Determining the Baseline Strategy in a Cost-Effectiveness Analysis with Treatment Sequences

Area of Science:

• Health economics
• Health technology assessment
• Decision science

Background:

• The proliferation of treatment options necessitates evaluating sequences of interventions, not just individual ones.
• Health technology assessments (HTAs) must adapt to compare complex treatment pathways.
• Defining the baseline strategy is crucial for accurate cost-effectiveness analysis of treatment sequences.

Purpose of the Study:

• To investigate how the choice of baseline strategy affects cost-effectiveness results when evaluating treatment sequences.
• To analyze current practices in defining baseline strategies in HTA submissions.
• To propose a method for selecting an appropriate baseline strategy in economic models.

Main Methods:

• A review of submissions to the UK National Institute for Health and Care Excellence (NICE) to understand baseline strategy definitions.
• Development of a simple Markov model with four hypothetical treatments (cost-effective, extendedly dominated, higher cost, non-cost-effective) and a 'best supportive care' control.
• Generation and ranking of all treatment sequence permutations by net monetary benefit.

Main Results:

• Most NICE submissions relied on existing guidelines, expert opinion, or prior models to define baseline strategies; some lacked clear explanations.
• A non-cost-effective treatment was never part of an optimal sequence in the case study.
• The selection of the baseline treatment sequence demonstrably altered the cost-effectiveness estimates of new interventions.

Conclusions:

• The baseline strategy significantly influences the cost-effectiveness of treatment sequences.
• For efficient healthcare resource allocation, baseline strategies should be determined by ranking interventions based on net monetary benefit.
• Ignoring individual treatment cost-effectiveness when defining the baseline can lead to misleading economic evaluations.