iGlarLixi vs IDegAsp in Chinese Participants After OAD(s): Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for iGlarLixi vs IDegAsp in Chinese Participants After OAD(s) (NCT05413369), a randomized phase 3 study. The primary outcome is Change in HbA1c From Baseline to Week 24: Non-Inferiority Analysis.

2. Clinical Question

The comparison concerns a change in a laboratory measure over the treatment period, not a comparison of long-term outcomes. Both groups continued background oral therapy as described in the registry record. The randomized, two-arm design provides a direct comparison between the assigned regimens, while the open-label design means participants and study personnel were not masked to treatment assignment. The registry record also lists secondary questions about body weight, glycemic targets, fasting plasma glucose, and self-monitored plasma glucose. Those outcomes add context, but they do not change what the primary endpoint asks: how HbA1c changed from baseline through Week 24 in each group.

3. Trial Design

This was a Phase 3, randomized, active-controlled, open-label, parallel-group study in Chinese participants with type 2 diabetes mellitus. The registry record reports enrollment of 582 participants. The posted primary outcome results include 291 participants in each treatment group. The comparison was between once-daily iGlarLixi and once-daily IDegAsp, each added to metformin with or without an SGLT-2 inhibitor. Treatment lasted 24 weeks.

The study is described as open-label, with no masking. Random assignment can help balance measured and unmeasured participant characteristics between groups, while an active comparator allows the two active regimens to be compared directly. Neither feature makes an open-label study equivalent to a blinded study: knowledge of treatment assignment can affect aspects of care, reporting, or assessment, although the primary endpoint was based on HbA1c blood samples.

Per-participant study duration was approximately up to 27 weeks. Following screening, visits were scheduled weekly from randomization through Week 4, every two weeks through Week 12, and then every three weeks through Week 24. The record describes 14 visits, including 7 phone calls and 7 on-site visits during screening and treatment, followed by a safety phone call 3 days after the last dose. The study is recorded as completed. The registry dates are 2022-07-07 for study start and 2023-10-20 for primary completion.

The trial’s original sample-size justification, design assumptions, and any interim analyses are not reported in the registry record. The record identifies the primary analysis population as all randomized participants. Details about how missing endpoint observations were handled and whether other analysis populations were used are not reported in the registry record.

4. Participant Flow and Accounting

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

Before assignment: A total of 582 participants were randomized in a 1:1 ratio to receive either insulin glargine/lixisenatide (iGlarLixi) or insulin degludec and insulin aspart (IDegAsp).

Overall Study

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: Analysis was performed on all randomized participants.

5. Baseline Characteristics

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

Population: Baseline characteristics were assessed for all randomized population.

6. Primary Endpoint

The registry lists the primary outcome as Change in HbA1c From Baseline to Week 24: Non-Inferiority Analysis, measured over Baseline, Week 24.

The primary endpoint was change in HbA1c from baseline to Week 24. HbA1c is a laboratory measure used to assess glycemic status over time; in this trial it served as the continuous outcome for comparing the two assigned treatment groups. The registry record defines baseline as the last available assessment before the first dose, on or before Day 1, and says blood samples were collected at the indicated timepoints for analysis.

The reported group values are least-squares means, rather than simple raw averages. In general, a least-squares mean is a model-based estimate that can account for terms included in the statistical model. The registry identifies ANCOVA for its reported group comparison, but does not report the complete model specification, covariates, or missing-data procedures. The endpoint is expressed as a change in percentage of HbA1c, so negative group estimates indicate a decrease from baseline; the amount of decrease is described by the signed estimate.

The primary result is identified as a non-inferiority analysis. In general, a non-inferiority analysis asks whether the observed difference is compatible with a prespecified margin that defines how much worse a treatment could be while still meeting the study’s criterion. That margin and the protocol’s decision rule are not reported in the registry record. The posted result can therefore be described as the registry’s non-inferiority analysis, but the margin-based criterion cannot be reconstructed from the available record.

7. Results Posted in the Registry

• IDegAsp: mean -1.68 versus -1.88 on control; difference 0.20 (95% CI 0.06 to 0.34), standardized effect d = 0.23; z-test p = 0.005.

