A Study to Compare the Effect of a Double Dose of Two Long-acting Insulin Therapies in Participants With Type 2 Diabetes: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for A Study to Compare the Effect of a Double Dose of Two Long-acting Insulin Therapies in Participants With Type 2 Diabetes (NCT02132637), a randomized phase 3 study. The primary outcome is Percentage of Participants With Clinically Significant Hypoglycemia.

2. Clinical Question

The question is about a pharmacodynamic and safety outcome after a single double dose, within a study that also used daily standard-dose treatment in each period. It is not a comparison of long-term clinical outcomes. The primary endpoint counts participants with an event during the specified observation window and expresses that count as a percentage of the analyzed participants. The posted results show 6.6% for insulin peglispro and 35.5% for insulin glargine. The comparison is presented as a risk difference with a confidence interval and an unadjusted p-value; those statistics describe the posted contrast, not a treatment recommendation.

3. Trial Design

This was a phase 3, randomized, double-blind crossover study sponsored by Eli Lilly and Company. The registry describes a comparison of pharmacodynamics after a double dose of insulin peglispro or insulin glargine in people with type 2 diabetes. In the crossover arrangement, each participant received both treatments, one in each of two 4-week study periods. Each treatment was given once daily at a standard dose during its period, followed by a single double dose on day 3 of the inpatient stay.

The registry reports enrollment of 68 and a completed study. The recorded start was 2014-05, and primary completion was 2015-07. These record dates describe the study timeline; they do not establish the date on which results were analyzed or posted. The registry record does not report the treatment sequence assignment, any washout interval, or further details about period transitions. Those details should not be inferred from the crossover label alone.

For the primary endpoint, the analysis population consisted of randomized participants who received the double dose and had evaluable hypoglycemia data. The posted group sizes were 61 for insulin peglispro and 62 for insulin glargine. These are endpoint-specific analysis counts, not the overall enrollment. The record also gives different denominators for adverse-event summaries. Because each participant received both treatments, treatment observations are related within a participant; interpreting the study design therefore requires keeping the crossover structure in mind. The registry’s posted summaries do not include a detailed statistical analysis or reported analysis results for the secondary endpoints.

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.

Study Period 1

Reasons for not completing this period:

Study Period 2 (Crossover)

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: All randomized participants who received the double dose of study drug and had evaluable hypoglycemia data were included in the analysis.

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 randomized participants

6. Primary Endpoint

The registry lists the primary outcome as Percentage of Participants With Clinically Significant Hypoglycemia, measured over Predose to 84 Hours Post Double Dose.

The primary endpoint is the percentage of participants with clinically significant hypoglycemia from predose through 84 hours after the double dose. The registry defines clinically significant hypoglycemia as blood glucose <54 mg/dL (3.0 mmol/L) or symptoms of severe hypoglycemia. The percentage is calculated from the number of participants meeting that definition and the number analyzed.

This endpoint turns an event into a participant-level binary outcome: each analyzed participant either met the event definition during the observation window or did not. A percentage makes the frequency interpretable relative to the analyzed group size, while the risk difference and its confidence interval summarize the contrast between the two treatment groups. This is one way to compare how often a specified event occurred in the posted observation period.

The endpoint is relevant to the study’s stated pharmacodynamic comparison because it records clinically significant low-glucose events after the double dose. Its interpretation is bounded by the definition, the observation window, and the participants with evaluable data. It does not summarize every glucose measurement or establish the pattern of events beyond the specified period. Nor does the percentage alone describe event severity beyond the registry’s definition, recurrence, or individual participant experience.

7. Results Posted in the Registry

• Insulin Glargine: 35.5% (22/62) versus 6.6% (4/61) on control; risk difference 28.9 percentage points (95% CI 15.5 to 42.4), risk ratio 5.41 (1.98–14.78); unadjusted two-proportion z-test p = <0.001.

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.

