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Cardiovascular Phase 3 LDL-C NCT03400800

ORION-11: Complete Statistical Analysis of Inclisiran in Elevated LDL-C

An independent statistical review of the randomized phase 3 ORION-11 trial evaluating inclisiran versus placebo in subjects with ASCVD or ASCVD-risk equivalents and elevated low-density lipoprotein cholesterol.

Trial status: COMPLETED  ·  Enrollment: 1617  ·  Primary completion: July 2019
Scope of this record

This page separates reported trial results from statistical interpretation. Numerical results are taken from the ClinicalTrials.gov record. The registry provides the official trial record.

Registry note: This page provides an independent statistical analysis and educational interpretation of publicly reported results. ClinicalTrials.gov provides the official trial registry record.

1. Trial at a Glance

ORION-11 was a randomized, double-blind, parallel-design phase 3 trial evaluating inclisiran sodium versus placebo in subjects with ASCVD or ASCVD-risk equivalents and elevated LDL-C. The registry reports 1617 enrolled subjects, two treatment arms, two registered primary endpoints, and formal ANCOVA analyses for both primary endpoints.

1617
Enrollment
Phase 3
2
Arms
Inclisiran vs placebo
2
Primary endpoints
Both formally analyzed
<0.0001
Primary p-values
Two-sided
FeatureORION-11
Trial nameORION-11
PhasePhase 3
ConditionASCVD; Risk Factor, Cardiovascular; Elevated Cholesterol
PopulationSubjects with ASCVD or ASCVD-risk equivalents and elevated low-density lipoprotein cholesterol
DesignRandomized, parallel
MaskingDouble
Primary purposeTreatment
Enrollment1617
InterventionsInclisiran Sodium; Placebo
Lead sponsorThe Medicines Company
Sponsor typeIndustry
ClinicalTrials.govNCT03400800

2. Clinical Question

The central question was whether treatment with inclisiran produced a greater reduction in LDL-C than placebo over the registered assessment periods in subjects with ASCVD or ASCVD-risk equivalents and elevated LDL-C.

Population

Subjects with ASCVD or ASCVD-risk equivalents and elevated low-density lipoprotein cholesterol.

Intervention

Inclisiran Sodium.

Comparator

Placebo.

Primary question

Does inclisiran produce a greater reduction in LDL-C than placebo at the registered endpoint time frames?

3. Trial Design

01
Enroll1617 subjects
02
Randomize2 treatment arms
03
MaskDouble-blind design
04
AssessLDL-C and other outcomes
05
AnalyzeANCOVA and reported estimates
ARM 1

Inclisiran

  • Inclisiran Sodium
  • Randomized treatment arm
  • Primary and secondary efficacy analyses compared with placebo where specified
ARM 2

Placebo

  • Placebo
  • Randomized comparator arm
  • Reference group for the reported treatment-effect estimates
Allocation
Randomized
Random allocation was part of the registered trial design.
Design model
Parallel
The registry identifies a parallel-group design.
Masking
Double
The registry identifies the trial as double-masked.
Hypothesis
Superiority
The posted statistical analyses use a superiority hypothesis.

Trial timing

November 2017

Trial start

The registry lists the trial start as 2017-11-01.

July 2019

Primary completion

The registry lists the primary completion date as 2019-07-31.

Completed

Registry status

The current ClinicalTrials.gov record in the ClinicalTrials.gov record identifies ORION-11 as completed.

4. Endpoints

The registry lists two primary endpoints. Both were posted with formal statistical analyses, and both used ANCOVA in the registry analysis records.

EndpointRegistry time frameAnalysis
Percentage Change in LDL-C From Baseline to Day 510 Baseline, Day 510 ANCOVA; ITT population
Time-adjusted Percent Change in LDL-C Levels From Baseline After Day 90 and up to Day 540 Baseline, Day 90 to Day 540 ANCOVA; ITT population

Secondary endpoints reported in the registry

Secondary endpointTime frameEffect measure
Absolute Change In LDL-C From Baseline To Day 510Baseline, Day 510Median Difference (Final Values)
Time-adjusted Absolute Change in LDL-C From Baseline After Day 90 and up to Day 540Baseline, Day 90 to Day 540Mean Difference (Final Values)
Percentage Change in Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9) From Baseline to Day 510Baseline, Day 510Mean Difference (Final Values)
Percentage Change in Total Cholesterol From Baseline to Day 510Baseline, Day 510Mean Difference (Final Values)
Percentage Change in Apolipoprotein B (ApoB) From Baseline to Day 510Baseline, Day 510Mean Difference (Final Values)
Percentage Change in Non-HDL-C From Baseline to Day 510Baseline, Day 510Mean Difference (Final Values)

5. Statistical Methodology

Analysis populations

The registry analyses identify the intention-to-treat (ITT) population for both primary endpoints. Several secondary endpoint analyses are also explicitly identified as using the ITT population.

