Comparison of the Efficacy and Safety of Two Different Dose Adjustment Regimens for Insulin Degludec/Insulin Aspart in Subjects With Type 2 Diabetes Mellitus Previously Treated With Insulin Glargine: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Comparison of the Efficacy and Safety of Two Different Dose Adjustment Regimens for Insulin Degludec/Insulin Aspart in Subjects With Type 2 Diabetes Mellitus Previously Treated With Insulin Glargine (NCT01680341), a randomized phase 3 study. The primary outcome is Change From Baseline in HbA1c (Glycosylated Haemoglobin) (%).

2. Clinical Question

The practical statistical contrast is between two approaches to adjusting the same combination insulin treatment in people already treated with insulin glargine. Because the trial was randomized, the intended comparison is between groups assigned to different titration algorithms rather than between people who selected their approach. The record describes the study as open-label, so participants and study personnel were not masked to assignment. That feature may matter for outcomes that involve behavior or judgment, although HbA1c is a measured laboratory outcome.

The central endpoint is a continuous measure of glycaemic change. Secondary registry outcomes cover fasting plasma glucose, reaching an HbA1c threshold, reaching that threshold without confirmed hypoglycaemia, and adverse events. They offer additional perspectives but are not substitutes for the primary comparison. The registry does not report a single combined clinical question covering all these outcomes, nor does it report a multiplicity strategy for interpreting them together.

3. Trial Design

This was a completed phase 3, randomized, open-label, parallel-group trial. It enrolled 272 participants. The two assigned groups were IDegAsp Simple and IDegAsp Step Wise, with 136 participants represented in each primary-outcome arm summary. Both arms received insulin degludec/insulin aspart; the distinction was the titration algorithm. The registry record gives no detailed arm descriptions explaining how dose adjustments differed in practice.

The trial's stated aim was to compare efficacy and safety of the two titration algorithms in people with type 2 diabetes mellitus previously treated with insulin glargine. The study took place in Africa, Asia, Europe and the United States of America. The registry lists the design as parallel and the masking as none. Thus, the reported comparison is between concurrently assigned groups, but treatment assignment was not concealed from participants and investigators through masking.

The primary outcome was change from baseline in HbA1c after 26 weeks of treatment. The primary analysis population was the full analysis set, defined in the record as all randomized subjects. Missing data for that analysis were imputed using last observation carried forward. Arm-level primary results are least-squares means with standard errors. The record also posts a comparison between the arms, including a mean difference, confidence interval and unadjusted p-value.

The registry record does not report the trial's original sample-size justification, planned allocation ratio, detailed randomization procedure, interim analyses, or a full statistical analysis plan. It also does not report detailed rules for either titration algorithm in the arm descriptions. Those omissions limit what can be inferred about the intended operating characteristics and implementation of the study, beyond the design features and analysis details explicitly recorded.

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: While entering the treatment period the subjects discontinued insulin glargine (IGlar) and sulfonylurea (SU)/glinides (if administered) but continued treatment with up to 3 other oral antidiabetic drugs (OADs) as prescribed.

Overall Study

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: The full analysis set (FAS) included all randomised subjects. Missing data were 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 Change From Baseline in HbA1c (Glycosylated Haemoglobin) (%), measured over Week 0, Week 26.

The primary endpoint is change from baseline in glycated haemoglobin, or HbA1c, measured as a percentage. The timeframe is Week 0 to Week 26. HbA1c reflects glycaemia over time rather than a single moment, so change in this measure provides a way to compare average glycaemic response across the treatment period. The registry reports an adjusted least-squares mean for each arm, with a standard error, and a between-arm mean difference.

For this comparison, a negative within-arm change indicates a lower HbA1c value at follow-up than at baseline. The Simple arm's least-squares mean change was -1.45, and the Step Wise arm's was -1.33. The reported Step Wise minus Simple mean difference was 0.12. These are estimates of average change, not statements about every participant's response. The group summaries do not show the full distribution of individual changes.

