Relative contrast estimation and inference for treatment recommendation

Area of Science:

• Biostatistics
• Health Economics
• Clinical Trial Design

Background:

• Resource constraints necessitate effective prioritization of individualized treatments.
• Current methods often focus on absolute treatment effect differences, which may not always be optimal.
• Relative treatment effect differences can offer a more appropriate metric in certain clinical settings.

Purpose of the Study:

• To develop a statistical framework for modeling relative individualized treatment benefits.
• To introduce a single index model for scale-invariant contrasts of conditional treatment effects.
• To propose efficient semiparametric methods for estimating treatment benefit indices.

Main Methods:

• Modeling scale-invariant contrasts between conditional treatment effects.
• Developing semiparametric estimating equations, including the efficient score.
• Proposing a two-step estimation procedure involving doubly robust loss minimization and efficiency augmentation.

Main Results:

• Demonstrated that scale-invariant contrasts are monotonic transformations of each other.
• Characterized semiparametric estimating equations for index parameter estimation.
• Proposed a novel two-step approach for semiparametric efficiency.

Conclusions:

• The proposed method effectively models relative individualized treatment benefits.
• The semiparametric approach achieves efficiency in estimating treatment benefit indices.
• Theoretical and numerical studies confirm the superiority of the developed approach for treatment prioritization.