Why ITT matters: An ITT analysis retains subjects according to their randomized treatment assignment rather than redefining the comparison according to later treatment behavior. This preserves the treatment-group comparison created by randomization and is a standard principle for superiority trials.

ANCOVA

All eight posted statistical analyses use ANCOVA as the reported method. The registry normalizes this method to the linear-model family. For the primary analyses, the reported effect measure is a mean difference in final values, and the analysis notes state that the estimate represents the least squares means difference from placebo.

Conceptual ANCOVA structure
Outcome = treatment effect + covariate effects + residual variation

ANCOVA combines a comparison of treatment groups with adjustment for specified covariates. The exact covariates used in the posted ORION-11 analyses are not identified in the registry method fields, so this page does not infer them.

Confidence intervals

Each of the eight posted statistical analyses supplies a 95% two-sided confidence interval. The interval describes statistical uncertainty around the reported treatment-effect estimate under the analysis framework; it is not a range containing the individual responses of 95% of subjects.

Superiority testing

The posted analyses identify superiority as the hypothesis type. In a superiority analysis, the treatment effect is assessed against the null hypothesis of no difference rather than against a prespecified non-inferiority margin.

P-values

The primary endpoint analyses report two-sided p-values of <0.0001. A p-value quantifies the compatibility of the observed data with the specified null hypothesis under the statistical model and testing framework. It does not measure the magnitude of the treatment effect or the probability that the treatment hypothesis is true.

6. Statistical Methods Explained

Why was ANCOVA used?

The registry reports ANCOVA for all eight posted analyses. ANCOVA is a linear-model approach that can compare treatment groups while accounting for prespecified covariates. For a continuous biochemical outcome such as change in LDL-C, the method provides an adjusted treatment contrast rather than relying solely on an unadjusted comparison of raw group means.

What is a least squares means difference?

The primary analyses report a mean difference in final values and state that the estimate represents the least squares means difference from placebo. Least squares means are model-based adjusted means. Their difference therefore represents the estimated treatment contrast after accounting for the covariate structure included in the ANCOVA model.

What does a negative mean difference indicate here?

The primary endpoint estimates are negative: -53.5 and -49.17. Because the endpoint is percentage change in LDL-C, a negative treatment-minus-placebo difference indicates a lower estimated percentage change in LDL-C for inclisiran relative to placebo under the reported model.

What does the 95% confidence interval tell us?

For the first primary endpoint, the 95% CI is -56.66 to -50.35. For the second, it is -51.57 to -46.77. These intervals quantify uncertainty around the respective estimated treatment differences. Their narrowness relative to the estimates indicates that the posted analyses produced fairly precise estimates within their statistical framework.

Why doesn't the p-value measure effect size?

The p-value depends on both the magnitude of an observed difference and the amount of statistical information available. A very small p-value can accompany either a large or small effect depending on sample size and variability. The effect estimate and its confidence interval are therefore essential for understanding the size and precision of the observed treatment contrast.

Why does the ITT population matter?

The ITT population preserves the randomized comparison by analyzing subjects according to their assigned treatment group. This is especially important when interpreting a superiority trial because excluding subjects after randomization can disrupt the balance created by randomization and potentially change the question being answered.

What does double masking contribute statistically?

Double masking can reduce the potential for knowledge of treatment assignment to influence behavior, outcome assessment, or other aspects of trial conduct. The registry identifies ORION-11 as double-masked; the ClinicalTrials.gov record does not provide additional detail about exactly which trial personnel were masked.

7. Primary Results

Percentage Change in LDL-C From Baseline to Day 510

Inclisiran vs placebo

-53.5

95% CI: -56.66 to -50.35   ·   P < 0.0001

Effect measure: Mean Difference (Final Values)

Analysis: ANCOVA in the ITT population; the registry analysis notes that the estimate represents the least squares means difference from placebo.

