Personalized medicine is revolutionizing healthcare by tailoring treatment decision- making to patients. This seminar delves into the critical aspects of causal inference and prediction modeling for the advancement of personalized medicine. It will cover the following key points:

1. Treatment Effect: Concepts and Definitions

   1. Why Estimate Population Average Treatment Effects (ATE, ATT, ATU)?

      We explore the rationale behind estimating population-level treatment effects rather than individual-level effects.

   2. Patient A is not Patient B: Conditional Average Treatment Effects (CATE)

      We discuss the concept of conditional average treatment effects, emphasizing the heterogeneity of treatment responses across patients.

2. Heterogeneous Treatment Effects According to Baseline Risk (45min)

   1. Development of Risk Prediction Models

      We delve into the creation of risk prediction models.

   2. Assessment of Risk Prediction Model Performance

      We present evaluation metrics like discrimination and calibration of risk prediction models.

   3. R Package “metamisc”

      An introduction to the R package “metamisc” for implementing these methodologies.

3. Heterogeneous Treatment Effects According to Baseline Covariates (45min)

   1. Development of CATE Prediction Models

      We present the development of models that predict CATE to identify subgroups of patients who may benefit most from specific treatments.

   2. Assessment of CATE Prediction Models

      Similar to risk prediction models, we present the performance metrics of CATE prediction models using discrimination and calibration.

   3. R Package “precmed"

      An overview of the R package “precmed” for implementing CATE prediction.

4. What’s next? (20min)

   1. Population Average Decision Effects According to Prediction Models

      We discuss the importance of estimating the effect of decision-making informed by prediction models.

   2. Synthesis of Randomized Studies & Real-World Data

      We discuss the synthesis of data from randomized controlled trials and real-world data to advance personalized medicine.