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Activity Number: 144 - Methods for Missing and/or Misclassified Data
Type: Contributed
Date/Time: Monday, August 8, 2022 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #322800
Title: Multiple Robustness in Missing Data Analysis Using Multiple Penalized Spline of Propensity Prediction
Author(s): Yu-Che Chung* and Sanjib Basu
Companies: University of Illinois Chicago and Biostatistics, University of Illinois Chicago
Keywords: Missing data; MAR; PSPP; mPSPP
Abstract:

In missing data analysis, properties such as doubly robust property provide protection against model misspecification. Little and An(2004), Zhang and Little(2009) and others proposed penalized spline of propensity prediction(PSPP) method that provides doubly robustness for predicting marginal and conditional means under missing at random(MAR). We develop multiple penalized spline of propensity prediction(mPSPP) method which incorporates multiple propensity scores models. We establish that mPSPP is robust against propensity score misspecification. We also develop stratified mPSPP that provides multiple robustness for marginal and conditional means. This approach uses regularization when a large number of propensity score models are included in the prediction model. A simulation study was conducted to assess the performance of mPSPP in both non-regularization settings and regularization settings.


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