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