Clinical Biostats interpretation

The estimated treatment difference was -53.5 percentage points for the registered endpoint, with inclisiran compared with placebo. Because the outcome is percentage change in LDL-C, the negative sign indicates a lower estimated percentage change in the inclisiran group relative to placebo under the reported ANCOVA model.

This does not mean that every subject experienced a 53.5% reduction, nor does it mean that the individual treatment effect was identical for every subject. The estimate is a model-based group comparison.

The 95% CI of -56.66 to -50.35 expresses uncertainty around the estimated treatment contrast. It does not describe the range of individual patient responses.

The p-value of <0.0001 indicates very strong incompatibility with the specified null hypothesis under the reported testing framework. It does not say that the treatment effect is "53.5% significant," and it does not measure clinical importance.

Because this is an ANCOVA analysis rather than a time-to-event analysis, proportional-hazards assumptions are not relevant to this particular estimate. Interpretation instead depends on the linear-model specification, covariates, outcome definition, analysis population, and handling of missing observations.

Time-adjusted Percent Change in LDL-C Levels From Baseline After Day 90 and up to Day 540

Inclisiran vs placebo

-49.17

95% CI: -51.57 to -46.77   ·   P < 0.0001

Effect measure: Mean Difference (Final Values)

Analysis: ANCOVA in the ITT population; the registry analysis notes that the estimate represents the least squares means difference from placebo.

Clinical Biostats interpretation

The estimated treatment difference for the time-adjusted percentage-change endpoint was -49.17 percentage points. The endpoint is defined from baseline after Day 90 and up to Day 540, so this estimate summarizes the registered time-adjusted outcome rather than representing a single instantaneous measurement.

The estimate does not mean that LDL-C was reduced by exactly 49.17% in every individual subject. It is the reported model-based treatment contrast between inclisiran and placebo.

The 95% CI of -51.57 to -46.77 provides the statistical precision reported for this estimate. The interval is entirely below zero, which is consistent with a negative treatment difference under the superiority framework.

The p-value of <0.0001 addresses the null hypothesis in the reported analysis; it is not a measure of the magnitude or clinical importance of the treatment effect.

As with the first primary endpoint, the analysis is ANCOVA rather than a survival model. Consequently, proportional-hazards assumptions and censoring rules are not the central interpretive issues for this estimate. The exact covariate specification and missing-data procedures are not provided in the registry analysis fields.

8. Secondary Endpoint Results

The registry contains six posted secondary endpoint analyses. All six use ANCOVA and all report superiority comparisons with 95% two-sided confidence intervals.

Secondary endpointEstimate95% CIP-value
Absolute Change In LDL-C From Baseline To Day 510 -51.87 -55.01 to -48.72 <.0001
Time-adjusted Absolute Change in LDL-C From Baseline After Day 90 and up to Day 540 -48.94 -51.39 to -46.48 <0.0001
Percentage Change in PCSK9 From Baseline to Day 510 -79.27 -81.97 to -76.57 <0.0001
Percentage Change in Total Cholesterol From Baseline to Day 510 -29.79 -31.78 to -27.81 <0.0001
Percentage Change in ApoB From Baseline to Day 510 -38.94 -41.21 to -36.67 <0.0001
Percentage Change in Non-HDL-C From Baseline to Day 510 -43.32 -46.04 to -40.60 <0.0001

Absolute change in LDL-C to Day 510

Inclisiran vs placebo

-51.87

95% CI: -55.01 to -48.72   ·   P < .0001

Reported effect measure: Median Difference (Final Values); registry method: ANCOVA.

The registry's analysis notes identify the estimate as representing the least squares means difference from placebo despite the posted effect-measure label of "Median Difference (Final Values)." This page preserves that registry wording rather than attempting to reconcile or recompute the estimate.

Time-adjusted absolute change in LDL-C

Inclisiran vs placebo

-48.94

95% CI: -51.39 to -46.48   ·   P < 0.0001

Effect measure: Mean Difference (Final Values); analysis: ANCOVA.

Percentage change in PCSK9

Inclisiran vs placebo

-79.27

95% CI: -81.97 to -76.57   ·   P < 0.0001

Effect measure: Mean Difference (Final Values); analysis: ANCOVA.

