Abstract Number:
3087
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Yihan Bao (1), Lauren Bell (2), Elizabeth Williamson (3), Claire Garnett (4), Tianchen Qian (5)
Institutions:
(1) Department of Statistics and Data Science, Yale University, New Haven, CT, USA, (2) Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom, (3) Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom, (4) Department of Behavioural Science and Health, University College London, London, United Kingdom, (5) University of California, Irvine, Irvine, California, USA
Co-Author(s):
Lauren Bell
Medical Research Council Biostatistics Unit, University of Cambridge
Elizabeth Williamson
Department of Medical Statistics, London School of Hygiene and Tropical Medicine
Claire Garnett
Department of Behavioural Science and Health, University College London
First Author:
Yihan Bao
Department of Statistics and Data Science, Yale University
Presenting Author:
Abstract Text:
Micro-randomized trials (MRTs) are commonly conducted for optimizing mobile health interventions such as push notifications for behavior change. In analyzing such trials, causal excursion effects are often of primary interest, and their estimation typically involves inverse probability weighting (IPW). However, in a MRT, additional treatments can often occur during the time window over which an outcome is defined, and this can greatly inflate the variance of the causal effect estimator because IPW would involve a product of numerous weights. To reduce variance and improve estimation efficiency, we propose a new estimator using a modified version of IPW, which we call "per-decision IPW". It is applicable when the outcome is binary and can be expressed as the maximum of a series of sub-outcomes defined over sub-intervals of time. We establish the estimator's consistency and asymptotic normality. Through simulation studies and real data applications, we demonstrate substantial efficiency improvement of the proposed estimator over existing estimators. The new estimator can be used to improve the precision of primary and secondary analyses for MRTs with binary outcomes.
Keywords:
causal excursion effect |inverse probability weighting |log relative risk |micro-randomized trial |per-decision importance weighting |mobile health
Sponsors:
Lifetime Data Science Section
Tracks:
Miscellaneous
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