Bromocriptine Quick Release (BCQR) as Adjunct Therapy in Type 1 Diabetes: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Bromocriptine Quick Release (BCQR) as Adjunct Therapy in Type 1 Diabetes (NCT02544321), a randomized phase 2 study. The primary outcome is Mean Glucose.

2. Clinical Question

3. Trial Design

This was a phase 2, randomized, double-blind, placebo-controlled crossover study of bromocriptine quick release as adjunct therapy in type 1 diabetes. The study is marked completed. The registry record gives enrollment as 108 and reports separate outcome arms for adolescents and adults. The arm entries list n as 40.0 for each reported group; the record does not explain how those outcome counts relate to the enrollment figure.

In a crossover design, participants receive interventions in a sequence, so a participant can contribute measurements under more than one condition. The summary describes a random-order study and 4-week intervention periods. It does not report the sequence allocation details, whether a washout period was used, or how period and carryover effects were handled in the analysis. Those details matter because outcomes measured later in a sequence may reflect more than the current intervention.

The interventions were BCQR and placebo. The study description says outcomes included fasting and postprandial glucose, glycemic variability, insulin dosing, hypoglycemia frequency and awareness, sleep quality, and metabolic hormone levels. Posted secondary outcomes also include vascular and heart-rate measures. The original statistical analysis plan, participant flow, and analysis populations are not reported in the registry record. The registry record also does not report the original sample-size justification or the assumptions used when planning the trial.

4. Participant Flow and Accounting

The registry reports how participants moved through each stage of the study. All counts below are exactly as posted; missing counts are not inferred from other stages.

Before assignment: Some participants screen failed or withdrew from the study after enrollment, but prior to starting the study.

Overall Study

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: Data reflects average glucose levels.

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: Includes all subjects who completed at least one phase of the study

6. Primary Endpoint

The registry lists the primary outcome as Mean Glucose, measured over 4 weeks.

The primary endpoint is mean glucose, measured in mg/dl by continuous glucose monitoring at the end of each 4-week intervention period. It is a continuous outcome: each participant’s glucose measurements over the monitoring period contribute to an average, and the registry reports a least-squares mean with a standard error.

Continuous glucose monitoring can capture glucose across time rather than relying on a single measurement. An average is useful for describing the overall glucose level during the measurement period, but it does not describe every feature of glucose control. For example, the same average could occur alongside different amounts of fluctuation or different frequencies of low readings. Those questions are addressed by separate outcomes, including mean glycemic variability and hypoglycemia-related measures in the study description.

The primary endpoint addresses a short-term, period-specific question. It does not by itself establish how glucose changes over longer periods, nor does it summarize all the outcomes named in the study description. In this record, the adult arm means are both 165.0, while the corresponding adult comparison entry gives a difference of -16.0 using a reference mean of 181.0. That mismatch limits a straightforward interpretation of the adult comparison as BCQR versus the posted adult placebo mean.

7. Results Posted in the Registry

• Bromocriptine QR (Adolescents): mean 182.00 versus 181.00 on control; difference 1.00 (95% CI -12.86 to 14.86), standardized effect d = 0.03; z-test p = 0.888.
• Bromocriptine QR (Adults): mean 165.00 versus 181.00 on control; difference -16.00 (95% CI -29.86 to -2.14), standardized effect d = -0.51; z-test p = 0.024.
• Placebo (Adults): mean 165.00 versus 181.00 on control; difference -16.00 (95% CI -29.86 to -2.14), standardized effect d = -0.51; z-test p = 0.024.

