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Recovering knot placements in Bayesian piecewise growth models with missing data.

Ihnwhi Heo1, Fan Jia2, Sarah Depaoli2

  • 1Department of Psychological Sciences, University of California, Merced, 5200 N. Lake Road, Merced, CA, 95343, USA. ihnwhi.heo@gmail.com.

Behavior Research Methods
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Summary
This summary is machine-generated.

Bayesian piecewise growth models (PGMs) help analyze nonlinear trends. Accurate knot location estimation in PGMs depends heavily on prior distributions and handling missing data, especially with smaller sample sizes.

Keywords:
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Area of Science:

  • Statistics
  • Biostatistics
  • Growth Modeling

Background:

  • Bayesian piecewise growth models (PGMs) are valuable for analyzing nonlinear data with distinct developmental phases.
  • Knot locations, representing transitions between phases, are crucial parameters in PGMs.
  • Estimating knot locations from data offers more flexibility than a priori specification.

Purpose of the Study:

  • To investigate the impact of prior distributions and missing data on knot location recovery in Bayesian PGMs.
  • To understand how these factors influence the accuracy of estimated changepoints.

Main Methods:

  • A Monte Carlo simulation study was conducted.
  • Different prior specifications and varying levels of missing data were systematically examined.
  • The recovery of knot placements in Bayesian PGMs was the primary outcome measure.

Main Results:

  • Knot location estimates were strongly influenced by prior distributions, particularly with small sample sizes.
  • Estimates remained sensitive to informative and inaccurate priors even with larger sample sizes.
  • Missing data complicated knot recovery and could introduce bias, though accurate priors could mitigate this.

Conclusions:

  • Prior distributions and missing data critically influence the accuracy of knot location estimation in Bayesian PGMs.
  • Careful consideration of priors and data imputation strategies is essential for reliable changepoint analysis.
  • Findings highlight the intertwined nature of priors and missing data in PGM analyses.