a separate instruction for health professionals; the placebo-arm wording about health professionals is ambiguous. The record does not clarify the practical implementation of that exception.

Statistically, the most defensible reading is descriptive: the observed counts were lower in the tetracycline arm, while the estimated contrast is imprecise. The posted data do not justify a categorical conclusion that the intervention works or does not work. Further interpretation would require information about the trial’s analysis plan, missing data, outcome ascertainment, participant characteristics, and implementation that is not reported in the registry record.

16. Why This Trial Matters Statistically

This trial illustrates why the size and direction of a point estimate should be considered alongside its uncertainty. The observed primary-outcome counts favor the tetracycline arm numerically, but the confidence interval for the risk difference includes zero and extends to positive values. A reader who focuses only on the counts or only on the p-value would miss part of the statistical picture: the estimate has a direction, yet the data do not locate the underlying contrast precisely.

It also demonstrates the value of reporting effect measures on more than one scale. The risk difference describes an absolute separation, while the risk ratio and odds ratio express relative contrasts. Each offers a different perspective, and all require interpretation in light of the event counts and their uncertainty. Here, the relative-effect confidence intervals also include their no-association values.

The planning illustrations make another statistical point: sample-size requirements depend on the effect a future study aims to detect and the power target. Planning for a smaller fraction of the observed effect produces larger requirements, as does choosing the higher power target. These calculations are planning illustrations based on the observed effect, not evidence that the completed trial had a particular power or that its original design used those assumptions.

Finally, registry summaries are most informative when their methods and analysis populations are clear. The record provides the randomization and masking labels, arms, primary endpoint, and posted outcome counts, but does not report the detailed analysis plan, missing-data handling, outcome ascertainment, or multiplicity strategy. Recognizing that boundary is part of sound statistical interpretation: the posted estimates can be described without claiming more than the record supports.

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 does not report eligibility criteria or detailed participant characteristics. That limits assessment of which population the results represent and whether the study group resembles a wider clinical population. It also does not provide the detailed method used to identify or confirm COVID-19 infection, even though the primary endpoint depends on whether infection was recorded during the study period.

The primary result is based on a limited number of observed events: 2 in the tetracycline arm and 5 in placebo. The corresponding confidence interval is broad and includes no difference. The precision of the estimated contrast is therefore limited, and the observed point estimate should not be treated as a settled estimate of the effect in other settings.

Important analysis details are not reported in the registry record. These include the statistical analysis plan, analysis population, handling of missing outcome data, any covariate adjustment, and multiplicity procedures. The record also does not describe interim analyses or the original sample-size justification. The planning illustrations are not a substitute for the trial’s original design assumptions.

The arm descriptions contain an ambiguity: the placebo description’s exception for health professionals refers to use of APT™ T3X. The record does not clarify how that instruction was implemented. Detailed adherence, exposure, and masking procedures are also not reported. These gaps make it difficult to assess how the assigned regimens were carried out and how well masking was maintained.

Safety summaries provide counts, but do not give detailed definitions, timing, severity, relatedness, or collection procedures for adverse events. The record also does not provide the secondary-outcome results in the summary described. These limitations constrain interpretation of both the primary comparison and the wider outcome profile.

18. Related Tutorials

19. Related Statistical Calculators

20. Sources

• ClinicalTrials.gov: APT™ T3X on the COVID-19 Contamination Rate — NCT04716426.
• Registry record last updated 2021-04-08; status last verified 2021-04.
• Results first posted 2021-04-08.
• 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

NCT04716426 is a completed, randomized, quadruple-masked, parallel-group trial sponsored by the University of Nove de Julho. The registry lists enrollment of 100 and compares intranasal tetracycline hydrochloride 3% (APT™ T3X) with placebo. The primary outcome is COVID-19 contamination rate, recorded as participants and assessed at 22 days after randomization.

The posted primary results report 2 events among 50 participants in the tetracycline arm and 5 among 50 in the placebo arm. The risk difference is -0.06, with a 95% confidence interval from -0.159 to 0.039 and an unadjusted p-value of 0.24. The interval includes no difference, so the point estimate is uncertain.

The registry lists four secondary outcomes and safety counts, including 0 serious adverse events and 0 deaths in each group. Detailed analysis methods, missing-data handling, participant characteristics, and the original sample-size justification are not reported in the registry record.