Stacking Exercises Aid the Decline in FVC and Sick Time: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Stacking Exercises Aid the Decline in FVC and Sick Time (NCT01999075), a randomized phase 4 study. The primary outcome is Change in FVC (%-Predicted) From Baseline to 2 Years..

2. Clinical Question

3. Trial Design

STEADFAST (NCT01999075) was a phase 4, randomized, single-masked, parallel-group trial sponsored by Children's Hospital of Eastern Ontario. The registry reports an enrollment of 70 and a completed status. The trial began in 2013-03, and primary completion was recorded as 2018-11-22. The registry record does not describe who was masked.

Participants were assigned to conventional treatment or conventional treatment plus lung volume recruitment twice per day. The posted primary outcome compares changes in FVC percent-predicted from baseline to 2 years. The registry reports primary-outcome group sizes of 25 for conventional treatment and 28 for lung volume recruitment. It says four children were excluded from the primary analyses because they did not have a reliable or reproducible baseline pulmonary function test: three in the conventional-treatment group and one in the lung-volume-recruitment group. The remainder were included except for a truncated qualification in the registry text, so the record does not fully explain the primary analysis population.

Ten outcome measures and five analyses are reported in the record, alongside seven posted secondary outcomes. The reported primary analysis is an ANCOVA comparison. The registry does not report the trial's original sample-size justification, detailed allocation procedures, masking procedures, or a complete statistical analysis plan. These missing details limit assessment of how closely the reported analysis follows the prespecified 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: Of the 70 children randomized to a treatment arm, four children (n=3 in conventional treatment group; n=1 in LVR group) were excluded from the analyses as they did not have a reliable or reproducible pulmonary function test at baseline. All remaining children were included in the analyses except where no measurement was available.

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: Of the 70 enrolled children, four did not have reliable or reproducible FVC measurements at baseline and were excluded.

6. Primary Endpoint

The registry lists the primary outcome as Change in FVC (%-Predicted) From Baseline to 2 Years., measured over 2 years.

The primary endpoint is change in FVC percent-predicted from baseline to 2 years. FVC, or forced vital capacity, is the amount of air a person can forcefully exhale after taking a full breath. Expressing it as percent-predicted relates the measurement to a reference value; the registry reports the endpoint as a change in that measure. The group summaries are means, with standard deviations describing the spread of individual changes.

The registry states that FVC change was selected because it is a strong predictor of subsequent respiratory failure and mortality. It also notes that survival was not a realistic endpoint for this pediatric trial and characterizes FVC change as an appropriate clinical laboratory measure and valid surrogate endpoint for the study. A surrogate is a measurable outcome used in place of a later clinical outcome. In general, a surrogate can be informative about the process under study, but it is not itself the later outcome it is intended to represent.

The endpoint therefore addresses whether average lung-function change differed between the randomized groups over the stated timeframe. It does not directly measure survival, respiratory failure, or every aspect of lung health or quality of life.

7. Results Posted in the Registry

• Lung Volume Recruitment: mean -6.40 versus -9.20 on control; difference 2.80 (95% CI -6.13 to 11.73), standardized effect d = 0.17; z-test p = 0.539.

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.

FVC Decline of 10% of Predicted

• 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:
  • Lung Volume Recruitment: mean 21.70 versus 19.14 on control; difference 2.56 (95% CI -1.37 to 6.49), standardized effect d = 0.32; z-test p = 0.201.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Lung Volume Recruitment and the control. The interval gives the range of effects that remain compatible with the data.

Number of Participants Prescribed Outpatient Oral Antibiotic Courses Between Baseline and 2 Years

• Endpoint type: Binary: the number of participants with the event, compared as proportions.
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • Lung Volume Recruitment: 5.6% (2/36) versus 3.3% (1/30) on control; risk difference 2.2 percentage points (95% CI -7.6 to 12.1), risk ratio 1.67 (0.16–17.49); unadjusted two-proportion z-test p = 0.666.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Lung Volume Recruitment and the control. The interval gives the range of effects that remain compatible with the data.