Percentage of Participants With Clinically Significant Hypoglycemia 12 Hours Post Double Dose

• 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:
  • Insulin Glargine: 22.6% (14/62) versus 1.6% (1/61) on control; risk difference 20.9 percentage points (95% CI 10.1 to 31.8), risk ratio 13.77 (1.87–101.55); unadjusted two-proportion z-test p = <0.001.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the proportion with the event is statistically higher for Insulin Glargine than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Nadir Glucose

• 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:
  • Insulin Glargine: mean 55.93 versus 61.70 on control; difference -5.77 (95% CI -9.04 to -2.50), standardized effect d = -0.83; z-test p = <0.001.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the mean is statistically lower for Insulin Glargine than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

Time to the Nadir Glucose

• Endpoint type: Continuous: a mean value per participant, compared as a difference in means.
• Test: Unadjusted two-sample z-test for the difference in means, using the posted standard deviations.
• Result:
  • Insulin Glargine: mean 28.15 versus 35.92 on control; difference -7.77 (95% CI -19.78 to 4.24), standardized effect d = -0.32; z-test p = 0.205.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Insulin Glargine and the control. The interval gives the range of effects that remain compatible with the data.

The secondary outcomes extend the primary question by looking at hypoglycemia over a shorter post-dose window, glucose nadir and its timing, and other glucose and pharmacodynamic measures. Taken together, they provide a broader descriptive view of the measurements collected around the double dose. The posted pattern is not a single uniform signal across every kind of outcome: binary event outcomes and continuous glucose summaries measure different features, on different scales and over different timeframes.

These outcomes can be considered alongside the primary result, but they do not replace it. A shorter-window hypoglycemia endpoint is related to the primary endpoint while covering less follow-up; nadir and time-to-nadir describe glucose measurements in another way. Such measures may help readers understand context, but their estimates should not be treated as interchangeable evidence about the primary endpoint. The registry record lists 10 outcome measures, but not all secondary outcomes have posted comparison analyses.

Unadjusted secondary results carry less weight because assessing several endpoints creates multiple opportunities for apparently notable findings by chance. This is a general statistical concern. The registry record lists no reported analyses for the secondary outcomes, and does not report a multiplicity adjustment strategy. Without that information, secondary p-values and estimates are best regarded as descriptive or exploratory, rather than as a confirmatory set of independent findings. The secondary results can suggest what deserves further study, but they cannot establish that the primary pattern is replicated across outcomes or that every outcome supports the same interpretation.

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

• Percentage of Participants With Clinically Significant Hypoglycemia 12 Hours Post Double Dose
• Percentage of Participants With Hypoglycemia
• Nadir Glucose
• Time to the Nadir Glucose
• Duration of Glucose ≤70 mg/dL
• Fasting Blood Glucose
• Pharmacodynamics: Three-Hour Postprandial Glucose Area Under the Concentration Time Curve (AUC)
• Pharmacodynamics: Three-Hour Postprandial Glucose Area Under the Concentration Time Curve (AUC) Excursion

9. Statistical Interpretation

10. Statistical Methods Explained

What kind of endpoint is this?

The primary outcome is binary at the participant level: the registry counts whether each participant had at least one qualifying clinically significant hypoglycemia event within the stated observation window. The reported percentage is the number of participants meeting the definition divided by the number analyzed, multiplied by 100. This differs from an event rate, which would count repeated events over time or relate events to person-time. The primary result does not report the total number or timing of repeated events for each participant.

What does the risk difference describe?

A risk difference compares the event proportions in two groups by expressing the contrast on the proportion scale. Here, the posted estimate is 0.289, and the interval is 0.155 to 0.424. The group percentages and event counts help readers see the observed data behind the comparison. A risk difference is an absolute contrast rather than a ratio: it describes separation between proportions on their original scale. The confidence interval expresses uncertainty about that estimated contrast under the analysis used. The record does not give enough detail to reconstruct the model or test used for this primary estimate.

Why does the crossover structure matter?

In a crossover study, participants receive both treatments at different times, so observations from one participant are not naturally independent in the way observations from separate people might be. A suitable analysis may need to account for the within-participant relationship and for aspects such as treatment period or sequence. This is a general statistical consideration, not a claim about the method used. The registry record identifies the study as randomized and crossover, but does not report the primary statistical model, whether period or sequence effects were evaluated, or how any within-participant relationship was handled. The posted results should therefore be read as registry-reported estimates rather than as a fully documented analysis that can be independently assessed from the record alone.

What does the p-value contribute?

The primary comparison has an unadjusted p-value of <0.001. A p-value describes how unusual data at least this discrepant would be under a specified null hypothesis and the assumptions of the test. It is not the probability that the null hypothesis is true, nor does it measure the size or clinical importance of the contrast. It also does not account automatically for testing multiple endpoints. Because the p-value is labelled unadjusted, it should not be treated as evidence that multiplicity has been addressed.

