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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Updated: Mar 14, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Investigating causal questions about temporal and cumulative developmental effects: An introduction to the devMSMs

Isabella C Stallworthy1, Meriah L DeJoseph2, Emily R Padrutt3

  • 1Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States.

Child Development
|March 12, 2026
PubMed
Summary

This study introduces marginal structural models (MSMs) to analyze how the dose and timing of economic strain affect child behavior problems. The novel devMSMs R package aids these causal inference analyses.

Keywords:
causal inferencedevelopmental scienceinverse-probability-of-treatment-weightingmarginal structural model

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Area of Science:

  • Developmental Psychology
  • Biostatistics
  • Epidemiology

Background:

  • Causal inference in developmental studies is complex, especially with time-varying exposures and confounding.
  • Traditional methods like regression adjustment are insufficient for time-varying confounders.

Purpose of the Study:

  • Introduce marginal structural models (MSMs) as a robust tool for analyzing dosage and timing effects in developmental research.
  • Demonstrate the application of MSMs using longitudinal data on economic strain and child behavior problems.

Main Methods:

  • Conceptual overview of the potential outcomes framework, exposure histories, and inverse-probability-of-treatment weighting.
  • Application of MSMs to examine economic strain effects from infancy to early childhood.
  • Utilized the longitudinal Family Life Project dataset (N=1,292).

Main Results:

  • MSMs effectively address time-varying confounding in developmental research.
  • Economic strain during early life stages significantly impacts later behavior problems.
  • The study provides a practical guide to the devMSMs R package for implementing these methods.

Conclusions:

  • Marginal structural models offer a powerful approach for causal inference in developmental science, particularly for time-varying exposures.
  • Understanding the dosage and timing of early life stressors is crucial for predicting developmental trajectories.
  • The devMSMs package facilitates the application of advanced causal inference techniques in developmental research.