8. Secondary Efficacy Endpoints

The registry also posts results for secondary outcomes. For each outcome below, the endpoint type, the test applied to the posted summary statistics, the result and its interpretation are given in turn. Secondary endpoints are usually not adjusted for multiple testing, so they support rather than confirm the primary finding.

Change in HbA1c From Baseline to Week 24: Superiority Analysis

• 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: mean -1.68 versus -1.88 on control; difference 0.20 (95% CI 0.06 to 0.34), standardized effect d = 0.23; z-test p = 0.005.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the mean is statistically higher for IDegAsp than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Change in Body Weight From Baseline to Week 24

• 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: mean 1.19 versus -0.30 on control; difference 1.49 (95% CI 0.74 to 2.24), standardized effect d = 0.32; z-test p = <0.001.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the mean is statistically higher for IDegAsp than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Percentage of Participants Reaching HbA1c <7% at Week 24

• 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: 59.8% (174/291) versus 72.5% (211/291) on control; risk difference -12.7 percentage points (95% CI -20.3 to -5.1), risk ratio 0.82 (0.73–0.93); unadjusted two-proportion z-test p = 0.001.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the proportion with the event is statistically lower for IDegAsp than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

The reported secondary outcomes broadly add information on HbA1c, body weight, and target attainment. The direction of the HbA1c findings is consistent with the primary result: the reported group comparison indicates a larger decrease for iGlarLixi, and the reported target-attainment analysis also favors iGlarLixi. The weight comparison describes a different dimension and does not follow the same pattern of change. Taken together, these outcomes give a broader but not uniform picture across the measures assessed.

Secondary outcomes are related to the primary question but are not substitutes for it. The primary endpoint is the change in HbA1c from baseline to Week 24, and the secondary measures examine other features, such as body weight or reaching a stated HbA1c threshold. A threshold outcome reduces a continuous measurement to whether or not a participant reached a cutoff, which answers a different question from the average change in HbA1c. The other listed secondary outcomes include fasting plasma glucose, self-monitored plasma glucose, and combinations of target attainment with weight gain or hypoglycemia. Their results are not reported in the registry record.

Unadjusted secondary results generally carry less inferential weight than a prespecified primary analysis. In general, testing multiple outcomes creates more opportunities for apparently notable results by chance, especially if no multiplicity adjustment is used. The registry record does not report whether or how multiplicity was controlled, so the strength of the secondary claims cannot be fully assessed from the record. The reported secondary p-values and confidence intervals should therefore be interpreted as outcome-specific results, not as proof that every measure was formally confirmed under a single testing plan.

The relationship between the primary and secondary findings is one of contextual consistency on HbA1c, with additional outcomes capturing different aspects of response. It does not establish that every secondary endpoint moved in the same direction, nor does it make the secondary results independent replications of the primary analysis. Their interpretation depends on their own definitions, analytic methods, uncertainty, and place in the trial’s testing plan.

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

• Change in HbA1c From Baseline to Week 24: Superiority Analysis
• Change in Body Weight From Baseline to Week 24
• Percentage of Participants Reaching HbA1c <7% at Week 24
• Percentage of Participants Reaching HbA1c <7% With no Body Weight Gain at Week 24
• Percentage of Participants Reaching HbA1c <7% With no Body Weight Gain at Week 24 and no Hypoglycemia During 24-Week Treatment Period
• Change in Fasting Plasma Glucose From Baseline to Week 24
• Change in Average 7-Point Self-Monitored Plasma Glucose (SMPG) Profile From Baseline to Week 24
• Percentage of Participants Reaching HbA1c <7% at Week 24 With no Hypoglycemia During 24-Week Treatment Period

9. Statistical Interpretation

10. Statistical Methods Explained

The primary endpoint is continuous: each participant’s HbA1c change is measured on a numeric scale. The registry reports least-squares mean changes for each group and an ANCOVA comparison. ANCOVA, or analysis of covariance, is a regression-based method that compares outcomes between groups while accounting for specified covariates. In general, adjustment can improve precision or account for baseline differences, depending on the model and data. The registry record does not identify the full set of covariates or provide enough detail to reproduce the sponsor’s ANCOVA.

What do the group estimates mean?

The iGlarLixi least-squares mean change was -1.88 and the IDegAsp estimate was -1.68. These are model-based estimates for change from baseline to Week 24. A negative change represents a decrease relative to baseline. The reported standard errors describe the estimated sampling uncertainty for each group mean; they are not standard deviations of individual participants’ outcomes. The standard error for each group estimate was 0.05.

