Mylan Insulin Aspart Study: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Mylan Insulin Aspart Study (NCT03760068), a randomized phase 3 study. The primary outcome is Treatment Emergent Antibody Response (TEAR).

2. Clinical Question

3. Trial Design

This was a phase III, randomized, multicenter, open-label, parallel-group clinical study of MYL-1601D and NovoLog® in patients with type 1 diabetes mellitus. It was listed as completed. The registry reports enrollment of 478 and identifies the two interventions as MYL-1601D Product and FlexPen NovoLog®, both at 100 U/mL. The record does not report the number of study centers.

The recorded study start was 2018-11-07, and primary completion was 2019-12-28. The primary outcome timeframe ran from baseline to week 24. Participants were assigned to the MYL-1601D group, with 238 participants, or the NovoLog® group, with 240 participants. The record identifies the design as randomized and parallel, but the sequence-generation and allocation-concealment procedures are not reported in the registry record.

Open-label means that the study did not use masking as recorded in the registry. That design feature can matter when an outcome depends on participant or clinician reporting or decisions. The primary outcome is a laboratory-defined antibody response, but the record does not describe who assessed the samples or whether laboratory personnel were masked to assignment. Those details are not reported in the registry record.

The stated aim was to demonstrate equivalence in safety and efficacy profiles. The posted registry results include a primary antibody-response count and selected secondary outcomes. The registry record does not report an equivalence margin, the original statistical design assumptions, a sample-size justification, or the full protocol. It also does not provide reported analyses for the secondary outcomes. These absences limit what can be inferred about how the study was originally planned and how its results were analyzed.

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: Type 1 Diabetes Patients

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: Type 1 Diabetes Patients

6. Primary Endpoint

The registry lists the primary outcome as Treatment Emergent Antibody Response (TEAR), measured over Baseline to week 24.

The primary endpoint counted participants with a treatment-emergent antibody response from baseline to week 24. The registry defines a response as either becoming anti-drug-antibody positive after a negative baseline result, or having a four-fold increase in titer at a post-baseline timepoint after being positive at baseline. The endpoint therefore combines two routes to meeting the response definition, depending on baseline antibody status.

Counting participants who meet a predefined event definition is useful for comparing how often that event occurred in randomized groups. It is a binary outcome at the participant level: a participant either met the registry definition during the specified timeframe or did not. The count alone does not describe the magnitude of antibody titers or how long a response persisted. Those details are not reported in the registry record.

Antibody response was selected as the primary outcome in the registry record, while the study’s stated aim concerned safety and efficacy more broadly. The record does not report the rationale for selecting this endpoint, the clinical interpretation assigned to a response, or a threshold for an acceptable between-group difference. It is therefore possible to describe the recorded event frequency and its uncertainty, but not to infer the full biological or clinical meaning of an individual response from this endpoint alone.

7. Results Posted in the Registry

• FlexPen NovoLog® (100 U/mL): 27.9% (67/240) versus 24.8% (59/238) on control; risk difference 3.1 percentage points (95% CI -4.8 to 11.0), risk ratio 1.13 (0.83–1.52); unadjusted two-proportion z-test p = 0.438.

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.

Change in HbA1c From Baseline

• 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:
  • FlexPen NovoLog® (100 U/mL): mean 0.04 versus 0.10 on control; difference -0.06 (95% CI -0.19 to 0.07), standardized effect d = -0.08; z-test p = 0.368.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between FlexPen NovoLog® (100 U/mL) and the control. The interval gives the range of effects that remain compatible with the data.

Change in Fasting Plasma Glucose From Baseline

• 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:
  • FlexPen NovoLog® (100 U/mL): mean 0.60 versus 0.40 on control; difference 0.20 (95% CI -0.79 to 1.18), standardized effect d = 0.04; z-test p = 0.692.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between FlexPen NovoLog® (100 U/mL) and the control. The interval gives the range of effects that remain compatible with the data.

