Comparison of the Blood Sugar Lowering Effect of Biphasic Insulin Aspart 30 and Insulin Glargine Both Combined With Metformin and Glimepiride in Chinese and Japanese Subjects With Type 2 Diabetes New to Insulin Treatment: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Comparison of the Blood Sugar Lowering Effect of Biphasic Insulin Aspart 30 and Insulin Glargine Both Combined With Metformin and Glimepiride in Chinese and Japanese Subjects With Type 2 Diabetes New to Insulin Treatment (NCT01123980), a randomized phase 4 study. The primary outcome is Change in Glycosylated Haemoglobin (HbA1c).

2. Clinical Question

This was a comparison of two insulin regimens added to ongoing metformin and glimepiride, not a comparison of insulin against no insulin or against oral medicines alone. The registry record describes the aim as investigating blood sugar lowering in Chinese and Japanese people with type 2 diabetes who had never received insulin before.

The treatment contrast matters for interpreting the result: each group received an insulin regimen alongside two other medicines, and the insulin was administered at a different time of day in the two groups. The trial therefore addresses the relative HbA1c changes observed under these assigned regimens during the treatment period. It does not isolate the effect of insulin from the background medicines.

3. Trial Design

The registry record describes a randomized, parallel-group, open-label trial. It was completed, with enrollment of 521. Its official title describes a 3 week run-in followed by 24 Week treat-to-target treatment. Masking was listed as none, so participants and trial personnel were not masked to assigned insulin regimen according to the record.

The trial compared once-daily biphasic insulin aspart 30, taken immediately before dinner, with once-daily insulin glargine, taken at bedtime. Both regimens were combined with metformin and glimepiride. Starting doses were listed as 0.1-0.2 U/kg for each insulin. Minimum daily doses of metformin were stated separately for Chinese and Japanese patients, and at least 4 mg of glimepiride was specified.

The registry lists the study as phase 4, while its brief summary says it was conducted as a phase 4 trial in China and phase 3 in Japan. The record identifies China and Japan as the settings and describes the participants as Chinese and Japanese subjects with type 2 diabetes who were insulin-naive.

The primary outcome was change in HbA1c from Week 0 to week 24. The reported primary analysis used a full analysis set with last observation carried forward. The record gives outcome results for 261.0 participants in the biphasic insulin aspart 30 group and 260.0 in the insulin glargine group. It lists six secondary outcomes, including plasma glucose profiles, HbA1c target attainment, and hypoglycaemic episodes. More detailed operational information about randomization, the treat-to-target titration algorithm, and the trial’s original sample-size justification is 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.

Before assignment: At the screening, eligible subjects entered the run-in period before being randomised. During the 3 week run-in period, subjects switched from insulin secretagogue to glimepiride. During the last 2 weeks, the total dose of glimepiride was kept at 4mg/day. Subjects continued their pre-trial metformin dose.

Overall Study

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: Full analysis set using LOCF (last observation carried forward) consists of all randomised subjects who were exposed to at least one dose of trial product(s)

5. Baseline Characteristics

The table reproduces the baseline characteristics posted in the registry, which describe the participants analysed at baseline and help show whether the randomized groups were comparable.

6. Primary Endpoint

The registry lists the primary outcome as Change in Glycosylated Haemoglobin (HbA1c), measured over Week 0, week 24.

HbA1c is a measure of glycosylated haemoglobin and reflects blood glucose exposure over time. The primary outcome is the change in this measure from Week 0 to week 24, expressed as a percentage of glycosylated haemoglobin. A negative change indicates a decrease from the starting measurement; the reported group estimates were negative.

The registry reports least-squares means rather than simple group averages. In general, a least-squares mean is an estimate from a statistical model and can account for specified covariates or design features. Which covariates were included in the primary model is not reported in the registry record. Standard errors are reported for the group estimates, but confidence limits for each group’s least-squares mean are not listed in the posted primary results.

HbA1c is useful as a continuous endpoint because it retains information about the magnitude of change rather than reducing the result to whether a participant crossed a threshold. The registry also lists secondary outcomes based on thresholds, including the percentage achieving HbA1c below 7.0% and below or equal to 6.5%, alongside glucose profiles and hypoglycaemic episodes. Those outcomes address different aspects of the trial and should not be substituted for the primary continuous measure.

The endpoint describes a laboratory measure over the trial’s stated period. By itself, it does not capture every aspect of glucose control, adverse events, or participant experience.

