Comparison of NN1250 With Insulin Glargine in Type 1 Diabetes: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Comparison of NN1250 With Insulin Glargine in Type 1 Diabetes (NCT01079234), a randomized phase 3 study. The primary outcome is Main Trial (Primary Endpoint): Change in Glycosylated Haemoglobin (HbA1c) After 26 Weeks of Treatment.

2. Clinical Question

The registry identifies the trial as a phase 3, randomized study with a parallel-group model and no masking. It reports enrollment of 493. Its stated aim was to investigate the efficacy and safety of NN1250 in people with type 1 diabetes. The clinical question is therefore about comparative changes in a continuous laboratory measure over the main trial period, rather than a comparison of a clinical event or time-to-event outcome.

HbA1c change is the primary endpoint, while fasting plasma glucose (FPG) is included among the secondary outcomes. A 52-week extension adds follow-up information for HbA1c and FPG. These endpoints describe different aspects of glycaemic measurements and are not interchangeable. The posted results can describe observed group means and the uncertainty around comparisons, but do not by themselves establish why any observed difference occurred or how it should guide an individual treatment decision.

3. Trial Design

This was a randomized, open-label, parallel-group phase 3 trial of NN1250, identified as insulin degludec, and insulin glargine in type 1 diabetes. The registry reports enrollment of 493 and a completed status. The listed start date is 2010-03, and primary completion is recorded as 2010-11. The trial’s official title describes a 26-week main trial and a 26-week extension, with dosing flexibility, efficacy and safety as topics of investigation.

For the primary endpoint, three groups are represented: IDeg OD FF, IDeg OD and IGlar OD. The reported sample sizes for change in HbA1c are 164, 165 and 164, respectively. The registry also lists insulin aspart as an intervention and includes it in the arm labels. Detailed arm descriptions are empty, so the exact meaning of the suffixes and the dosing arrangements cannot be established from the registry record.

The main trial endpoint is change in HbA1c from week 0 to week 26. The extension endpoint compares change from week 0 to week 52 for the recorded extension groups. The registry lists three secondary outcomes: HbA1c change after 52 weeks and FPG change after both 26 and 52 weeks. The FPG results are reported in mmol/L; HbA1c is reported as a percentage of glycosylated haemoglobin.

The main primary analysis uses the full analysis set, described as including all randomized subjects, with missing values imputed using last observation carried forward. This describes the posted endpoint population and missing-data approach. Further details about randomization procedures, concealment, eligibility criteria, treatment adherence, and the original sample-size justification are not reported in the registry record. The same is true of the trial’s detailed statistical analysis plan and any interim analyses.

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: All subjects who completed the 26-week main trial and were found to be eligible for the extension trial were offered to participate in the 26-week extension trial (NCT01079234). Total duration of trial was up to 52 weeks (26 weeks+ 26 weeks) with two times 7-12 day follow-up periods.

Main: Week 0 to 26

Reasons for not completing this period:

Extension: Week 27 to 52

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: Full analysis set (FAS) includes all randomised subjects and missing data is imputed using last observation carried forward (LOCF).

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 Main Trial (Primary Endpoint): Change in Glycosylated Haemoglobin (HbA1c) After 26 Weeks of Treatment, measured over Week 0, Week 26.

The primary endpoint is change from baseline in glycosylated haemoglobin (HbA1c) after 26 weeks of treatment. It is recorded as a mean, with standard deviation describing the spread of participant-level changes. The registry lists the timeframe as week 0 and week 26 and gives the unit as percentage of glycosylated haemoglobin. The analysis population is the full analysis set, including all randomized subjects, with missing data imputed using last observation carried forward.

HbA1c is a continuous measure of glycaemia over time, so a change score provides a way to compare average movement from baseline across randomized groups. In general, a continuous endpoint can retain information about the range of participant outcomes rather than reducing results to a count of whether a threshold was crossed. Mean change is therefore useful for describing the group-level pattern, while its standard deviation indicates how variable individual changes were.

