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Estimating transformations for repeated measures modeling of continuous bounded outcome data.

Matthew M Hutmacher1, Jonathan L French, Sriram Krishnaswami

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This study introduces a new statistical transformation method to simplify complex models for continuous bounded outcome data, improving random effect assumptions. This approach enhances the analysis of health assessment data, particularly for rheumatoid arthritis patients.

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Area of Science:

  • Statistics
  • Biostatistics
  • Rheumatology

Background:

  • Continuous bounded outcome data often violate standard mixed-effects model assumptions.
  • Complex models are frequently required to address time-dependent and treatment effects.

Purpose of the Study:

  • To develop a transformation strategy with censoring likelihood for simpler statistical models.
  • To improve the plausibility of random effects assumptions in mixed-effects models.

Main Methods:

  • A transformation strategy incorporating a likelihood component for censoring was developed.
  • The method was motivated by Health Assessment Questionnaire Disability Index (HAQ-DI) data.
  • A simulation study was used for evaluation.

Main Results:

  • The proposed transformation strategy simplifies model structures.
  • It enhances the plausibility of assumptions on random effects.
  • The method is applicable to continuous bounded outcome data.

Conclusions:

  • The developed transformation strategy offers a more parsimonious and assumption-friendly approach for analyzing continuous bounded outcome data.
  • This method is particularly relevant for longitudinal studies in rheumatology, such as those using HAQ-DI.