Estimation of causal quantile effects with a binary instrumental variable and censored data

• Published in: Journal of the Royal Statistical Society. Series B, Statistical methodology Vol. 83; no. 3; pp. 559 - 578
• Main Authors: Wei, Bo, Peng, Limin, Zhang, Mei‐Jie, Fine, Jason P.
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.07.2021
• Subjects: Asymptotic properties; B-cell lymphoma; Bone marrow; bone marrow transplant; censored quantile regression; complier quantile causal effect; computer software; data collection; Health sciences; Immunotherapy; instrumental variable (IV); Lymphoma; Monoclonal antibodies; Parameter estimation; Regression analysis; Robustness (mathematics); Statistical methods; Statistics; stochastic integral estimating equation; unmeasured Confounder; Weighting; Stochastic integral estimating equation; Instrumental variable (IV); Censored quantile regression; Unmeasured Confounder; Complier quantile causal effect
• ISSN: 1369-7412; 1467-9868
• DOI: 10.1111/rssb.12431

The causal effect of a treatment is of fundamental interest in the social, biological and health sciences. Instrumental variable (IV) methods are commonly used to determine causal treatment effects in the presence of unmeasured confounding. In this work, we study a new binary IV framework with randomly censored outcomes where we propose to quantify the causal treatment effect by the concept of complier quantile causal effect (CQCE). The CQCE is identifiable under weaker conditions than the complier average causal effect when outcomes are subject to censoring, and it can provide useful insight into the dynamics of the causal treatment effect. Employing the special characteristic of the binary IV and adapting the principle of conditional score, we uncover a simple weighting scheme that can be incorporated into the standard censored quantile regression procedure to estimate CQCE. We develop robust non‐parametric estimation of the derived weights in the first stage, which permits stable implementation of the second stage estimation based on existing software. We establish rigorous asymptotic properties for the proposed estimator, and confirm its validity and satisfactory finite‐sample performance via extensive simulations. The proposed method is applied to a bone marrow transplant data set to evaluate the causal effect of rituximab in diffuse large B‐cell lymphoma patients.