Comparing costs associated with risk stratification rules for t-year survival

Area of Science:

• Biostatistics
• Health Economics
• Clinical Decision-Making

Background:

• Accurate risk prediction is crucial for effective disease prevention and treatment strategies.
• Patient stratification into risk categories guides clinical interventions, but suboptimal assignments incur significant costs.
• Evaluating risk stratification (RS) rules necessitates considering associated clinical intervention costs.

Purpose of the Study:

• To propose a method for quantifying the value of RS rules based on expected costs of misclassification.
• To establish the relationship between cost parameters and optimal thresholds for minimizing total expected costs.
• To develop statistical inference procedures for evaluating and comparing RS rules.

Main Methods:

• Quantifying RS rule value by total expected cost of incorrect risk group assignment.
• Deriving optimal threshold values based on cost parameters to minimize population-wide expected costs.
• Developing statistical inference for evaluating and comparing RS rules, validated via simulation.

Main Results:

• A framework to link cost parameters with optimal RS thresholds was established.
• Statistical procedures for RS rule evaluation and comparison were developed and tested.
• The methodology was demonstrated using data from the Cardiovascular Health Study.

Conclusions:

• Incorporating cost-effectiveness into RS rule development optimizes clinical interventions and reduces healthcare expenses.
• The proposed methods provide a robust framework for evaluating and selecting superior RS rules.
• This approach enhances the clinical utility of risk prediction by directly addressing economic and medical consequences.