The sponsor’s reported least-squares mean difference is -0.20 for the comparison in the order iGlarLixi minus IDegAsp. A negative value means the estimated change was more negative in the first group. The simple recalculation is expressed in the opposite order, IDegAsp relative to iGlarLixi, and is 0.2. When comparing signed estimates, the group order must be kept in view: reversing the order reverses the sign, not the underlying contrast.

What is a p-value?

In general, a p-value describes how compatible the observed data are with a specified null hypothesis, under the statistical model and its assumptions. A small p-value is evidence against that null hypothesis; it is not the probability that the null hypothesis is true, nor does it measure the size or practical importance of an effect. The registry reports p < 0.001 for the primary non-inferiority analysis, p = 0.003 for the secondary superiority analysis, and the simple unadjusted recalculation gives p = 0.005. These values come from analyses with different specifications and should not be treated as interchangeable.

The non-inferiority and superiority labels refer to different inferential questions. In general, non-inferiority evaluates a treatment difference against a prespecified margin, whereas superiority evaluates whether the data support a difference in a specified direction relative to no difference. The margin and the full testing procedure are not reported in the registry record. The sponsor’s primary analysis is explicitly labelled non-inferiority; the secondary HbA1c analysis is labelled superiority. The inferential label alone does not supply the missing protocol details.

What does the recalculation add?

The recalculated comparison is a simple, unadjusted estimate based on the posted group summaries. It is useful as a check on the direction and approximate scale of the difference, but it does not recreate ANCOVA. The recalculated standard deviation is described as approximate, and the recalculated interval and p-value do not incorporate the sponsor model’s covariate adjustment. General statistical practice treats a summary-statistic recalculation as a supplementary view, not as a substitute for the prespecified analysis.

The posted recalculation gives a mean difference of 0.2 and an unadjusted p-value of 0.005. Its sign is positive because it compares IDegAsp with iGlarLixi, opposite to the sponsor-reported group order. The recalculation also supplies planning illustrations based on the observed effect. Those illustrations are not evidence about the trial’s original design or the probability that a result would be statistically significant under an alternative sample size.

Why does the analysis population matter?

The registry says the primary analysis was performed on all randomized participants. In general, an analysis that follows randomized assignment helps preserve the balance created by randomization, but interpretation also depends on outcome availability and the specified handling of missing data. The missing-data method and the number of participants with endpoint observations are not reported in the registry record. The number assigned to each group in the posted outcome summaries is 291, but those numbers alone do not establish how every randomized participant’s missing observations were handled.

More generally, statistical conclusions depend on the design, outcome definition, model specification, and assumptions about the data. The registry provides the endpoint, group estimates, ANCOVA method, and selected inferential results, but it does not report the full model details, non-inferiority margin, missing-data approach, or multiplicity procedure. Those omissions limit independent verification of the complete analysis plan.

11. Confidence Intervals and Precision

A confidence interval gives a range of estimates compatible with the observed data under the analysis and its assumptions. In general, it reflects sampling uncertainty; it is not a range expected to contain every individual participant’s outcome, nor is it a probability statement about a fixed parameter after the data have been observed. A narrower interval indicates greater statistical precision than a wider interval, but precision does not establish that an effect is clinically important.

For the primary non-inferiority analysis, the sponsor reports a least-squares mean difference of -0.20 with a 95% confidence interval from -0.33 to -0.07. The secondary superiority analysis reports -0.20 with a 97.5% confidence interval from -0.35 to -0.05. These are model-based intervals from the reported ANCOVA analyses. The confidence levels differ, and each interval belongs to its own analysis; they should not be interpreted as identical estimates with identical procedures.

The simple recalculation reports a mean difference of 0.2, with an interval that lies above zero. That recalculation uses the opposite group order and an unadjusted method, so its interval is not expected to reproduce the ANCOVA interval exactly. The group estimates are accompanied by standard errors of 0.05, which quantify uncertainty in the estimated group means rather than spread among individual outcomes.