Change in Maximum Inspiratory Pressures (MIP), From Baseline to 2 Years

• 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 deviations.
• Result:
  • Lung Volume Recruitment: mean -4.60 versus -0.50 on control; difference -4.10 (95% CI -18.43 to 10.23), standardized effect d = -0.19; z-test p = 0.575.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Lung Volume Recruitment and the control. The interval gives the range of effects that remain compatible with the data.

Across the posted secondary outcomes, the estimates do not form a clear, precise pattern that independently settles the primary question. The endpoints cover time until a specified FVC decline, antibiotic courses, quality of life, cough flow, maximal insufflation capacity, and respiratory pressures. They assess related but distinct aspects of respiratory function and health. The primary outcome remains the central comparison identified by the registry, while the secondary outcomes supply supporting context.

The available secondary results include differences in both directions, and the reported intervals for the recalculated comparisons encompass no difference. Some outcomes have smaller analyzed groups than the primary endpoint, which limits precision. The record lists seven secondary outcomes, but the posted numerical results described in the registry are not complete for all of them. Missing endpoint results are not reported in the registry record.

In general, secondary outcomes are often assessed in a larger family of comparisons. Unless multiplicity is addressed, repeated testing can increase the chance that at least one result appears noteworthy by chance. The registry does not report a multiplicity adjustment or a hierarchy for interpreting secondary endpoints. Their unadjusted results therefore carry less weight than a prespecified primary analysis and should not be treated as independent confirmation based on nominal p-values alone.

Secondary outcomes can still help characterize the trial's findings, especially when they are biologically and clinically related to the primary endpoint. But different scales, incomplete reporting, smaller samples, and the absence of a reported multiplicity strategy make it inappropriate to combine them informally into a single conclusion. The secondary evidence does not remove the uncertainty in the primary FVC estimate.

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

• FVC Decline of 10% of Predicted
• Number of Participants Prescribed Outpatient Oral Antibiotic Courses Between Baseline and 2 Years
• Health-related Quality of Life From Baseline to 2 Years
• Change in Difference Between Assisted and Unassisted Peak Cough Flow (PCF) From Baseline to 2 Years
• Change in Maximal Insufflation Capacity (MIC)-Vital Capacity (VC) From Baseline to 2 Years
• Change in Maximum Inspiratory Pressures (MIP), From Baseline to 2 Years
• Change in Maximal Expiratory Pressures (MEP), From Baseline to 2 Years

9. Statistical Interpretation

10. Statistical Methods Explained

What does the mean difference represent?

The primary outcome is continuous, so the arm summaries are means: -9.2 for conventional treatment and -6.4 for lung volume recruitment. The unadjusted recalculation expresses the contrast as a mean difference of 2.8. In general, a mean difference describes the separation between group averages on the outcome's original scale. It does not describe the experience of every participant, and it can conceal variation among individuals. The reported standard deviations, 17.3 and 15.7, describe that variation in the two groups.

How should the confidence interval and p-value be read?

In general, a confidence interval gives a range of estimates compatible with the data under the method used. The unadjusted primary interval is -6.133 to 11.73. Because it includes zero, the results are compatible with no group difference as well as with differences in either direction. A p-value summarizes how unusual results at least as extreme as the observed result would be under a specified no-difference model and its assumptions. The unadjusted p-value is 0.539. Neither a p-value nor an interval measures the probability that a treatment works, and neither settles clinical importance by itself.

Why is the reported analysis different?

The registry reports ANCOVA, with a net mean difference of 1.9, a 95% confidence interval from -6.9 to 10.7, and p = 0.68. In general, ANCOVA compares groups while accounting for covariates included in the model. The registry does not identify the covariates or provide enough detail to reproduce that model. The unadjusted recalculation is based on the posted group summaries and is not a replacement for the sponsor's analysis.

What do the secondary outcome measures add?

The secondary outcomes include a restricted-mean time measure for FVC decline of 10% of predicted, a count of participants prescribed outpatient oral antibiotic courses, and changes in respiratory pressure measures. The FVC-decline measure is reported in months and uses a restricted mean. In general, a restricted mean summarizes time accumulated up to a specified analysis limit rather than relying only on a hazard ratio. The antibiotic outcome is a participant count, while the pressure measures are continuous outcomes summarized by means. The registry's posted estimates and methods vary by endpoint, and only the primary and MIP results have a reported sponsor analysis in the displayed record.

