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Monotonic single-index models to assess drug interactions
Yubing Wan1, Susmita Datta2, J Jack Lee3
1Precision for Medicine, Frederick, MD, U.S.A.
Statistics in Medicine
|November 3, 2016
Summary
This study introduces a novel monotonic single-index model using penalized I-splines. The model effectively estimates dose-response surfaces and drug interactions, demonstrating strong performance in simulations and a case study.
Area of Science:
- Statistics
- Biometrics
- Econometrics
Background:
- Single-index models are widely used but lack focus on link function monotonicity.
- Monotonicity is often desirable for interpretability and applicability in fields like economics and biometrics.
Purpose of the Study:
- To propose and develop a monotonic single-index model.
- To estimate dose-response surfaces and assess drug interactions.
- To provide a robust estimation algorithm and variance estimation for the model.
Main Methods:
- Constructing the link function using penalized I-splines.
- Implementing coefficient constraints to ensure monotonicity.
- Developing an algorithm for estimating single-index parameters and the link function.
- Utilizing sandwich estimation for variance of index parameters.
Main Results:
- The proposed model successfully achieves monotonicity of the link function.
- The estimation algorithm and variance estimation are effective.
- The model accurately estimates dose-response surfaces and assesses drug interactions.
Conclusions:
- The monotonic single-index model is a valuable tool for analyzing dose-response relationships and drug interactions.
- The proposed methodology shows excellent performance in both simulated data and a real-world case study.
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