Causal survival analysis under competing risks using longitudinal modified treatment policies

Iván Díaz (), Katherine L. Hoffman () and Nima S. Hejazi ()
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Iván Díaz: New York University Grossman School of Medicine
Katherine L. Hoffman: Columbia University
Nima S. Hejazi: Harvard University

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2024, vol. 30, issue 1, No 8, 213-236

Abstract: Abstract Longitudinal modified treatment policies (LMTP) have been recently developed as a novel method to define and estimate causal parameters that depend on the natural value of treatment. LMTPs represent an important advancement in causal inference for longitudinal studies as they allow the non-parametric definition and estimation of the joint effect of multiple categorical, ordinal, or continuous treatments measured at several time points. We extend the LMTP methodology to problems in which the outcome is a time-to-event variable subject to a competing event that precludes observation of the event of interest. We present identification results and non-parametric locally efficient estimators that use flexible data-adaptive regression techniques to alleviate model misspecification bias, while retaining important asymptotic properties such as $$\sqrt{n}$$ n -consistency. We present an application to the estimation of the effect of the time-to-intubation on acute kidney injury amongst COVID-19 hospitalized patients, where death by other causes is taken to be the competing event.

Keywords: Modified treatment policies; Competing risks; Targeted minimum loss-based estimation; Double machine learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10985-023-09606-7

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