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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

A sensitivity analysis for shared-parameter models for incomplete longitudinal outcomes.

An Creemers1, Niel Hens, Marc Aerts

  • 1I-BioStat, Universiteit Hasselt, Diepenbeek, Belgium.

Biometrical Journal. Biometrische Zeitschrift
|November 26, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a sensitivity analysis framework for incomplete data using shared-parameter models. It assesses how assumptions about unobserved data impact treatment effect estimates in clinical trials.

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Last Updated: Jun 18, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Clinical Trial Analysis

Background:

  • Models for incomplete data rely on assumptions about unobserved outcomes.
  • Different models can explain observed data but yield varying predictions for missing data.

Purpose of the Study:

  • To develop a sensitivity analysis framework for shared-parameter models with incomplete data.
  • To evaluate the impact of unverifiable assumptions on parameters of interest.

Main Methods:

  • Utilized results on random missingness within shared-parameter models.
  • Connected outcome and missingness models via common latent variables or random effects.
  • Devised a sensitivity analysis framework to study unverifiable assumptions.

Main Results:

  • The framework assesses the impact of varying assumptions on unobserved measurements.
  • Applied methodology to a clinical trial for toenail dermatophyte onychomycosis.
  • Demonstrated utility in analyzing longitudinal outcomes with incomplete data.

Conclusions:

  • The proposed sensitivity analysis is valuable for shared-parameter models.
  • Applicable to longitudinal data and other scenarios with potential missingness.
  • Enhances understanding of how missing data assumptions affect results.