8. Secondary Efficacy Endpoints

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

Mean Glycemic Variability

• 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:
  • Bromocriptine QR (Adolescents): mean 71.00 versus 73.00 on control; difference -2.00 (95% CI -7.82 to 3.82), standardized effect d = -0.15; z-test p = 0.501.
  • Bromocriptine QR (Adults): mean 61.00 versus 73.00 on control; difference -12.00 (95% CI -18.40 to -5.60), standardized effect d = -0.82; z-test p = <0.001.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Bromocriptine QR (Adolescents) and the control. The interval gives the range of effects that remain compatible with the data.
  • The 95% confidence interval excludes no difference, so the mean is statistically lower for Bromocriptine QR (Adults) than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Augmentation Index

• 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:
  • Bromocriptine QR (Adolescents): mean 10.42 versus 12.52 on control; difference -2.10 (95% CI -6.85 to 2.65), standardized effect d = -0.19; z-test p = 0.387.
  • Bromocriptine QR (Adults): mean -2.00 versus 12.52 on control; difference -14.52 (95% CI -19.40 to -9.64), standardized effect d = -1.30; z-test p = <0.001.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Bromocriptine QR (Adolescents) and the control. The interval gives the range of effects that remain compatible with the data.
  • The 95% confidence interval excludes no difference, so the mean is statistically lower for Bromocriptine QR (Adults) than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Heart Rate Variability (Adults)

• 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:
  • Bromocriptine QR (Adults): mean 1.09 versus 1.14 on control; difference -0.05 (95% CI -0.19 to 0.09), standardized effect d = -0.16; z-test p = 0.480.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Bromocriptine QR (Adults) and the control. The interval gives the range of effects that remain compatible with the data.

The posted secondary results cover different aspects of glucose regulation and cardiovascular or autonomic measures. The general pattern is not uniform across age groups or outcomes: some recalculated comparisons show lower values during BCQR than the comparison reference, while others are close to zero and have intervals that include zero. This pattern should be read as exploratory rather than as a single consistent secondary finding.

Secondary outcomes can add context to the primary mean glucose measure. Glycemic variability concerns fluctuations around glucose levels rather than the average alone, while augmentation index and heart rate variability describe different physiological features. A result on one of these measures does not establish a corresponding change in mean glucose or show that the measures move together. The registry lists additional outcomes relating to hypoglycemia awareness, sleep, and metabolic markers, but a full set of inferential results is not reported in the registry record.

The secondary comparisons are unadjusted. When researchers examine multiple outcomes or comparisons, the chance of finding at least one apparently unusual result by chance can increase. Adjustment methods or a prespecified hierarchy can help address that issue, but a multiplicity plan is not reported in the registry record. The secondary p-values should therefore carry less weight than a clearly prespecified, appropriately analyzed primary comparison.

There is also a comparison-definition concern: the adult secondary comparison entries use adolescent placebo means as their reference values, while adult placebo arm means are separately posted. This means some apparent adult contrasts do not line up with the adult placebo summaries. Without a registry explanation or the sponsor’s analysis, the secondary results cannot be treated as clear adult treatment-versus-placebo estimates. Their main value is to identify outcomes that may merit careful evaluation in a complete analysis, not to establish efficacy or a coherent mechanism.

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

• Mean Glycemic Variability
• Hypoglycemia Awareness
• Augmentation Index
• Heart Rate Variability (Adults)
• Heart Rate Variability (Adolescents)
• Sleep Duration
• Sleep Quality
• Metabolic Markers-glucose and Triglycerides

9. Statistical Interpretation

10. Statistical Methods Explained

Several statistical ideas help interpret the posted results. The calculations accompanying the arm summaries are unadjusted comparisons of means. They use the reported means and standard errors to produce a mean difference, an approximate standard deviation, a confidence interval, and a p-value. They are not reported as the trial sponsor’s crossover-model analysis, and the registry record lists no reported analysis entries.

What does a mean difference describe?

A mean difference subtracts one group or condition mean from another, using the order specified for that comparison. A negative value indicates that the mean used as the second value is lower than the reference mean; a positive value indicates that it is higher. The sign alone does not establish whether the difference is important. Interpretation also depends on the outcome scale, the comparison being made, uncertainty, and whether the contrast matches the intended trial question.

For adolescents, the comparison entry uses placebo at 181.0 and BCQR at 182.0, yielding the listed difference of 1.0. For the adult comparison entry, the reference value is 181.0, even though the posted adult placebo arm mean is 165.0. This is not a minor interpretive detail: the means used in a contrast define what that contrast represents. The registry record does not explain why the adult comparison entry and adult arm results differ in this way.

What does a confidence interval express?