For binary outcomes, a risk difference is the difference between the proportions experiencing the event in the groups. In general, that contrast is interpretable on the probability scale, while a confidence interval indicates its uncertainty. The antibiotic comparison has a risk difference of 0.022 and an interval from -0.076 to 0.121. The interval includes zero, so the data do not provide a precise distinction between the groups for this outcome.

Why can secondary results be difficult to interpret?

General statistical practice treats a set of many outcome comparisons cautiously. When multiple outcomes are tested, the chance of finding an apparently unusual result by chance can increase unless the analysis plan addresses multiplicity. The registry record does not report whether or how multiplicity was handled. The secondary results should therefore be read as exploratory evidence unless additional prespecified analysis details are available. Small analyzed groups and wide uncertainty can further limit what these estimates establish.

What does over-dispersion mean in general?

For count outcomes, over-dispersion means that observed variability is greater than a chosen count model expects. It can make standard errors and confidence intervals too narrow if the model does not accommodate the extra variability. This is a general consideration for count data; the registry record does not report a count-model analysis or an assessment of over-dispersion for these outcomes.

11. Confidence Intervals and Precision

Precision is about how narrowly the data locate an estimate, not whether the estimate is important. The unadjusted primary mean difference is 2.8, and its 95% confidence interval extends from -6.133 to 11.73. The interval includes zero and values on both sides of zero. This means the posted primary summary data do not pin down the direction or magnitude of a group difference with much certainty.

The sponsor's ANCOVA estimate is 1.9, with a 95% confidence interval from -6.9 to 10.7. This interval also includes zero and spans appreciable differences in either direction. The adjusted and unadjusted estimates are both uncertain; their distinct methods should be kept in mind when comparing them.

Secondary comparisons likewise have intervals that include no difference in the reported examples. For FVC decline of 10% of predicted, the mean difference is 2.56 months, with a 95% interval from -1.368 to 6.488. For outpatient oral antibiotic courses, the risk difference is 0.022, with an interval from -0.076 to 0.121. The intervals express uncertainty on different scales, so their widths should not be compared without considering the outcome units and measure.

In general, wider intervals indicate less precision, which can arise from limited information, variability, or both. The primary analysis has fewer participants than the randomized enrollment, and some secondary measures have still smaller analyzed groups. The registry does not report a prespecified clinically important difference for the primary outcome. Accordingly, interval inclusion of zero should not be mistaken for proof of equivalence, and an estimate's direction alone should not be treated as a definitive result.

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 illustrations based on the observed effect, not the trial's original design justification. They assume a two-sided significance level of 0.05, equal allocation between two arms, and a normal approximation. Each row considers an effect equal to 100%, 75%, or 50% of the observed effect. The displayed continuous and binary outcome planning figures are participants per arm; the planning note specifies that time-to-event calculations would instead report total events. These figures are not observed power.

The 80% power column gives the estimated participants per arm under those assumptions for a design with an 80% chance of detecting the stated effect if that effect were the true one. The 90% power column describes the corresponding design target for a 90% chance. These are prospective planning concepts, not evidence that the completed trial had a particular probability of detecting an effect.

Smaller effects require more participants to detect under the same assumptions. For the primary outcome, planning for 100% of the observed effect gives 544 participants per arm for 80% power and 728 for 90% power. Planning for 75% gives 966 and 1,293 participants per arm, while planning for 50% gives 2,174 and 2,910. The increase as the target effect gets smaller illustrates why detecting modest differences generally requires larger studies.

These illustrations should be interpreted cautiously because the effect used is estimated from this trial's data and may itself be uncertain. The registry record does not report the assumptions used for the trial's original design, so these illustrations cannot establish whether the original enrollment target was appropriate.