The endpoint is not a direct measure of every aspect of diabetes control, nor does a group mean describe the experience of each participant. The registry does not state a clinically important difference threshold or provide a rationale for choosing this measure as primary. The posted endpoint should be interpreted as the measured average change over the specified period, with attention to the comparison estimates and their confidence intervals.

7. Results Posted in the Registry

• IDeg OD: mean -0.41 versus -0.40 on control; difference -0.01 (95% CI -0.15 to 0.13), standardized effect d = -0.02; z-test p = 0.889.
• IGlar OD: mean -0.58 versus -0.40 on control; difference -0.18 (95% CI -0.32 to -0.04), standardized effect d = -0.27; z-test p = 0.013.

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.

Extension Trial (Secondary Endpoint): Change in Glycosylated Haemoglobin (HbA1c) After 52 Weeks of Treatment

• 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:
  • IDeg OD F: mean -0.13 versus -0.21 on control; difference 0.08 (95% CI -0.05 to 0.21), standardized effect d = 0.12; z-test p = 0.239.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between IDeg OD F and the control. The interval gives the range of effects that remain compatible with the data.

Main Trial (Secondary Endpoint): Change in Fasting Plasma Glucose (FPG) After 26 Weeks of Treatment

• 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:
  • IDeg OD: mean -2.54 versus -1.28 on control; difference -1.26 (95% CI -2.36 to -0.16), standardized effect d = -0.25; z-test p = 0.025.
  • IGlar OD: mean -1.33 versus -1.28 on control; difference -0.05 (95% CI -1.17 to 1.07), standardized effect d = -0.01; z-test p = 0.930.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the mean is statistically lower for IDeg OD than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between IGlar OD and the control. The interval gives the range of effects that remain compatible with the data.

Extension Trial (Secondary Endpoint): Change in Fasting Plasma Glucose (FPG) After 52 Weeks of Treatment

• 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:
  • IDeg OD F: mean -1.73 versus -0.61 on control; difference -1.12 (95% CI -2.11 to -0.13), standardized effect d = -0.21; z-test p = 0.027.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the mean is statistically lower for IDeg OD F than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Across the secondary outcomes, the pattern is mixed rather than uniform. The FPG comparisons at the main-trial and extension timepoints do not all point in the same direction or have the same degree of uncertainty, and the extension HbA1c comparison is also distinct from the primary endpoint comparison. This spread is important: the secondary outcomes add context about different measurements and follow-up periods, but they do not reproduce a single consistent estimate across all endpoints.

The primary result concerns mean HbA1c change at week 26 in the main trial. Secondary endpoints include FPG change at week 26, then HbA1c and FPG change at week 52 in the extension. The extension measures therefore address a later timeframe, and the registry lists extension groups with different recorded sample sizes from the main primary analysis. Results at these timepoints are not interchangeable with the primary result. They can help describe whether the observed pattern appears in other measures, but cannot replace the primary comparison.

The registry posts no reported analyses for the secondary endpoints, while the displayed secondary comparisons have unadjusted p-values. When multiple outcomes and contrasts are examined, some apparently notable results can arise by chance even if no underlying difference exists. Unless a multiplicity strategy is specified, individual secondary p-values should be treated as exploratory rather than as independent confirmation. The registry record does not report a multiplicity adjustment or a hierarchy for interpreting the secondary endpoints.

Accordingly, the secondary results are best read as supporting detail about the range of observed outcomes, not as a collection of separate definitive conclusions. Their confidence intervals, timepoints, units and analysis populations matter. The overall interpretation should remain anchored to the prespecified primary endpoint while acknowledging that the registry does not report enough analysis-plan detail to determine how the secondary findings were intended to be weighted.