Change in Prandial Insulin Dose From Baseline

• 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:
  • FlexPen NovoLog® (100 U/mL): mean -0.00 versus 0.01 on control; difference -0.01 (95% CI -0.03 to 0.01), standardized effect d = -0.07; z-test p = 0.444.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between FlexPen NovoLog® (100 U/mL) and the control. The interval gives the range of effects that remain compatible with the data.

The registry names six secondary outcome areas: change in HbA1c, fasting plasma glucose, prandial insulin dose, basal insulin dose, total daily insulin dose, and a seven-point self-monitored blood-glucose profile. Results are posted for three of these outcomes: HbA1c, fasting plasma glucose, and prandial insulin dose. The remaining named secondary outcomes do not have posted numerical results in the record. The record lists no reported analyses for the secondary outcomes.

Across the three posted continuous outcomes, the recalculated unadjusted mean-difference intervals include zero. This is a broad pattern of uncertainty rather than evidence that all outcomes were identical between groups. These endpoints measure different aspects of glycemic control and insulin use, use different units, and have different variability. They should not be combined into a single conclusion without a prespecified analysis strategy, which is not reported in the registry record.

The secondary outcomes provide context alongside the primary antibody-response result, but they do not answer the same question. The primary endpoint counts participants meeting the antibody-response definition; the secondary outcomes summarize changes in glucose-related measures and insulin doses. Findings on these different measures should be interpreted separately, while recognizing that the record does not describe how the secondary outcomes were prioritized or analyzed.

Unadjusted secondary results generally carry less inferential weight when several outcomes are examined. In general, considering many outcomes can increase the chance of apparently notable findings arising by chance, and interpretation depends on whether the outcomes and analyses were prespecified. The registry does not report a multiplicity strategy, a hierarchy for testing outcomes, or adjusted secondary analyses. The secondary results are therefore most appropriately treated as descriptive and exploratory rather than as independent confirmation of an overall equivalence claim.

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

• Change in HbA1c From Baseline
• Change in Fasting Plasma Glucose From Baseline
• Change in Prandial Insulin Dose From Baseline
• Change in Basal Insulin Dose From Baseline
• Change in Total Daily Insulin Dose From Baseline
• Change in 7-point Self-monitored Blood Glucose (SMBG) Profile From Baseline

9. Statistical Interpretation

10. Statistical Methods Explained

The posted results include a binary primary endpoint and continuous secondary outcomes. A binary endpoint records whether each participant met the stated event definition. Continuous outcomes record a measurement, such as change in HbA1c, and can be summarized by a group mean and standard deviation. The methods used to generate the original sponsor analyses are not reported in the registry record; the contrasts discussed in the posted results are unadjusted recalculations from the available summary statistics.

What does the primary contrast represent?

The primary comparison is expressed as a risk difference. In general, a risk difference subtracts the event proportion in one group from the event proportion in the other group. Here the posted contrast is oriented as NovoLog® minus MYL-1601D. A value above zero therefore corresponds to a higher recorded event proportion in the NovoLog® group; a value below zero corresponds to a higher proportion in the MYL-1601D group. This direction describes the arithmetic of the contrast, not a cause for the observed difference.

The event counts are 59 of 238 participants for MYL-1601D and 67 of 240 for NovoLog®. The posted proportions are 0.248 and 0.279, and the risk difference is 0.031. The confidence interval runs from -0.048 to 0.110. The unadjusted p-value is 0.438. These are summary-statistic calculations and do not include adjustment for covariates, stratification, or other design features; whether such adjustments were part of the sponsor’s original analysis is not reported in the registry record.

How should a confidence interval be understood?

In general, a confidence interval gives a range of estimates compatible with the data and the method used to calculate it. Its width conveys precision: a narrower interval indicates less uncertainty about the estimated contrast, while a wider interval indicates more. It is not a guarantee that the true effect lies within the particular interval, and it does not by itself define which differences are clinically or scientifically acceptable. For an equivalence question, the interval must be considered in relation to a prespecified equivalence margin. No margin is reported in the registry record.