Percentage change in total cholesterol

Inclisiran vs placebo

-29.79

95% CI: -31.78 to -27.81   ·   P < 0.0001

Effect measure: Mean Difference (Final Values); analysis: ANCOVA; analysis population: ITT Population.

Percentage change in ApoB

Inclisiran vs placebo

-38.94

95% CI: -41.21 to -36.67   ·   P < 0.0001

Effect measure: Mean Difference (Final Values); analysis: ANCOVA; analysis population: ITT Population.

Percentage change in non-HDL-C

Inclisiran vs placebo

-43.32

95% CI: -46.04 to -40.60   ·   P < 0.0001

Effect measure: Mean Difference (Final Values); analysis: ANCOVA; analysis population: ITT Population.

Secondary-endpoint interpretation: The six secondary analyses consistently report negative treatment differences and very small p-values. However, a collection of statistically significant secondary endpoints should not automatically be interpreted as six independent confirmatory claims. The ClinicalTrials.gov record identifies superiority testing but do not provide a multiplicity-adjustment strategy or endpoint hierarchy. Accordingly, this page reports the posted results without assigning an additional confirmatory interpretation to the collection as a whole.

9. Understanding the Pattern of Treatment Effects

The posted estimates cover several related lipid measures. The largest numerical percentage-change contrast among the secondary outcomes is for PCSK9 (-79.27), while the other reported percentage-change contrasts include total cholesterol (-29.79), ApoB (-38.94), and non-HDL-C (-43.32).

Reported secondary percentage-change estimates
PCSK9
-79.27
Non-HDL-C
-43.32
ApoB
-38.94
Total cholesterol
-29.79

The graphic is a visual representation of the posted estimates rather than a new statistical analysis. Because these endpoints are measured on different biological scales, their numerical magnitudes should not be treated as directly comparable measures of clinical importance.

10. Safety Results

The ClinicalTrials.gov record reports serious adverse events by randomized arm. The affected and at-risk counts are provided directly in the registry-derived data.

Safety measureInclisiranPlacebo
Serious adverse events 181 / 811 181 / 804

Inclisiran

181 subjects with serious adverse events among 811 at risk.

Placebo

181 subjects with serious adverse events among 804 at risk.

Safety interpretation: The reported serious-adverse-event counts are identical between the two arms in the ClinicalTrials.gov record, but the denominators differ. The ClinicalTrials.gov record does not include a formal statistical comparison of serious adverse events, a confidence interval, or a p-value, so this page does not construct one.

11. Randomization and Blinding

ORION-11 is identified as randomized, parallel, and double-masked. These design features address different sources of bias.

Randomization

Random assignment is intended to create comparable treatment groups in expectation, allowing differences in outcomes to be attributed more credibly to treatment assignment rather than systematic baseline differences.

Double masking

Masking can reduce the possibility that knowledge of treatment assignment influences trial conduct, participant behavior, or assessment.

Parallel design

Each randomized subject belongs to a treatment arm rather than sequentially receiving both randomized interventions in a crossover structure.

Superiority framework

The posted analyses are labeled superiority analyses, so the treatment effect is evaluated as a difference rather than against a non-inferiority margin.

12. What the Primary Estimates Do — and Do Not — Mean

Effect size

The primary estimate of -53.5 is a between-group treatment contrast for the registered percentage-change endpoint. It is not an individual-level prediction and should not be interpreted as saying that every subject's LDL-C changed by exactly the same amount.

Confidence interval

The 95% CI of -56.66 to -50.35 gives a measure of statistical uncertainty around the first primary estimate. The second primary estimate has a 95% CI of -51.57 to -46.77. Neither interval describes the distribution of individual patient responses.

P-value

The reported primary p-values are <0.0001. These values indicate strong evidence against the relevant null hypothesis under the reported statistical framework. They do not quantify the size of the treatment effect or its practical importance.

Model dependence

Both primary estimates come from ANCOVA. Consequently, they are conditional on the model specification used by the registry analysis. The ClinicalTrials.gov record does not identify every model covariate or the detailed missing-data strategy, so those elements should not be inferred.

13. Multiplicity and the Two Primary Endpoints

ORION-11 has two registered primary endpoints, and both have formal statistical analyses posted. The ClinicalTrials.gov record identifies superiority as the hypothesis type and report two-sided p-values for both analyses.