The confidence intervals help describe how precisely the group contrast was estimated in this study. They do not resolve whether the difference is clinically meaningful, because a clinical-importance threshold is not reported in the registry record. For the non-inferiority claim in particular, the margin required to interpret the interval against the trial’s criterion is also not reported. The intervals therefore support a description of statistical uncertainty but do not provide all information needed to audit the protocol-level decision.

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 give participants per arm for the continuous HbA1c comparison in this trial; they are not total-event counts. The general description of the planning method specifies a two-sided significance level of 0.05, equal allocation between two arms, a normal approximation, and an effect to detect set to a stated fraction of the observed effect. The trial’s original design assumptions and sample-size justification are not reported in the registry record. These illustrations are not observed power.

For the scenario using 100% of the observed effect, the illustration gives 286 participants per arm for 80% power and 383 participants per arm for 90% power. For 75% of the observed effect, it gives 508 participants per arm for 80% power and 680 participants per arm for 90% power. For 50% of the observed effect, it gives 1,143 participants per arm for 80% power and 1,529 participants per arm for 90% power.

In general, a power target describes the chance of detecting a specified effect under the assumptions of a planning calculation, if that effect and the assumptions are correct. The 80% column means the planning calculation targets an 80% chance of detecting the stated effect; the 90% column targets a 90% chance. Neither figure describes the probability that the observed result is true or the power achieved by this completed trial. The illustrations show the expected planning tradeoff: when the effect to detect is smaller, more participants are needed to reach the same power target.

The displayed scenarios use fractions of the observed effect as hypothetical planning inputs. They should not be read as the original protocol’s targeted effect, nor as evidence that the trial was designed with these assumptions. The sample sizes in the illustration are per arm, whereas the registry reports enrollment of 582 and 291 participants in each posted outcome group.

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 = 0.200; pooled standard deviation σ = 0.853.
2. n = 2 × (1.96 + 0.842)² × 0.853² / 0.200² = 285.5, rounded up to 286 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 <- 0.2                      # observed difference in means
sigma <- 0.852936105461599                     # 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(5413369)
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 = 286 (80% power), 383 (90% power)
 75% of observed effect: n per arm = 508 (80% power), 680 (90% power)
 50% of observed effect: n per arm = 1,143 (80% power), 1,529 (90% power)
power.t.test: 286.5 
Simulated power at n = 286 per arm: 0.798 (20000 replicates)

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

14. Analyses Reported by the Sponsor

The registry reports the sponsor’s primary ANCOVA analysis as a least-squares mean difference of -0.20, with a 95% confidence interval from -0.33 to -0.07 and p < 0.001. It labels this analysis non-inferiority. The sponsor also reports a secondary superiority analysis of the HbA1c change, with a least-squares mean difference of -0.20, a 97.5% confidence interval from -0.35 to -0.05, and p = 0.003.

The recalculation is a simple unadjusted comparison of the posted group summaries. It gives a difference of 0.2 because it expresses IDegAsp relative to iGlarLixi, the reverse of the sponsor-reported order. The sign and interval endpoints therefore appear different in form, while the direction of the underlying group contrast is consistent. The recalculated p-value is 0.005, which is not the same as either sponsor-reported p-value because the recalculation does not reproduce the ANCOVA analysis.

The sponsor’s estimates should be used to describe the registry’s reported trial analyses. The recalculation is supplementary and does not override the adjusted analysis. The discrepancy flag is false, but the methods are still distinct: an unadjusted summary-statistic calculation cannot serve as an exact reproduction of the sponsor’s model. The non-inferiority margin and full model specification are not reported in the registry record, so the numerical result can be quoted while the protocol-level basis for the non-inferiority conclusion remains unauditable from that record.

15. Multiplicity, Missing Data and Analysis Populations

The registry lists a primary HbA1c non-inferiority analysis, a secondary HbA1c superiority analysis, and a range of additional secondary outcomes. Multiplicity arises when several hypotheses or outcomes are tested: even if each test uses a conventional threshold, the chance of at least one apparently positive result can increase across a collection of tests. General approaches include prespecifying a hierarchy, adjusting significance thresholds, or treating secondary analyses as exploratory. The registry record does not report a multiplicity-control procedure or a hierarchy governing the listed outcomes.

