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dynr.mi: An R Program for Multiple Imputation in Dynamic Modeling.

Yanling Li1, Linying Ji1, Zita Oravecz2

  • 1Department of Human Development and Family Studies, The Pennsylvania State University, University Park, PA 16802 USA.

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Summary
This summary is machine-generated.

This study introduces dynr.mi(), an R function for handling missing data in intensive longitudinal data (ILD) using multiple imputation (MI). It demonstrates MI

Keywords:
Dynamic modelingmissing datamultiple imputationphysiological measures

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Area of Science:

  • Psychometrics
  • Longitudinal Data Analysis
  • Statistical Modeling

Background:

  • Intensive longitudinal data (ILD) offers rich insights but presents challenges, notably increased missingness over time, potentially under non-ignorable scenarios.
  • Multiple Imputation (MI) is a method to address missing data by creating multiple datasets and pooling results for robust inference.

Purpose of the Study:

  • To introduce dynr.mi(), a novel function within the R package 'dynr' for estimating dynamic systems models with missing data using MI.
  • To integrate 'dynr' estimation capabilities with 'MICE' (Multivariate Imputation by Chained Equations) for handling non-ignorable missingness in dynamic models.
  • To compare the performance of dynr.mi() with listwise deletion in analyzing complex longitudinal relationships.

Main Methods:

  • Development and utilization of the dynr.mi() function in R, integrating dynamic systems modeling with Multivariate Imputation by Chained Equations (MICE).
  • Application of dynr.mi() to a vector autoregressive model examining relationships between ambulatory physiological measures and self-reported affect.
  • Comparison of results obtained via MI with those from listwise deletion, with convergence diagnostics guiding the number of imputation iterations.

Main Results:

  • Significant differences in the statistical significance of covariate parameters were observed between the MI approach and listwise deletion.
  • The choice of imputation iterations, guided by convergence diagnostics, influenced the observed differences in parameter significance.
  • The study highlights the impact of handling missing data appropriately on the interpretation of dynamic systems models.

Conclusions:

  • The dynr.mi() function provides a valuable tool for addressing missing data challenges in intensive longitudinal data analysis within dynamic systems frameworks.
  • Emphasizes the critical importance of utilizing convergence diagnostics when implementing multiple imputation procedures for reliable statistical inference.
  • Findings underscore that robust handling of missing data is essential for accurate conclusions in complex longitudinal research.