7. Results Posted in the Registry

• Insulin Glargine: mean -0.56 versus -0.68 on control; difference 0.12 (95% CI -0.05 to 0.29), standardized effect d = 0.12; z-test p = 0.157.

8. Statistical Interpretation

9. Statistical Methods Explained

General statistical context: A randomized comparison aims to make treatment groups comparable on average at assignment, while a parallel-group design follows each participant in one assigned group. Randomization is listed in the registry record, but its sequence generation and allocation concealment procedures are not reported there. The study was open-label, so assignment was not masked.

What does a least-squares mean describe?

A least-squares mean is a model-based estimate of a group’s average outcome. It is often used in clinical trials when an analysis adjusts for baseline values or other specified variables. The registry identifies the primary parameter as a least-squares mean, but the covariates and model specification for that primary analysis are not reported in the registry record. The group estimates should therefore be read as the posted model-based results, not as independently verified raw averages.

How should the standard errors be read?

The standard error describes uncertainty in an estimated mean. A smaller standard error generally indicates a more precise estimate, given the model and data used. The posted primary estimates each have a standard error of 0.06. A standard error is not the same as the spread of individual participant measurements; it describes the estimated mean’s sampling uncertainty. The registry does not provide group-level confidence intervals for these least-squares means in the posted primary outcome figures.

What does the unadjusted recalculation add?

The recalculated comparison uses posted summary statistics to estimate an unadjusted difference between the groups. It is a simple comparison, not a reconstruction of the sponsor’s ANCOVA. It does not incorporate any model adjustments that may have been used in the sponsor analysis. As a result, its estimate, confidence interval, and p-value can differ from those of the sponsor’s analysis. The trial’s own reported analysis takes precedence when describing the sponsor’s result, particularly because the registry marks that analysis as non-inferiority or equivalence.

General statistical context: A confidence interval gives a range of values compatible with the data under the assumptions of the analysis. It conveys uncertainty about the estimated contrast and should be considered alongside the point estimate and the question the analysis was designed to answer. A p-value measures how compatible the data are with a specified statistical hypothesis under a model; it is not the probability that a treatment is effective or that the null hypothesis is true.

Why are ANCOVA and the recalculation different?

ANCOVA is a model-based method that can compare groups while adjusting for selected variables, often including a baseline measure. The posted sponsor analysis is identified as ANCOVA and reports a net mean difference. The exact model terms, handling of baseline values, and hypothesis-testing details are not reported in the registry record. The recalculated estimate is explicitly unadjusted and uses summary statistics, so it answers a more limited question. Differences between these estimates do not by themselves identify which model feature caused the discrepancy.

The sponsor’s 95% confidence interval runs from -0.25 to 0.02, while the associated p-value is reported as p < 0.001. Because the analysis is classified as non-inferiority or equivalence, the p-value may address a margin-based hypothesis rather than a zero-difference hypothesis. The margin and the exact null hypothesis are not reported in the registry record. Without them, the p-value cannot be interpreted as a conventional test of a difference from zero. The confidence interval and p-value should be described as posted rather than reconciled by assuming an unreported testing procedure.

What is over-dispersion, and is it relevant?

General statistical context: Over-dispersion occurs when variability in count data exceeds what a statistical model assumes. It can affect standard errors and confidence intervals for event counts. The primary endpoint in this trial is continuous HbA1c change, not a count outcome, so over-dispersion is not the central issue for its primary analysis. Hypoglycaemic episodes are listed among secondary outcomes, but numerical results and modeling details for those counts are not reported in the registry record.

What do the population and missing-data labels mean?

The full analysis set is described as all randomized subjects exposed to at least one dose of trial products. The posted primary outcome used last observation carried forward, a method that substitutes a participant’s last available measurement for a later missing value. This creates an analysis dataset but relies on an assumption about the unobserved outcome after the last measurement. The registry does not report the amount or pattern of missing endpoint data, reasons for missingness, or sensitivity analyses using other missing-data methods.

In general, missing-data assumptions can affect treatment estimates if participants with unavailable outcomes differ from those with observed outcomes, or if the method does not reflect how their outcomes would have changed. The registry’s description identifies the method but does not establish that the assumption is appropriate for every participant. The record also does not report whether the primary analysis population or missing-data approach was specified before results were examined.

What does the non-inferiority or equivalence label establish?

The registry classification identifies the type of sponsor analysis, but the label alone is not enough to assess its design or conclusion. A non-inferiority or equivalence interpretation depends on the prespecified margin, the direction of the hypothesis, and the analysis plan. These details are not reported in the registry record. The analysis result can be quoted as posted, but the margin-based claim cannot be independently evaluated from the available information.

