Treatment Heterogeneity for Survival Outcomes

Xu, Yizhe; Ignatiadis, Nikolaos; Sverdrup, Erik; Fleming, Scott W.; Wager, Stefan; Shah, Nigam H.

doi:10.48550/arxiv.2207.07758

Cited by 2 publications

(5 citation statements)

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“…These IPD requirements may be so high as to be impractical and rarely applicable to policy decisions. In a clinical trial case study Xu et al address the trial follow up limitation of IPD by using a decision model to predict long term outcomes for the IPD (Xu et al, 2022). We classify this as an "augmented IPD" method which, in contrast to the "pure IPD" approach, uses disease simulation modeling methods to augment IPD.…”

Section: Incorporating Heterogeneity and Machine Learning Into Decisi...mentioning

confidence: 99%

“…In the introduction, we classified the methodology of Bonander et al and Xu et al as "pure IPD" and "augmented IPD" respectively (Bonander & Svensson, 2021;Xu et al, 2022). One novel contribution in this paper is to extend the application of ML in cost effectiveness analysis to a "full decision modeling" approach.…”

Section: Value Of Heterogeneitymentioning

confidence: 99%

“…Machine learning (ML) techniques can be used to find the patient characteristics that best predict risk and variation in treatment effects, and have been proposed for use in health decision analysis and economic evaluation (Bonander & Svensson, 2021;Padula et al, 2022;Sadique et al, 2022;Xu et al, 2022). There has been limited discussion, however, of the best approach to integrate ML-derived parameter estimates into decision models.…”

Section: Introductionmentioning

confidence: 99%

“…While there has been a recent explosion of ML methods that estimate heterogeneous parameters for survival data (see Hu et al, Curth et al and Xu et al, for reviews and comparative simulation studies), the estimation of uncertainty derived from most of these methods is not well understood (Curth et al, 2021;Hu et al, 2021;Xu et al, 2022). This is a general phenomenon, as many ML methods are developed to optimize prediction success and are less focused on uncertainty quantification (Athey & Imbens, 2019).…”

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confidence: 99%

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Integrating decision modeling and machine learning to inform treatment stratification

Glynn,

Giardina,

Hatamyar

et al. 2024

Health Economics

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There is increasing interest in moving away from “one size fits all (OSFA)” approaches toward stratifying treatment decisions. Understanding how expected effectiveness and cost‐effectiveness varies with patient covariates is a key aspect of stratified decision making. Recently proposed machine learning (ML) methods can learn heterogeneity in outcomes without pre‐specifying subgroups or functional forms, enabling the construction of decision rules (‘policies’) that map individual covariates into a treatment decision. However, these methods do not yet integrate ML estimates into a decision modeling framework in order to reflect long‐term policy‐relevant outcomes and synthesize information from multiple sources. In this paper, we propose a method to integrate ML and decision modeling, when individual patient data is available to estimate treatment‐specific survival time. We also propose a novel implementation of policy tree algorithms to define subgroups using decision model output. We demonstrate these methods using the SPRINT (Systolic Blood Pressure Intervention Trial), comparing outcomes for “standard” and “intensive” blood pressure targets. We find that including ML into a decision model can impact the estimate of incremental net health benefit (INHB) for OSFA policies. We also find evidence that stratifying treatment using subgroups defined by a tree‐based algorithm can increase the estimates of the INHB.

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Section: Incorporating Heterogeneity and Machine Learning Into Decisi...mentioning

confidence: 99%

Section: Value Of Heterogeneitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Integrating decision modeling and machine learning to inform treatment stratification

Glynn,

Giardina,

Hatamyar

et al. 2024

Health Economics

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“…The estimation of HTEs in the presence of censored time-to-events has been limited. Xu et al (2022) explore the problem and describe standard approaches to estimate treatment effect heterogeneity with survival outcomes. They also describe challenges associated with existing risk models when assessing treatment effect heterogeneity in the case of cardiovascular health.…”

Section: Related Workmentioning

confidence: 99%

Counterfactual Phenotyping with Censored Time-to-Events

Nagpal

Goswami

Dufendach

et al. 2022

Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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Studies involving both randomized experiments as well as observational data typically involve time-to-event outcomes such as time-to-failure, death or onset of an adverse condition. Such outcomes are typically subject to censoring due to loss of follow-up and established statistical practice involves comparing treatment efficacy in terms of hazard ratios between the treated and control groups. In this paper we propose a statistical approach to recovering sparse phenogroups (or subtypes) that demonstrate differential treatment effects as compared to the study population. Our approach involves modelling the data as a mixture while enforcing parameter shrinkage through structured sparsity regularization. We propose a novel inference procedure for the proposed model and demonstrate its efficacy in recovering sparse phenotypes across large landmark real world clinical studies in cardiovascular health.

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Treatment Heterogeneity for Survival Outcomes

Cited by 2 publications

References 0 publications

Integrating decision modeling and machine learning to inform treatment stratification

Integrating decision modeling and machine learning to inform treatment stratification

Counterfactual Phenotyping with Censored Time-to-Events

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