Gan & Lee Insulin Glargine Target Type (1) Evaluating Research: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Gan & Lee Insulin Glargine Target Type (1) Evaluating Research (NCT03371082), a randomized phase 3 study. The primary outcome is Treatment-induced Anti-Insulin Antibody (TI-AIA).

2. Clinical Question

The clinical question concerns whether the two insulin glargine products had comparable immunogenicity during the study period. Immunogenicity refers to an immune response against a therapeutic product; in this trial, the primary measure was a composite of newly confirmed positive anti-insulin antibodies or an important increase in antibody titer. The registry also lists safety and efficacy as secondary objectives. The primary outcome is therefore about an antibody response, not a direct measure of glucose control or a broad assessment of clinical benefit.

3. Trial Design

This was a randomized, open-label, multicenter, parallel-group phase 3 study. The registry lists enrollment as 576 and the trial status as completed. Masking was listed as none, so the study was open-label. Participants were assigned to Gan & Lee Insulin Glargine Injection or Lantus®. Both were described as 100 U/mL products for subcutaneous injection in pre-filled pens, and participation was planned for 26 weeks.

The registry identifies adults with type 1 diabetes mellitus as the study population. The primary outcome was assessed in the Safety Analysis Set, defined as randomly assigned participants who received any amount of study treatment and had non-missing values. That definition is narrower than simply describing all randomized participants: receipt of treatment and availability of outcome values were part of the stated analysis population. Details on the number of participants excluded from any analysis, and reasons for missing values, are not reported in the registry record.

The primary objective was to evaluate equivalence between the products in immunogenicity. Secondary objectives covered additional anti-insulin antibody measures, safety, and efficacy. Glycosylated hemoglobin, antibody outcomes, fasting blood glucose control, and HbA1c control are listed among the secondary outcomes. The registry record does not report the original sample-size justification, the detailed randomization procedure, or the planned statistical analysis method for the sponsor’s equivalence analysis. The study dates are listed in the registry record, but the key design features for interpreting the posted results are randomization, open-label treatment, parallel groups, and follow-up through Week 26.

4. Participant Flow and Accounting

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

Overall Study

Analysis population for the primary outcome, as defined in the registry: The Safety Analysis Set (SS) was comprised of all subjects whose treatment assignment was randomly assigned who received any of the study treatment, even a partial dose, and had non-missing values.

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: All Subjects Assigned randomly to treatment (full analysis set).

6. Primary Endpoint

The registry lists the primary outcome as Treatment-induced Anti-Insulin Antibody (TI-AIA), measured over Assessed up to Week 26.

The primary endpoint was treatment-induced anti-insulin antibody (TI-AIA), measured in titers and assessed up to Week 26. The registry defines TI-AIA as a composite of newly confirmed positive anti-insulin antibody results or an important increase in antibody titer. Thus, the outcome combines two ways a participant’s antibody response could meet the endpoint definition rather than measuring only a change in average titer.

Immunogenicity is relevant when comparing biological products because the immune system may form antibodies in response to a therapeutic product. The trial’s primary objective was to assess whether the two insulin glargine products were equivalent on this immunogenicity outcome. That objective explains why the endpoint focuses on antibody status and titers. It does not, by itself, measure glucose control, symptoms, or overall clinical benefit.

The posted group summaries report mean titers and standard deviations. The Gan & Lee group mean was 4.8 with a standard deviation of 88.88; the Lantus® group mean was 42.5 with a standard deviation of 332.9. These summaries describe group averages and variation, while the composite endpoint definition determines which antibody responses count as treatment-induced. The primary population was the Safety Analysis Set, which included randomly assigned participants who received any study treatment and had non-missing values. The registry record does not report further detail on how missing primary endpoint values were handled.

7. Results Posted in the Registry

• Lantus®: mean 42.50 versus 4.80 on control; difference 37.70 (95% CI -2.03 to 77.43), standardized effect d = 0.15; z-test p = 0.063.

