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
NSABP B-28 was a breast cancer biomarker study designed to evaluate the relationship between the 21-gene recurrence score (RS) and clinically relevant outcomes.
| Feature | NSABP B-28 |
|---|---|
| NCT ID | NCT01420185 |
| Status | UNKNOWN |
| Sponsor | NSABP Foundation Inc |
| Sponsor type | NETWORK |
| Condition | Breast Cancer |
| Enrollment | 1300 |
| Interventions | RNA analysis; gene expression analysis; microarray analysis; reverse transcriptase-polymerase chain reaction; laboratory biomarker analysis |
2. Clinical Question
Population
Patients with breast cancer included in the NSABP B-28 study population.
Biomarker
The 21-gene recurrence score (RS).
Measurements
Gene expression and biomarker analyses using RNA and related laboratory approaches.
Primary question
How is the 21-gene RS associated with local recurrence risk and treatment-related outcomes?
3. Trial Design
Clinical biomarker analysis study.
1300 participants.
Started 2011-10 with primary completion in 2015-12.
NSABP Foundation Inc, sponsor type NETWORK.
4. Endpoints
| Endpoint | Time frame |
|---|---|
| Association between low, intermediate, and high 21-gene recurrence score (RS) and risk of LRR | approximately 4 years |
| Identification of a subgroup of patients who may or may not need radiotherapy after surgery | approximately 4 years |
| 21-gene RS in predicting treatment benefit, reducing LRR risk, and improving DFS and OS in node-positive ER+ patients | approximately 4 years |
5. Planned Analysis
The ClinicalTrials.gov record lists primary endpoints but does not report posted statistical analyses or endpoint results.
The registry-defined endpoints are association and prediction questions. For a recurrence-risk biomarker, statistical analysis would typically evaluate relationships between recurrence score categories and time-to-event outcomes such as local recurrence, disease-free survival, or overall survival. Analyses of this type commonly involve survival methods, regression modeling, and assessment of whether biomarker categories provide clinically meaningful separation of risk groups.
6. Statistical Methodology
Biomarker association analysis
The central statistical challenge in a genomic biomarker study is determining whether a measured biological characteristic is associated with future clinical outcomes. The recurrence score is treated as a prognostic or predictive marker depending on whether it describes baseline risk or modifies treatment benefit.
Clinical outcome = biomarker information + treatment information + patient characteristics + statistical uncertainty
Risk groups and clinical interpretation
The registry specifies low, intermediate, and high 21-gene recurrence score groups. Categorizing a continuous biomarker can make results easier to interpret clinically, but it may also reduce information compared with analyzing the underlying continuous measurement.
Time-to-event outcomes
Local recurrence risk, disease-free survival, and overall survival are examples of outcomes where follow-up time and censoring must be considered. Patients without an observed event contribute information until their last known follow-up.
7. Statistical Methods Explained
Why study the association between recurrence score categories and LRR?
Local recurrence risk is a clinically meaningful outcome. Comparing recurrence patterns across recurrence-score groups can evaluate whether the biomarker identifies groups with different observed risks.
What is the difference between prognostic and predictive biomarkers?
A prognostic biomarker is associated with outcome regardless of treatment. A predictive biomarker is associated with differential treatment benefit between treatment approaches.
Why are time frames important in biomarker studies?
A risk estimate is always connected to a follow-up period. The registry specifies approximately 4 years for the listed primary endpoints.
Why are confidence intervals important?
Confidence intervals describe uncertainty around an estimated association. They help distinguish a precise estimate from one based on limited information.
Why does association not prove causation?
A biomarker associated with an outcome may reflect underlying disease biology rather than directly causing the outcome.
8. Limitations
- No posted statistical analyses: The ClinicalTrials.gov record does not report formal statistical analyses or numerical endpoint results.
- Biomarker interpretation: Associations between genomic measurements and outcomes require careful interpretation because correlation does not establish mechanism.
- Risk-group categorization: Grouping patients into recurrence-score categories may simplify interpretation but can reduce statistical information.
- Time-dependent outcomes: Recurrence and survival outcomes require appropriate handling of follow-up and censoring.
9. Why This Trial Matters Statistically
| Concept | Application |
|---|---|
| Biomarker validation | Evaluating whether a molecular measurement is associated with clinical outcomes. |
| Risk stratification | Comparing low, intermediate, and high recurrence score groups. |
| Time-to-event analysis | Relevant for recurrence and survival outcomes. |
| Predictive modeling | Assessing whether biomarkers relate to treatment benefit. |