A parsimonious personalized dose-finding model via dimension reduction

Wenzhuo Zhou, Ruoqing Zhu and Donglin Zeng

Biometrika, 2021, vol. 108, issue 3, 643-659

Abstract: SummaryLearning an individualized dose rule in personalized medicine is a challenging statistical problem. Existing methods often suffer from the curse of dimensionality, especially when the decision function is estimated nonparametrically. To tackle this problem, we propose a dimension reduction framework that effectively reduces the estimation to an optimization on a lower-dimensional subspace of the covariates. We exploit the fact that the individualized dose rule can be defined in a subspace spanned by a few linear combinations of the covariates to obtain a more parsimonious model. Owing to direct maximization of the value function, the proposed framework does not require the inverse probability of the propensity score under observational studies. This distinguishes our approach from the outcome-weighted learning framework, which also solves decision rules directly. Within the same framework, we further propose a pseudo-direct learning approach that focuses more on estimating the dimensionality-reduced subspace of the treatment outcome. Parameters in both approaches can be estimated efficiently using an orthogonality-constrained optimization algorithm on the Stiefel manifold. Under mild regularity assumptions, results on the asymptotic normality of the proposed estimators are established. We also derive the consistency and convergence rate of the value function under the estimated optimal dose rule. We evaluate the performance of the proposed approaches through extensive simulation studies and analysis of a pharmacogenetic dataset.

Keywords: Dimension reduction; Direct learning; Individualized dose rule; Propensity score; Semiparametric inference; Stiefel manifold (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1093/biomet/asaa087 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:biomet:v:108:y:2021:i:3:p:643-659.

Ordering information: This journal article can be ordered from
https://academic.oup.com/journals

Access Statistics for this article

Biometrika is currently edited by Paul Fearnhead

More articles in Biometrika from Biometrika Trust Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK.
Bibliographic data for series maintained by Oxford University Press ().