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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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Published on: December 9, 2015

Additive transformation models for clustered failure time data.

Donglin Zeng1, Jianwen Cai

  • 1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. dzeng@bios.unc.edu

Lifetime Data Analysis
|December 17, 2009
PubMed
Summary
This summary is machine-generated.

We introduce new additive transformation risk models for clustered failure time data. These models generalize existing methods and provide reliable estimators for regression coefficients in complex survival analyses.

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Last Updated: Jun 17, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

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Published on: December 9, 2015

Area of Science:

  • Statistics
  • Survival Analysis
  • Biostatistics

Background:

  • Clustered failure time data presents unique analytical challenges.
  • Existing additive risk models are typically for independent failure times.
  • Generalizing these models to multivariate settings requires advanced statistical approaches.

Purpose of the Study:

  • To propose a novel class of additive transformation risk models for clustered failure time data.
  • To extend the standard additive risk model to handle correlated failure times within clusters.
  • To develop robust statistical methods for analyzing such data.

Main Methods:

  • Development of additive transformation risk models incorporating frailty.
  • Utilizing an estimating equation approach based on marginal hazards.
  • Theoretical analysis of estimator properties under random cluster sizes.

Main Results:

  • The proposed models naturally generalize the univariate additive risk model.
  • Consistent and asymptotically normal estimators for regression coefficients are derived.
  • Goodness-of-fit test statistics are developed for model selection.

Conclusions:

  • The new models offer a flexible framework for clustered failure time data.
  • The proposed methods provide statistically sound and reliable analysis.
  • Further validation through simulations and real-world data confirms performance.