Clinical Evaluation of Effects of an Investigational Tampon on Vaginal Microflora: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Clinical Evaluation of Effects of an Investigational Tampon on Vaginal Microflora (NCT00913523), a randomized na study. The primary outcome is Percentage of Subjects Showing Unfavorable Changes in Primary Microflora.

2. Clinical Question

3. Trial Design

The registry describes a randomized, single-masked, parallel-group study of tampon use during menstruation. Participants were assigned to one of three groups: tampons with GML, study tampons without GML, or their normal tampon brand. The brief summary describes two study sites and states that a total of 450 women would participate. The registered enrollment is 482. The identity of the party or parties who were masked is not reported in the registry record.

The intervention was a device, and the investigational product was a regular or Super tampon with GML added to its cover. The sham-control tampon had no GML. Participants in the usual-tampon group were not issued study tampons. The record describes use during menstrual periods and microbiological samples collected at specified points relative to the menstrual cycle.

The primary endpoint compares mid-cycle baseline with mid-menstrual samples. A secondary microflora endpoint uses post-menstrual samples, and a Nugent-score endpoint is listed for mid-menstrual sampling. The registry record also names outcomes concerning selected microflora in tampons, their abundance in tampons, and Nugent-score changes. Detailed results for every named secondary outcome are not reported in the registry record.

The study status is completed. The recorded start is 2009-01 and primary completion is 2009-08. The trial phase is listed as not applicable. The registry record does not report the original sample-size justification, the intended allocation ratio, the masking procedures, or a full statistical analysis plan. The three analysis groups should not be treated as interchangeable: the study-tampon comparison and the usual-tampon comparison address different comparator conditions.

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: Microbiological analyses are for the "per protocol" population, and no data imputation was performed. Analysis was limited to those subjects whose samples were viable for culture upon receipt at the laboratory.

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 Percentage of Subjects Showing Unfavorable Changes in Primary Microflora, measured over Mid-Cycle Baseline to Mid-Menstrual Samples.

The primary endpoint is the percentage of participants showing an unfavorable change in primary microflora between mid-cycle baseline and mid-menstrual samples. It counts a decrease of at least 1-log in lactobacilli or an increase of at least 1-log in C. albicans or G. vaginalis. The outcome therefore converts microbiological measurements into a participant-level event: each participant either meets the stated unfavorable-change definition or does not.

This type of percentage is useful for describing how often the specified event occurred in each group. It gives the result a direct denominator and makes the groups’ event frequencies comparable. However, it compresses several distinct biological changes into one indicator. It does not show which organism accounted for an event, the size of a change beyond the threshold, or whether more than one organism changed for a participant.

The analysis population is also important to interpretation. The registry says microbiological analyses used the per-protocol population, with no data imputation, and were limited to samples viable for culture upon laboratory receipt. Thus, the result describes analyzed participants meeting those conditions, rather than necessarily every randomized participant. The number of randomized participants whose samples were not viable, and the reasons for any other exclusions, are not reported in the registry record.

7. Results Posted in the Registry

• Tampon Without GML: 52.9% (74/140) versus 52.7% (68/129) on control; risk difference 0.1 percentage points (95% CI -11.8 to 12.1), risk ratio 1.00 (0.80–1.26); unadjusted two-proportion z-test p = 0.981.
• Tampon Normally Used: 60.3% (85/141) versus 52.7% (68/129) on control; risk difference 7.6 percentage points (95% CI -4.2 to 19.4), risk ratio 1.14 (0.93–1.41); unadjusted two-proportion z-test p = 0.210.

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.

Percentage of Subjects Showing Unfavorable Changes in Primary Microflora

• Endpoint type: Binary: the percentage of participants with the event (counts rebuilt from the posted percentages and group sizes).
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • Tampon Without GML: 34.3% (48/140) versus 31.6% (43/136) on control; risk difference 2.7 percentage points (95% CI -8.4 to 13.8), risk ratio 1.08 (0.77–1.52); unadjusted two-proportion z-test p = 0.637.
  • Tampon Normally Used: 36.2% (51/141) versus 31.6% (43/136) on control; risk difference 4.6 percentage points (95% CI -6.6 to 15.7), risk ratio 1.14 (0.82–1.59); unadjusted two-proportion z-test p = 0.424.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Tampon Without GML and the control. The interval gives the range of effects that remain compatible with the data.
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Tampon Normally Used and the control. The interval gives the range of effects that remain compatible with the data.