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

• Extension Trial (Secondary Endpoint): Change in Glycosylated Haemoglobin (HbA1c) After 52 Weeks of Treatment
• Main Trial (Secondary Endpoint): Change in Fasting Plasma Glucose (FPG) After 26 Weeks of Treatment
• Extension Trial (Secondary Endpoint): Change in Fasting Plasma Glucose (FPG) After 52 Weeks of Treatment

9. Statistical Interpretation

10. Statistical Methods Explained

For a continuous outcome, a group mean is the average of the recorded changes for participants included in that analysis. The accompanying standard deviation describes the spread of those changes around the group mean. A small standard deviation indicates that values are more tightly clustered around the mean than a larger standard deviation would, although it does not indicate whether an estimate of the group mean is precise by itself. Precision also depends on the number of observations and the analysis method.

What does a mean difference represent?

A mean difference compares the average change in one group with the average change in a reference group. For the primary endpoint, the reference is IDeg OD FF. The reported estimate for IDeg OD is -0.01, and the estimate for IGlar OD is -0.18. A negative estimate means the second group named in the comparison has a more negative mean change than the reference group, based on the direction used in the posted contrasts. The estimate is in the endpoint’s units, percentage of glycosylated haemoglobin. For FPG, estimates are in mmol/L.

The estimate is a point summary, not a statement that all participants changed by that amount. The group means and standard deviations show the distribution at a broad descriptive level, while the mean difference focuses on the contrast between group averages. Neither provides a complete account of individual variation.

How should confidence intervals be read?

In general, a confidence interval expresses uncertainty about an estimated quantity. It gives a range of values reasonably compatible with the data and the method used to construct the interval. A narrower interval indicates greater precision than a wider interval, when comparing estimates on the same scale and under comparable methods. It does not mean that every value inside the interval is equally plausible, and it does not express the probability that the true effect lies in the particular interval after the data have been observed.

For a mean difference, zero represents no difference between the group means. An interval that includes zero is compatible with no difference as well as with differences in either direction within the interval. An interval that lies entirely on one side of zero is more consistent with a difference in that direction under the interval’s assumptions. This is one way to read uncertainty, not a substitute for considering the study design, outcome relevance and analysis plan.

What does an unadjusted p-value tell the reader?

In general, a p-value measures how incompatible the observed data are with a specified null model, assuming that model and the analysis assumptions hold. It does not measure the size or importance of an effect, and it is not the probability that the null hypothesis is true. A smaller p-value is not, on its own, evidence that a result matters clinically. The p-values in the posted comparisons are explicitly unadjusted. The registry record does not report whether the investigators used a multiplicity adjustment in a separate analysis.

When several comparisons or outcomes are considered, the chance of seeing at least one apparently unusual result can increase. This is why individual unadjusted p-values should be interpreted cautiously alongside the full set of results and the prespecified analysis plan. The registry record does not report a detailed plan for handling multiple testing.

What is the role of the analysis population and imputation?

The primary endpoint population is described as the full analysis set, including all randomized subjects. The record states that missing data are imputed using last observation carried forward (LOCF). In general, LOCF fills a missing later measurement with the participant’s last observed value. This approach relies on an assumption that the last observed value is an appropriate substitute for the missing value. If participant measurements would have changed after the last observation, the imputed value may not represent the unobserved outcome. The registry record does not report sensitivity analyses or other missing-data methods, so the robustness of the result to different missing-data assumptions cannot be assessed from these entries.

Randomization supports comparison of groups by assigning participants to treatment groups through a chance-based process. In general, randomization can reduce systematic differences between groups at baseline, but it does not prevent missing outcomes, deviations from assigned treatment or bias in an open-label trial. The registry identifies the trial as randomized and unmasked; details of sequence generation and allocation concealment are not reported in the registry record.

The registry lists standard deviations for the continuous outcomes and group sizes for the analyses. It also posts confidence intervals and unadjusted p-values for comparisons. The particular statistical model, covariate adjustment, handling of protocol deviations and any model diagnostics are not reported in the registry record. These details matter because different analysis choices can affect estimates and their uncertainty, especially when data are incomplete or assumptions do not fit well. No additional model assumptions should be inferred beyond the methods explicitly recorded.

