A Framework for Reliable, Transparent, and Reproducible Population-Adjusted Indirect Comparisons

Area of Science:

• Health Economics and Outcomes Research
• Biostatistics
• Evidence Synthesis

Background:

• Conventional indirect treatment comparisons (ITCs) may suffer from imbalances in patient characteristics.
• Population-adjusted indirect comparison (PAIC) methods offer solutions for adjusting these imbalances and handling disconnected evidence networks.
• Existing PAIC implementations show variability and lack transparency, impacting reproducibility and reimbursement decisions.

Purpose of the Study:

• To propose a systematic framework for conducting and reporting Population-Adjusted Indirect Comparisons (PAICs).
• To enhance the transparency, reproducibility, and reliability of PAIC analyses in health technology assessment.

Main Methods:

• Development of a six-element systematic framework for PAIC analyses.
• Considerations include defining the comparison, selecting PAIC methods and adjustment variables, applying adjustment methods, assessing risk of bias, and ensuring comprehensive reporting.

Main Results:

• The proposed framework addresses key challenges in PAIC implementation.
• It guides consistent decision-making across six critical analytical elements.
• This promotes standardization and transparency in the application of PAIC.

Conclusions:

• A systematic framework is crucial for improving the consistency and transparency of PAIC methods.
• Standardized PAIC approaches will enhance the interpretability and reproducibility of health technology assessments.
• This framework supports informed reimbursement decision-making by ensuring robust evidence synthesis.