Changes in Nugent Score

• Endpoint type: Binary: the percentage of participants with the event (counts rebuilt from the posted percentages and group sizes).
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • Tampon Without GML: 19.3% (27/140) versus 17.8% (23/129) on control; risk difference 1.5 percentage points (95% CI -7.8 to 10.7), risk ratio 1.08 (0.65–1.79); unadjusted two-proportion z-test p = 0.759.
  • Tampon Normally Used: 24.8% (35/141) versus 17.8% (23/129) on control; risk difference 7.0 percentage points (95% CI -2.7 to 16.7), risk ratio 1.39 (0.87–2.22); unadjusted two-proportion z-test p = 0.162.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Tampon Without GML and the control. The interval gives the range of effects that remain compatible with the data.
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Tampon Normally Used and the control. The interval gives the range of effects that remain compatible with the data.

The secondary results cover a post-menstrual version of the unfavorable-microflora endpoint and a Nugent-score outcome at mid-menstrual sampling. The registry also lists outcomes about selected microflora in tampons and their abundance, as well as Nugent-score changes. The posted data cover only some of the named secondary outcomes; results for the other named outcomes are not reported in the registry record.

Across the posted secondary comparisons, the group percentages do not create a clear contrast with the GML group, and the unadjusted p-values do not indicate strong evidence of a difference. That broad pattern is compatible with the primary result, which likewise does not show a clear separation in the posted unadjusted comparisons. This agreement across outcomes is descriptive, not independent confirmation: endpoints may be related, use overlapping participants, or reflect related aspects of the same biological process.

Secondary results generally carry less weight than a prespecified primary endpoint, particularly when the record does not report how many comparisons were planned, whether results were ranked in advance, or whether multiplicity was addressed. When multiple outcomes and comparisons are examined, chance alone can produce apparently notable findings. Conversely, a non-small p-value for a secondary outcome does not establish that groups are equivalent. The registry record does not provide enough information to determine whether the secondary outcomes were intended as confirmatory or exploratory. Their best role is to give context to the primary endpoint and indicate where further study might clarify patterns, not to replace the primary comparison or support a broad clinical conclusion.

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

• Percentage of Subjects Showing Unfavorable Changes in Primary Microflora
• Percentage of Subjects With Selected Microflora in Tampons
• Abundance of Selected Microflora in Tampons
• Changes in Nugent Score
• Changes in Nugent Score

9. Statistical Interpretation

10. Statistical Methods Explained

The primary result is a binary participant-level outcome. Under the registry definition, an unfavorable change is present if at least one of the specified organism changes crosses the stated threshold in the specified direction. The percentage is the number of analyzed participants meeting that definition relative to the analyzed group. This is a straightforward way to summarize event occurrence, but it does not preserve the full range of underlying microbiological measurements.

What does a risk difference describe?

A risk difference compares two event proportions by subtracting one group’s proportion from the other’s. Its reference value is zero, which corresponds to equal event proportions. A positive estimate means the event proportion is higher in the comparison group under the orientation used for the estimate; a negative estimate means it is lower. The posted primary comparisons use the GML group as the reference and compare it separately with the no-GML and usual-tampon groups. No combined comparison across all three groups is reported.

The registry also gives risk ratios and odds ratios. A risk ratio compares event proportions as a ratio, with one as its no-difference reference. An odds ratio compares odds rather than proportions and also uses one as its no-difference reference. These measures express contrasts on different scales, so their numerical values are not directly interchangeable. The most accessible summary for this binary endpoint is often the event percentage in each group together with the risk-difference interval, because those statistics remain on a percentage-point scale. The record’s reported p-values are unadjusted.

How should a confidence interval be read?

A confidence interval describes a range of effect estimates compatible with the observed data under the interval’s statistical assumptions. A narrow interval indicates greater precision than a wide interval; an interval spanning the no-difference value indicates that the data do not distinguish the estimate from no difference at the corresponding confidence level. It does not assign a probability that the true effect lies in the particular interval, and it does not show that all values inside it are equally plausible. For the posted primary risk differences, the intervals include zero, and they extend to values on both sides of zero.