A p-value evaluates compatibility of the observed data with a specified null model under the test’s assumptions. An unadjusted p-value does not measure the probability that the null hypothesis is true, nor does it quantify the size or importance of a difference. The record does not report a sponsor analysis for the primary outcome, and the posted p-value should be read as an unadjusted recalculation rather than as a substitute for an unavailable planned analysis.

What do the continuous-outcome summaries add?

For a continuous outcome, the group means summarize average change, while the standard deviations describe variation among participants around each group mean. A mean difference compares the two group averages; its sign depends on the stated subtraction order. The posted secondary contrasts use NovoLog® minus MYL-1601D. A standardized mean difference expresses a mean difference in standard-deviation units, which can help describe scale-independent magnitude, but it does not establish clinical importance. The record posts no sponsor-reported analyses for the secondary outcomes.

Standard deviation and standard error describe different forms of variability. Standard deviation describes spread among individual measurements; standard error describes uncertainty in an estimated group mean. In general, the standard error depends on both the variability of measurements and the amount of information available. A confidence interval for a mean difference reflects uncertainty in the contrast, rather than the spread of individual participants’ outcomes.

Why do analysis details matter?

Randomization supports a fair comparison by aiming to balance measured and unmeasured characteristics between groups, but it does not guarantee balance in every realized sample. An adjusted analysis may account for prespecified factors or improve precision. The registry record does not report whether the original analysis used adjustment, how missing observations were handled, which participants were included in each analysis, or how the randomization was implemented. These details are important for interpreting estimates and their uncertainty.

For binary outcomes, the choice of effect measure matters: a risk difference describes an absolute difference in event proportions, while a risk ratio or odds ratio describes a relative comparison. The primary posted contrast is the risk difference, and the record also lists relative measures. Each measure answers a different question; none changes the underlying event counts. A reader should follow the measure and direction stated for each contrast rather than treating the different scales as interchangeable.

Finally, an equivalence objective requires more than observing a non-significant difference. In general, an equivalence analysis asks whether the uncertainty interval falls entirely within prespecified bounds around no difference. The registry record does not report those bounds or a formal sponsor equivalence result. The posted unadjusted estimates can be interpreted descriptively, but they cannot establish that criterion on their own.

11. Confidence Intervals and Precision

The primary risk-difference estimate is 0.031, with a 95% confidence interval from -0.048 to 0.110. Because the interval crosses zero, the posted data do not isolate a single direction of difference. Its lower end is compatible with a lower event proportion in the NovoLog® group under the stated subtraction order, while its upper end is compatible with a higher event proportion in that group. The interval’s breadth indicates uncertainty around the estimated contrast.

In general, a confidence interval is a range generated by a statistical procedure that reflects the precision of an estimate under its assumptions. It does not say that every value in the interval is equally likely, and it does not establish that values inside the interval are acceptable. For an equivalence claim, the interval must be compared with an equivalence margin chosen before results are examined. The registry record does not report such a margin, so the interval cannot be used to conclude that the groups were equivalent.

The continuous-outcome contrasts also have uncertainty intervals. The posted interval for change in HbA1c is -0.191 to 0.071; for fasting plasma glucose it is -0.787 to 1.185; and for prandial insulin dose it is -0.031 to 0.014. Each includes zero. This describes uncertainty in the unadjusted differences in means, not certainty that the underlying groups have identical outcomes. Units differ across outcomes, so interval widths should not be compared directly across these measures.

The record does not provide confidence intervals for the individual group counts. The primary contrast interval is more informative for comparing groups than looking at the two counts separately because it directly quantifies uncertainty in the between-group difference. Interpretation remains conditional on the calculation method, the observed participants, and any assumptions needed for the analysis. Details of the sponsor’s planned inferential procedure and analysis populations are 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 participants per arm for the binary primary endpoint and the continuous outcomes with posted recalculated contrasts. They are not total event counts. The planning illustration uses a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. It considers detecting the stated fraction of the observed effect: 100%, 75%, or 50%. The original trial’s own design assumptions and sample-size justification are not reported in the registry record.