For planning illustrations, the stated approach uses a two-sided significance level of 0.05, equal allocation between two arms and a normal approximation. The effect to detect is a stated fraction of the observed effect. In general, these are planning calculations based on assumptions, not an account of the trial’s original design or its actual probability of detecting a result. The registry record does not report the original sample-size justification.

11. Confidence Intervals and Precision

The primary HbA1c contrast between IDeg OD and IDeg OD FF is estimated at -0.01, with a confidence interval from -0.151 to 0.131. This interval spans zero and allows for small differences in either direction. The estimate is close to zero, but its uncertainty means the data do not identify an exact difference. The interval should be read together with the group means, standard deviations and analysis population rather than as a stand-alone verdict.

The primary contrast between IGlar OD and IDeg OD FF is estimated at -0.18, with a confidence interval from -0.323 to -0.037. This interval does not include zero and lies on the negative side of the comparison scale. It still covers a range of possible magnitudes, so it does not pin down one exact effect. The unadjusted p-value is 0.013, but neither that value nor the interval indicates whether the difference is clinically important; the registry does not report an importance threshold.

In general, confidence intervals help distinguish a relatively precise estimate from one that remains uncertain. Their widths depend on the observed variability, available sample size and statistical method. The endpoint standard deviations are 0.59, 0.71 and 0.72 across the primary groups, with 164, 165 and 164 participants respectively. The posted interval is the direct summary of uncertainty for each comparison and should take precedence over trying to infer precision from group sizes alone.

Confidence intervals also do not address every source of uncertainty. They reflect sampling uncertainty under the analysis method, but do not automatically account for bias from missing data, open-label conduct, outcome measurement, selective reporting or multiple comparisons. The primary analysis uses LOCF for missing data, and the registry does not report sensitivity analyses or multiplicity adjustments. Those limitations are separate from the numerical width of the intervals.

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 illustrations report participants per arm for 80% power and participants per arm for 90% power. They are not total sample sizes and do not refer to total events, because the endpoints in these calculations are continuous measures. The calculations use the observed comparison effect as a reference and show planning values for detecting the full observed effect, 75% of the observed effect and 50% of the observed effect.

The stated planning assumptions are a two-sided significance level of 0.05, equal allocation between the two arms and a normal approximation. The effect to detect is the stated fraction of the observed effect. These are planning assumptions for the illustration; the trial’s own original design assumptions and sample-size justification are not reported in the registry record.

In plain language, 80% power means that, under the assumptions used for the calculation and if the targeted effect were the true effect, a study of the indicated size would have an 80% chance of producing a result that meets the chosen statistical criterion. The 90% column uses the same idea with a higher target chance of detecting the specified effect. These descriptions are general explanations of power, not claims about what happened in this trial.

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. The figures are planning illustrations, not observed power. They should not be read as evidence that the original study was adequately or inadequately sized, because the original design assumptions are not reported.

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 = -0.010; pooled standard deviation σ = 0.653.
2. n = 2 × (1.96 + 0.842)² × 0.653² / -0.010² = 66,925.6, rounded up to 66,926 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 <- -0.00999999999999995                      # observed difference in means
sigma <- 0.652946040733912                     # 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(1079234)
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 = 66,926 (80% power), 89,595 (90% power)
 75% of observed effect: n per arm = 118,979 (80% power), 159,279 (90% power)
 50% of observed effect: n per arm = 267,703 (80% power), 358,378 (90% power)
power.t.test: 66926.6 
Simulated power at n = 66,926 per arm: 0.800 (20000 replicates)

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

14. Multiplicity, Missing Data and Analysis Populations

The primary population is described as the full analysis set, including all randomized subjects. For the primary endpoint, missing data are imputed using last observation carried forward (LOCF). In general, this method carries a participant’s last observed measurement forward to replace a missing later measurement. It is a specific assumption about what the missing outcome would have been, not a direct observation. If the outcome would have changed after the last observed value, LOCF may not reflect the unobserved result.