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 = 2.800; pooled standard deviation σ = 16.472.
2. n = 2 × (1.96 + 0.842)² × 16.472² / 2.800² = 543.3, rounded up to 544 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 <- 2.8                      # observed difference in means
sigma <- 16.4723118845999                     # 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(1999075)
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 = 544 (80% power), 728 (90% power)
 75% of observed effect: n per arm = 966 (80% power), 1,293 (90% power)
 50% of observed effect: n per arm = 2,174 (80% power), 2,910 (90% power)
power.t.test: 544.3 
Simulated power at n = 544 per arm: 0.805 (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's reported primary analysis is an ANCOVA comparison: net mean difference 1.9, 95% confidence interval from -6.9 to 10.7, and p = 0.68. The unadjusted recalculation from posted group summaries gives a mean difference of 2.8, 95% confidence interval from -6.133 to 11.73, and p = 0.539. Both estimates have intervals that include zero, but they are not identical because the sponsor's reported ANCOVA is adjusted and the recalculation is unadjusted.

The sponsor's own analysis takes precedence for interpreting the trial's reported primary result. The unadjusted recalculation is useful as a transparent comparison based on summary statistics, but it does not reproduce the adjusted model. The registry does not report the ANCOVA covariates or enough model detail to independently verify how adjustment was performed.

For the reported MIP outcome, the registry lists a mixed models analysis with p = 1.00, but does not give an estimate or confidence interval for that analysis. The unadjusted recalculation of the posted group summaries is not a substitute for the mixed-model result. No sponsor analysis is listed for the other secondary comparisons discussed in the record.

15. Multiplicity, Missing Data and Analysis Populations

The registry identifies a primary outcome and lists secondary outcomes, but the posted record does not report a complete statistical analysis plan. It does not state whether a formal multiplicity procedure was prespecified, whether secondary outcomes were ordered in a testing hierarchy, or whether any adjustment was applied. When many outcomes are examined, general statistical practice recognizes that the chance of a false-positive finding can rise. Without the analysis plan, the appropriate inferential status of the secondary comparisons is uncertain.

The record says four children were excluded from primary analyses because baseline pulmonary function tests were not reliable or reproducible: three in conventional treatment and one in lung volume recruitment. It also says all remaining children were included except for an incomplete qualification. The complete analysis-population definition and the handling of missing follow-up measurements are not reported in the registry record. The record does not state whether the primary analysis was intention-to-treat, per-protocol, or another approach.

The outcome summaries use different numbers of participants. The primary groups contain 25 and 28 participants; several secondary analyses use larger groups, while the MIP groups contain 15 and 17. The registry does not fully explain the endpoint-specific availability of measurements or the reasons for these group-size differences. It also does not describe imputation, sensitivity analyses, or other missing-data methods.

These omissions do not establish that the analyses were inappropriate; they limit what can be assessed from the registry record alone. A protocol or full statistical analysis report could clarify prespecification, multiplicity, analysis populations, and missing-data handling, but those details are not reported in the registry record.

16. 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 zero affected participants in both groups: 0 among 30 at risk in conventional treatment and 0 among 36 at risk in lung volume recruitment. The registry lists no serious adverse event terms. It also reports zero deaths in both groups.

For other, non-serious adverse events, 0 of 30 participants in the conventional-treatment group and 3 of 36 in the lung-volume-recruitment group were affected. The registry does not identify the types of these other events in the posted figures. These counts describe events recorded in the trial; they do not establish whether an event was caused by an intervention.

In general, safety counts should be interpreted with attention to how many participants were observed, the duration and completeness of observation, and how events were defined and collected. The registry record does not provide detail about those procedures in the adverse-event figures. Zero reported serious events is not proof that serious events cannot occur, particularly when the number of participants is limited.

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

The primary result estimates a difference in average change in FVC percent-predicted between randomized groups over 2 years. The posted unadjusted difference is 2.8, and the sponsor's ANCOVA estimate is 1.9. Both confidence intervals include zero and extend to differences in either direction. The data therefore do not precisely establish the size or direction of a group difference for this endpoint.

This is not the same as demonstrating that the groups are equivalent. Equivalence requires a defined margin and an analysis designed to assess whether the interval lies within that margin; the registry record does not report such a margin or an equivalence analysis. Nor does a non-significant result prove that no difference exists.

The trial focused on FVC change rather than survival. Although the registry describes FVC as a predictor of subsequent respiratory failure and mortality and as a surrogate endpoint, a finding on FVC is not itself a direct finding about survival, respiratory failure, infection prevention, or quality of life. The posted secondary outcomes touch on some related measures, but their results are not sufficient to turn the primary estimate into a broader claim about every aspect of respiratory health.

