Comparing treatment policies with assistance from the structural nested mean model

Area of Science:

• Biostatistics
• Causal Inference
• Health Services Research

Background:

• Treatment policies, or dynamic treatment regimes, guide patient care based on history.
• Multiple competing policies often exist, representing different management strategies.
• Comparing these policies' effectiveness is crucial for optimal patient outcomes.

Purpose of the Study:

• To develop an "assisted estimator" for comparing the mean outcomes of competing treatment policies.
• To provide a method for evaluating different dynamic treatment regimes.

Main Methods:

• Utilized estimators from the Structural Nested Mean Model (SNMM).
• Employed a parametric model to assess the causal effect of treatment over time.
• Applied the assisted estimator to real-world data from the ExTENd study.

Main Results:

• The assisted estimator facilitates the comparison of mean outcomes between different treatment policies.
• Demonstrated the utility of SNMM-based estimators in policy comparison.
• Provided insights into treatment strategies for alcohol dependence.

Conclusions:

• The assisted estimator is a valuable tool for comparing competing treatment policies.
• This methodology enhances causal inference in dynamic treatment regimes.
• Findings support evidence-based decision-making in managing chronic conditions like alcohol dependence.