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.

Glycosylated Hemoglobin HbA1c

• 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:
  • Lantus®: mean 0.00 versus -0.08 on control; difference 0.08 (95% CI -0.10 to 0.26), standardized effect d = 0.07; z-test p = 0.397.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Lantus® and the control. The interval gives the range of effects that remain compatible with the data.

Number of Subjects in Each Treatment Group With Negative AIA at Baseline Who Develop Confirmed Positive AIA After Baseline

• 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:
  • Lantus®: 24.7% (59/239) versus 26.7% (63/236) on control; risk difference -2.0 percentage points (95% CI -9.9 to 5.8), risk ratio 0.92 (0.68–1.26); unadjusted two-proportion z-test p = 0.616.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Lantus® and the control. The interval gives the range of effects that remain compatible with the data.

Number of Subjects in Each Treatment Group With Confirmed Positive AIA at Baseline and at Least a 4-fold Increase in Titers After Baseline.

• 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:
  • Lantus®: 29.2% (14/48) versus 22.4% (11/49) on control; risk difference 6.7 percentage points (95% CI -10.7 to 24.1), risk ratio 1.30 (0.66–2.57); unadjusted two-proportion z-test p = 0.449.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Lantus® and the control. The interval gives the range of effects that remain compatible with the data.

The registry lists secondary outcomes covering HbA1c, several anti-insulin antibody measures, fasting blood glucose control, and HbA1c control. Together, these outcomes broaden the assessment beyond the primary composite antibody endpoint: some concern glucose-related measures, while others describe antibody status, titer changes, or neutralizing antibodies. The detailed numerical comparisons include a continuous HbA1c measure and antibody outcomes expressed as participant counts and risk differences.

Across the recalculated secondary comparisons, the confidence intervals include zero, and the unadjusted p-values do not provide a clear signal of a group difference. This overall pattern does not establish that the groups are identical. The intervals retain uncertainty, and the secondary measures represent distinct questions on different scales. They should be interpreted as supporting context for the primary immunogenicity result, not as substitutes for the sponsor’s equivalence analysis.

Unadjusted secondary results carry less weight when several outcomes are examined because the chance of finding at least one apparently unusual result can increase with the number of comparisons. This is a general statistical concern, not a claim about a particular result in this trial. The registry record does not report a multiplicity adjustment or a hierarchy specifying how secondary outcomes were to be interpreted. Consequently, individual secondary p-values should be treated as exploratory evidence rather than as independent confirmation of a treatment difference or equivalence.

The secondary outcomes also do not all have the same analysis population or interpretation stated in the registry entry. For example, antibody counts are based on participants with specified baseline antibody status, whereas the primary endpoint is defined in the Safety Analysis Set. Comparisons across outcomes therefore need to respect each outcome’s denominator and definition. Taken together, the secondary results add context about antibody and glycemic measures but do not resolve the uncertainty around the primary equivalence question.

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

• Glycosylated Hemoglobin HbA1c
• Number of Subjects in Each Treatment Group With Negative AIA at Baseline Who Develop Confirmed Positive AIA After Baseline
• Number of Subjects in Each Treatment Group With Confirmed Positive AIA at Baseline and at Least a 4-fold Increase in Titers After Baseline.
• Mean Change From Baseline in Each Treatment Group in AIA Titers After Baseline
• Number of Subjects With Confirmed Positive AIA After Baseline Who Develop Any Anti-insulin Neutralizing Antibodies After Baseline.
• Number of Subjects With Confirmed Positive AIA After Baseline.
• Postbaseline FBG Control
• HbA1c Control.

9. Statistical Interpretation

10. Statistical Methods Explained

Several statistical ideas help distinguish what the posted results can and cannot establish. The primary outcome is a continuous measure summarized by a group mean and standard deviation. The mean describes the average observed titer in a group. The standard deviation describes how dispersed individual measurements are around that average. A mean difference compares the two group averages; in the recalculation, the direction is Lantus® minus Gan & Lee Insulin Glargine Injection. A positive estimate therefore means the Lantus® group mean was higher in the posted summaries.

