Causal Inference in Oncology: Why, What, How and When

Area of Science:

• Oncology
• Biostatistics
• Epidemiology

Background:

• Randomized controlled trials (RCTs) provide average treatment effects but may not generalize to diverse real-world patient populations.
• Observational data offers insights into treatment effectiveness in routine clinical practice, but estimating causal effects presents challenges.

Purpose of the Study:

• To introduce the principles of causal inference in the context of oncology.
• To explain the estimation of treatment effects using both RCTs and observational data.
• To highlight the value of causal inference from real-world data for individualized cancer treatment decisions.

Main Methods:

• Review of causal inference concepts and methodologies.
• Discussion of challenges in estimating treatment effects from observational data.
• Presentation of a framework for conducting causal inference studies in oncology.

Main Results:

• Causal inference provides a formal framework for defining and estimating treatment effects.
• Observational data, when analyzed with causal inference methods, can supplement RCT findings.
• Understanding the strengths and limitations of both RCTs and observational causal inference is crucial.

Conclusions:

• Causal inference from observational data can enhance treatment effect estimation in oncology.
• Integrating insights from RCTs and real-world data supports more informed clinical decision-making.
• This approach facilitates personalized treatment strategies for cancer patients.