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Parametric variance function estimation for nonnormal repeated measurement data.

M C Paik1

  • 1Division of Biostatistics, Columbia University, School of Public Health, New York, New York 10032.

Biometrics
|March 1, 1992
PubMed
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This study extends generalized linear models for nonnormal longitudinal data to account for varying variances. Ignoring this variance heterogeneity can lead to a loss of statistical efficiency.

Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Generalized linear models (GLMs) are commonly used for longitudinal data.
  • Zeger and Liang (1986) proposed a GLM-based procedure for nonnormal longitudinal data.
  • Standard GLMs often assume homogeneity of variance, which may not hold for longitudinal data.

Purpose of the Study:

  • To extend the Zeger and Liang (1986) procedure to model variance heterogeneity in nonnormal longitudinal data.
  • To investigate the impact of ignoring variance heterogeneity on statistical efficiency.

Main Methods:

  • Utilized a generalized linear model framework.
  • Extended the model to incorporate differing scale parameters for observations.
  • Evaluated the loss of efficiency when variance heterogeneity is not modeled.

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Main Results:

  • The proposed extension effectively models variance heterogeneity in nonnormal longitudinal data.
  • Ignoring scale factor heterogeneity results in a demonstrable loss of statistical efficiency.

Conclusions:

  • The extended procedure provides a robust method for analyzing nonnormal longitudinal data with heterogeneous variances.
  • Accounting for variance heterogeneity is crucial for maintaining statistical efficiency in such analyses.