Several analytical techniques are employed to interpret clinical data in cancer:
Statistical Analysis: Basic statistical methods help summarize data and identify significant differences between treatment groups. Machine Learning: Advanced algorithms can uncover complex patterns and predict outcomes based on large datasets. Survival Analysis: This technique assesses the time until an event of interest, such as death or disease recurrence. Bioinformatics: Computational tools analyze genomic and proteomic data to identify biomarkers and therapeutic targets.
