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Substantive model compatible multilevel multiple imputation: A joint modeling approach.

Matteo Quartagno1, James R Carpenter1,2

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|August 12, 2022
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Summary
This summary is machine-generated.

Substantive model compatible multiple imputation (SMC-MI) offers a superior approach for handling complex analysis models. This joint modeling strategy effectively addresses interactions, non-linearities, and random slopes in data imputation.

Keywords:
joint modelingmissing datamultilevelmultiple imputation

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Area of Science:

  • Statistics
  • Biostatistics
  • Data Science

Background:

  • Substantive model compatible multiple imputation (SMC-MI) is an advanced technique for handling missing data.
  • It is particularly beneficial for analysis models incorporating interactions, non-linearities, and partially observed random slopes.

Purpose of the Study:

  • To investigate a novel SMC-MI strategy using joint modeling of analysis model covariates.
  • To provide practical code implementation and conduct extensive simulations to evaluate the strategy's performance.

Main Methods:

  • Developed and implemented a joint modeling (JM) strategy for SMC-MI.
  • Conducted simulations to assess performance under varying conditions (e.g., missing data level, non-linearities, imputation model specification).
  • Applied the imputation methods to a motivating dataset.

Main Results:

  • The proposed SMC-JM strategy outperformed standard JM imputation, especially with significant random slope variation, non-linearities, and interactions.
  • Results demonstrated robustness to minor imputation model mis-specifications for covariates.
  • Sufficient observed clusters at level 2 are crucial for unbiased estimation when imputing level 2 data.

Conclusions:

  • SMC-JM is recommended over standard JM imputation for analysis models with complexities like non-linearities or random slopes.
  • This method enhances the accuracy and reliability of imputation in sophisticated statistical modeling.