Safety Study of Subcutaneously-Injected Prandial INSULIN-PH20 NP Compared to Insulin Lispro Injection in Participants With Type 1 Diabetes Mellitus: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Safety Study of Subcutaneously-Injected Prandial INSULIN-PH20 NP Compared to Insulin Lispro Injection in Participants With Type 1 Diabetes Mellitus (NCT00883558), a randomized phase 2 study. The primary outcome is Postprandial Glucose Excursion.

2. Clinical Question

The study’s stated focus was the absorption and action of insulin. The primary measure captures the difference between a post-meal glucose value and the corresponding pre-meal value, then averages meal excursions over the measurement days in each treatment cycle. It is therefore a measure of post-meal glucose change, rather than a measure of glucose concentration at a single time point. The registry record also lists time spent outside a specified glucose range during continuous monitoring and hypoglycemic events as secondary outcomes.

3. Trial Design

This was a Phase II, randomized, open-label, two-way crossover safety study. In a crossover design, participants receive both treatments in different treatment periods, so each participant can contribute data under each treatment. That design can support a within-participant comparison, although interpretation depends on features such as treatment sequence, period effects, and any carryover between periods. The details of those design features are not reported in the registry record.

The registry reports enrollment of 48 participants. Before the two treatment cycles, all enrolled participants underwent a one-month dose-titration period and received insulin lispro at 100 units per milliliter, injected subcutaneously before meals, with doses titrated to each participant individually. Participants were then randomly assigned to one of two treatment sequences. The record identifies INSULIN-PH20 NP and insulin lispro as the treatments, but the full treatment-period descriptions are not available in the recorded arm description. Treatment duration within each cycle, any washout, and the allocation ratio are not reported in the registry record.

The primary outcome was assessed at Week 14 and Week 26. Its analysis population consisted of participants who completed both treatment cycles and had evaluable postprandial glucose data; 37 participants contributed to each treatment summary. These counts describe the posted outcome data, not necessarily the number randomized to each treatment sequence. The registry record does not report why participants were excluded from this outcome analysis or whether missing measurements were handled using a particular method.

The study is listed as completed. Its start is recorded as 2009-05 and primary completion as 2010-02. The registry identifies Halozyme Therapeutics as sponsor. Blinding was not used, so participants and study personnel were not masked to treatment assignment. The protocol’s original sample-size justification, planned interim analyses, and detailed statistical analysis plan are not reported in the registry record.

4. Participant Flow and Accounting

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

Titration Period (Weeks 0 to 4)

Reasons for not completing this period:

First Treatment Cycle (Weeks 4 to 16)

Reasons for not completing this period:

Second Treatment Cycle (Weeks 16 to 28)

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: Participants who completed both treatment cycles with evaluable postprandial glucose data.

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: Participants who were randomized and received at least 1 dose of study drug.

6. Primary Endpoint

The registry lists the primary outcome as Postprandial Glucose Excursion, measured over Week 14 and Week 26.

The primary endpoint was postprandial glucose excursion, measured in milligrams per deciliter. For each meal, an excursion was defined as the post-meal glucose value minus the pre-meal value, using eight-point glucose monitoring. Measurements covered three meals over three days in each treatment cycle. The reported value for a cycle was the average of those excursions across the meals and days.

This measure is used to describe the change in glucose following meals, rather than the overall glucose level or the time spent within a target range. A positive excursion represents a post-meal value above the corresponding pre-meal value; a negative excursion would represent a lower post-meal value. That is a general interpretation of the measure’s definition, not a claim about the participants’ individual readings.

The registry reports means and standard deviations for participants with evaluable data who completed both treatment cycles. A mean describes the group’s average excursion, while the standard deviation describes how spread out the observed values are around that average. The endpoint is therefore informative about average post-meal change and the variability of those changes in the analyzed participants. It does not by itself describe every participant’s response, glucose values at other times, or clinical outcomes beyond the measured excursion.

7. Results Posted in the Registry

• Insulin Lispro: mean 14.47 versus 17.23 on control; difference -2.76 (95% CI -18.97 to 13.45), standardized effect d = -0.08; z-test p = 0.739.

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.

Time Spent With Blood Glucose Value Outside a 71-139 Milligrams Per Deciliter (mg/dL) Range During Continuous Glucose Monitoring

• 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:
  • Insulin Lispro: mean 13.37 versus 14.58 on control; difference -1.21 (95% CI -2.77 to 0.35), standardized effect d = -0.34; z-test p = 0.129.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Insulin Lispro and the control. The interval gives the range of effects that remain compatible with the data.

