Assessing Causal Effects in a Longitudinal Observational Study With “Truncated” Outcomes Due to Unemployment and Nonignorable Missing Data

Bia, Michela; Mattei, Alessandra; Mercatanti, Andrea

doi:10.1080/07350015.2020.1862672

Cited by 3 publications

(1 citation statement)

References 54 publications

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“…However, these bounds are often too wide to be informative for real (i.e., clinical or policy) applications (Yang and Ding, 2018). Beyond interval identification, the second stream of literature invokes additional structural and parametric modeling assumptions to point identify the SACE, e.g., Hayden et al (2005), Egleston et al (2006), Zhang et al (2009), Chiba andVanderWeele (2011), Frumento et al (2012), Wang et al (2017) and Bia et al (2021). While convenient to implement, fully parametric modeling necessitates invoking various assumptions that are often challenging to verify, and further, their violations can lead to biased SACE estimates, as we demonstrate in Section 5.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Machine Learning Approach for Estimating Heterogeneous Survivor Causal Effects: Applications to a Critical Care Trial

Chen¹,

Harhay²,

Tong³

et al. 2022

Preprint

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Assessing heterogeneity in the effects of treatments has become increasingly popular in the field of causal inference and carries important implications for clinical decision-making. While extensive literature exists for studying treatment effect heterogeneity when outcomes are fully observed, there has been limited development of tools for estimating heterogeneous causal effects when patientcentered outcomes are truncated by a terminal event, such as death. Due to mortality occurring during study follow-up, the outcomes of interest are unobservable, undefined, or not fully observed for specific subgroups of participants, therefore requiring the principal stratification framework to draw valid causal conclusions. Motivated by the Acute Respiratory Distress Syndrome Network (ARDSNetwork) ARDS respiratory management (ARMA) trial, we developed a flexible Bayesian machine learning approach to estimate the average causal effect and heterogeneous causal effects among the always-survivors stratum when clinical outcomes are subject to truncation. We adopted Bayesian additive regression trees (BART) to flexibly specify separate models for the potential outcomes and latent strata membership. In the analysis of the ARMA trial, we found that the low tidal volume treatment had an overall benefit for participants sustaining acute lung injuries on the outcome of time to returning home, but substantial heterogeneity in treatment effects among the always-survivors, driven most strongly by sex and the alveolar-arterial oxygen gradient at baseline (a physiologic measure of lung function and source of hypoxemia) These findings illustrate how the proposed methodology could guide the prognostic enrichment of future trials in the field. We also demonstrated through a simulation study that our proposed Bayesian machine learning approach outperforms other parametric methods in reducing the estimation bias in both the average causal effect and heterogeneous causal effects for always-survivors.

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