A confidence interval conveys uncertainty around an estimate under the calculation’s assumptions. Narrower intervals indicate greater precision; wider intervals indicate more uncertainty. An interval that includes zero is compatible with no difference as well as with differences in either direction at the confidence level used. An interval that excludes zero does not, by itself, show that the result is clinically important, free from bias, or based on the intended comparison.

Here, the intervals are recalculated from summary statistics and should be understood as unadjusted. The registry reports least-squares means and standard errors, but it does not describe the model used to generate those means or provide a covariance estimate for the within-participant crossover data. The recalculated intervals therefore do not demonstrate that the correlation between measurements from the same participant has been accounted for.

Why does the crossover design matter?

In a crossover study, outcomes from the same participant under different conditions are related. A suitable analysis can use that within-participant pairing and may also account for sequence and period. Treating the observations as independent can fail to represent that structure. Whether this materially changes a result depends on the data and analysis assumptions; the required paired data and the sponsor’s analysis are not reported in the registry record. The posted unadjusted calculations should not be mistaken for a full analysis of the crossover design.

How should p-values be read?

A p-value is calculated under a null hypothesis and a statistical model. It summarizes how compatible the observed result is with that model, not the probability that the null hypothesis is true. A small p-value does not quantify the size or importance of an effect. Nor does it address whether an outcome was selected from many possible outcomes or whether the comparison was prespecified. The registry record does not report a multiplicity plan, and the adult comparison’s reference mean does not match the adult placebo arm mean.

What does the approximate standard deviation mean?

The posted standard errors describe uncertainty in the reported means, while a standard deviation describes variation among individual observations. The recalculated comparisons flag the pooled standard deviation as approximate. It is derived from summary information rather than individual participant data. In a crossover design, a pooled standard deviation from arm summaries may not capture the within-participant variability that is relevant to a paired contrast. The individual observations and the covariance between conditions are not reported in the registry record.

These methods are useful for reading the posted summaries, but they cannot replace a clearly documented analysis plan and participant-level analysis. In particular, the registry record does not report the primary analysis model, how missing observations were handled, or which analysis population was used.

11. Confidence Intervals and Precision

Precision is reflected in the width of a confidence interval: a narrower interval indicates a more precise estimate under the assumptions used, while a wider interval leaves more values compatible with the data. For the adolescent mean glucose comparison, the recalculated difference is 1.0 mg/dl and the 95% confidence interval ranges from -12.86 to 14.86. The interval includes zero and also allows for differences in either direction. The point estimate alone therefore gives an incomplete account of the uncertainty.

The adult comparison entry reports a difference of -16.0, with a 95% confidence interval from -29.86 to -2.141. That interval is calculated from a comparison whose reference mean is 181.0. The posted adult placebo mean is 165.0, so the interval does not match a contrast between the two adult arm means as displayed. Its apparent precision cannot resolve that inconsistency.

Standard errors are reported for the arm means, but the registry does not provide individual-level measurements or the within-participant covariance needed to evaluate a crossover contrast directly. Thus, the displayed recalculated intervals characterize the unadjusted summary-statistic calculations, not necessarily the precision of an analysis that properly accounts for repeated measurements. The registry’s original inferential analysis is not reported in the registry record.

Confidence intervals also do not measure every source of uncertainty. They do not account automatically for outcome selection, missing data, model misspecification, or a mismatch between the comparison inputs and the intended contrast. Statistical precision should therefore be considered alongside the design and reporting limitations.

12. Planning a Similar Trial: Sample Size

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

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

The 80% power column gives the estimated participants per arm for a design with an 80% chance of detecting the chosen effect if the planning assumptions and effect are correct. The 90% power column gives the corresponding estimate for a 90% chance. Higher planned power generally requires more participants for the same effect. The rows for 100% of the observed effect, 75% of the observed effect, and 50% of the observed effect show how the calculation changes when the target effect is smaller. Smaller effects need more participants because they are harder to distinguish from random variation.

