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Acute Ischemic Stroke Randomized Trial Terminated NCT01062698

THRACE: Complete Statistical Analysis of Intra-Arterial Thrombectomy in Acute Ischemic Stroke

An independent statistical review of the THRACE randomized trial evaluating intra-arterial thrombectomy combined with alteplase in acute ischemic stroke.

ClinicalTrials.gov identifier: NCT01062698
Scope of this record

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

THRACE evaluated intra-arterial thrombectomy as a treatment strategy for patients with acute ischemic stroke. The randomized trial compared alteplase alone with alteplase combined with mechanical thrombectomy.

412
Enrollment
2
Study Arms
2010-06
Start Date
2015-12
Primary Completion
FeatureTHRACE
ConditionCerebral Stroke; Cerebrovascular Accident
AllocationRandomized
Design modelParallel
MaskingSingle
Primary purposeTreatment
Lead sponsorCentral Hospital, Nancy, France

2. Clinical Question

Population

Patients with acute ischemic stroke.

Intervention

Alteplase (rt-PA)/Actilyse combined with mechanical thrombectomy using MERCI, PENUMBRA, CATCH, or SOLITAIRE devices.

Comparator

Alteplase (rt-PA)/Actilyse.

Primary question

Does adding intra-arterial thrombectomy improve functional outcome compared with alteplase alone?

3. Trial Design

01
Randomize
412 participants
02
Arm 1
Alteplase
03
Arm 2
Alteplase + thrombectomy
04
Assess
Modified Rankin Score
05
Time point
3 months

4. Treatment Arms

Control

Alteplase (rt-PA)/Actilyse drug treatment.

Intervention

Alteplase (rt-PA)/Actilyse plus mechanical thrombectomy using MERCI, PENUMBRA, CATCH, or SOLITAIRE.

5. Endpoints

EndpointTime frame
Modified Rankin Score (mRs)3 months after treatment

6. Planned Analysis

The registry identifies Modified Rankin Score at 3 months after treatment as the primary endpoint. No posted statistical analyses or outcome results are available in the ClinicalTrials.gov record.

For a functional outcome scale such as the Modified Rankin Score, analysis commonly depends on how the score is modeled in the statistical analysis plan. Approaches may include ordinal analysis across the full disability scale, dichotomized functional independence comparisons, or other prespecified methods. The appropriate method depends on the protocol-defined analysis population and statistical plan.

7. Statistical Methodology

Randomized treatment comparison

Randomization creates the framework for comparing treatment groups while reducing the influence of measured and unmeasured baseline differences. The analysis question is whether outcomes differ between groups assigned to the competing treatment strategies.

Functional outcome analysis

The Modified Rankin Score is an ordered disability scale. Statistical interpretation must account for the fact that categories have a natural ordering but the distance between categories is not necessarily equal.

Conceptual framework
Treatment comparison โ†’ Functional outcome measurement โ†’ Statistical model โ†’ Estimate of treatment effect

The choice of statistical model determines how information contained in the outcome scale is represented.

8. Statistical Methods Explained

Why is the Modified Rankin Score suitable for stroke trials?

The Modified Rankin Score measures global disability and functional dependence after stroke. It captures clinically meaningful differences in recovery.

Why does the analysis method matter for an ordinal endpoint?

Ordinal outcomes contain ranking information. Treating categories as simple numerical measurements may not reflect their statistical structure.

What does randomization accomplish?

Randomization balances treatment assignment at the population level, allowing outcome differences to be interpreted as comparisons between assigned strategies.

Why is the three-month time point important?

The registry defines the primary endpoint assessment at three months after treatment, providing a prespecified outcome window.

Why are confidence intervals important?

Confidence intervals describe uncertainty around an estimated treatment effect and provide information beyond whether a statistical test crosses a significance threshold.

9. Limitations

10. Why This Trial Matters Statistically

ConceptApplication in THRACE
RandomizationComparison of two treatment strategies
Ordinal outcomesModified Rankin Score as a functional endpoint
Time-defined endpointAssessment at 3 months after treatment
Clinical trial methodologyParallel randomized design

11. Sources