The 80% power column gives the estimated participants per arm for a future study to have an 80% chance of detecting the chosen effect under the listed assumptions, if that effect and the model assumptions are correct. The 90% power column uses a higher target chance of detecting that same chosen effect, and therefore generally requires more participants per arm. For example, the primary-outcome illustration lists 3,116 participants per arm for 80% power and 4,170 participants per arm for 90% power when planning for 100% of the observed effect. The corresponding entries for planning for 75% of the observed effect are 5,486 and 7,344; for 50%, they are 12,223 and 16,363.

Smaller effects are harder to distinguish from random variation, so planning to detect a smaller fraction of the observed effect requires more participants per arm under these assumptions. The figures are planning illustrations, not a reconstruction of the trial’s original enrollment plan. They are not observed power: observed power is not a useful substitute for interpreting the estimate and its confidence interval after a study has been completed.

13. How the Sample Size Is Derived

Power is the probability that a study of a given size rejects the null hypothesis when a specific true effect exists. It depends on four things: the effect to detect, the variability of the outcome, the significance level (here a two-sided α of 0.05, so z_1−α/2 = 1.960) and the desired power (z_1−β = 0.842 for 80% and 1.282 for 90%).

Formula

n is the number of participants in each arm, p₀ and p₁ are the control and treatment proportions, and p̄ is their average.

Worked calculation for this trial

1. Control proportion p₀ = 0.248; treatment proportion p₁ = 0.279; average p̄ = 0.264.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.6230 = 1.2211.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.6226 = 0.5240.
4. n = (1.2211 + 0.5240)² / (0.0313)² = 3,115.1, rounded up to 3,116 participants per arm for 80% power.

The smaller-effect rows in the table above repeat the same calculation with the effect multiplied by 0.75 and 0.5 (for ratios, applied to the logarithm of the ratio).

R code

alpha <- 0.05
z_a   <- qnorm(1 - alpha / 2)          # two-sided
p0 <- 0.247899159663866                          # control proportion
p1 <- 0.279166666666667                          # treatment proportion
n_per_arm <- function(power, frac = 1) {
  q1 <- p0 + (p1 - p0) * frac               # smaller effect = fraction of the observed difference
  pbar <- (p0 + q1) / 2
  z_b <- qnorm(power)
  ceiling(round((z_a * sqrt(2 * pbar * (1 - pbar)) + z_b * sqrt(p0 * (1 - p0) + q1 * (1 - q1)))^2 / (q1 - p0)^2, 6))
}
for (f in c(1, 0.75, 0.5))
  cat(sprintf("%3.0f%% of observed effect: n per arm = %s (80%% power), %s (90%% power)\n", 100 * f,
              format(n_per_arm(0.80, f), big.mark = ","), format(n_per_arm(0.90, f), big.mark = ",")))

# Check against R's built-in function (unrounded n per arm)
cat("power.prop.test:", round(power.prop.test(p1 = p0, p2 = p1, power = 0.80)$n, 1), "\n")

# Simulation: how often does the z-test reject at the 80%-power sample size?
set.seed(3760068)
n    <- n_per_arm(0.80)
reps <- 20000
x0 <- rbinom(reps, n, p0); x1 <- rbinom(reps, n, p1)
ph <- (x0 + x1) / (2 * n)
z  <- ((x1 - x0) / n) / sqrt(ph * (1 - ph) * 2 / n)
cat(sprintf("Simulated power at n = %s per arm: %.3f (%d replicates)\n", format(n, big.mark = ","), mean(abs(z) > z_a, na.rm = TRUE), reps))

Output from running the code

100% of observed effect: n per arm = 3,116 (80% power), 4,170 (90% power)
 75% of observed effect: n per arm = 5,486 (80% power), 7,344 (90% power)
 50% of observed effect: n per arm = 12,223 (80% power), 16,363 (90% power)
power.prop.test: 3115.1 
Simulated power at n = 3,116 per arm: 0.803 (20000 replicates)

The simulation draws many pairs of event counts at the planned sample size and counts how often the two-proportion z-test rejects the null. It should land close to the target power; at very small required sample sizes it can fall below it, because the normal approximation is rough there.