Primary endpointEstimate95% CIP-valueHypothesis
Percentage Change in LDL-C From Baseline to Day 510 -53.5 -56.66 to -50.35 <0.0001 Superiority
Time-adjusted Percent Change in LDL-C Levels From Baseline After Day 90 and up to Day 540 -49.17 -51.57 to -46.77 <0.0001 Superiority

The presence of two primary endpoints is statistically important because a trial may define a family of confirmatory hypotheses that requires an explicit multiplicity strategy. The ClinicalTrials.gov record does not state whether a multiplicity adjustment, hierarchical testing procedure, or other alpha-allocation rule was used. Therefore, this page does not infer one.

Important distinction: observing p-values below 0.0001 for both primary endpoints does not by itself reveal how the trial's overall type I error was allocated across the two primary hypotheses. The registry results should be reported as posted unless the protocol or statistical analysis plan provides the missing multiplicity details.

14. Missing Data and Model Assumptions

The registry analysis fields identify ANCOVA and the ITT population but do not specify the complete missing-data or imputation strategy. That distinction matters because repeated lipid measurements and time-adjusted outcomes can involve observations that are unavailable at particular assessment times.

General ANCOVA interpretation
Estimated treatment contrast = adjusted mean under inclisiran − adjusted mean under placebo

The equation is conceptual rather than a reconstruction of the ORION-11 analysis. The registry's posted analysis identifies the treatment contrast as a least squares means difference from placebo, but the ClinicalTrials.gov record does not contain the full model specification.

For an educational interpretation, it is therefore important to distinguish what is directly documented from what would normally be examined in a statistical analysis plan: covariate specification, treatment-by-covariate interactions if relevant, missing-data assumptions, sensitivity analyses, and the precise estimand being targeted.

15. Time Frames and Estimands

The two primary endpoints are related but are not identical statistical questions. One is defined specifically at Day 510, while the other is defined as a time-adjusted percent change from baseline after Day 90 and up to Day 540.

Day 510 endpoint

Asks about percentage change in LDL-C from baseline to the specific registered Day 510 assessment.

Day 90–540 endpoint

Uses a time-adjusted percentage-change definition over the registered period after Day 90 and up to Day 540.

Why the distinction matters

A single-time-point endpoint and a time-adjusted endpoint summarize treatment effects differently even when they measure the same underlying biomarker.

Interpretation

The two estimates should therefore be viewed as complementary primary analyses rather than treated as duplicate measurements of exactly the same statistical quantity.

16. Statistical Interpretation of the Secondary Outcomes

The secondary analyses extend the statistical story beyond LDL-C to PCSK9, total cholesterol, ApoB, and non-HDL-C. They also include both absolute and time-adjusted absolute LDL-C change.

Outcome domainReported estimate95% CIStatistical method
LDL-C absolute change-51.87-55.01 to -48.72ANCOVA
LDL-C time-adjusted absolute change-48.94-51.39 to -46.48ANCOVA
PCSK9 percentage change-79.27-81.97 to -76.57ANCOVA
Total cholesterol percentage change-29.79-31.78 to -27.81ANCOVA
ApoB percentage change-38.94-41.21 to -36.67ANCOVA
Non-HDL-C percentage change-43.32-46.04 to -40.60ANCOVA

Every confidence interval in this table lies below zero, matching the direction of the corresponding point estimate. The statistical interpretation remains model-dependent: these are estimated group differences, not individual treatment effects.

17. Clinical Interpretation vs Statistical Interpretation

Statistical interpretation

The two registered primary analyses report negative ANCOVA treatment differences with 95% confidence intervals entirely below zero and two-sided p-values below 0.0001.

Clinical interpretation

The reported estimates describe differences in LDL-C-related outcomes between randomized treatment groups. Whether a numerical change represents a clinically important benefit requires a separate clinical framework and is not determined by the p-value alone.

18. What the Registry Data Support — and What They Do Not

A strong statistical analysis distinguishes reported evidence from information that is not contained in the available record.