10. Confidence Intervals and Precision

The primary group estimates are -0.68 for biphasic insulin aspart 30 and -0.56 for insulin glargine, each with a standard error of 0.06. These standard errors describe uncertainty in the estimated group means; they do not show the full range of individual responses. The registry does not give confidence intervals for the two group-specific least-squares means.

For the unadjusted recalculation, the reported mean difference is 0.12 and its confidence interval spans zero. That interval indicates uncertainty about the direction and magnitude of the unadjusted contrast under the recalculation’s assumptions. The unadjusted p-value is 0.157. This should be distinguished from the sponsor’s model-based result and should not be treated as a substitute for it.

The sponsor’s ANCOVA estimate is -0.12, with a 95% confidence interval from -0.25 to 0.02. The interval includes zero, so it does not by itself show a nonzero contrast under a conventional two-sided interpretation. However, the registry classifies the sponsor analysis as non-inferiority or equivalence, and the margin and testing details are not reported. The reported p-value, p < 0.001, therefore cannot be interpreted fully from the interval alone.

Precision is not the same as clinical importance. A confidence interval expresses statistical uncertainty under an analysis; it does not establish whether values in the interval matter to patients or whether the analysis assumptions are appropriate. No clinical threshold for interpreting the primary difference is reported in the registry record. The open-label design, missing-data approach, and absent details about the sponsor’s model and hypothesis further limit independent assessment of precision.

11. 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, not total events. The stated planning assumptions are a two-sided significance level 0.05, equal allocation between the two arms, and a normal approximation. The effect to detect is set to the stated fraction of the observed effect. These are planning illustrations based on the observed effect, not the trial’s own original design assumptions; those original assumptions are not reported in the registry record.

The 80% power column gives the number of participants per arm estimated to provide an 80% chance of detecting the specified effect under those assumptions. The 90% power column gives the corresponding number per arm for a 90% chance. These are prospective planning quantities under an assumed effect and model. They are not a measure of the probability that the completed trial found a true effect, and they are not observed power.

The planning rows use 100% of the observed effect, 75% of the observed effect, and 50% of the observed effect. The listed figures are 1,023 and 1,369 participants per arm for the 100% planning effect, 1,818 and 2,434 per arm for 75%, and 4,090 and 5,475 per arm for 50%, for 80% and 90% power respectively. The figures increase as the assumed effect becomes smaller: smaller effects are harder to distinguish from random variation and therefore require more participants under the stated assumptions.

The trial enrolled 521, whereas these planning illustrations are derived from the observed estimate rather than the trial’s original planning process. They should be used to understand how sample-size requirements vary with assumed effect size and target power, not to judge the trial by an unreported design calculation.

12. 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 = 0.120; pooled standard deviation σ = 0.968.
2. n = 2 × (1.96 + 0.842)² × 0.968² / 0.120² = 1,022.3, rounded up to 1,023 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 <- 0.12                      # observed difference in means
sigma <- 0.968402534180953                     # 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(1123980)
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 = 1,023 (80% power), 1,369 (90% power)
 75% of observed effect: n per arm = 1,818 (80% power), 2,434 (90% power)
 50% of observed effect: n per arm = 4,090 (80% power), 5,475 (90% power)
power.t.test: 1023.3 
Simulated power at n = 1,023 per arm: 0.801 (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.

13. Analyses Reported by the Sponsor

The sponsor’s reported result is an ANCOVA estimate of a net mean difference of -0.12, with a 95% confidence interval from -0.25 to 0.02 and p < 0.001. The registry classifies this as a non-inferiority or equivalence analysis. This is the trial’s own reported analysis and takes precedence over the simple unadjusted recalculation.

The recalculation gives a mean difference of 0.12, a confidence interval that spans zero, and an unadjusted p-value of 0.157. It is based on posted summary statistics and does not reproduce the sponsor’s ANCOVA model. The estimates use different methods and are not a like-for-like reanalysis of the same model.

The sponsor’s confidence interval includes zero, while the p-value is reported as less than 0.001. That combination should not be interpreted as an ordinary test of a zero difference without knowing the hypothesis and analysis details. For a non-inferiority or equivalence test, a margin can define the relevant comparison. The margin, the exact null hypothesis, model covariates, and further testing details are not reported in the registry record. The available information permits reporting the sponsor’s stated result, but not independently verifying its margin-based interpretation.