The registry lists two secondary outcomes: time spent outside the specified glucose range during continuous glucose monitoring, and the number of participants with hypoglycemic events. Quantitative summary information is available for the continuous monitoring outcome. A result for the hypoglycemic-event outcome is not reported in the registry record. Consequently, the posted secondary information does not permit a complete comparison across both listed outcomes or an overall account of their pattern.

The continuous monitoring outcome measures time outside a range, which differs from the primary endpoint’s meal-specific glucose excursion. The primary endpoint focuses on average post-meal change; time outside the range summarizes glucose monitoring over a broader period. These measures address related but non-identical aspects of glucose data. A secondary estimate should therefore not be treated as a replication of the primary endpoint.

The available secondary result does not remove the uncertainty in the primary comparison, and the missing event-count result limits assessment of the full set of secondary outcomes. The registry record does not report whether these secondary outcomes were prespecified in a statistical hierarchy, whether any multiplicity adjustment was applied, or how missing secondary measurements were handled.

As a general statistical consideration, testing or estimating multiple outcomes creates more opportunities for a seemingly notable result to arise by chance. Unadjusted secondary results are usually interpreted more cautiously than a primary result because they may be exploratory and because multiplicity can increase false-positive risk. Here, the lack of reported multiplicity procedures and the absence of a posted result for one listed secondary outcome make it especially important not to infer a broad treatment pattern from the secondary findings alone.

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

• Time Spent With Blood Glucose Value Outside a 71-139 Milligrams Per Deciliter (mg/dL) Range During Continuous Glucose Monitoring
• Number of Participants With Hypoglycemic Events

9. Statistical Interpretation

10. Statistical Methods Explained

The primary endpoint is continuous: each participant’s outcome is a measured quantity, and the posted summaries describe its mean and standard deviation. A mean gives the arithmetic average for a group. A standard deviation describes the degree to which individual values vary around that mean. When variability is large, group means alone may not capture the range of individual observations; uncertainty in a comparison also depends on the number of observations and the study design.

What does a mean difference represent?

A mean difference expresses the gap between two group averages in the endpoint’s original units. For the recalculation, the contrast is insulin lispro minus INSULIN-PH20 NP. A negative estimate means the insulin lispro average is lower under that subtraction order; it does not, by itself, establish that the treatments differ reliably. The estimate is an unadjusted comparison of posted summary statistics. It does not account for the paired structure of a crossover trial in the way a participant-level crossover analysis may do.

Why does the crossover structure matter?

In a crossover study, each participant receives both treatments, so the two observations from one participant are related. A comparison that uses this pairing can account for stable participant characteristics shared across treatment periods. By contrast, a simple unadjusted calculation from separate group summaries does not use the within-participant correlation. That correlation can affect the standard error and confidence interval. The registry does not report the participant-level data or enough detail to reconstruct the paired analysis from the summaries.

Period effects and carryover can also matter in a crossover design. A period effect means outcomes may differ between treatment periods for reasons other than the treatment; carryover means an earlier treatment may affect outcomes during a later period. Randomization of treatment sequence can help address some design concerns, but the handling of period, sequence, and carryover in the sponsor’s model is not reported in the registry record. The exact mixed-model specification is also not reported.

What does the confidence interval add?

A confidence interval describes uncertainty around an estimated difference under the assumptions of the analysis. It gives a range of values compatible with the observed data and the method used. A wide interval indicates that the data do not pin down the difference precisely. If an interval includes zero, it includes no average difference as one possibility; it does not prove that the difference is zero. Interpretation also depends on what differences would matter in the study context, and a threshold for clinical importance is not reported in the registry record.

What does a p-value say?

A p-value measures how inconsistent the observed data are with a specified null hypothesis, under the assumptions of the statistical test. It is not the probability that the null hypothesis is true, and it does not express the size or importance of an effect. The recalculated p-value is unadjusted and derives from a simple comparison of the posted summaries. The sponsor’s p-value comes from the reported mixed-model analysis. They should not be treated as interchangeable because the analyses differ in method and may use different assumptions.

Why can over-dispersion matter?

