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Related Experiment Videos

Regression models for recurrent event data: parametric random effects models with measurement error

B W Turnbull1, W Jiang, L C Clark

  • 1Statistics Center, Cornell University, Ithaca, NY 14853-3801, USA.

Statistics in Medicine
|April 30, 1997
PubMed
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This study introduces statistical methods to correct for measurement errors in non-linear models. These adjustments improve the accuracy of statistical analyses, particularly for complex data like recurrent event data.

Area of Science:

  • Statistical methodology
  • Biostatistics
  • Epidemiology

Background:

  • Measurement error in covariates can bias statistical analyses.
  • Non-linear models are frequently used in various scientific fields.
  • Accurate statistical inference is crucial for reliable scientific conclusions.

Purpose of the Study:

  • To present statistical methodology for analyzing non-linear measurement error models.
  • To adjust maximum likelihood estimators, standard errors, and significance tests for measurement error.
  • To demonstrate the application of these methods in a real-world scenario.

Main Methods:

  • Development of adjusted maximum likelihood estimators.
  • Calculation of adjusted standard errors and significance tests.

Related Experiment Videos

  • Application of a mixed effects Poisson regression model for recurrent event data.

    • Main Results:

    • The proposed methodology provides adjustments for statistical analysis in the presence of measurement error.
    • The technique was successfully illustrated using a mixed effects Poisson regression model.
    • The methods are applicable to recurrent event data and clinical trial settings.

    Conclusions:

    • The presented statistical methodology effectively accounts for measurement error in non-linear models.
    • Adjustments improve the reliability of statistical inference.
    • The approach is valuable for analyzing complex data, such as recurrent event data from clinical trials.