A p-value measures how unusual data at least as discrepant as those observed would be under a specified null model and its assumptions. It is not the probability that the null hypothesis is true, nor does it measure the clinical importance of an effect. The unadjusted p-values for the primary comparisons are 0.981 and 0.21. These values should be interpreted alongside the estimated event percentages and interval uncertainty, not as a stand-alone verdict.

Why does the analysis population matter?

The registry says the microbiological analysis was per protocol, used no data imputation, and included only participants whose samples were viable for culture upon receipt at the laboratory. A per-protocol analysis focuses on participants meeting the specified analysis conditions, whereas an analysis of all randomized participants would retain assignment as the basis for inclusion. The latter is not described for this endpoint. Without the counts and reasons for exclusions, it is not possible to assess the scale or direction of selection introduced by requiring viable samples. No imputation means missing microbiological values were not filled in by a statistical procedure, but the amount and pattern of missingness are not reported in the registry record.

General statistical context: random assignment is intended to make groups comparable at baseline on average, but it does not remove uncertainty from finite samples, nor does it guarantee that a restricted per-protocol subset remains comparable. The record does not report baseline balance details or adjusted models for this outcome. The estimates should therefore be interpreted as unadjusted group comparisons within the stated analysis population.

What is and is not reported about analysis choices?

The registry record lists no reported analysis entries for the posted secondary outcomes. It does not report a multiplicity adjustment, a prespecified hierarchy among endpoints, or a detailed model specification. The comparisons presented are unadjusted. General statistical context: examining multiple endpoints and contrasts increases the chance that at least one result appears notable by chance, so individual secondary p-values generally carry less weight when no multiplicity strategy is reported. The absence of a reported adjustment does not mean one was or was not used in another analysis; the registry record does not establish that.

The sample-size illustrations use a two-sided significance level of 0.05, equal allocation between the two compared arms, and a normal approximation, with the effect to detect set to a stated fraction of the observed effect. Those are assumptions for the posted planning illustrations, not documented assumptions for the original trial design. The registry record does not report the original design’s sample-size calculation, interim analyses, or a complete statistical plan. The planning numbers are not observed power and should not be used to reinterpret the completed trial as though its design had been chosen from those illustrations.

General statistical context: over-dispersion matters when observed variation exceeds what a simple model assumes. A model that ignores extra variation can understate uncertainty and produce intervals that are too narrow. The primary outcome is presented as a percentage of participants with a binary event; a separate over-dispersion parameter is not reported. There is no basis in the registry record to claim that a particular over-dispersion model was used or required.

11. Confidence Intervals and Precision

Precision is conveyed by the uncertainty around each group contrast, not only by the number of participants or the point estimate. For the primary comparison of no-GML tampons with GML tampons, the estimated risk difference is 0.001 and its confidence interval extends from -0.118 to 0.121. For usual tampons compared with GML tampons, the estimate is 0.076 and the interval extends from -0.042 to 0.194. These intervals include zero, so the posted data are compatible with no difference as well as with differences in either direction.

The estimates are based on analyzed group sizes of 129.0, 140.0, and 141.0. Those sample sizes give context but do not, by themselves, determine precision: event frequency and the variability inherent in a binary outcome also matter. The intervals are broad relative to the point estimates, particularly for the comparison with usual tampons, so the point estimates alone should not be read as precise measures of a group effect.

A confidence interval is not a guarantee that the true value lies within its endpoints. It summarizes sampling uncertainty under the method’s assumptions and does not account automatically for issues such as exclusions, unreported missingness, or multiple testing. The registry record does not report a multiplicity adjustment or enough detail to evaluate every assumption behind the interval calculations. Precision should therefore be understood as uncertainty within the posted unadjusted comparison, not total uncertainty about all possible biases or clinical interpretations.

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 planning figures are participants per arm for the binary outcomes; they are not total events. Each power column answers a planning question: under the stated assumptions, how many participants would be needed in each of two equally allocated arms to have an 80% or 90% chance of detecting the chosen effect if that effect were the true one. The 90% target is more demanding than the 80% target and therefore generally calls for a larger planned sample.