These figures are not observed power. They do not indicate the probability that the completed trial found an effect, and they do not replace a prospective sample-size justification. Because the calculations use the observed effect, they can be especially sensitive to how large or small that estimate happens to be. The original sample-size assumptions and justification are not reported in the registry record. In addition, the planning calculation assumes equal allocation between two arms and a normal approximation; it does not, as described, incorporate the details of a crossover analysis or address the mismatch in the adult comparison means.

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 = 1.000; pooled standard deviation σ = 31.623.
2. n = 2 × (1.96 + 0.842)² × 31.623² / 1.000² = 15,697.8, rounded up to 15,698 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 <- 1                      # observed difference in means
sigma <- 31.6227766016838                     # 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(2544321)
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 = 15,698 (80% power), 21,015 (90% power)
 75% of observed effect: n per arm = 27,908 (80% power), 37,360 (90% power)
 50% of observed effect: n per arm = 62,792 (80% power), 84,060 (90% power)
power.t.test: 15698.7 
Simulated power at n = 15,698 per arm: 0.797 (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. Multiplicity, Missing Data and Analysis Populations

The registry record lists multiple outcome measures, including the primary mean glucose endpoint and secondary measures involving glycemic variability, hypoglycemia awareness, vascular measures, heart rate variability, sleep, and metabolic markers. Examining many outcomes can make isolated small p-values less persuasive, because the chance of at least one apparently notable result can rise as the number of tests increases. A prespecified testing hierarchy or an adjustment for multiple comparisons can help control that issue. Such a plan is not reported in the registry record.

The record gives no reported analyses. It does not identify the primary analysis model, explain whether the analysis used the crossover pairing, or state how sequence, period, or carryover were considered. It also does not report an analysis population, such as whether all randomized participants or only participants with usable measurements were included. These details affect how results should be interpreted and whether the reported summaries represent the intended trial estimand.

Missing-data methods are not reported in the registry record. The available summaries do not describe the amount, timing, or reasons for missing outcome measurements, nor whether missingness differed by intervention or age group. In longitudinal and crossover settings, missing measurements can affect both the estimates and their uncertainty, especially if the probability of missingness is related to health status or the outcome.

The record also does not report a statistical analysis plan or document whether analyses were prespecified. Without those details, it is not possible to determine which secondary results were planned as confirmatory, how multiplicity was handled, or how deviations from an intended analysis were addressed. These gaps do not prove that an analysis was not performed; they mean that its methods and results are not reported in the registry record.

15. 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 3% reporting threshold.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: severe hypoglycemia (2.4%), diagnosed with ra (2.4%).

The adverse-event summaries list participants at risk and counts of participants affected by serious and other adverse events for each reported group. The serious-event entries show 1 participant affected in each BCQR group and 0 in each placebo group; the listed at-risk count is 42 in each group. Deaths are listed as 1 in each BCQR group and 0 in each placebo group. The registry also lists other adverse events affecting 13 in the adolescent BCQR group, 17 in the adult BCQR group, 1 in the adolescent placebo group, and 3 in the adult placebo group.

The serious-event terms listed at 2.4% are severe hypoglycemia and diagnosed with RA. The record does not make clear which group each term belongs to, so these terms should not be assigned to a particular intervention group based on this summary. Counts describe what was recorded; they do not establish that an event was caused by an intervention. The registry record does not provide the event narratives, timing, severity details beyond the listed categories, or an assessment of causality. These summaries should therefore be read descriptively, not as a complete comparative safety analysis.

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

The posted results describe mean glucose and several secondary outcomes over 4-week intervention periods in a randomized, double-blind crossover study. The adolescent primary comparison is close to zero, with a confidence interval that includes zero and spans differences in either direction. The adult comparison record reports a difference whose reference mean conflicts with the posted adult placebo mean. The two sources of adult results cannot be reconciled from the registry record alone.

These findings do not establish that BCQR improves or worsens glucose control. The reported endpoint is an average over a limited period, not a comprehensive measure of glucose patterns or longer-term outcomes. Mean glucose does not capture all variation, hypoglycemia, treatment burden, or other clinical outcomes. The study description lists these as distinct areas of interest, but complete results and analysis details are not reported in the registry record.

