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Related Experiment Videos

Partly functional temporal process regression with semiparametric profile estimating functions.

Jun Yan1, Jian Huang

  • 1Department of Statistics, University of Connecticut, Unit 4120, Storrs, Connecticut 06269, USA. jun.yan@uconn.edu

Biometrics
|June 13, 2008
PubMed
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This study introduces a partly functional temporal process regression (TPR) model for event time data. This new model allows for both time-independent and time-varying covariate effects, enhancing flexibility in data analysis.

Area of Science:

  • Statistics
  • Biostatistics
  • Survival Analysis

Background:

  • Marginal mean models offer milder assumptions than traditional intensity models for temporal processes.
  • Fully functional temporal process regression (TPR) provides flexibility but lacks methods for model comparison.
  • Existing TPR estimation procedures hinder goodness-of-fit testing for covariate effects.

Purpose of the Study:

  • To propose a partly functional TPR model for event time data analysis.
  • To develop an estimation procedure enabling successive goodness-of-fit tests and model selection.
  • To extend the application of marginal mean models in survival analysis.

Main Methods:

  • Developed a partly functional TPR model, encompassing fully functional and semiparametric models.

Related Experiment Videos

  • Proposed semiparametric profile estimating equations solved via an iterative algorithm.
  • Utilized empirical process theory to establish weak convergence of estimators.

    • Main Results:

    • The proposed model allows for both time-independent and time-varying covariate effects.
    • The estimation method does not require smoothing, unlike other varying-coefficient approaches.
    • Enabled successive tests of time-varying effects and backward model selection.

    Conclusions:

    • The partly functional TPR model offers a flexible and statistically sound approach for analyzing temporal processes in event time data.
    • The developed methodology facilitates robust model comparison and selection.
    • Demonstrated practical utility through simulation and analysis of cystic fibrosis patient data.