Comment: Clarifying Endogeneous Data Structures and Consequent Modelling Choices Using Causal Graphs

We read with great interest the article by Qian. Klasnja and Murphy (2020), and commend the authors for focusing on principled estimation and providing a quantitative approach to healthcare delivery through mobile devices. The quantitative analyses studied here could have wide-ranging applications t...

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Bibliographic Details
Published in:Statistical science 2020-08, Vol.35 (3), p.391
Main Authors: Moodie, Erica E. M., Stephens, David A.
Format: Article
Language:English
Subjects:
Data structures
Datasets
Electronic devices
Health care
Health care delivery
Monitoring systems
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Summary:We read with great interest the article by Qian. Klasnja and Murphy (2020), and commend the authors for focusing on principled estimation and providing a quantitative approach to healthcare delivery through mobile devices. The quantitative analyses studied here could have wide-ranging applications that may serve to increase patient empowerment by taking medical monitoring and even intervention out of the clinic and into the home. Here, we wish to delve into two complementary aspects of the work: first, we attempt to give clarifications concerning the parameters) of interest, and second, we provide visualizations of potential scenarios that may help to clarify estimands and when biases due to endogeneity may arise.
ISSN:0883-4237
2168-8745
DOI:10.1214/20-STS777