Estimating Individualized Treatment Rules for Ordinal Treatments

Chen, Jingxiang; Fu, Haoda; He, Xuanyao; Kosorok, Michael R.; Liu, Yufeng

doi:10.1111/biom.12865

Cited by 28 publications

(35 citation statements)

References 29 publications

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“…(), and Chen et al. (), in some situations the finite sample performance of the rule is better if we modify the outcome weight. For example, Zhou et al.…”

Section: Discussionmentioning

confidence: 98%

Optimal Treatment Assignment to Maximize Expected Outcome with Multiple Treatments

Lou

Shao

2017

Biometrics

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When there is substantial heterogeneity of treatment effectiveness, it is crucial to identify individualized treatment assignment rules for comparative treatment selection. Traditional approaches directly model clinical outcome and define optimal treatment rule according to the interactions between treatment and covariates. This approach relies on the success of separating the main effects from the covariate-treatment interaction effects, which may not be easy. To overcome this shortcoming, a recent approach, called outcome weighted learning, focuses on building an optimal treatment rule by maximizing the expected clinical outcome related with differential treatments. However, there seems to be a lack of approaches to explicitly deal with three or more treatments. In this article, we propose an outcome weighted learning method that extends estimating individualized treatment rules to multi-treatment case by using a vector hinge loss as a target function. Consistency of the resulting estimator is shown in the article. We demonstrate the performance of our approach in simulation studies and in a real data analysis.

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“…(), and Chen et al. (), in some situations the finite sample performance of the rule is better if we modify the outcome weight. For example, Zhou et al.…”

Section: Discussionmentioning

confidence: 98%

Optimal Treatment Assignment to Maximize Expected Outcome with Multiple Treatments

Lou

Shao

2017

Biometrics

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“…Few authors also have developed methods extending from the outcome weighted learning (OWL) concept described by Zhao et al For example, Mi et al used OWL framework along with an ensemble deep learning neural networks. Chen et al extended OWL for estimating individualized treatment rules for ordinal treatments. Some authors focused their methods on special data types.…”

Section: Discussionmentioning

confidence: 99%

Personalized treatment selection using data from crossover designs with carry‐over effects

Siriwardhana

Kulasekera

Datta

2019

Statistics in Medicine

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In this work, we propose a semiparametric method for estimating the optimal treatment for a given patient based on individual covariate information for that patient when data from a crossover design are available. Here, we assume there are carry‐over effects for patients switching from one treatment to another. For the K treatment (K ≥ 2) scenario, we show that nonparametric estimation of carry‐over effects can have the undesirable property that comparison of treatment means can only be done using independent outcome measurements from different groups of patients rather than using available joint measurements for each patient. To overcome this barrier, we compare probabilities of outcome variable of each treatment dominating outcome variables for all other treatments conditional on patient‐specific scores constructed from patient covariates. We suggest single‐index models as appropriate models connecting outcome variables to covariates and our empirical investigations show that frequencies of correct treatment assignments are highly accurate. The proposed method is also rather robust against departures from a single‐index model structure. We also conduct a real data analysis to show the applicability of the proposed procedure.

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“…Crossover GOWL shows marked improvement in both misclassification and estimated value for small n. When n is large, ridge regression yields competitive results with that from crossover GOWL, but crossover GOWL still appears to have marginal gains. Although GOWL in the parallel setting does not perform as well as OWL in any of the presented scenarios, Chen et al (2018) discuss scenarios where improvements in misclassification and estimated value are observed when using GOWL as opposed to OWL.…”

Section: Simulation Studiesmentioning

confidence: 94%

“…Therefore, the estimated decision function in OWL depends on the chosen shift in the outcomes. Chen et al (2018) propose GOWL, an extension of OWL, which handles negative rewards by modifying the hinge loss to be piecewise and weighting the misclassified observations by |Y |. With GOWL, there is no need to shift rewards.…”

Section: Existing Methodsmentioning

confidence: 99%

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Estimating individualized treatment regimes from crossover designs

et al. 2019

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The field of precision medicine aims to tailor treatment based on patientspecific factors in a reproducible way. To this end, estimating an optimal individualized treatment regime (ITR) that recommends treatment decisions based on patient characteristics to maximize the mean of a pre-specified outcome is of particular interest. Several methods have been proposed for estimating an optimal ITR from clinical trial data in the parallel group setting where each subject is randomized to a single intervention. However, little work has been done in the area of estimating the optimal ITR from crossover study designs. Such designs naturally lend themselves to precision medicine, because they allow for observing the response to multiple treatments for each patient. In this paper, we introduce a method for estimating the optimal ITR using data from a 2 × 2 crossover study with or without carryover effects. The proposed method is similar to policy search methods such as outcome weighted learning (OWL); however, we take advantage of the crossover design by using the difference in responses under each treatment as the observed reward. We establish Fisher and global consistency, present numerical experiments, and analyze data from a feeding trial to demonstrate the improved performance of the proposed method compared to standard methods for a parallel study design.

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Estimating Individualized Treatment Rules for Ordinal Treatments

Cited by 28 publications

References 29 publications

Optimal Treatment Assignment to Maximize Expected Outcome with Multiple Treatments

Optimal Treatment Assignment to Maximize Expected Outcome with Multiple Treatments

Personalized treatment selection using data from crossover designs with carry‐over effects

Estimating individualized treatment regimes from crossover designs

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