What does a confidence interval express?

A confidence interval describes the uncertainty around an estimated effect under the assumptions of the analysis. A narrower interval indicates greater precision, while a wider interval leaves more uncertainty about the size and direction of the effect. For the recalculated primary mean difference, the 95% interval extends from -2.034 to 77.43. Because it spans zero, the posted summary-statistic calculation is compatible with no mean difference as well as with differences in either direction. It is also compatible with a range of nonzero differences, so the estimate should not be reduced to a simple yes-or-no conclusion.

A confidence interval does not give the probability that the true effect lies within that particular interval. It is a range produced by a method that, under its assumptions, has a specified long-run coverage property. The registry record does not describe the method used for the sponsor’s equivalence interval. Its 90% interval should therefore be reported as posted, without assuming how it was constructed or how it relates to the recalculated 95% interval.

Why distinguish a difference test from an equivalence analysis?

A conventional difference analysis asks whether the data are sufficiently inconsistent with a zero difference under a specified model. Equivalence asks a different question: whether the estimated difference is sufficiently constrained to fall within a prespecified range considered acceptably small. The equivalence margin is essential to interpreting that question. A confidence interval can be compared with the margin only when the margin and the relevant analysis details are known. The registry record labels the sponsor analysis as equivalence, but does not report its margin or method. The recalculated p-value of 0.063 is for an unadjusted mean comparison and is not an equivalence test.

Why can over-dispersion matter?

As a general statistical point, large variation among individual measurements can make a group mean less precise, particularly when the number of observations is limited. It can also make a mean sensitive to relatively extreme observations. The posted standard deviations, 88.88 for Gan & Lee and 332.9 for Lantus®, are large compared with the respective means of 4.8 and 42.5. This makes it important to avoid treating the averages as a complete description of the antibody response. The registry record does not provide a distribution plot, median, or other summary that would establish the shape of the individual titers.

For binary secondary outcomes, a risk difference expresses the difference between the proportions meeting an outcome definition in the two groups. A confidence interval around that difference describes uncertainty in the estimated contrast. These outcomes should not be conflated with the primary continuous mean-titer measure: they count participants meeting specified antibody criteria, whereas the primary result summarizes titers using means. The registry also lists HbA1c as a secondary continuous outcome, summarized by least-squares means and standard errors. Least-squares means are model-based averages; however, the model details are not reported in the registry record, so further assumptions about adjustment should not be made.

The recalculations are described as simple, unadjusted calculations from posted summary statistics. They do not reproduce an unknown covariate-adjusted or otherwise prespecified sponsor model. The sample-size planning illustrations use the observed effect as a starting point and apply stated planning assumptions. They do not establish what the study was originally designed to detect. The original design assumptions, including the sponsor’s sample-size justification, are not reported in the registry record.

Finally, statistical significance and clinical meaning are distinct concepts. A p-value is not the size of an effect, and it does not convey the full uncertainty captured by an interval. Likewise, an interval’s width does not determine whether a difference matters clinically. Clinical interpretation would require a justified threshold for meaningful antibody differences and the relevant outcome context; such a threshold is not reported in the registry record. The appropriate reading is therefore descriptive and bounded: identify the endpoint, population, estimate, uncertainty, and analysis type, then avoid claims that exceed those details.

11. Confidence Intervals and Precision

The recalculated primary mean difference was 37.7, with a 95% confidence interval from -2.034 to 77.43. This interval is broad and includes zero. It therefore leaves uncertainty about whether the average titers differ and, if they do, about the size and direction of the difference. The lower end is slightly below zero, while the upper end permits a substantially higher mean in the Lantus® group under the subtraction order used. The point estimate alone should not be read as a precise measure of the underlying difference.