The unadjusted calculations do not show whether the analysis appropriately used the crossover structure. Measurements from the same participant under different conditions are related, and a direct analysis of that design may yield estimates and uncertainty that differ from calculations based on summary means. The original statistical model, missing-data approach, and multiplicity plan are not reported in the registry record.

Nor do the planning illustrations indicate what the completed trial was powered to detect. They are calculations based on observed effects and specified assumptions, not observed power or the original design justification. The results should be interpreted as registry summaries with substantial reporting limitations, rather than as a definitive estimate of a treatment effect or a complete evaluation of safety.

17. Why This Trial Matters Statistically

This trial is statistically notable because its crossover design can, when analyzed appropriately, compare conditions within participants. That design can make efficient use of participant-level information, but it also requires methods that reflect the dependence between repeated measurements. The registry record does not report whether its original analysis used that structure. The simple recalculations are useful for describing the posted summaries, but they cannot establish the result of a crossover-aware analysis.

The record also illustrates why estimates must be checked against the means they are meant to compare. The adult primary comparison is reported as -16.0, yet the posted adult BCQR and placebo means are both 165.0. The comparison entry instead lists 181.0 as its reference mean. The interval and p-value cannot repair that mismatch; clear identification of the analyzed contrast is necessary before interpreting an apparent difference.

More broadly, statistical interpretation depends on more than a point estimate or a p-value. Confidence intervals show uncertainty under stated assumptions, while analysis plans clarify how multiple outcomes, missing data, and the crossover design were handled. Those details are not reported in the registry record. The secondary outcomes and safety summaries may be informative descriptively, but without a documented inferential plan they should not be treated as a unified set of confirmatory findings.

The record therefore offers a useful case in reading clinical-trial summaries critically: distinguish arm means from comparison estimates, consider the design behind the data, and separate planning calculations from trial results. These distinctions are important even when a trial is randomized and blinded, because design features and reporting determine what the statistical estimates can support.

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.

Several limitations specific to this record constrain interpretation. First, the primary adult comparison does not use the posted adult placebo mean as its reference: the comparison record lists 181.0, whereas the adult placebo arm mean is 165.0. The adult BCQR arm mean is also 165.0. The registry record does not explain this discrepancy, so the reported adult estimate cannot be taken as a direct contrast between the displayed adult arms.

Second, the study is described as a crossover trial, but the registry does not report whether the posted comparison calculations account for paired observations, sequence, period, or carryover. No sponsor analysis is listed. Without those details, the unadjusted calculations may not reflect the intended analysis of the trial design.

Third, arm summaries give standard errors rather than participant-level variation, and the recalculated pooled standard deviations are approximate. Individual data and within-participant covariance are not reported. The record also does not report how missing observations were handled, which participants contributed to each outcome, or the analysis population.

Finally, the registry lists several outcome areas, but a multiplicity plan and original analysis plan are not reported. The record does not explain how the outcome counts relate to the enrollment figure, and it does not report the original sample-size justification. These reporting gaps limit assessment of precision, selective reporting, and the strength of conclusions from secondary outcomes. They do not establish that the trial lacked these procedures; they indicate that the relevant details are not reported in the registry record.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: Bromocriptine Quick Release (BCQR) as Adjunct Therapy in Type 1 Diabetes — NCT02544321.
• Registry record last updated 2021-09-13; status last verified 2021-08.
• Results first posted 2021-09-13.
• 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 registry record describes a completed phase 2, randomized, double-blind, placebo-controlled crossover study of bromocriptine quick release in type 1 diabetes. Enrollment is listed as 108, and the study description specifies 4-week intervention periods. The primary outcome is mean glucose measured by continuous glucose monitoring.

Posted arm summaries include results for adolescents and adults. The adolescent primary comparison is a small unadjusted difference with a confidence interval that includes zero. The adult comparison entry does not align with the posted adult placebo mean, so it is not straightforward to interpret as the adult treatment-versus-placebo contrast. Several secondary outcomes and adverse-event summaries are also listed.

The registry record does not report the original statistical analysis plan, crossover-model analysis, missing-data methods, multiplicity plan, or original sample-size justification. The recalculated comparisons and planning illustrations should be read with those limitations in mind.