14. Multiplicity, Missing Data and Analysis Populations

The registry lists six secondary outcomes, including 9-point plasma glucose profiles, HbA1c threshold attainment, and hypoglycaemic episodes. It also lists 7 outcome measures and 12 analyses. Multiple outcomes or analyses can create multiplicity: when many hypotheses are tested, the chance of at least one apparently notable result can increase. Statistical approaches to multiplicity may include adjustment procedures or a prespecified hierarchy, but the approach used for this trial is not reported in the registry record.

The primary outcome is identified as change in HbA1c, but the registry information does not establish how the primary and secondary hypotheses were ordered, whether any multiplicity adjustment was planned, or which analyses were considered confirmatory. Numerical results for the secondary outcomes are not included in the registry record for this analysis. Their findings therefore cannot be used to judge consistency with the primary result.

The primary analysis population is described as the full analysis set, comprising randomized subjects exposed to at least one dose of trial product. The primary endpoint used last observation carried forward. This method carries the last observed endpoint-related value forward when a later measurement is missing. The registry does not report the amount of missing data, the timing or reasons for missingness, or whether missingness differed between the groups.

Last observation carried forward can be simple to apply, but it assumes that the last available value is a suitable replacement for the unobserved value. If outcomes would have changed after that measurement, the method may not represent the missing data well. The registry does not report sensitivity analyses that test alternative assumptions or methods, nor does it state whether an intention-to-treat analysis using a different population or missing-data approach was conducted.

Further details about prespecification, protocol deviations, analysis-plan amendments, and interim analyses are not reported in the registry record. Their absence limits assessment of whether the posted analyses fully reflect a prespecified statistical plan; it does not establish that such procedures were or were not used.

15. Safety Results

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

Other (non-serious) adverse events

Participants with at least one non-serious event at or above the registry's 5% reporting threshold.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: coronary artery stenosis (0.8%), hypoglycaemia (0.4%), multiple myeloma (0.4%), prostate cancer (0.4%), adrenal adenoma (0.4%).

The registry reports serious adverse events in 2 of 261 participants in the biphasic insulin aspart 30 group and 5 of 260 participants in the insulin glargine group. These are reported counts within the listed group sizes. The record also gives other adverse event counts of 26 and 28 for the respective groups. No deaths are listed in the available record fields; whether deaths were absent or simply not reported is not established by those fields.

The serious adverse event terms listed among the most frequent include coronary artery stenosis at 0.8%, hypoglycaemia at 0.4%, multiple myeloma at 0.4%, prostate cancer at 0.4%, adrenal adenoma at 0.4%, and cerebral infarction at 0.4%. These percentages should be read as registry-reported values, not as evidence that treatment caused the events.

These figures are descriptive. The registry information does not provide a statistical comparison of adverse-event risks, event timing, exposure-adjusted rates, or detailed criteria for attribution. Counts alone do not show whether events were related to treatment, and the listed serious-event terms do not capture every element of safety. Differences in counts should not be interpreted as proof of a safety difference.

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

The posted primary estimates describe reductions in HbA1c in both groups over the trial’s assessed period: -0.68 with biphasic insulin aspart 30 and -0.56 with insulin glargine. The sponsor’s ANCOVA analysis reports a net mean difference of -0.12, with a 95% confidence interval from -0.25 to 0.02. The registry labels this a non-inferiority or equivalence analysis, but the margin and testing details are not reported.

The results apply to the trial’s studied setting and population: Chinese and Japanese subjects with type 2 diabetes who were new to insulin treatment, receiving metformin and glimepiride alongside one of the assigned insulin regimens. They do not directly describe people outside that population or treatment context. The open-label design and the specified analysis using last observation carried forward are also relevant when judging the estimates.

The comparison concerns group-level HbA1c change. It does not show how much any particular participant’s measurement changed, how outcomes varied among participants, or whether a given numerical difference is important to patients. The primary outcome also does not capture the full balance of benefits and harms. Secondary outcomes are listed, but their numerical results are not available in the registry record used for this analysis.

The unadjusted recalculation and sponsor analysis differ in method and reported inferential result. Because the sponsor’s analysis is the trial’s own analysis, it takes precedence; the recalculation should be understood as a separate, simple summary-statistic comparison. Missing information about the margin, model specification, multiplicity, and missing-data sensitivity analyses limits independent evaluation of the sponsor’s result.

These findings should therefore be read as registry-reported estimates from a particular randomized trial, not as a broad conclusion about treatment effectiveness or a recommendation for an individual. The numerical results alone do not settle questions about clinical importance, safety, or which regimen should be selected.