As a general statistical point, over-dispersion means observed variability is greater than a model or analysis assumption allows for. If variability is underestimated, standard errors may be too small and uncertainty intervals too narrow; if the model accounts for variability appropriately, that concern may be reduced. The primary endpoint is reported with standard deviations, but the registry record does not identify an over-dispersion assessment or a particular variance adjustment. The posted spread alone does not establish that over-dispersion occurred.

The sponsor labels its analysis “Mixed Models Analysis” and reports an LS mean difference. In general, a least-squares mean is a model-based mean estimated under the specified model, and mixed models can represent structured or correlated observations. The registry record does not report the covariates, covariance structure, missing-data assumptions, or other model details, so those cannot be evaluated from the posted description. The sponsor analysis is the trial’s reported analysis; the simple recalculation is a separate, unadjusted check based on summary statistics.

11. Confidence Intervals and Precision

Precision concerns how narrowly the data locate the treatment difference. For the unadjusted recalculation of the primary outcome, the estimated mean difference is -2.76 mg/dL, with a 95% confidence interval from -18.97 to 13.45 mg/dL. Because this interval is broad and includes zero, the posted summaries do not locate the difference precisely or rule out differences in either direction under this calculation. The interval should be interpreted in the measurement’s units and in light of the analysis method.

The sponsor’s mixed-model analysis reports an LS mean difference of 3.06 and an upper 95% confidence limit of 14.11. Its lower confidence limit is not reported in the registry record, so the full interval cannot be assessed from the recorded results. The sponsor’s model-based estimate and the unadjusted recalculation differ in method and estimate; the registry record does not provide enough model detail to explain or reproduce the sponsor result.

Precision is also affected by the available primary analysis population: 37 participants contributed to each treatment summary, and eligibility for this analysis required completion of both cycles and evaluable postprandial data. The registry record does not report the reasons for missing outcome data or the extent to which missingness could affect the estimates. The observed standard deviations indicate substantial variation among the recorded outcomes, which contributes to uncertainty but does not alone determine it.

A confidence interval is not a range containing a fixed proportion of individual participant outcomes. It describes uncertainty in an estimated group difference under the method’s assumptions. Nor does an interval that includes zero show equivalence. Assessing equivalence or non-inferiority requires a prespecified margin and a complete interval or decision rule; the margin is not reported in the registry record.

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 illustrative calculations based on the observed effect, not the trial’s original design assumptions. For this continuous-outcome comparison, the figures are participants per arm, not total events. The listed assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. The assumed effect is a stated fraction of the observed effect. The trial’s own sample-size justification and original design assumptions are not reported in the registry record.

The 80% power column gives the estimated participants per arm for a planning scenario designed to have an 80% chance of detecting the assumed effect, if that effect and the calculation assumptions hold. The 90% power column gives the corresponding per-arm estimate for a 90% chance. These are prospective planning quantities; they are not evidence about the probability that this completed trial detected an effect.

The planning rows consider 100%, 75%, and 50% of the observed effect. As the assumed effect becomes smaller, the required number of participants per arm increases. This reflects a general feature of statistical planning: smaller differences are harder to distinguish from random variation, so more information is needed to detect them with the same target power.

For the primary outcome, the displayed planning values are based on the recalculated unadjusted effect, not the sponsor’s mixed-model estimate. They are not a reconstruction of the study’s original sample-size calculation, and the registry does not state that the trial was designed around these values. This is not observed power. The planning curve, if read alongside the figures, should likewise be understood as an illustration under the listed assumptions rather than as a report of the trial’s achieved ability to detect an effect.

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 = -2.760; pooled standard deviation σ = 35.580.
2. n = 2 × (1.96 + 0.842)² × 35.580² / -2.760² = 2,608.7, rounded up to 2,609 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 <- -2.76                      # observed difference in means
sigma <- 35.5795191718494                     # 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(883558)
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 = 2,609 (80% power), 3,493 (90% power)
 75% of observed effect: n per arm = 4,638 (80% power), 6,209 (90% power)
 50% of observed effect: n per arm = 10,435 (80% power), 13,970 (90% power)
power.t.test: 2609.6 
Simulated power at n = 2,609 per arm: 0.795 (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 registry reports the sponsor’s primary analysis as a mixed models analysis of LS mean difference. It gives an estimate of 3.06, an upper 95% confidence limit of 14.11, and a p-value of 0.322; the lower confidence limit is not reported in the registry record. The analysis is classified in the registry as non-inferiority or equivalence. The margin and full decision criteria are not reported, so the classification alone does not show whether a prespecified criterion was met.