14. Multiplicity, Missing Data and Analysis Populations

The registry record does not report a multiplicity adjustment or a hierarchy specifying how the primary and secondary outcomes were to be tested. Multiplicity matters when a study examines several outcomes or conducts several comparisons: in general, more opportunities to test hypotheses can increase the chance of a nominally notable result occurring by chance. Whether and how to control that risk depends on the trial’s prespecified objectives and analysis plan, which are not reported in the registry record.

The record identifies one primary outcome and names six secondary outcome areas, while posting numerical results for three secondary outcomes. It reports no analyses for the secondary outcomes. The results displayed for those outcomes are unadjusted recalculations, so they should not be interpreted as multiplicity-adjusted evidence. The record does not report whether the sponsor used other analyses or adjustments in a separate analysis plan.

Information about missing data and analysis populations is also limited. The registry lists group sizes for each posted outcome, but it does not describe whether those counts represent all randomized participants, participants with an available measurement, or another defined analysis population. It does not report the amount or pattern of missing outcome data, reasons for missingness, imputation procedures, or sensitivity analyses. The approach to missing data is therefore not reported in the registry record.

These omissions do not establish that the original trial lacked a statistical plan or that no analyses were conducted. They mean that the registry record does not supply enough information to assess those procedures. For an equivalence objective, this is particularly relevant: interpretation depends on prespecified margins, a suitable analysis population, and a planned inferential method. The posted summary statistics alone do not document those elements.

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 1% reporting threshold.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: hypoglycemia (6.3%), anemia (0.4%), colititis (0.4%), chest pain (0.4%), anal abscess (0.4%).

The registry reports serious adverse events affecting 22 of 238 participants in the MYL-1601D group and 15 of 240 participants in the NovoLog® group. The record lists zero deaths in the MYL-1601D group and one in the NovoLog® group. These are descriptive participant counts, not estimates from a reported comparative safety analysis.

The listed serious adverse-event terms include Hypoglycemia at 6.3%, and Anemia, Colititis, Chest Pain, Anal Abscess, and Cellulitis at 0.4% each. These term-level percentages should be read as the registry recorded them. The record does not provide further event-level detail, exposure-adjusted rates, confidence intervals, or a statistical comparison for these terms. It also does not report whether events were judged related to treatment.

Serious adverse-event counts describe what was recorded during the study; they do not by themselves establish that a treatment caused an event or that a difference between groups is meaningful. Differences in exposure time, event recurrence, ascertainment, and participant characteristics can matter to safety interpretation, but these details are not reported in the registry record.

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

The posted primary result describes the frequency of a defined treatment-emergent antibody response over baseline to week 24 in two randomized groups. The raw counts were 59 of 238 participants for MYL-1601D and 67 of 240 for NovoLog®. The unadjusted risk-difference estimate was 0.031, with a 95% confidence interval from -0.048 to 0.110. The interval includes zero and permits differences in either direction under the calculation used.

This result does not by itself establish that the treatments are equivalent. Equivalence is a claim about whether a difference is small enough to fall within prespecified acceptable bounds. The registry record does not report those bounds or a formal equivalence analysis. A non-significant unadjusted p-value, including the posted p-value of 0.438, is not proof that there is no difference.

The same caution applies to efficacy and safety more broadly. The registry states an aim of comparing safety and efficacy, but the posted primary endpoint is antibody response, and only selected secondary measures have numerical results. The record does not report sponsor analyses for the listed outcomes, detailed definitions and results for every secondary measure, or a multiplicity plan. A limited set of summary statistics cannot stand in for the complete prespecified evaluation.

The safety figures are also descriptive. They report serious adverse events and deaths by group, but do not establish causal attribution or provide a formal comparative analysis. In general, a clinical trial’s group-level event counts need to be interpreted in light of how events were collected, the duration participants were observed, and the analysis approach. Those details are not reported in the registry record.