The large standard deviations in both groups are relevant to precision. The posted standard deviations were 88.88 and 332.9, compared with group means of 4.8 and 42.5. Those summaries indicate substantial variability around the averages. The confidence interval reflects uncertainty in the recalculated mean difference; it does not describe the range of individual titers, and it does not show how antibody responses were distributed within either group.

The sponsor’s posted equivalence result is a separate estimate: a risk difference of 0.6 with a 90% confidence interval from -5.4 to 6.5. The registry labels this an equivalence analysis, but does not report the equivalence margin or the analysis method. Without the margin, it is not possible to determine from the interval alone whether it meets the sponsor’s criterion. Its 90% confidence level also differs from the recalculated primary mean-difference interval, so the two intervals are not interchangeable measures of the same analysis.

For the secondary outcomes, the recalculated intervals also leave uncertainty around the comparisons. Their interpretation should account for the different outcome scales and definitions, as well as the fact that the registry does not describe a multiplicity adjustment. Overall, the posted estimates are more informative when read with their intervals than when reduced to a single p-value or point estimate.

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 sample-size illustrations translate an observed effect into hypothetical future-study planning scenarios. The listed figures for this trial’s continuous and binary outcomes are participants per arm, not total participants or total events. The general convention in the planning assumptions is that time-to-event calculations give total events; that convention does not apply to the outcomes presented for this trial.

The stated planning assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. Each scenario targets an effect that is the stated fraction of the observed effect. For example, planning for 75% of the observed effect means designing to detect an effect smaller than the one estimated from the posted trial results. The original trial’s own sample-size assumptions and justification are not reported in the registry record.

The 80% power column means the hypothetical design would have an 80% chance of detecting the specified effect under the planning assumptions, if that effect were the true effect and the assumptions held. The 90% power column represents a higher chance under the same type of assumptions. Greater power generally requires more participants when other assumptions are held constant. The illustrations show the same pattern across effect scenarios: smaller effects need more participants to detect reliably than larger effects.

These are planning illustrations based on observed effects, not observed power for the completed trial. They do not tell whether the original study was adequately powered, because the original design assumptions and target effect are not reported. Estimates based on an observed effect are also uncertain and should not be treated as a promise that a future study will achieve the stated power.

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 = 37.700; pooled standard deviation σ = 244.009.
2. n = 2 × (1.96 + 0.842)² × 244.009² / 37.700² = 657.6, rounded up to 658 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 <- 37.7                      # observed difference in means
sigma <- 244.00889356043                     # 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(3371082)
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 = 658 (80% power), 881 (90% power)
 75% of observed effect: n per arm = 1,170 (80% power), 1,566 (90% power)
 50% of observed effect: n per arm = 2,631 (80% power), 3,522 (90% power)
power.t.test: 658.6 
Simulated power at n = 658 per arm: 0.804 (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 analysis as an equivalence analysis with a risk difference of 0.6 and a 90% confidence interval from -5.4 to 6.5. The equivalence margin, statistical method, and any associated p-value are not reported in the registry record. Without the margin, the interval cannot be judged against the criterion needed to establish equivalence.

The recalculation uses posted group means and standard deviations to estimate an unadjusted mean difference of 37.7, with a 95% confidence interval from -2.034 to 77.43 and an unadjusted p-value of 0.063. This is a different statistic and analysis from the sponsor’s reported risk-difference equivalence result. It is useful as a transparent summary-statistic comparison, but it does not reproduce or replace the sponsor’s analysis.

The two results should not be treated as conflicting estimates of an identical quantity: one is labeled a risk difference, while the recalculation compares means of titers. The registry record does not give enough detail to reconcile their scales or methods. The sponsor’s own reported equivalence analysis remains the relevant analysis for the stated equivalence objective, but its conclusion cannot be independently assessed from the registry entry without the missing margin and method details.