How should the other summaries be read?

The posted secondary outcomes include binary percentages and continuous measures. For a binary endpoint, a percentage summarizes the proportion of participants with the event. For a continuous measure, a mean or least-squares mean summarizes the location of measurements; a standard deviation describes participant-level spread, while a standard error describes uncertainty around an estimated mean. These dispersion measures answer different questions and should not be interchanged.

For example, the nadir glucose results are reported as least-squares means with standard errors, whereas time to nadir is reported as means with standard deviations. The registry gives secondary comparison estimates and some unadjusted p-values, but the reported analyses list is empty for the displayed secondary outcomes. Accordingly, readers should avoid assuming that the displayed summaries arise from a particular adjustment strategy or a fully specified model. A mean difference is also not a direct description of the distribution of individual participant values.

What can planning calculations say?

The sample-size illustrations use the observed effect, or a stated fraction of it, to show hypothetical per-arm planning requirements at specified power levels. They are planning calculations under the listed assumptions, not a report of the trial’s original design calculation. In general, a confidence interval concerns uncertainty in an estimated effect, while a power calculation asks how often a planned study would meet a test criterion if a specified effect and assumptions held. Neither calculation replaces information about the actual analysis model, missing data, or crossover features. The registry record does not report the study’s own sample-size justification or full statistical analysis plan.

11. Confidence Intervals and Precision

The primary estimate is reported with a confidence interval from 0.155 to 0.424 around a risk difference of 0.289. A confidence interval is a range of values compatible with the data and the statistical procedure under its assumptions. It communicates uncertainty about the estimated contrast; it is not a range containing a known fixed proportion of individual participants, and it does not guarantee that the true effect lies within the interval.

The interval lies above zero, so the posted analysis indicates a positive risk difference in the direction of a higher event percentage in the insulin glargine group. Its width also matters: the estimate is not known with perfect precision, and the values across the interval represent a range of plausible contrasts under the analysis. The displayed arm percentages, 6.6% and 35.5%, should be read alongside the counts of 4 of 61 and 22 of 62. The interval is a measure of uncertainty about the comparison, not an interval for each arm’s percentage.

These precision statements depend on the analysis and its assumptions. The registry record does not report the primary model, details of how the crossover structure was represented, or the handling of missing outcome data beyond defining the analyzed population as participants with evaluable data. The confidence interval therefore gives useful information about the posted estimate, but cannot settle those methodological questions. A confidence interval also does not address whether other outcomes were tested or whether any adjustment for multiple comparisons was applied.

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 table is a planning illustration based on the observed effect and the assumptions listed for that calculation, not a report of the trial’s original design assumptions. The calculations use a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. The assumed effect to detect is the stated fraction of the observed effect. The record does not report the trial’s original sample-size justification.

For these comparisons, the table gives participants per arm. It does not give a total number of events. Each row changes the assumed effect: planning for 100% of the observed effect uses the full observed contrast, while planning for 75% or 50% uses a smaller contrast. The columns for 80% and 90% power describe the number of participants per arm required, under the stated assumptions, for a planned study to have those respective chances of meeting the statistical test criterion if the assumed effect were the true effect.

Higher power targets generally require more participants for the same assumed effect. Likewise, smaller effects are harder to distinguish from random variation, so the table shows larger participant requirements when planning for a smaller fraction of the observed effect. The table and curve are planning aids: they illustrate how the required sample size changes with the effect assumption and power target, rather than documenting what happened in this study.

This is not observed power. It is not a retrospective measure of whether the completed trial was adequate, and it does not change the uncertainty in the posted results. The figures rely on simplified assumptions, including equal allocation, and do not reflect unreported features of the crossover analysis or missing-data handling.

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, p₀ and p₁ are the control and treatment proportions, and p̄ is their average.