HbA1c is used as the primary endpoint because it gives a continuous outcome that can capture changes across the range of measured values, instead of reducing response to whether a participant crossed one threshold. A continuous endpoint can be informative about average differences, while threshold outcomes may help describe how many participants reached a specified level. These endpoints answer related but distinct questions. A difference in mean change does not automatically imply a difference in the probability of reaching a threshold, and neither result alone describes the full balance of benefits and harms.

7. Results Posted in the Registry

• IDegAsp Step Wise: mean -1.33 versus -1.45 on control; difference 0.12 (95% CI -0.13 to 0.37), standardized effect d = 0.11; z-test p = 0.346.

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 From Baseline in Fasting Plasma Glucose (FPG)

• Endpoint type: Continuous: a mean value per participant, compared as a difference in means.
• Test: Unadjusted two-sample z-test for the difference in means, using the posted standard errors.
• Result:
  • IDegAsp Step Wise: mean -1.98 versus -1.68 on control; difference -0.30 (95% CI -0.94 to 0.34), standardized effect d = -0.11; z-test p = 0.356.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between IDegAsp Step Wise and the control. The interval gives the range of effects that remain compatible with the data.

Subjects With HbA1c Below 7.0%

• Endpoint type: Binary: the number of participants with the event, compared as proportions.
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • IDegAsp Step Wise: 62.5% (85/136) versus 66.9% (91/136) on control; risk difference -4.4 percentage points (95% CI -15.8 to 6.9), risk ratio 0.93 (0.78–1.11); unadjusted two-proportion z-test p = 0.446.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between IDegAsp Step Wise and the control. The interval gives the range of effects that remain compatible with the data.

Percentage of Subjects With HbA1c Below 7.0% Without Confirmed Hypoglycaemia

• Endpoint type: Binary: the percentage of participants with the event (counts rebuilt from the posted percentages and group sizes).
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • IDegAsp Step Wise: 32.0% (40/125) versus 25.4% (31/122) on control; risk difference 6.6 percentage points (95% CI -4.7 to 17.8), risk ratio 1.26 (0.85–1.87); unadjusted two-proportion z-test p = 0.253.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between IDegAsp Step Wise and the control. The interval gives the range of effects that remain compatible with the data.

The secondary outcomes provide a mixed descriptive pattern rather than a consistent directional signal across every measure. Fasting plasma glucose change is numerically more negative with Step Wise, while the HbA1c threshold-only result numerically favors Simple. The combined outcome requiring the threshold without confirmed hypoglycaemia numerically favors Step Wise. These outcomes address different features of glycaemic response and should not be expected to move in lockstep.

This pattern does not clearly reinforce the primary comparison in one direction. The primary result is a small estimated difference in average HbA1c change with uncertainty spanning no difference. The secondary outcomes offer related observations, but each uses its own endpoint definition and, in some cases, a different denominator. A threshold outcome discards information about how far values lie above or below the cutoff, while the combined endpoint incorporates hypoglycaemia as an additional condition. These distinctions explain why secondary directions can differ from the continuous primary endpoint without being logically contradictory.

The secondary comparisons are unadjusted, and the registry reports no secondary analyses. When multiple outcomes are examined, some apparent differences can arise by chance even if no underlying effects exist. Without a reported multiplicity strategy, secondary p-values have less confirmatory weight than a prespecified primary analysis. Their confidence intervals and estimates can still be useful for describing the data and for identifying questions that merit further study, but they should not be treated as independent proof of benefit or harm.

The posted safety outcomes also belong to a different evidentiary category from efficacy endpoints. Serious and other treatment-emergent adverse events describe event experience in the recorded groups; they do not resolve the uncertainty around glycaemic comparisons. A full assessment would require considering endpoint definitions, follow-up, event ascertainment and the missing details in the registry record. The registry does not provide enough information to infer a comprehensive benefit-harm balance from these summaries alone.

