Abstract:
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Patient-reported outcome (PRO) measures are increasingly collected as a means of measuring healthcare quality and value. However, PRO measures often suffer from a high missing rate, and the missingness may depend on many patient factors. Under such a complex missing mechanism, statistical inference of the parameters in a model for predicting PRO measures is challenging. In this work, we propose to use an informative surrogate that can lead to a flexible imputation model lying in a low-dimensional subspace to efficiently infer the parameters of interest. To remove the bias due to the flexible imputation model, we identify a class of weighting functions as alternatives to the traditional propensity score and estimate a low-dimensional weighting function within the identified function class. Based on the estimated low-dimensional weighting function, we construct a one-step debiased estimator without using any information of the true missing propensity. We establish the asymptotic normality of the one-step debiased estimator. Simulation and an application to real-world data demonstrate the superiority of the proposed method.
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