The registry record does not report how many participants had missing primary endpoint measurements, why measurements were missing, or whether the missingness differed between groups. It also does not report sensitivity analyses using alternative missing-data assumptions. This means the effect of missing values on the posted estimates cannot be assessed from the registry entries alone. The use of LOCF is known, but the extent and consequences of imputation are not.

The reported comparisons include unadjusted p-values, and the registry does not report a multiple-testing procedure. Multiplicity arises when several hypotheses are tested; without an adjustment or an explicitly ordered testing strategy, the chance of one or more misleadingly small p-values can be greater than for a single prespecified test. This is particularly relevant when interpreting secondary outcomes, which include several measures and timepoints. The registry record does not report which secondary comparisons, if any, were confirmatory.

The registry lists no reported analyses for the secondary outcomes. That absence does not establish that no further analyses were conducted; it means those analyses are not reported in the registry record. Details of the statistical analysis plan, interim analyses, covariate adjustment, handling of protocol deviations and sensitivity analyses are also not reported in the registry record. As a result, the posted values can be described, but the broader analysis strategy and its safeguards against missing-data and multiplicity concerns remain unclear.

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: hypoglycaemia (2.5%), hypoglycaemic unconsciousness (2.5%), ulna fracture (0.6%), hypoglycaemic seizure (0.6%), spinal osteoarthritis (0.6%).

The registry reports serious adverse events for two groups. In the IDeg OD F group, 25 of 329 participants at risk were affected; in the IGlar OD group, 12 of 161 participants at risk were affected. These are counts of participants reported as affected among the stated group sizes at risk. They do not describe the number of all serious-event episodes, and the registry entry does not provide additional detail about event timing, duration, severity categories or attribution to treatment.

For other adverse events, the reported affected counts are 187 of 329 in IDeg OD F and 105 of 161 in IGlar OD. The registry also lists serious-event terms and percentages: hypoglycaemia and hypoglycaemic unconsciousness are each listed at 2.5%; ulna fracture, hypoglycaemic seizure, spinal osteoarthritis and femur fracture are each listed at 0.6%. The record does not specify group attribution for each listed term in the information posted.

Deaths are not reported in the registry record. These safety entries are descriptive counts and term listings, not a complete assessment of causality or comparative risk. Differences in follow-up, event ascertainment and participant exposure would matter for a fuller comparison, but those details are not reported in the registry record.

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

The posted primary results describe average change in HbA1c over the main 26-week period for three randomized groups. The comparison of IDeg OD with IDeg OD FF is estimated close to zero, with an interval spanning zero. The comparison of IGlar OD with IDeg OD FF is estimated at -0.18, with an interval that lies below zero. These findings characterize the observed group averages and their statistical uncertainty; they do not describe every participant’s response.

The endpoint is one laboratory measure over a specified period. It does not capture every possible clinical outcome, treatment burden, adverse effect or longer-term experience. The secondary FPG and extension measures add information, but their results vary and are unadjusted. They should not be treated as a substitute for the primary result or as definitive confirmation without a reported multiplicity plan.

A confidence interval that excludes zero indicates that the estimated comparison is inconsistent with zero under the interval’s assumptions, but it does not establish clinical importance. The registry does not report a clinically important difference threshold. Likewise, a p-value does not express the probability that a treatment is effective or that the result will recur. The findings are conditional on the analysis method, including the stated LOCF approach to missing primary endpoint data.

The trial was open-label, and the record does not report sufficient details to assess all possible influences on conduct or analysis. Randomization supports comparative interpretation, but it does not resolve questions about missing outcomes, multiplicity, generalizability or the meaning of the arm-label suffixes. The results should therefore be understood narrowly as comparisons of posted mean changes in the stated analysis populations and timeframes. They do not establish a mechanism, predict an individual’s outcome, or provide medical advice.

