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Martingale R-learner: estimating time-varying heterogeneous treatment effects for survival data

Abstract Number:

1100 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Yuchen Qi (1), Jue Hou (2), Ronghui Xu (3)

Institutions:

(1) UC San Diego, Department of Family Medicine & Public Health, N/A, (2) N/A, N/A, (3) University of California-San Diego, N/A

Co-Author(s):

Jue Hou  
N/A
Ronghui Xu  
University of California-San Diego

First Author:

Yuchen Qi  
UC San Diego, Department of Family Medicine & Public Health

Presenting Author:

Yuchen Qi  
UC San Diego, Department of Family Medicine & Public Health

Abstract Text:

Future precision medicine requires accurate assessment on the explainable variability in treatment effects, known as heterogeneous treatment effects (HTE), to guide the optimal clinical decision at individual level. Measuring HTE by the ratio of survival probabilities under structural failure time model, we develop a martingale R-learner to estimate HTE. Our martingale R-learner incorporates flexible estimators for 1) marginal survival or cumulative hazards for association between outcome and confounders, and 2) time-varying propensity score in risk sets, which enables leveraging advances in machine learning. To reduce the impact of estimation bias in these two nuisance models on HTE, we proposed a Neyman orthogonal score based on an orthogonal decomposition of conditional model martingale residuals into residuals of propensity score and marginal model martingale. The resulting martingale R-learner attains the quasi-oracle property, i.e. estimation error of nuisance models have no impact on HTE if their estimators are consistent at o(n^(-1/4)) rate. Numerical experiments in various settings demonstrated valid empirical performance consistent with theoretical properties.

Keywords:

heterogeneous treatment effect|causal inference|survival analysis|orthogonal score| |

Sponsors:

Biometrics Section

Tracks:

Personalized/Precision Medicine

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I have read and understand that JSM participants must abide by the Participant Guidelines.

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I understand that JSM participants must register and pay the appropriate registration fee by June 3, 2025. The registration fee is non-refundable.

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