The strongest supported reading is therefore narrow: the registry records these primary event counts and selected secondary and safety summaries, with uncertainty in the unadjusted comparisons. It does not provide enough information to make a definitive equivalence conclusion or to resolve all questions about efficacy, safety, missing data, or the original analysis plan.

17. Why This Trial Matters Statistically

This trial illustrates the distinction between a study’s stated objective and what can be concluded from posted summary results. The registry describes an equivalence aim, but the result most fully reported is a difference estimate with a confidence interval. In general, equivalence requires prespecified bounds and an analysis designed to evaluate whether the uncertainty interval lies within them. Those bounds and a formal equivalence analysis are not reported in the registry record.

The primary endpoint is a participant-level antibody-response count, while secondary outcomes address changes in glucose measures and insulin doses. Keeping these questions distinct is important: a result for one endpoint does not automatically settle the interpretation of the others. The record’s limited reported analyses make it especially important to distinguish descriptive summaries from planned inferential conclusions.

The trial also shows why effect estimates and precision are more informative than a p-value alone. The risk difference and its confidence interval convey both the direction and the range of uncertainty in the posted comparison. The interval includes zero, but without an equivalence margin it cannot identify which possible differences would be considered acceptable. That remains true regardless of whether an unadjusted test crosses a conventional significance threshold.

Finally, the registry record contains important gaps for statistical appraisal, including the original sample-size rationale, analysis populations, handling of missing data, and multiplicity approach. Recognizing those limits helps readers separate what is directly recorded from what would require a protocol, statistical analysis plan, or fuller results report. The result is a more disciplined interpretation of the evidence available in the registry.

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.

The results in the registry record are limited to summary counts and selected continuous-outcome summaries. The primary outcome is reported as a count of participants meeting a composite antibody-response definition; the record does not provide participant-level antibody trajectories, persistence of responses, or further details about titers. Those omissions prevent a more detailed description of response patterns.

The comparison is open-label, and the record does not describe masking of laboratory personnel or outcome assessors. The primary endpoint is defined using antibody measurements, but the registry does not describe sample handling, assay characteristics, or adjudication procedures. These procedural details are not reported in the registry record and cannot be assessed from the posted summaries.

The record does not report the original sample-size justification, equivalence margin, randomization procedure, analysis populations, missing-data methods, multiplicity approach, or sponsor-reported analyses. While the posted planning illustrations can describe hypothetical future sample sizes under stated assumptions, they are not evidence about the original trial’s planning. The record also provides numerical results for only some of the secondary outcomes it names.

The adverse-event summaries do not include a reported comparative analysis, exposure-adjusted rates, or event-level treatment attribution. The registry record does not provide enough detail to evaluate whether differences in observation time, repeated events, or ascertainment affected the group summaries. These limitations constrain interpretation of both safety and efficacy and make the posted statistics best understood as a partial account rather than a complete report of the trial’s analysis.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: Mylan Insulin Aspart Study — NCT03760068.
• Registry record last updated 2022-02-25; status last verified 2022-02.
• Results first posted 2021-01-27.
• 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

NCT03760068 is a completed phase III randomized, multicenter, open-label, parallel-group study comparing MYL-1601D Product (100 U/mL) with FlexPen NovoLog® (100 U/mL) in patients with type 1 diabetes mellitus. Enrollment was 478, with 238 participants assigned to MYL-1601D and 240 to NovoLog®. The recorded study start was 2018-11-07, and primary completion was 2019-12-28.

The primary outcome counted participants with a treatment-emergent antibody response from baseline to week 24. The registry reports 59 such participants in the MYL-1601D group and 67 in the NovoLog® group. The posted unadjusted risk difference is 0.031, with a 95% confidence interval from -0.048 to 0.110. The record states an equivalence aim but does not report an equivalence margin or formal sponsor analysis.

Selected secondary outcomes and descriptive safety counts are also posted. Analysis details, including missing-data procedures, multiplicity methods, and the original sample-size rationale, are not reported in the registry record.