Interpretation is also limited by the analysis population and incomplete reporting. Four randomized children were excluded from the primary analyses for unreliable or unreproducible baseline testing, and the record's remaining analysis-population description is truncated. The posted summaries cannot fully explain missing outcome data, the model covariates, or the plan for multiple outcomes.

The results should therefore be read as estimates with substantial uncertainty from a completed randomized comparison in children with Duchenne Muscular Dystrophy. They do not by themselves establish benefit, lack of benefit, equivalence, or effects beyond the outcomes and participants represented in the registry record.

18. Why This Trial Matters Statistically

This trial illustrates why an estimated effect and its precision must be read together. The primary point estimate is positive, but its confidence interval includes zero and also allows differences in the opposite direction. A point estimate alone can make a result appear more definite than the data support; the interval communicates the remaining uncertainty.

It also demonstrates the distinction between an adjusted sponsor analysis and an unadjusted recalculation from group summaries. Both are legitimate statistical descriptions of different analyses, but they answer the comparison using different information and methods. The registry reports ANCOVA as the primary analysis, while the recalculation provides a simple contrast based on the displayed means and standard deviations. Keeping those roles separate avoids treating a summary-statistic calculation as a reproduction of the sponsor's model.

Another statistical lesson is that enrollment and analysis sample sizes are not interchangeable. The registry reports 70 randomized children, while the primary summaries include 25 and 28. Exclusions and endpoint-specific availability affect how much information contributes to each estimate. Transparent reporting of analysis populations and missing-data methods is essential for understanding that transition.

Finally, secondary outcomes and planning illustrations require distinct interpretation. Multiple outcomes can broaden clinical context but complicate inference if multiplicity is not addressed. Planning figures based on the observed effect describe hypothetical designs under stated assumptions; they are not observed power and do not reveal the original design rationale. These issues are common in clinical research and make careful reading of the registry record central to sound statistical interpretation.

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.

Several limitations are specific to the information reported for this trial. The primary analysis excludes four randomized children because baseline pulmonary function testing was not considered reliable or reproducible. The registry's explanation of the remaining primary analysis population is cut off, and it does not describe the handling of missing follow-up measures. This makes it difficult to assess whether the analyzed participants represent the randomized groups fully.

The primary group sizes are smaller than the randomized enrollment, and the analyzed sample sizes differ across outcomes. Some secondary measures, including MIP, are based on fewer participants than the primary comparison. Smaller samples generally yield less precise estimates, though the registry does not explain all reasons for the endpoint-specific group sizes.

The sponsor's ANCOVA estimate is reported without its covariates or sufficient model detail for reproduction. The registry also does not report the trial's original sample-size justification, interim analyses, complete analysis plan, or multiplicity approach. The posted secondary outcomes are not all accompanied by numerical results in the record, and the available adverse-event figures do not identify the other event types.

These are reporting limitations as well as limits on inference from the trial data. They do not establish that unreported procedures were absent or that the analyses were improperly conducted. They mean that the registry record alone cannot resolve those questions.

20. Related Tutorials

21. Related Statistical Calculators

22. Sources

• ClinicalTrials.gov: Stacking Exercises Aid the Decline in FVC and Sick Time — NCT01999075.
• Registry record last updated 2025-01-09; status last verified 2024-11.
• Results first posted 2025-01-09.
• 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

STEADFAST was a completed, randomized, single-masked, parallel-group phase 4 trial sponsored by Children's Hospital of Eastern Ontario, with 70 children enrolled. It compared conventional treatment with conventional treatment plus twice-daily lung volume recruitment in children with Duchenne Muscular Dystrophy.

The primary endpoint was change in FVC percent-predicted from baseline to 2 years. The unadjusted recalculation gives a mean difference of 2.8, with a 95% confidence interval from -6.133 to 11.73. The registry's ANCOVA analysis reports a net mean difference of 1.9, with a 95% confidence interval from -6.9 to 10.7. Both estimates are uncertain and compatible with no difference.

The registry also reports secondary respiratory and health outcomes, as well as adverse-event counts. Interpretation is constrained by exclusions from the primary analysis, incomplete reporting of the analysis population and statistical plan, and uncertainty across secondary outcomes.