15. Multiplicity, Missing Data and Analysis Populations

The registry record lists multiple secondary outcomes in addition to the primary endpoint. When many outcomes are tested, there is a general risk that some results may appear notable by chance, even when there is no corresponding underlying difference. A prespecified testing hierarchy or multiplicity adjustment can help control how such results are interpreted. The registry record does not report whether a multiplicity strategy was used, how outcomes were prioritized, or whether any secondary tests were intended to be confirmatory.

Missing-data handling is also not reported in sufficient detail to determine how unavailable outcome values affected the analyses. The primary Safety Analysis Set required randomly assigned participants to have received some study treatment and to have non-missing values. That definition identifies who was eligible for the stated primary set, but it does not explain how missing values arose, how many were missing for each outcome, or whether any imputation or sensitivity analysis was used. The registry record does not report those details.

The primary analysis population is not described as an all-randomized population. Since randomization supports comparability at assignment, exclusions based on treatment receipt or missing outcome values can affect how closely an analysis retains that benefit. The posted definition is important for interpreting the estimate, but the registry entry does not provide enough information to assess the impact of exclusions. The secondary analysis populations and their missing-data methods are not reported in the registry record in comparable detail.

The registry also does not report the original sample-size justification or a full statistical analysis plan. Those materials would clarify the planned estimand, handling of protocol deviations, missing-data rules, and any prespecified tests. Their absence from the registry record limits independent assessment of whether all analyses followed the original plan. These are reporting limitations; they do not by themselves show that the analyses were inappropriate.

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 0.3% reporting threshold.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: hypoglycemia (1.0%), acute kidney injury (0.7%), depression (0.7%), benign neoplasm of spinal cord (0.3%), carotid artery occlusion (0.3%).

The posted serious adverse event counts were 10 among 287 participants at risk in the Gan & Lee group and 14 among 289 at risk in the Lantus® group. The registry also lists other adverse events for 160 of 287 and 161 of 289 participants, respectively. These categories should be kept distinct: the first concerns serious adverse events, while the second is the posted count for other adverse events. The registry reports 0 deaths in the Gan & Lee group and 1 in the Lantus® group.

The listed serious adverse event terms include Hypoglycemia at 1.0%, Acute kidney injury at 0.7%, Depression at 0.7%, Benign neoplasm of spinal cord at 0.3%, Carotid artery occlusion at 0.3%, and Cellulitis at 0.3%. The registry entry does not assign these term percentages to a particular treatment group, so they should not be attributed to either arm.

These figures are descriptive counts from the study, not estimates of causation. The registry record does not provide enough detail to compare event timing, duration, severity beyond the serious-event classification, or the relationship of each event to treatment. Differences in counts alone do not establish that one product caused more or fewer events.

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

The results describe a randomized comparison of two insulin glargine products in adults with type 1 diabetes, focusing primarily on treatment-induced anti-insulin antibody response assessed through Week 26. The recalculated mean-titer contrast is uncertain: its confidence interval includes zero and extends across a broad range. The sponsor’s result is labeled as an equivalence analysis, but the registry entry does not report the margin and method needed to judge that analysis independently.

These findings do not establish that the products are identical. Failure of a recalculated difference analysis to show a clear difference is not the same as evidence of equivalence. Equivalence requires a defined acceptable range and an analysis that demonstrates the estimate is sufficiently precise relative to that range. Those details are not reported in the registry record. Nor does the recalculated mean comparison replace the sponsor’s analysis, because it uses a different reported statistic.

The results also do not establish that the products differ in a clinically important way. The posted mean difference and its interval concern the antibody titer summary, not a patient-centered clinical outcome. Secondary outcomes include HbA1c and antibody measures, but their unadjusted comparisons do not erase the limitations in the primary analysis or establish broader conclusions. Safety counts are descriptive and cannot by themselves attribute events to treatment.

