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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Published on: October 23, 2020

Correlated bivariate continuous and binary outcomes: issues and applications.

Armando Teixeira-Pinto1, Sharon-Lise T Normand

  • 1Faculty of Medicine, Department of Biostatistics and Medical Informatics, University of Porto, Porto, Portugal. tpinto@med.up.pt

Statistics in Medicine
|April 10, 2009
PubMed
Summary
This summary is machine-generated.

Analyzing multiple non-commensurate outcomes requires advanced methods. Multivariate models offer significant efficiency gains over univariate approaches when outcomes depend on different covariates.

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Area of Science:

  • Statistics
  • Biostatistics
  • Health Services Research

Background:

  • Multiple non-commensurate outcomes are increasingly collected to assess treatment effectiveness and policy impacts.
  • Current common practice often models each outcome separately, ignoring potential correlations.
  • This univariate approach can lead to inefficiencies and suboptimal inference.

Purpose of the Study:

  • To describe and contrast various multivariate statistical methods for analyzing non-commensurate outcomes.
  • To introduce a novel multivariate model for jointly analyzing correlated binary and continuous outcomes using a latent variable.
  • To evaluate the efficiency gains of multivariate methods compared to traditional univariate analyses.

Main Methods:

  • Comparison of full likelihood and quasi-likelihood multivariate approaches.
  • Development and application of a new latent variable multivariate model for mixed binary and continuous data.
  • Efficiency analysis of univariate versus multivariate methods under different covariate structures.

Main Results:

  • All methods provide consistent parameter estimates with complete data.
  • Multivariate approaches show negligible efficiency gains when outcomes share the same covariate structure.
  • Substantial efficiency gains are observed when outcomes depend on distinct sets of covariates.

Conclusions:

  • Multivariate statistical methods are crucial for efficiently analyzing multiple non-commensurate outcomes.
  • The choice of method and covariate structure significantly impacts efficiency.
  • The proposed latent variable model offers a flexible approach for correlated mixed-type outcomes.