The unadjusted recalculation uses the posted group means and standard deviations and yields a different estimate and interval. It is a simple check based on summary statistics and does not use the crossover pairing or reproduce the mixed-model analysis. A difference between the two results can arise because the analyses use different methods and information; the registry does not give enough detail to identify the exact reason for the difference.

The sponsor’s mixed-model result is the reported trial analysis, while the recalculation is supplementary and should not replace it. The registry does not identify a discrepancy between the reported and recalculated analyses. Neither result should be read as proof of equivalence or as proof that there is no treatment difference without the margin, complete analysis details, and the corresponding decision criteria.

15. Multiplicity, Missing Data and Analysis Populations

The registry reports one primary outcome and lists two secondary outcomes. It does not report a procedure for adjusting significance tests or confidence intervals for multiple outcomes, nor does it describe a testing hierarchy. Thus, whether the sponsor controlled the overall chance of a false-positive finding across outcomes is not reported in the registry record. The recalculated p-value is explicitly unadjusted and should be interpreted as a single comparison rather than a multiplicity-controlled result.

The primary analysis population is described as participants who completed both treatment cycles with evaluable postprandial glucose data. The registry does not report a full participant flow for this outcome, reasons for missing data, or a missing-data strategy. It also does not state whether an intention-to-treat analysis, a per-protocol analysis, or other analysis populations were defined for the sponsor’s non-inferiority or equivalence analysis. Without those details, it is not possible to assess how exclusions or missing observations may have influenced the result.

The registry does not report interim analyses, stopping rules, or any adjustment connected to repeated analyses over time. It also does not provide the full statistical analysis plan, including the treatment of period and sequence in the crossover design, the mixed-model specification, or assumptions about covariance and missingness. These are relevant because the primary outcome is measured in two treatment cycles for participants who completed both.

These absences do not establish that such methods were not used; they mean the procedures are not reported in the registry record. The stated outcome summaries and the sponsor’s reported model result can be described, but fuller assessment of confirmatory interpretation requires the planned analysis details, completeness information, and the criteria underlying the non-inferiority or equivalence classification.

16. Safety Results

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

Other (non-serious) adverse events

Participants with at least one non-serious event at or above the registry's 3% reporting threshold.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: uterine haemorrhage (4.5%), haemothorax (2.3%), rib fracture (2.3%), intervertebral disc degeneration (2.2%).

Serious adverse events are summarized by treatment period. The registry reports 2 affected participants among 45 at risk during the INSULIN-PH20 NP treatment period and 1 among 43 at risk during the insulin lispro treatment period. It also reports other adverse events affecting 17 of 45 and 11 of 43 participants, respectively. These are treatment-period summaries in a crossover study; they should not be interpreted as if they were comparisons between two independent groups of participants.

The registry lists serious-event terms and percentages, including uterine haemorrhage, haemothorax, rib fracture, and intervertebral disc degeneration. The record does not establish from these figures whether an event was caused by a treatment, nor does the listed information support a comparative risk analysis. Serious event counts are small, and no confidence intervals or statistical comparison for adverse events are reported in the registry record.

Deaths are not reported in the registry record. The adverse-event summaries describe reported events in this study and are not a complete assessment of safety beyond the recorded periods and outcomes. Interpretation should remain descriptive, particularly because the study enrolled 48 participants and the available summaries do not provide detailed event timing, attribution, or exposure-adjusted comparisons.

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

The primary outcome summaries describe average postprandial glucose excursions among participants who completed both treatment cycles and had evaluable data. The recalculated comparison is uncertain: its confidence interval includes zero and extends to differences in either direction. The sponsor also reports a mixed-model analysis, but the lower confidence limit, non-inferiority or equivalence margin, and detailed model specification are not reported in the registry record. Those limitations prevent the registry result from independently establishing equivalence or non-inferiority.

The findings do not show that the treatments are identical, and a non-significant p-value does not prove that no difference exists. Conversely, the estimates alone do not establish a meaningful difference. Assessing whether a difference matters clinically requires a relevant threshold and complete interpretation of the analysis, neither of which is reported in the registry record. No medical advice or treatment recommendation follows from these statistical summaries.

The primary endpoint concerns meal-related glucose change during specified assessment periods. It does not summarize every aspect of glucose control, long-term outcomes, or the experience of participants without evaluable primary data. The secondary outcomes concern time outside a glucose range and hypoglycemic events, but the latter’s numerical result is not reported in the registry record. The available findings therefore have a limited scope.