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

• Change From Baseline in Fasting Plasma Glucose (FPG)
• Subjects With HbA1c Below 7.0%
• Percentage of Subjects With HbA1c Below 7.0% Without Confirmed Hypoglycaemia
• Incidence of Treatment Emergent Adverse Events (TEAEs)
• Number of Treatment Emergent Confirmed Hypoglycaemic Episodes
• Number of Treatment Emergent Confirmed Hypoglycaemic Episodes in the Maintenance Period
• Number of Treatment Emergent Nocturnal (00:01-05:59) Confirmed Hypoglycaemic Episodes

9. Statistical Interpretation

10. Statistical Methods Explained

What does the mean change represent?

HbA1c is a continuous measure, so the primary outcome is expressed as an average change rather than a count of participants meeting a yes-or-no definition. The reported least-squares mean summarizes each arm's estimated average change under the analysis model. The record gives -1.45 for IDegAsp Simple and -1.33 for IDegAsp Step Wise. A negative change indicates a reduction from baseline. The between-arm estimate is Step Wise minus Simple, so its positive value of 0.12 indicates that the estimated reduction was numerically less negative in the Step Wise arm.

Least-squares means are model-based adjusted means. They are often used to compare groups while accounting for terms in a statistical model. Which terms were included in this trial's model is not reported in the registry record. The standard error describes the estimated variability of the mean estimate under the model. It is not the same as the standard deviation of individual participant outcomes.

How should the between-arm comparison be read?

The mean difference puts both arms on one scale and makes the direction explicit. The reported contrast is Step Wise minus Simple. Its confidence interval includes zero, the value corresponding to no difference in the stated contrast. The unadjusted p-value is 0.346. Taken together, these indicate that the registry result does not distinguish the observed estimate from no difference at conventional levels of statistical evidence. They do not show that the two algorithms are interchangeable or that any possible difference is clinically unimportant.

A p-value describes the degree of incompatibility between data and a statistical model that assumes no difference, given the analysis assumptions. It is not a measure of effect size, clinical importance, or the probability that a result arose by chance. The point estimate describes the estimated direction and size, while the confidence interval communicates uncertainty around it. Neither should be interpreted in isolation.

Why is over-dispersion relevant to counts?

General statistical explanation: binary and event-count outcomes can show more variability than a simple model assumes. Over-dispersion means observed variation exceeds the model's expected variation. If present and not accounted for, uncertainty can be understated and intervals can be too narrow. The registry's posted secondary comparisons include binary outcomes, but it does not report a model specification or an over-dispersion assessment for them. That assessment is therefore not reported in the registry record, and no conclusion about it can be drawn from the posted summaries alone.

What does last observation carried forward do?

For the primary analysis, missing values were imputed using last observation carried forward. In general, this method substitutes a participant's most recent observed value for a missing later value. It creates a complete analysis value but assumes, in effect, that the participant's outcome did not change after the last observation. That assumption may not match the true course of the outcome. The record identifies the method but does not report the number or pattern of missing primary endpoint values, sensitivity analyses using alternative assumptions, or the reasons for missingness.

The full analysis set included all randomized subjects, according to the registry description. This links the main analysis population to randomized assignment rather than only to those who completed treatment. However, the use of imputation means that the analysis also depends on how missing outcomes were handled. The registry does not report a per-protocol population or other analysis population for the primary endpoint.

How are secondary outcomes different?

Fasting plasma glucose is continuous and is summarized as a least-squares mean change. The HbA1c threshold outcomes are binary: each participant either meets the threshold or does not, and one outcome adds the condition of having no confirmed hypoglycaemia. For such outcomes, a risk difference compares the observed proportions between arms. The posted secondary comparisons are unadjusted. They do not account for the number of endpoints examined, and the registry does not report adjusted analyses.

The secondary outcome summaries use different analysis denominators for some outcomes. For the outcome requiring HbA1c below the threshold without confirmed hypoglycaemia, the arm-level denominators are 122 and 125, rather than the primary outcome's 136 in each arm. The reason for these denominator differences is not reported in the registry record. Counts, percentages and model-based estimates should be interpreted using the denominator and definition attached to each specific outcome, rather than assumed to represent the same analysis set.