Worked calculation for this trial

1. Control proportion p₀ = 0.066; treatment proportion p₁ = 0.355; average p̄ = 0.210.
2. First term: z_1−α/2 × √[2p̄(1−p̄)] = 1.96 × 0.5762 = 1.1294.
3. Second term: z_1−β × √[p₀(1−p₀) + p₁(1−p₁)] = 0.842 × 0.5387 = 0.4534.
4. n = (1.1294 + 0.4534)² / (0.2893)² = 29.9, rounded up to 30 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
p0 <- 0.0655737704918033                          # control proportion
p1 <- 0.354838709677419                          # treatment proportion
n_per_arm <- function(power, frac = 1) {
  q1 <- p0 + (p1 - p0) * frac               # smaller effect = fraction of the observed difference
  pbar <- (p0 + q1) / 2
  z_b <- qnorm(power)
  ceiling(round((z_a * sqrt(2 * pbar * (1 - pbar)) + z_b * sqrt(p0 * (1 - p0) + q1 * (1 - q1)))^2 / (q1 - p0)^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 (unrounded n per arm)
cat("power.prop.test:", round(power.prop.test(p1 = p0, p2 = p1, power = 0.80)$n, 1), "\n")

# Simulation: how often does the z-test reject at the 80%-power sample size?
set.seed(2132637)
n    <- n_per_arm(0.80)
reps <- 20000
x0 <- rbinom(reps, n, p0); x1 <- rbinom(reps, n, p1)
ph <- (x0 + x1) / (2 * n)
z  <- ((x1 - x0) / n) / sqrt(ph * (1 - ph) * 2 / n)
cat(sprintf("Simulated power at n = %s per arm: %.3f (%d replicates)\n", format(n, big.mark = ","), mean(abs(z) > z_a, na.rm = TRUE), reps))

Output from running the code

100% of observed effect: n per arm = 30 (80% power), 40 (90% power)
 75% of observed effect: n per arm = 47 (80% power), 63 (90% power)
 50% of observed effect: n per arm = 89 (80% power), 118 (90% power)
power.prop.test: 29.9 
Simulated power at n = 30 per arm: 0.829 (20000 replicates)

The simulation draws many pairs of event counts at the planned sample size and counts how often the two-proportion z-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 registry record does not report whether the study used a prespecified hierarchy of endpoints, an adjustment for multiple testing, or another strategy for controlling the chance of false-positive findings across outcomes. The primary comparison is labelled with an unadjusted p-value. The record lists secondary outcome measures, but reports no analyses for the secondary outcomes in the analysis listings. In the absence of a documented multiplicity plan, secondary comparisons should be interpreted cautiously and should not be treated as confirmatory evidence simply because estimates or p-values are displayed.

The primary analysis population is described as all randomized participants who received the double dose and had evaluable hypoglycemia data. The posted denominators are 61 for insulin peglispro and 62 for insulin glargine. The registry does not explain why some randomized participants were not included in those endpoint denominators, provide participant-level missingness, or describe an imputation or sensitivity-analysis approach. The exact handling of missing data is not reported in the registry record. The term evaluable identifies the stated inclusion condition but does not, by itself, show whether missingness could have affected the comparison.

Because this is a crossover study, the analysis population and missing data also need to be considered in relation to treatment periods and within-participant information. The registry record does not report whether incomplete treatment sequences or period-specific data were included, nor how such cases were handled. It also does not report a detailed primary model, treatment-sequence analysis, or interim-analysis plan. These gaps limit independent assessment of the assumptions behind the posted comparison. They do not establish that an analysis was inappropriate; they mean the relevant methods are not documented in the registry record.

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 2% reporting threshold.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: gastritis erosive (1.6%), otitis media (1.6%).

The serious adverse-event summaries use groups at risk of 64 for insulin peglispro and 67 for insulin glargine. The registry reports 2 participants with serious adverse events in the insulin peglispro group and 1 in the insulin glargine group. These denominators differ from the primary endpoint analysis counts, so the safety figures should be read using their own at-risk group sizes rather than substituted into the hypoglycemia comparison.

The record names gastritis erosive and otitis media among the top serious adverse-event terms, each reported as 1.6%. It also reports other adverse events affecting 31 participants in the insulin peglispro group and 26 in the insulin glargine group, with 64 and 67 at risk, respectively. The registry record does not report death figures. These summaries do not give a full account of event timing, duration, recurrence, or individual clinical context, and the figures alone do not establish a causal relationship with study treatment.

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

The posted primary result describes the percentage of analyzed participants who met the registry’s definition of clinically significant hypoglycemia from predose to 84 hours after a double dose. The observed percentages were 6.6% with insulin peglispro and 35.5% with insulin glargine. The reported risk difference and its confidence interval indicate a contrast in those analyzed data, with the interval above zero. This is what the registry result shows for this endpoint and timeframe.

