P-value and Testing of Hypothesis in Cancer Oncology Research
This webinar introduces the foundations of hypothesis testing in cancer research and explains how statistical evidence is used to answer clinical questions. Participants will learn the concepts of null and alternative hypotheses, Type I and Type II errors, statistical power, confidence intervals, and interpretation of p-values in oncology studies. The session also discusses common misconceptions surrounding p-values, multiple testing issues, and practical examples from cancer clinical trials and observational studies to strengthen evidence-based decision making.
Session
Content
Updates
ICER, QALY are buzzwords for drug worth. Economic evaluations are a trial must-do.
Takeaway: Money talks, learn the lingo.
Platforms
Decision trees, Markov models, sensitivity tools turn numbers into payer-friendly stories.
Takeaway: Models sell the big picture.
Strategy
Craft data-driven models, test hard to win budget folks and policymakers.
Takeaway: Solid models mean wins.
Latest Story
A diabetes drug trial’s Markov model proved it was a steal, changing patient lives.
Takeaway: Economics makes or breaks drugs.
Analytics Challenge: Sketch a decision tree, post on X with #TrialsUnraveled!
Takeway
ICER (Incremental Cost-Effectiveness Ratio) and QALY (Quality-Adjusted Life Year) are core tools for assessing a drug’s value relative to its cost.
Integrating economic evaluations into clinical trials strengthens evidence for reimbursement and policy decisions.
Mastering health economics terminology empowers researchers to translate trial results into real-world healthcare value.