TopicWhat the ClinicalTrials.gov record supports
RandomizationORION-11 was randomized.
BlindingThe registry identifies the design as double-masked.
Primary endpointsTwo registered primary endpoints are identified, and both have formal analyses.
Primary methodANCOVA was reported for both primary endpoints.
Analysis populationThe primary analyses used the ITT population.
Effect measureMean Difference (Final Values), with analysis notes describing least squares means differences from placebo.
Confidence intervals95% two-sided intervals are reported for both primary analyses and all six secondary analyses.
HypothesisThe analyses are identified as superiority analyses.
Serious adverse events181/811 for inclisiran and 181/804 for placebo.
Detailed baseline characteristicsNot contained in the ClinicalTrials.gov record.
Detailed multiplicity strategyNot contained in the ClinicalTrials.gov record.
Detailed missing-data strategyNot contained in the ClinicalTrials.gov record.
Subgroup analysesNot contained in the ClinicalTrials.gov record.
Time-to-event analysesNot contained in the ClinicalTrials.gov record.

19. Important Limitations and Interpretation Issues

20. Why This Trial Matters Statistically

ORION-11 is a useful teaching case because it illustrates how a randomized superiority trial can use ANCOVA to estimate treatment differences for continuous biomarker outcomes while simultaneously requiring careful attention to the endpoint definition, analysis population, confidence intervals, and multiplicity.

ConceptHow it appears in ORION-11
RandomizationRandomized parallel-group design with two treatment arms.
BlindingDouble-masked trial.
Intention-to-treat analysisBoth primary analyses use the ITT population.
ANCOVAReported method for all eight posted statistical analyses.
Least squares meansPrimary analysis notes describe the estimates as least squares means differences from placebo.
Confidence intervals95% two-sided confidence intervals accompany all eight posted analyses.
P-valuesBoth primary analyses report P < 0.0001; all six secondary analyses also report very small p-values.
SuperiorityThe posted analyses identify superiority as the hypothesis type.
Multiple primary endpointsTwo primary endpoints require attention to the overall testing framework.
Safety analysisSerious adverse events are reported as affected subjects over subjects at risk by arm.

21. A Practical Framework for Reading ORION-11

A reader evaluating the statistical evidence can work through the trial in a structured sequence.

1. Identify the estimand

Start with exactly what was measured: percentage or absolute change from baseline and the specified assessment period.

2. Identify the population

Check whether the reported efficacy analysis is based on the ITT population. For ORION-11, the primary analyses are.

3. Read the effect estimate

Interpret the signed treatment difference before looking at the p-value. The sign establishes the direction of the reported contrast.

4. Read the confidence interval

Use the interval to understand the statistical precision of the treatment estimate rather than treating it as an individual-response range.

5. Then read the p-value

The p-value addresses compatibility with the null hypothesis. It should not replace the effect estimate or confidence interval.

6. Check the testing structure

Because there are two primary endpoints and six secondary analyses, the interpretation of multiple p-values depends on the prespecified multiplicity framework.

22. Statistical Concepts in This Trial

Learn more about the methods used in this trial:

23. Related Statistical Calculators

Apply the same statistical concepts with Clinical Biostats calculators:

24. Sources

Continue through the Clinical Biostats statistical library

Connect this trial's design and analytical methods to deeper tutorials, statistical calculators, and other clinical-trial analyses.

25. Record Summary

ORION-11 provides a clear example of statistical analysis for randomized clinical-trial biomarker endpoints. The trial was a randomized, double-masked, parallel phase 3 study with 1617 enrolled subjects and two treatment arms. Both registered primary endpoints were formally analyzed using ANCOVA in the ITT population, with least squares means differences from placebo reported as the principal treatment contrasts. The first primary endpoint produced an estimate of -53.5 with a 95% CI of -56.66 to -50.35 and P < 0.0001; the second produced an estimate of -49.17 with a 95% CI of -51.57 to -46.77 and P < 0.0001.

The six posted secondary analyses show the same general statistical structure, extending the analysis to absolute LDL-C change, time-adjusted absolute LDL-C change, PCSK9, total cholesterol, ApoB, and non-HDL-C. Serious adverse events were reported as 181/811 in the inclisiran arm and 181/804 in the placebo arm. The principal interpretive issues are therefore not limited to whether p-values are small: the reader should also examine the endpoint definitions, ITT population, model-based effect estimates, confidence intervals, the distinction between primary and secondary endpoints, and the unspecified multiplicity and missing-data details.

Clinical Biostats methodology: A trial-results page should not merely repeat reported numbers. The goal is to reconstruct the statistical story of the trial while clearly separating registry-reported evidence from educational interpretation and avoiding unsupported assumptions about analyses that are not documented in the ClinicalTrials.gov record.