17. Why This Trial Matters Statistically

This trial offers a useful statistical example because it reports a continuous primary endpoint with group means, standard deviations, comparison estimates, confidence intervals and unadjusted p-values. Considering these together is more informative than treating a p-value as the whole result. One primary contrast is near zero with an interval spanning zero, while the other has an interval on one side of zero. The contrast in precision and direction illustrates why each comparison needs its own estimate and uncertainty range.

The study also includes secondary measures at more than one follow-up time. This makes clear why a set of results should be interpreted as a whole: outcomes can differ in scale, population size and timing, and multiple unadjusted comparisons can make isolated findings less persuasive. The extension results provide another timeframe but should not be merged mechanically with the primary endpoint.

Missing-data handling is also visible in the registry description. The full analysis set includes all randomized subjects, but primary endpoint missingness is addressed through LOCF. That method makes an assumption about unobserved values, underscoring that an analysis population label alone does not explain how incomplete data influence estimates.

Finally, the record allows readers to distinguish the trial’s reported comparisons from planning illustrations based on observed effects. Planning figures are not observed power, and they do not reveal the original design rationale. For statistical interpretation, the clearest conclusions come from describing the measured differences and their uncertainty while keeping the unreported analysis details and limitations in view.

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 gives limited detail about the trial’s operational and statistical methods. It identifies randomization, an open-label design and a parallel model, but does not report the randomization procedure or allocation concealment. Arm descriptions are empty, so labels such as IDeg OD FF and IDeg OD F cannot be fully interpreted from the record. The exact dosing schedules and the role of insulin aspart beyond its appearance in the intervention list and arm names are not reported in the registry record.

The primary endpoint uses LOCF for missing values, but the number and pattern of missing observations are not reported. No sensitivity analyses or alternative missing-data analyses are posted. The results may therefore depend on an assumption whose practical impact cannot be evaluated from the registry entry.

The posted p-values are unadjusted, and the registry does not report a multiplicity strategy. There are multiple comparisons, measures and timepoints, so secondary findings require particular caution. The detailed statistical analysis plan, covariate adjustment, interim analyses and original sample-size justification are not reported in the registry record. The planning illustrations use stated assumptions but should not be mistaken for the trial’s original design calculations.

The record also does not report a clinically important difference threshold for HbA1c or FPG, so statistical uncertainty cannot be translated into a registry-defined judgment of clinical importance. Safety information is limited to affected counts, listed serious-event terms and percentages; deaths are not reported in the registry record. The extent to which the trial population and conduct generalize beyond the enrolled setting cannot be determined from the information recorded.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: Comparison of NN1250 With Insulin Glargine in Type 1 Diabetes — NCT01079234.
• Registry record last updated 2017-02-09; status last verified 2016-12.
• Results first posted 2015-12-31.
• 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

NCT01079234 is a completed, randomized, open-label, parallel-group phase 3 trial with enrollment of 493. It investigated insulin degludec, identified in the trial description as NN1250, compared with insulin glargine in people with type 1 diabetes. The primary endpoint was mean change in HbA1c from week 0 to week 26 in the full analysis set, with missing data imputed using LOCF.

The posted primary comparison of IDeg OD with IDeg OD FF is -0.01, with a confidence interval from -0.151 to 0.131. The IGlar OD comparison with IDeg OD FF is -0.18, with a confidence interval from -0.323 to -0.037. Secondary outcomes cover FPG at week 26 and HbA1c and FPG at week 52. Their unadjusted comparisons should be interpreted cautiously because a multiplicity approach is not reported.

The registry does not report the original sample-size justification, detailed statistical analysis plan, or sensitivity analyses for missing data. Safety entries include serious and other adverse-event counts; deaths are not reported in the registry record.