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
CALGB 9344 was a completed clinical study in breast cancer focused on laboratory and biomarker analyses using tissue samples from women treated with paclitaxel in CALGB-9344 or CALGB-9741.
| Feature | CALGB 9344 |
|---|---|
| Condition | Breast Cancer |
| Status | COMPLETED |
| Lead sponsor | Alliance for Clinical Trials in Oncology |
| Sponsor type | OTHER |
| Interventions | Gene expression analysis; polymerase chain reaction; laboratory biomarker analysis |
2. Clinical Question
Population
Women with breast cancer treated with paclitaxel on clinical trial CALGB-9344 or CALGB-9741.
Intervention
Tissue sample evaluation using genetic and laboratory biomarker analysis methods.
Comparator
The registry lists one arm for this study.
Primary question
The study evaluated biomarker relationships with disease-free survival.
3. Trial Design
The registry describes CALGB 9344 as a completed study with 3677 participants and one listed arm. The study interventions involved analysis of tissue samples using gene expression analysis, polymerase chain reaction, and laboratory biomarker analysis.
Study population
Women treated with paclitaxel for breast cancer on CALGB-9344 or CALGB-9741.
Laboratory approach
Genetic and biomarker laboratory analyses were used to evaluate tissue samples.
4. Endpoints
| Endpoint | Time frame |
|---|---|
| Disease-free survival (DFS) | Up to 10 years |
5. Planned Analysis
The ClinicalTrials.gov record identifies disease-free survival (DFS) as the primary endpoint with a follow-up time frame of up to 10 years. The registry does not report posted statistical analyses or endpoint results.
Disease-free survival is a time-to-event endpoint. Analyses of this type commonly use methods that account for censoring, such as Kaplan-Meier estimation and regression approaches for time-to-event outcomes. Results have not been posted on ClinicalTrials.gov.
6. Statistical Methodology
Time-to-event analysis
Disease-free survival measures the time until a defined event occurs or until the participant is last known to be event-free. Statistical analysis must account for participants who have incomplete follow-up through censoring.
A survival endpoint is different from a simple proportion because it incorporates both whether an event occurred and when it occurred. A patient followed longer without an event contributes different information from a patient with a shorter observation period.
Biomarker analysis context
Biomarker studies often investigate whether measurable biological characteristics are associated with clinical outcomes. Interpretation requires attention to the relationship between laboratory measurements, endpoint definitions, and the statistical framework used for evaluation.
7. Statistical Methods Explained
Why is disease-free survival analyzed differently from a simple rate?
Because follow-up times can differ between participants, time-to-event methods incorporate both event timing and censoring rather than only counting events.
What does censoring mean?
Censoring occurs when a participant's exact event time is not observed during follow-up. Proper methods use available follow-up information without assuming the participant experienced an event afterward.
Why are biomarkers not automatically predictive?
A biomarker association requires statistical evidence showing that measured biological characteristics relate to clinical outcomes. Associations must be interpreted within the design and analysis framework.
Why is endpoint definition important?
The meaning of a statistical result depends on the exact endpoint being analyzed. Disease-free survival has a different interpretation from overall survival or response measures.
Why are confidence intervals important?
Confidence intervals describe uncertainty around an estimate. They help show how precisely an observed association has been estimated.
8. Limitations
- The registry does not report posted statistical analyses for this study.
- The registry does not report endpoint results for disease-free survival.
- The available registry description does not provide subgroup analyses, safety comparisons, or detailed statistical analysis plans.
- Biomarker findings require interpretation within the specific laboratory and clinical context in which they were measured.
9. Why This Trial Matters Statistically
CALGB 9344 illustrates how clinical trial resources can be used beyond the primary treatment comparison by studying biological information from collected tissue samples.
| Concept | Application |
|---|---|
| Time-to-event analysis | Disease-free survival over up to 10 years |
| Biomarker analysis | Gene expression and laboratory measurements |
| Censoring | Important consideration for survival endpoints |
| Clinical interpretation | Connecting laboratory findings with patient outcomes |
10. Sources
- ClinicalTrials.gov: NCT00991263