Data analysis in cancer research is crucial for interpreting results:
- Statistical Methods: Advanced statistical techniques are often required to handle large datasets and control for confounding variables.
- Bioinformatics: This is increasingly important in analyzing genomic data and identifying patterns that may indicate new therapeutic targets.
- Interdisciplinary Collaboration: Successful analysis often requires collaboration among biostatisticians, bioinformaticians, and clinicians to ensure that findings are both statistically sound and clinically relevant.