The posted assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, a normal approximation, and an effect set to the stated fraction of the observed effect. The planning rows consider 100% of the observed effect, 75% of the observed effect, and 50% of the observed effect. A smaller effect is harder to distinguish from sampling variation, so it requires more participants under the same assumptions. These figures are planning illustrations based on the observed effect, not the original design calculation.

In general, a sample-size curve illustrates how the required sample changes as the target effect changes; its meaning depends on the same assumptions as the accompanying planning figures. The calculations do not establish what effect is important in practice, and a very large calculated sample does not show that the actual trial had a particular chance of success. This is not observed power. The registry record does not report the original sample-size justification or the assumptions used when the trial was designed.

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

Worked calculation for this trial

1. Control proportion p₀ = 0.527; treatment proportion p₁ = 0.529; average p̄ = 0.528.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.7060 = 1.3838.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.7060 = 0.5942.
4. n = (1.3838 + 0.5942)² / (0.0014)² = 1,887,628.6, rounded up to 1,887,629 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 <- 0.527131782945736                          # control proportion
p1 <- 0.528571428571429                          # 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(913523)
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 = 1,887,629 (80% power), 2,526,999 (90% power)
 75% of observed effect: n per arm = 3,355,920 (80% power), 4,492,624 (90% power)
 50% of observed effect: n per arm = 7,551,122 (80% power), 10,108,809 (90% power)
power.prop.test: 1887629 
Simulated power at n = 1,887,629 per arm: 0.797 (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.

14. Multiplicity, Missing Data and Analysis Populations

The registry record describes a primary endpoint and multiple secondary outcomes, including microflora changes at more than one sampling time and Nugent-score outcomes. It does not report a multiplicity strategy, such as a prespecified sequence of hypothesis tests or an adjustment to account for testing several outcomes and group contrasts. The posted comparison p-values are unadjusted. Without a reported plan, the reader cannot tell how confirmatory claims were intended to be controlled across the set of analyses.

Multiplicity matters because each additional statistical test creates another opportunity for a result to look unusual under a no-difference model. This does not make an individual result invalid, but it affects how much evidential weight should be placed on isolated findings. The secondary comparisons are therefore most appropriately read as descriptive or supportive unless the full analysis plan establishes otherwise. The registry record does not report a full statistical analysis plan or interim analyses.

For the microbiological outcomes, the analysis population is described as per protocol. No data imputation was performed, and samples were included only if viable for culture upon laboratory receipt. This makes sample viability a condition of inclusion in the microbiological analysis. The number of participants excluded for nonviable samples, the reasons for other exclusions, and the amount or pattern of missing data are not reported in the registry record. As a result, the potential effect of missing or excluded observations cannot be assessed from the posted information.

The record does not report an intention-to-treat result for the primary endpoint, a comparison of analysis populations, or adjusted estimates. Randomization supports the original assigned-group comparison, while per-protocol restrictions can change the composition of the analyzed groups. These limitations do not establish that the posted findings are biased; they mean the available record does not permit a full assessment of missing-data or population-selection effects.

15. 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 0% reporting threshold.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: pregnancy (1.9%).

The registry reports serious adverse events in groups with 162 participants at risk in the GML arm, 159 in the no-GML arm, and 161 in the usual-tampon arm. The serious adverse event counts are 1, 3, and 0, respectively. The leading serious-event term is Pregnancy, reported as 1.9%. Death counts are not reported in the registry record.

These figures describe the events recorded in each safety group; they do not by themselves establish whether an event was caused by the assigned tampon or whether rates differ reliably between groups. The safety denominators differ from the analyzed microbiology sample sizes, so they should not be substituted for the primary endpoint denominators. The registry record does not provide event-level details, timing, seriousness criteria, relatedness assessments, or a formal statistical comparison of adverse events. The figures are therefore best read as counts and the reported term, not as a comparative safety conclusion.

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

The posted results describe the frequency of defined microbiological changes among participants included in the per-protocol culture analyses. For the primary mid-menstrual endpoint, event percentages were similar in the GML and no-GML groups, while the usual-tampon group had a higher reported percentage. The unadjusted comparisons and their intervals do not establish a clear difference between GML and either comparator. The results are uncertain estimates, not proof that the groups are identical.