The registry reports confidence levels and p-values for selected analyses, but those figures alone do not show whether adjustment for multiple testing was applied. The non-inferiority primary result and the superiority secondary result address different questions, and the record does not provide the full statistical analysis plan explaining how these analyses were ordered or interpreted together. This limits assessment of the formal confirmatory status of the secondary findings.

Missing-data handling is also not reported in the registry record. The primary analysis population is described as all randomized participants, but that statement does not specify how missing Week 24 HbA1c measurements were handled, whether any imputation was used, or what assumptions were made about outcomes not observed. The reported group sizes in the outcome summaries do not by themselves answer those questions.

The record does not report the non-inferiority margin, the complete ANCOVA model specification, details of covariate selection, or interim analyses. The sample-size justification for the original trial is likewise not reported in the registry record. These gaps do not invalidate the posted estimates, but they limit independent reconstruction of the analysis and evaluation of whether every inferential decision followed a prespecified plan. The conclusions should be read as descriptions of the registered results, with those aspects of the design and analysis remaining unresolved.

16. Safety Results

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

Deaths

Other (non-serious) adverse events

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

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: cerebral infarction (0.7%), bronchopulmonary aspergillosis (0.3%), pneumonia (0.3%), ischaemic cerebral infarction (0.3%), vertebrobasilar insufficiency (0.3%).

The registry reports serious adverse events among 290 participants at risk in the iGlarLixi group and 291 at risk in the IDegAsp group. Serious adverse events affected 15 participants in the iGlarLixi group and 12 in the IDegAsp group. The record lists 0 deaths in each group. These are participant counts from the safety results; they should not be confused with the total number of event episodes, which is not specified in the summarized figures.

The registry also reports other adverse events affecting 152 of 290 participants at risk in the iGlarLixi group and 101 of 291 in the IDegAsp group. The term-specific serious-event list includes Cerebral Infarction at 0.7%, and Bronchopulmonary Aspergillosis, Pneumonia, Ischaemic Cerebral Infarction, Vertebrobasilar Insufficiency, and Cataract each at 0.3%. The record does not assign those term-specific percentages to a treatment group in the reported list.

In general, adverse-event counts describe events observed during a defined study period; they do not by themselves establish that a treatment caused an event or explain its severity, timing, or clinical course. The registry record does not provide detail about event definitions, exposure-adjusted rates, or individual event narratives. The 24-week follow-up and the reported counts should therefore be interpreted as the safety information posted for this trial, not as a complete characterization of longer-term safety.

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

The results describe a randomized comparison of two active regimens over 24 weeks in Chinese adults with type 2 diabetes insufficiently controlled with oral antidiabetic drug(s). In the posted analysis, both groups had decreases in estimated HbA1c from baseline. The sponsor’s ANCOVA results report a difference between the groups, with a negative estimate in the iGlarLixi-minus-IDegAsp order. The secondary HbA1c and target-attainment results provide related evidence, while body weight addresses a separate outcome.

These findings do not establish the consequences of treatment beyond the 24-week period. They do not answer questions about long-term outcomes or populations not represented by the stated eligibility and setting. Nor do they show that every participant experienced the group-average estimate: least-squares means describe estimated group-level outcomes, not individual responses.

The registry labels its primary analysis non-inferiority, but the non-inferiority margin and full decision rule are not reported in the registry record. The result can be identified as the sponsor’s posted non-inferiority analysis, but the margin-dependent criterion cannot be independently evaluated from the registry alone. The record also does not report a clinically meaningful difference threshold for HbA1c, so statistical evidence of a group contrast should not be equated with a judgment about clinical importance.

The reported p-values and confidence intervals describe statistical uncertainty under their respective analyses. They do not prove that an effect is certain, measure the probability that a treatment will produce a particular outcome for an individual, or establish causality for adverse events. The open-label design, missing-data details, incomplete analysis-plan information, and unreported multiplicity procedures further define the limits of what can be concluded from the posted record. Overall, the results are best read as evidence about the specified measures, groups, and follow-up period, rather than as a universal claim about treatment effects.

18. Why This Trial Matters Statistically

This trial is statistically informative because it sets out a randomized, active-controlled comparison and reports both a primary non-inferiority analysis and a secondary superiority analysis for the same continuous HbA1c endpoint. Reading the estimate alongside its confidence interval and p-value helps distinguish the direction of the comparison, its uncertainty, and the inferential question being asked. The need to track the subtraction order is also clear: the sponsor reports iGlarLixi relative to IDegAsp, while the simple recalculation uses the reverse order.