The registry provides no interim analysis details, formal multiplicity procedure, or full statistical analysis plan. Those elements would help clarify which comparisons were prespecified as confirmatory and how error rates were controlled across outcomes. Without them, the posted p-values for secondary comparisons are best treated as exploratory descriptive evidence, not as a set of independent confirmations.

11. Confidence Intervals and Precision

The primary between-arm estimate is 0.12, with a confidence interval that crosses zero. The interval communicates uncertainty about the underlying difference compatible with the observed data and analysis. Because it includes values on both sides of zero, it does not establish which approach has the lower average change. The unadjusted p-value of 0.346 is consistent with that uncertainty, but the interval is more useful for seeing the range of effects that remain compatible with the estimate.

A confidence interval is not a guarantee that the true effect lies within its endpoints, nor is it a range containing a stated proportion of individual participant outcomes. In general, it is a procedure that, under its assumptions, would produce intervals containing the true parameter at the stated confidence level in repeated samples. The confidence level is not reported in the registry result, so it should not be inferred from the posted record.

The arm-level standard errors are 0.09 for both primary estimates. Standard errors describe uncertainty in estimated means; they do not describe how much individual HbA1c changes vary. The registry does not post arm-level confidence intervals for these means. The comparison interval is the relevant uncertainty summary for the difference between the arms.

The interval's span matters: it permits a range of possible differences rather than identifying one precise effect. The record does not specify a threshold for clinical importance, so statistical uncertainty cannot be translated into a judgment about whether all effects allowed by the interval would matter clinically. The sample-size planning illustrations elsewhere are based on observed effects and stated assumptions; they do not change the precision of the estimate already obtained in this trial.

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 describe hypothetical future studies under the assumptions stated for the illustration, not the original design of this trial. For the continuous and binary outcomes shown, the figures are participants per arm, not total participants and not total events. The planning assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. Each scenario uses the stated fraction of the observed effect as the effect to detect. The trial's own original sample-size 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, if that effect is truly present. The 90% power column uses the same interpretation with a higher target chance of detection. These are planning quantities, not guarantees; actual power depends on the true effect, variability, analysis choices and assumptions. They are not observed power.

Rows that plan for 100% of the observed effect use the observed effect as the target. Rows planning for 75% or 50% use smaller target effects. Smaller effects are harder to distinguish from random variation, so they require more participants under the same assumptions. The figures are illustrations tied to the observed estimates, not evidence that those estimates are the true or clinically important effects.

The registry's sample-size assumptions specify the illustration method, but they do not establish that the original trial used the same method or targets. For the time-to-event outcome type, the convention would be total events under the listed assumptions; no such outcome is the primary endpoint in this trial's posted results.

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 = 0.120; pooled standard deviation σ = 1.050.
2. n = 2 × (1.96 + 0.842)² × 1.050² / 0.120² = 1,200.9, rounded up to 1,201 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 <- 1.04957134107215                     # 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(1680341)
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 = 1,201 (80% power), 1,608 (90% power)
 75% of observed effect: n per arm = 2,135 (80% power), 2,859 (90% power)
 50% of observed effect: n per arm = 4,804 (80% power), 6,431 (90% power)
power.t.test: 1201.8 
Simulated power at n = 1,201 per arm: 0.799 (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 record identifies one primary outcome and several secondary outcomes, but it does not report a multiplicity adjustment or a prespecified hierarchy for testing them. Multiplicity arises when researchers make several statistical comparisons: the chance of at least one apparently unusual result can increase as the number of tests grows. Adjustments or a clear testing sequence can address that issue, but no such method is reported in the registry record. The secondary results should therefore be read as unadjusted and exploratory rather than as a collection of confirmatory findings.

The registry reports an unadjusted p-value for the primary comparison and unadjusted comparison results for the posted secondary outcomes. It lists no reported secondary analyses. The exact analysis plan, including whether particular secondary endpoints had formal testing roles, is not reported in the registry record. Interim analyses and any rules for stopping or changing the trial are also not reported in the registry record. Their absence from the record does not establish whether they were or were not performed; it means the record does not document them.

