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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Individual participant data meta-analysis with mixed-effects transformation models.

Bálint Tamási1, Michael Crowther2, Milo Alan Puhan3

  • 1Institut für Epidemiologie, Biostatistik und Prävention, Departement Biostatistik, Universität Zürich, Hirschengraben 84, CH-8001 Zürich, Switzerland.

Biostatistics (Oxford, England)
|December 30, 2021
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Summary

This study introduces a new statistical framework for analyzing individual participant data (IPD) in meta-analyses. It addresses challenges in time-to-event outcomes, improving survival prediction for diseases like COPD.

Keywords:
Individual participant dataMeta-analysisMixed-effects modelPrognostic modelingRegressionTime-to-event outcomesTransformation model

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Area of Science:

  • Biostatistics
  • Clinical Epidemiology
  • Statistical Modeling

Background:

  • One-stage meta-analysis of individual participant data (IPD) presents significant statistical and computational hurdles.
  • Accurate modeling of time-to-event outcomes requires complex nonlinear mixed-effects models to capture IPD characteristics.
  • Existing methods struggle with arbitrary censoring patterns and heterogeneity in baseline risks and covariate effects.

Purpose of the Study:

  • To develop a flexible statistical framework for one-stage IPD meta-analysis of time-to-event data.
  • To incorporate general normally distributed random effects into linear transformation models.
  • To extend the framework for handling between-study heterogeneity and non-proportional hazards.

Main Methods:

  • Introduction of a novel class of linear transformation models with general normally distributed random effects.
  • Development of extensions to model heterogeneity in baseline risks and covariate effects.
  • Utilizing Laplace approximation and automatic differentiation within the R package 'tramME' for efficient maximum likelihood estimation.

Main Results:

  • The proposed model class effectively handles arbitrary random censoring patterns.
  • Application to chronic obstructive pulmonary disease (COPD) prognostic data demonstrates predictive utility.
  • Simulation studies confirm the correctness and efficiency of the implemented 'tramME' package compared to alternatives.

Conclusions:

  • The presented statistical framework offers a robust solution for complex IPD meta-analyses.
  • The 'tramME' R package provides an efficient tool for fitting mixed-effects transformation models.
  • This approach enhances survival prediction and analysis of time-to-event data in clinical research.