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Case-only analysis in small studies of predictive biomarkers
M Hauptmann1, V H Nguyen2,3, L Sollfrank2
1Brandenburg Medical School Theodor Fontane, Institute of Biostatistics and Registry Research, Fehrbelliner Straße 39, 16816, Neuruppin, Germany. michael.hauptmann@mhb-fontane.de.
Case-only analysis for biomarkers in cancer treatment selection is generally inferior to full cohort analysis. However, it shows promise for cost savings in specific scenarios, like rare events and independent treatment assignment.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Cancer Research
Background:
- Biomarkers are crucial for guiding cancer treatment selection.
- Many potential biomarkers fail due to small studies and inconclusive results.
- Traditional Cox regression for biomarker-treatment interaction has limitations.
Purpose of the Study:
- To evaluate the performance of case-only logistic regression with Firth correction for biomarker-treatment interaction analysis.
- To assess the small sample properties of this method in a breast cancer context.
Main Methods:
- Simulation study using breast cancer data.
- Comparison of case-only logistic regression with Firth correction against full cohort analysis.
- Evaluation of bias-eliminating Firth correction and profile likelihood confidence intervals.
Main Results:
- Case-only analysis is generally inferior to full cohort analysis.
- Acceptable properties observed when the biomarker is protective/null, event rate is low, and treatment is independent of marker.
- Substantial cost savings are possible under these specific conditions.
Conclusions:
- Case-only analysis can be a cost-effective alternative in specific biomarker-treatment interaction studies.
- The method's reliability is sensitive to assumptions regarding biomarker effect, event rate, and treatment independence.
- Careful consideration of study design and assumptions is necessary for valid case-only analysis.
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