It does not show how the treatments compare on every possible outcome, or what would happen outside the study conditions. The study used standard daily dosing during two 4-week periods and a single double dose on day 3 of the inpatient stay. Its primary endpoint focuses on clinically significant hypoglycemia after that double dose, not long-term outcomes. The result should not be extended to all participants with type 2 diabetes or to circumstances that differ from the study design.

The result also does not establish why the groups differed, determine the experience of each participant, or describe the full frequency and recurrence of events. The registry record does not report detailed analysis methods, missing-data sensitivity analyses, or a multiplicity strategy. Those absences constrain how comprehensively the result can be evaluated, but do not negate the numerical findings that are posted. The confidence interval reflects statistical uncertainty under the analysis; it does not account for every uncertainty about study design and reporting.

Finally, a statistical contrast is not the same as a clinical recommendation. The posted data should be interpreted as a comparison of a defined event measure under the recorded study conditions. They do not, by themselves, answer broader questions about benefits, harms, or treatment choices.

17. Why This Trial Matters Statistically

This trial is statistically notable because it uses a randomized crossover design to compare two treatments within participants across separate treatment periods. In general, crossover designs can make within-participant comparisons informative, because each participant receives both treatments. They also require careful attention to dependence between observations, treatment period, sequence, and missing treatment-period data. The registry identifies the design, while leaving important analysis details unreported.

The primary outcome is a clearly defined participant-level event percentage over a specified observation window. The posted result includes group counts, percentages, a risk-difference estimate, a confidence interval, and an unadjusted p-value. Seeing these quantities together is more informative than relying on a p-value alone: the event counts show the observed data, the difference summarizes the contrast, and the interval conveys uncertainty. The tables and plots on this page should be read as complementary displays of the same posted results, not as additional independent evidence.

The record also illustrates why secondary endpoints, sample-size planning, and analysis transparency matter. Multiple outcomes can provide context, but unadjusted secondary findings need cautious interpretation. Planning calculations based on an observed effect can illustrate future-study requirements, but they are not the original study justification and do not measure observed power. Finally, the distinct denominators for endpoint and safety summaries show why readers should check the analysis population attached to each result instead of assuming a single denominator applies throughout.

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.
• Participant counts were reconstructed from posted percentages and arm sizes, so small rounding differences are possible.

The registry record identifies the study as a randomized, double-blind crossover design, but does not report the treatment sequence, any washout interval, or the details of the crossover analysis. This matters because the treatment observations are linked within participants, and period or sequence effects could affect interpretation. The posted summaries do not allow an independent check of how these features were handled.

The primary endpoint population required receipt of the double dose and evaluable hypoglycemia data. The record does not explain exclusions from the endpoint analysis, describe missingness, or report sensitivity analyses. The primary analysis denominators are not the same as the overall enrollment. Safety summaries also use their own at-risk denominators, so results across sections are not based on a single common analysis population.

The endpoint is limited to the stated definition and observation window. It does not report repeated event counts, participant-level event patterns, or the broader glucose profile. The study’s treatment periods and double-dose inpatient procedure also limit what can be inferred about settings or outcomes not described in the record.

Finally, the registry record does not report the primary statistical model, a full analysis plan, the original sample-size justification, or how multiplicity was addressed. Secondary analysis listings are empty. These reporting gaps do not establish that methods were absent or defective; they prevent readers from assessing those methods from the registry record. The interpretation should therefore remain tied to the posted estimates and their documented definitions.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: A Study to Compare the Effect of a Double Dose of Two Long-acting Insulin Therapies in Participants With Type 2 Diabetes — NCT02132637.
• Registry record last updated 2019-09-18; status last verified 2019-09.
• Results first posted 2018-04-20.
• 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

NCT02132637 is a completed phase 3 randomized, double-blind crossover study sponsored by Eli Lilly and Company. It enrolled 68 participants with type 2 diabetes and compared insulin peglispro with insulin glargine. Each participant was to receive the treatments in separate 4-week periods, with a single double dose on day 3 of the inpatient stay in each period.

The primary endpoint was the percentage of participants with clinically significant hypoglycemia from predose to 84 hours after the double dose. The registry reports 6.6% for insulin peglispro and 35.5% for insulin glargine, with a risk difference of 0.289 and a confidence interval from 0.155 to 0.424. The unadjusted p-value is <0.001. The posted findings are specific to the defined endpoint, analyzed participants, and study conditions; several analysis details are not reported in the registry record.