17. Why This Trial Matters Statistically

This trial illustrates why a treatment contrast cannot be understood from the point estimates alone. It reports group-level least-squares mean changes with standard errors, a sponsor ANCOVA comparison, and a separate unadjusted recalculation. These quantities address related but not identical statistical questions, so method and analysis purpose matter when interpreting their differences.

The sponsor analysis is classified as non-inferiority or equivalence, which makes the hypothesis and margin especially important. A confidence interval that includes zero and a small reported p-value cannot be fully reconciled without knowing the precise hypothesis being tested. The registry record does not report the margin or enough model details to independently assess the result. This is a useful example of why p-values should be interpreted in the context of the analysis design rather than in isolation.

The primary endpoint is continuous, while several listed secondary outcomes involve thresholds or event counts. Those outcome types provide different information and may require different statistical methods. When a trial reports multiple outcomes and analyses, prespecification and multiplicity handling also matter; these details are not reported in the registry record.

The record further identifies a full analysis set and last observation carried forward. Knowing the label for a population or missing-data method is useful, but evaluating robustness also requires information about missingness and sensitivity analyses. Planning illustrations based on the observed effect likewise answer a different question from the trial’s original design justification. Together, these features show how careful interpretation depends on understanding the estimand, model, assumptions, and reporting context—not only the headline estimate.

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 registry record does not report the non-inferiority or equivalence margin, the precise hypothesis tested, or enough detail to reproduce the sponsor’s ANCOVA. This restricts independent assessment of the sponsor’s p-value and the intended interpretation of its confidence interval. The unadjusted recalculation is not a substitute for that analysis because it is based on summary statistics and does not reproduce the sponsor’s model.

The primary analysis used last observation carried forward, but the amount and pattern of missing endpoint data and any sensitivity analyses are not reported in the registry record. The record also does not describe the covariates in the primary least-squares mean model, randomization procedures, allocation concealment, or protocol deviations. Details about interim analyses, multiplicity handling, and the original sample-size justification are not reported.

The trial was open-label, and the registry lists masking as none. The record describes a treat-to-target comparison but does not provide the titration algorithm or implementation details. These omissions limit evaluation of how treatment adjustment was conducted and how the open-label design may have affected measurements or trial conduct.

The available outcome details include the primary HbA1c estimates and selected safety counts, but not numerical results for the listed secondary outcomes. Consequently, interpretation cannot incorporate the trial’s reported glucose profiles, HbA1c target attainment, or hypoglycaemic episode results. Safety figures are descriptive, with no comparative statistical analysis or exposure-adjusted rates reported in the available record.

The registry’s brief summary describes phase 4 in China and phase 3 in Japan, while the phase field lists phase 4. Both descriptions are reported without attempting to resolve the difference.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: Comparison of the Blood Sugar Lowering Effect of Biphasic Insulin Aspart 30 and Insulin Glargine Both Combined With Metformin and Glimepiride in Chinese and Japanese Subjects With Type 2 Diabetes New to Insulin Treatment — NCT01123980.
• Registry record last updated 2017-02-24; status last verified 2017-01.
• Results first posted 2012-07-09.
• Data retrieved from ClinicalTrials.gov on 2026-11-09.
• Group sizes, means, event counts and sponsor-reported p-values are reproduced as posted. Effect estimates, confidence intervals and p-values not attributed to the sponsor are independently calculated from the posted summary statistics.

22. Record Summary

NCT01123980 was a completed, randomized, open-label, parallel-group trial with enrollment of 521. It compared once-daily biphasic insulin aspart 30 before dinner with once-daily insulin glargine at bedtime, both combined with metformin and glimepiride, in Chinese and Japanese subjects with type 2 diabetes who were new to insulin treatment.

The official title describes a 3 week run-in and 24 Week treat-to-target comparison. The primary outcome was change in HbA1c from Week 0 to week 24, analyzed in a full analysis set using last observation carried forward. The posted least-squares mean changes were -0.68 and -0.56, respectively. The sponsor reported an ANCOVA net mean difference of -0.12, with a 95% confidence interval from -0.25 to 0.02 and p < 0.001; the analysis is classified as non-inferiority or equivalence.

The registry also lists secondary glucose, HbA1c-threshold, and hypoglycaemia outcomes and reports serious adverse event counts. Important analysis details, including the sponsor’s margin and further missing-data and multiplicity information, are not reported in the registry record.