For the primary analysis, the full analysis set included all randomized subjects, and missing data were imputed using last observation carried forward. The registry does not report the amount of missing primary endpoint data, the timing or reasons for missingness, or sensitivity analyses evaluating alternative missing-data assumptions. Last observation carried forward can be sensitive to assumptions about what happens after a participant's last measurement. Without those details, the robustness of the primary estimate to missing data cannot be assessed from the registry results alone.

Some secondary outcome denominators are smaller than the primary analysis arm counts. The reasons and handling rules for those denominators are not reported in the registry record. The record also does not give a detailed account of analysis populations for each secondary endpoint. This matters because differing data availability or endpoint eligibility can affect how results compare across outcomes. Detailed protocol and analysis-plan information would be needed to evaluate those issues more fully.

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 (1.5%), acute coronary syndrome (0.7%), aortic valve stenosis (0.7%), coronary artery disease (0.7%), coronary artery occlusion (0.7%).

The registry reports serious adverse event counts of 7 for IDegAsp Simple and 10 for IDegAsp Step Wise, with 135 and 134 participants at risk, respectively. It also reports other treatment-emergent adverse event counts of 45 and 53, with the same respective at-risk counts. These are group summaries, not estimates of causation. The record does not state that every event was caused by the assigned treatment, and the figures should not be interpreted that way.

The serious-event terms listed include hypoglycaemia at 1.5%, and acute coronary syndrome, aortic valve stenosis, coronary artery disease, coronary artery occlusion and ventricular fibrillation at 0.7% each. The registry does not provide additional detail in these summaries about event timing, recurrence, severity beyond the serious classification, or relatedness assessments. Death counts are not reported in the registry record.

Event counts and at-risk counts are not interchangeable with a complete assessment of safety. Differences in follow-up, ascertainment and event definitions can affect comparisons, and those details are not fully. The counts describe what the registry posts for these groups; they do not establish that one algorithm is safer than the other.

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

The primary result estimates the difference in average HbA1c change between two dose-adjustment approaches for insulin degludec/insulin aspart over 26 weeks. The estimated difference was 0.12 for Step Wise minus Simple, and the confidence interval includes no difference. The unadjusted p-value was 0.346. These findings do not provide clear evidence of a between-arm difference under the reported analysis, but they also do not prove that the algorithms have identical effects.

The result does not establish equivalence or non-inferiority. Those conclusions require a prespecified margin and an analysis designed to test that question; such a framework is not reported in the registry record. Nor does the result identify which approach is preferable in clinical practice. It is a statistical comparison of a specified laboratory endpoint in a particular study population and follow-up period, not a recommendation for an individual.

The study concerns people with type 2 diabetes previously treated with insulin glargine and compares two titration algorithms for the same combination insulin. Generalizing beyond that population, treatment context, or duration requires evidence beyond this registry result. The open-label design, missing detail about algorithm instructions, and limited reporting of missing-data sensitivity analyses all qualify interpretation. The data do not give a full account of how treatment was implemented or how participants' outcomes would have differed under other regimens.

Secondary outcomes and adverse-event counts add context but do not resolve the primary uncertainty. Their directions are mixed, and the secondary comparisons are unadjusted. Safety counts are descriptive and do not by themselves establish a causal relation or a comparative safety conclusion. The registry record also does not report enough detail about event assessment or follow-up to support a complete safety evaluation.

Overall, the posted results support a cautious reading: the estimated primary difference is modest in the reported units and imprecise enough to remain compatible with effects in either direction. Statistical uncertainty, clinical importance and safety are related but separate questions. The registry record does not provide all the design and analysis details needed to settle each of them.