The inference is bounded by the study population, analysis set, endpoint definitions, and follow-up through Week 26. Open-label assignment, substantial variability in the primary summaries, and unreported details about missing data and the equivalence procedure limit what can be concluded from the registry record alone. The findings should be read as evidence about the measured outcomes in this study, not as a comprehensive answer about longer-term outcomes, other populations, or all aspects of treatment.

18. Why This Trial Matters Statistically

This trial illustrates why the statistical meaning of an endpoint must match the question being asked. Its primary objective was equivalence in immunogenicity, yet the posted summaries include both a mean-titer comparison recalculated from group statistics and a sponsor analysis labeled as a risk-difference equivalence test. Those are not interchangeable quantities. Readers need the estimand, scale, equivalence margin, and method to judge whether an equivalence claim is supported.

It also shows why a point estimate should be read with its uncertainty. The recalculated mean difference is 37.7, but the 95% confidence interval extends from -2.034 to 77.43. That breadth leaves room for materially different interpretations of the underlying contrast. The standard deviations are large relative to the means, underscoring that a group average does not capture the full variation in antibody titers.

Finally, the registry entry provides a useful reminder that analysis populations, missing values, multiplicity, and planning assumptions are part of the evidence, not administrative details. The primary set is defined, but the missing-data approach, multiplicity plan, original sample-size justification, and key elements of the equivalence analysis are not reported. These omissions constrain independent evaluation even when numerical results are available. Statistical interpretation is strongest when estimates, uncertainty, outcome definitions, and analysis plans can all be considered together.

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.

Several limitations are specific to interpreting this trial from the registry record. First, treatment was open-label, with masking listed as none. The record does not describe whether any outcome assessment procedures were blinded. Because the primary outcome is based on antibody measurements, the open-label design should be noted, while avoiding an assumption that it necessarily changed the measurements.

Second, the primary endpoint is a composite of newly confirmed positive anti-insulin antibodies or an important increase in titer. The registry defines the composite but does not provide the operational threshold for an important increase or further assay details. Without those details, the precise construction and reproducibility of the endpoint cannot be fully evaluated from the record.

Third, the primary summaries show large standard deviations relative to the group means. The registry entry does not provide additional distributional summaries that would clarify the shape of the titers or the influence of unusual values. The mean comparison may therefore be an incomplete description of the observed antibody responses.

Fourth, the sponsor’s equivalence analysis is reported without its margin or method. The recalculated summary-statistic comparison does not resolve this gap because it uses a different statistic. The record also does not report the original sample-size justification, the detailed analysis plan, missing-data handling, or a multiplicity approach for secondary outcomes.

Finally, the primary assessment was through Week 26. The record does not establish how these antibody findings relate to longer follow-up or outcomes outside those measured. These points limit interpretation of the registry results; they do not establish that the trial itself was conducted improperly.

20. Related Tutorials

21. Related Statistical Calculators

22. Sources

• ClinicalTrials.gov: Gan & Lee Insulin Glargine Target Type (1) Evaluating Research — NCT03371082.
• Registry record last updated 2024-05-14; status last verified 2024-04.
• Results first posted 2024-05-14.
• 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

The registry describes a completed randomized, open-label, multicenter, parallel-group phase 3 trial in adults with type 1 diabetes. Enrollment was 576, and the study compared Gan & Lee Insulin Glargine Injection with Lantus® over 26 weeks. Its primary objective was equivalence in immunogenicity, measured by treatment-induced anti-insulin antibody titers.

The recalculated primary mean difference was 37.7, with a 95% confidence interval from -2.034 to 77.43 and an unadjusted p-value of 0.063. The sponsor’s posted result was labeled as an equivalence analysis and gave a risk difference of 0.6 with a 90% confidence interval from -5.4 to 6.5. The equivalence margin and method are not reported in the registry record, limiting independent interpretation of that result.

Secondary outcomes covered antibody measures and glycemic outcomes. Safety counts are descriptive. Missing-data methods, multiplicity handling, the original sample-size justification, and several details of the statistical plan are not reported in the registry record.