The endpoint is not a diagnosis of infection and does not measure symptoms, treatment benefit, or prevention of disease. The registry explicitly states that the tampons were not intended to prevent, treat, or diagnose infection or another disease condition. A threshold-defined change in named organisms is a microbiological outcome, and its significance should not be extended beyond what was measured.

The trial’s randomized design is relevant to comparing assigned groups, but the microbiological analyses were restricted to the per-protocol population and viable samples. Without full information on exclusions and missingness, it is not possible to determine how well these analyses represent all randomized participants. In addition, the posted comparisons are unadjusted, and multiplicity handling is not reported. These features limit the strength of conclusions that can be drawn from small differences or individual p-values.

The findings also apply to the studied tampon groups, participants, and sample timing described in the registry record. They do not establish outcomes for other products, populations, or clinical settings. The appropriate interpretation is narrow: the registry posts group-level microbiological percentages and unadjusted comparisons at specified menstrual-cycle time points, with uncertainty and analysis-population restrictions that should remain part of any reading of the results.

17. Why This Trial Matters Statistically

This trial is statistically instructive because it places three comparator conditions alongside one another. Comparing a GML tampon with a no-GML study tampon isolates the contrast between the two study products more directly than comparing either with participants’ usual tampon. The usual-tampon arm answers a different practical comparison and may reflect variation in participants’ customary products. Keeping those contrasts separate helps prevent an overly simple reading of a three-arm study.

The endpoint also illustrates the trade-off in converting laboratory measurements into a binary event. A threshold makes the percentage readily interpretable, but it discards information about the magnitude and pattern of organism changes. Participants can meet the same endpoint through different organisms and directions of change. The outcome therefore gives a concise summary without representing every feature of the microbiological data.

Finally, the record illustrates why effect estimates, confidence intervals, and analysis-population details should be considered together. The primary comparisons have uncertain intervals that include no difference, while the analysis excludes samples not viable for culture and is described as per protocol. The registry does not report the original design assumptions, multiplicity plan, or full missing-data counts. Statistical interpretation depends not only on the percentages, but also on what they measure, who contributes to the analysis, and what uncertainty remains around the comparisons.

18. 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.
• Participant counts were reconstructed from posted percentages and arm sizes, so small rounding differences are possible.

Several limitations specific to this trial affect interpretation. First, the microbiological analysis was per protocol and limited to samples viable for culture upon laboratory receipt. The number of participants who did not contribute viable samples and the reasons for exclusion are not reported in the registry record. The results may therefore describe a subset of those enrolled rather than all randomized participants.

Second, the primary endpoint combines decreases in lactobacilli with increases in C. albicans or G. vaginalis into one binary indicator. The posted percentage does not identify which component drove an event or describe the underlying distribution of changes. The outcome is consequently useful as a summary but does not provide a complete account of each organism’s response.

Third, the registry record does not report a multiplicity approach, full statistical analysis plan, or detailed masking procedures. It does not describe the original sample-size justification, interim analyses, baseline microbiological balance, or the extent of missing observations. The posted unadjusted comparisons cannot resolve these unreported design and analysis details.

Fourth, results for all named secondary outcomes are not reported in the registry record. Safety reporting includes group counts and a leading serious-event term but does not provide a formal comparison or detailed event descriptions. These limits do not negate the posted results; they define the scope of what can be concluded from the registry record.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: Clinical Evaluation of Effects of an Investigational Tampon on Vaginal Microflora — NCT00913523.
• Registry record last updated 2011-08-23; status last verified 2011-08.
• Results first posted 2011-05-06.
• 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.

22. Record Summary

The completed study was a randomized, single-masked, parallel-group trial of tampons with GML, tampons without GML, and participants’ usual tampons. The registry lists enrollment of 482 and records a start of 2009-01 and primary completion of 2009-08. The primary endpoint was the percentage with an unfavorable change in primary microflora from mid-cycle baseline to mid-menstrual sampling.

Reported primary event percentages were 52.7%, 52.9%, and 60.0% across the GML, no-GML, and usual-tampon groups. The unadjusted comparisons do not show a clear statistical separation, and their confidence intervals include no difference. Microbiological analyses were per protocol, used no imputation, and were limited to samples viable for culture. Original design assumptions, multiplicity handling, and the extent of missing or excluded data are not reported in the registry record.