The study is a randomized, open-label crossover trial with a relatively limited primary analysis population. Its design permits within-participant treatment comparisons, but the available registry detail does not fully show how that structure was handled in the sponsor’s model. The most defensible reading is descriptive: the posted data give treatment-period averages and a model-based comparison, with uncertainty and reporting gaps that preclude a definitive claim about equality or clinical importance.

18. Why This Trial Matters Statistically

This trial illustrates why a treatment comparison should be read through both its estimate and its uncertainty. The reported group means are close in scale, but the observed spread is substantial and the recalculated interval remains wide. Looking only at point estimates or a p-value would obscure how much uncertainty remains about the size and direction of a difference.

It also demonstrates the importance of matching the statistical method to the design. In a crossover trial, the same participant receives both treatments, creating paired observations. A simple calculation from separate summary statistics is not equivalent to a participant-level mixed-model analysis that may use the pairing. Reporting the model estimate is useful, but fuller model specifications are needed to understand its assumptions and reproduce its result.

The registry’s non-inferiority or equivalence classification highlights another central issue: those claims depend on a prespecified margin and complete decision rules. The margin is not reported in the registry record, and the sponsor’s lower confidence limit is also absent. Without that information, the classification cannot be evaluated from the recorded result alone.

Finally, the primary endpoint population consists of completers with evaluable data, while information about missing outcomes and the full analysis populations is not reported. This makes transparent participant flow and analysis plans important to interpretation. Taken together, the study is a useful example of the distinction between a reported analysis, a simple recalculation, and the broader evidence required to make a precise statistical claim.

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.

The primary analysis is based on 37 participants in each treatment summary, selected from participants who completed both cycles and had evaluable postprandial data. The registry reports enrollment of 48 but does not explain the difference between enrollment and the primary outcome population. Reasons for non-completion or missing endpoint data are not reported in the registry record, limiting assessment of possible selection effects.

The sponsor’s mixed-model result is only partly reported: the upper confidence limit and p-value are available, but the lower confidence limit is not. The model details, including how the crossover structure and any period or sequence effects were handled, are not reported. The registry’s non-inferiority or equivalence classification also lacks the margin needed to assess the claim.

The study was open label, and the registry does not report blinding of participants or study personnel. The full treatment-period descriptions are incomplete in the recorded arm description; treatment-period duration, washout details, and other implementation details are not reported in the registry record. These omissions constrain interpretation of the crossover comparison.

For the secondary outcomes, a quantitative result for hypoglycemic events is not reported in the registry record, and no multiplicity procedure is described. The safety summaries are descriptive and do not provide a comparative statistical analysis or establish causality. The registry also does not report the original sample-size justification, interim analyses, detailed analysis plan, or threshold for a clinically important endpoint difference. These limitations apply to interpretation of the posted record and do not demonstrate that unreported procedures were absent from the study.

20. Related Tutorials

21. Related Statistical Calculators

22. Sources

• ClinicalTrials.gov: Safety Study of Subcutaneously-Injected Prandial INSULIN-PH20 NP Compared to Insulin Lispro Injection in Participants With Type 1 Diabetes Mellitus — NCT00883558.
• Registry record last updated 2014-09-08; status last verified 2014-08.
• Results first posted 2014-09-08.
• Data retrieved from ClinicalTrials.gov on 2026-11-09.
• Group sizes, means, event counts and sponsor-reported p-values are reproduced as posted. Effect estimates, confidence intervals and p-values not attributed to the sponsor are independently calculated from the posted summary statistics.

23. Record Summary

NCT00883558 is a completed Phase II, randomized, open-label, two-way crossover safety study sponsored by Halozyme Therapeutics. It enrolled 48 participants with type 1 diabetes and compared INSULIN-PH20 NP with insulin lispro.

The primary outcome was mean postprandial glucose excursion, assessed at Week 14 and Week 26. The primary analysis included participants who completed both treatment cycles with evaluable outcome data. The sponsor reported a mixed-model analysis classified as non-inferiority or equivalence; the margin and full model details are not reported in the registry record.

The registry also lists time outside a specified glucose range during continuous monitoring and hypoglycemic events as secondary outcomes. Serious and other adverse events are summarized by treatment period. Important details about missing data, multiplicity, the original sample-size justification, and the complete analysis plan are not reported in the registry record.