17. Why This Trial Matters Statistically

This trial illustrates why the estimated difference and its precision need to be considered together. The primary point estimate is 0.12, but its confidence interval crosses zero. A point estimate alone can give a misleading impression of certainty; the interval makes clear that the data allow more than one interpretation of the underlying difference. The p-value is another summary of compatibility with a no-difference model, not a verdict on clinical importance.

The outcome choice also matters. HbA1c change is continuous and describes average movement, whereas threshold outcomes describe whether participants crossed a specified boundary. A continuous endpoint preserves more gradation in values, while a threshold can be easier to communicate but may conceal how close participants were to the cutoff. The distinct secondary patterns in this trial show why endpoint definitions affect the question being answered.

The record also demonstrates how missing-data and multiplicity details shape interpretation. Last observation carried forward is stated for the primary outcome, but the amount and pattern of missingness and sensitivity analyses are not reported. Multiple secondary outcomes are posted, yet no adjustment strategy or analysis hierarchy is reported. Those gaps matter when deciding how much weight to give each result and how robust the conclusions may be.

Finally, the comparison isolates titration approach within a treatment product rather than contrasting different drugs. That makes the statistical question focused, but the missing algorithm details limit reproducibility and the ability to understand implementation. The registry result is informative as a comparison of assigned strategies, while remaining bounded by the population, endpoint, follow-up, and analysis details documented in the record.

18. Limitations

• The posted summary statistics are the only data these results rest on; patient-level data, full protocols and statistical analysis plans are not available.
• Recalculated results are simple, unadjusted calculations from posted summaries. They do not replace the sponsor's prespecified analysis.

Several trial-specific limitations affect interpretation. The registry describes the study as open-label, so participants and study personnel were not masked. Although the primary endpoint is a laboratory measure, lack of masking can still matter for treatment behavior, co-interventions, reporting and event ascertainment. The registry does not provide enough detail to evaluate how these possibilities were managed.

The arm descriptions do not explain the Simple or Step Wise titration algorithms. Consequently, the exact treatment procedures behind the comparison are not reproducible from the registry record alone. The record also does not report the randomization method, concealment details, or the original sample-size justification. These omissions limit review of allocation procedures and the study's planned ability to detect a meaningful difference.

For the primary outcome, missing data were handled using last observation carried forward. The amount and pattern of missing data and sensitivity analyses are not reported in the registry record. Some secondary endpoints have different arm denominators from the primary endpoint, but reasons for those differences and endpoint-specific missing-data rules are not reported. These details could affect comparability and interpretation.

The registry gives no multiplicity adjustment or inferential hierarchy for secondary outcomes, and no secondary analyses are listed. Their unadjusted results therefore have limited confirmatory value. The statistical model behind the least-squares means and the confidence level for the comparison interval are not specified in the record. Finally, adverse-event summaries lack detail on timing, recurrence and relatedness, and deaths are not reported in the registry record. These limits mean the posted summaries cannot provide a complete account of efficacy, robustness or safety.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: Comparison of the Efficacy and Safety of Two Different Dose Adjustment Regimens for Insulin Degludec/Insulin Aspart in Subjects With Type 2 Diabetes Mellitus Previously Treated With Insulin Glargine — NCT01680341.
• Registry record last updated 2018-04-02; status last verified 2018-03.
• Results first posted 2015-11-17.
• 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

NCT01680341 is a completed phase 3 randomized, open-label, parallel-group trial with enrollment of 272. It compared IDegAsp Simple with IDegAsp Step Wise in people with type 2 diabetes previously treated with insulin glargine. The primary endpoint was change in HbA1c from baseline to Week 26, analyzed in the full analysis set with last observation carried forward for missing data.

The reported least-squares mean changes were -1.45 for Simple and -1.33 for Step Wise. The Step Wise minus Simple estimate was 0.12, with an unadjusted p-value of 0.346 and a confidence interval that includes zero. Secondary outcomes show mixed directions and are unadjusted. Serious adverse event counts were 7 and 10, respectively; deaths are not reported in the registry record.

Important details, including the original sample-size justification, titration instructions, multiplicity strategy and missing-data sensitivity analyses, are not reported in the registry record.