Abstract Number:
3235
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Cuong Pham (1), Benjamin Baer (1), Ashkan Ertefaie (2)
Institutions:
(1) N/A, N/A, (2) University of Rochester, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
Dynamic treatment regimes have increasingly become more popular as they allow the personalization of treatments based on patient characteristics and medical history. Selecting the optimal dynamic treatment regime hinges on identifying the best marginal structural model. The assessment of the "goodness-of-fit" among different marginal structural models commonly involves utilizing the risk associated with each model. However, calculating the risk of a marginal structural model involves unobserved potential outcomes under a treatment regime, prompting the frequent use of the inverse probability weighting method. While inverse probability weighting is easy to implement, it is inefficient and yields biased estimates if the propensity model is incorrectly specified. To overcome these limitations, we propose a multiply robust estimator. The multiply robust estimator is efficient. It also remains unbiased even with misspecified propensity score models. Despite the advantages, the multiply robust estimator is challenging to compute in practice because of the substantial number of nuisance parameters. In order to overcome this issue, we propose a procedure to enhance the inverse probability.
Keywords:
Marginal Structural Model|Model Selection|Inverse Probability Weighted Estimator|Undersmoothing|Causal Inference|Dynamic Treatment Regimes
Sponsors:
Biometrics Section
Tracks:
Model/Variable Selection
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