The record illustrates why adjusted trial analyses and summary-statistic recalculations should not be conflated. The posted ANCOVA estimates are model-based, while the recalculation is unadjusted. They provide compatible descriptions of the direction of the group contrast, but the recalculated p-value and uncertainty do not reproduce the sponsor’s model. For researchers, that distinction is important when auditing registry results or using posted summaries for exploratory planning.

The record also highlights the importance of protocol information. A non-inferiority result cannot be fully assessed without the margin and decision rule, and multiple secondary outcomes cannot be given a confirmatory interpretation without knowing the testing plan. Missing-data methods and model details matter for reproducibility as well. Their absence does not erase the posted estimates, but it limits independent verification of the complete statistical argument.

Finally, the trial combines a continuous primary outcome with secondary measures that address thresholds, body weight, and other glucose measures. This illustrates how related endpoints can provide complementary context while answering distinct questions. A coherent interpretation must preserve those distinctions, account for uncertainty, and avoid extending a 24-week result beyond the study population and follow-up that were recorded.

19. Limitations

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

The registry record does not report the non-inferiority margin, so the central protocol criterion for interpreting the primary non-inferiority analysis cannot be checked. It also does not provide the full ANCOVA specification, including all covariates and model details. The sponsor’s estimates can be described, but the complete adjusted analysis cannot be independently reproduced from the record.

The study was open-label, with no masking. Randomization supports a structured group comparison, but open-label assignment may affect aspects of care or reporting that are not captured solely by the HbA1c laboratory measure. The record does not report how missing Week 24 measurements were handled, and it does not state whether sensitivity analyses were performed. The primary analysis population is described as all randomized participants, but that alone does not establish the missing-data assumptions or the exact set of observations used in each analysis.

The registry lists multiple secondary outcomes, but a multiplicity procedure or testing hierarchy is not reported. This limits how strongly secondary p-values can be treated as confirmatory. Results for several listed outcomes are also not reported in the registry record. The original sample-size justification and design assumptions are not reported, so the separate planning illustrations should not be mistaken for the trial’s original design rationale.

The reported treatment period was 24 weeks, with a safety follow-up phone call 3 days after the last dose. The record therefore supports interpretation of outcomes over the stated follow-up, not longer-term effects. The participant population is described as Chinese adults at least 18 years of age with type 2 diabetes insufficiently controlled with oral antidiabetic drug(s); extrapolation beyond that population is not established by this record. Adverse-event summaries provide counts and selected terms but do not provide detailed event narratives or exposure-adjusted rates in the reported information.

20. Related Tutorials

21. Related Statistical Calculators

22. Sources

• ClinicalTrials.gov: iGlarLixi vs IDegAsp in Chinese Participants After OAD(s) — NCT05413369.
• Registry record last updated 2025-09-24; status last verified 2025-09.
• Results first posted 2024-10-30.
• Data retrieved from ClinicalTrials.gov on 2026-11-09.
• Group sizes, means, event counts and sponsor-reported p-values are reproduced as posted. Effect estimates, confidence intervals and p-values not attributed to the sponsor are independently calculated from the posted summary statistics.

23. Record Summary

NCT05413369 is a completed, randomized, Phase 3, open-label, parallel-group study comparing iGlarLixi with IDegAsp in Chinese adults with type 2 diabetes insufficiently controlled with oral antidiabetic drug(s). The registry reports 582 participants enrolled and 24 weeks of treatment, with metformin and optional SGLT-2 inhibitor as background therapy.

The primary endpoint was change in HbA1c from baseline to Week 24, analyzed in all randomized participants. The sponsor reports an ANCOVA non-inferiority result with a least-squares mean difference of -0.20, a 95% confidence interval from -0.33 to -0.07, and p < 0.001. A secondary superiority analysis reports a difference of -0.20, a 97.5% confidence interval from -0.35 to -0.05, and p = 0.003. The non-inferiority margin, multiplicity procedure, detailed model specification, and missing-data method are not reported in the registry record.

Secondary results address HbA1c, body weight, and target attainment; safety results include serious and other adverse-event counts. Interpretation is limited to the posted measures and the 24-week treatment period.