A parsimonious personalized dose-finding model via dimension reduction

Zhou, Wenzhuo; Zhu, Ruoqing; Zeng, Donglin

doi:10.1093/biomet/asaa087

Cited by 10 publications

(4 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, Chen et al (2016) and other direct methods (Kallus and Zhou, 2018;Bica et al, 2020;Sondhi et al, 2020;Zhu et al, 2020;Schulz and Moodie, 2020;Zhou et al, 2021) use inverse probability weighted (IPW) or augmented IPW estimators of the value function to address confounding issues by reweighting the observational data to mimic randomized trials. However, none of these approaches are directly applicable for the PDI problem.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Policy Learning for Optimal Individualized Dose Intervals

Chen¹,

Li²,

Yu³

2022

Preprint

View full text Add to dashboard Cite

We study the problem of learning individualized dose intervals using observational data. There are very few previous works for policy learning with continuous treatment, and all of them focused on recommending an optimal dose rather than an optimal dose interval. In this paper, we propose a new method to estimate such an optimal dose interval, named probability dose interval (PDI). The potential outcomes for doses in the PDI are guaranteed better than a pre-specified threshold with a given probability (e.g., 50%). The associated nonconvex optimization problem can be efficiently solved by the Difference-of-Convex functions (DC) algorithm. We prove that our estimated policy is consistent, and its risk converges to that of the best-in-class policy at a root-n rate. Numerical simulations show the advantage of the proposed method over outcome modeling based benchmarks. We further demonstrate the performance of our method in determining individualized Hemoglobin A1c (HbA1c) control intervals for elderly patients with diabetes.

show abstract

Section: Related Workmentioning

confidence: 99%

“…To estimate the unknown function f opt (x), we can use the existing methods for optimal dose finding (Chen et al, 2016;Kallus and Zhou, 2018;Zhu et al, 2020;Zhou et al, 2021). The choice of which PDI (onesided or two-sided) to use usually depends on practical considerations.…”

Section: Non-convex Surrogate Loss Relaxationmentioning

confidence: 99%

Policy Learning for Optimal Individualized Dose Intervals

Chen¹,

Li²,

Yu³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Learning good strategies in a continuous action space is important for many real-world problems (Lillicrap et al, 2015), including precision medicine, autonomous driving, etc. In particular, when developing a new dynamic regime to guide the use of medical treatments, it is often necessary to decide the optimal dose level (Murphy, 2003;Laber et al, 2014;Chen et al, 2016;Zhou et al, 2021b). In infinite horizon sequential decision-making settings (Luckett et al, 2019;Shi et al, 2021), learning such a dynamic treatment regime falls into a reinforcement learning (RL) framework.…”

Section: Introductionmentioning

confidence: 99%

Quasi-optimal Learning with Continuous Treatments

Li¹,

Zhou²,

Zhu³

2023

Preprint

View full text Add to dashboard Cite

Many real-world applications of reinforcement learning (RL) require making decisions in continuous action environments. In particular, determining the optimal dose level plays a vital role in developing medical treatment regimes. One challenge in adapting existing RL algorithms to medical applications, however, is that the popular infinite support stochastic policies, e.g., Gaussian policy, may assign riskily high dosages and harm patients seriously. Hence, it is important to induce a policy class whose support only contains near-optimal actions, and shrink the action-searching area for effectiveness and reliability. To achieve this, we develop a novel quasi-optimal learning algorithm, which can be easily optimized in off-policy settings with guaranteed convergence under general function approximations. Theoretically, we analyze the consistency, sample complexity, adaptability, and convergence of the proposed algorithm. We evaluate our algorithm with comprehensive simulated experiments and a dose suggestion real application to Ohio Type 1 diabetes dataset.

show abstract

“…Zhu et al (2020) study a kernel assisted optimal dose rule method. Zhou et al (2021) propose a dimension reduced kernel approximation method. These existing works focus on the estimation of optimal policy or effect of continuous policy with low-dimensional covariates.…”

Section: Related Literaturementioning

confidence: 99%

Statistical learning for asset allocation

Ding¹

View full text Add to dashboard Cite

We establish a high-dimensional statistical learning framework for individualized asset allocation. Our proposed methodology addresses continuous-action decision-making with a large number of characteristics. We develop a discretization approach to model the effect from continuous actions and allow the discretization level to be large and diverge with the number of observations. The value function of continuous-action is estimated using penalized regression with generalized penalties that are imposed on linear transformations of the model coefficients. We show that our estimators using generalized folded concave penalties enjoy desirable theoretical properties and allow for statistical inference of the optimal value associated with optimal decision-making. Empirically, the proposed framework is exercised with the Health and Retirement Study data in finding individualized optimal asset allocation. The results show that our individualized optimal strategy improves individual financial well-being and surpasses benchmark strategies.

show abstract

A parsimonious personalized dose-finding model via dimension reduction

Cited by 10 publications

References 55 publications

Policy Learning for Optimal Individualized Dose Intervals

Policy Learning for Optimal Individualized Dose Intervals

Quasi-optimal Learning with Continuous Treatments

Statistical learning for asset allocation

Contact Info

Product

Resources

About