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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

Regression splines in the time-dependent coefficient rates model for recurrent event data.

Leila D Amorim¹, Jianwen Cai, Donglin Zeng

¹Department of Statistics, Federal University of Bahia, Salvador, Brazil. leiladen@ufba.br

Statistics in Medicine

|August 13, 2008

Summary

Vitamin A supplementation significantly reduced recurrent diarrhea in children. The treatment effect peaked after two months and waned after the third dose, highlighting time-varying impacts in recurrent event analysis.

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Published on: October 23, 2020

Area of Science:

Epidemiology
Biostatistics

Background:

Recurrent event data analysis is crucial in epidemiology.
Existing models often assume constant treatment effects over time.

Purpose of the Study:

To introduce a time-dependent coefficient rates model for recurrent events.
To analyze the time-varying effect of vitamin A supplementation on childhood diarrhea.

Main Methods:

Utilized regression splines for model estimation.
Employed simulation studies to assess estimator properties.
Applied the model to data from a vitamin A supplementation trial.

Main Results:

The vitamin A supplementation showed a substantial impact on reducing diarrhea after two months.
The beneficial effect diminished after the third dosage.
The model captured the time-varying nature of the treatment effect.

Conclusions:

The proposed time-dependent coefficient rates model offers a flexible alternative to constant-effect models.
This approach is valuable for analyzing time-varying covariate effects in recurrent event data.
Findings underscore the dynamic impact of vitamin A on diarrheal episodes.