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

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Event-weighted proportional hazards modelling for recurrent gap time data.

G A Darlington¹, S N Dixon

¹Department of Mathematics and Statistics, University of Guelph, Guelph, Ontario, Canada. gdarling@uoguelph.ca

Statistics in Medicine

|July 25, 2012

Summary

Analyzing recurrent event gap times is complex. This study introduces a computationally efficient Cox proportional hazards model adjustment, mimicking resampling for similar parameter estimates in event analysis.

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Area of Science:

Biostatistics
Statistical Modeling
Survival Analysis

Background:

Recurrent event data analysis necessitates adjustments to standard marginal models.
Existing methods like modified within-cluster resampling are computationally intensive.
Accurate analysis of gap times between recurrent events is crucial in many fields.

Purpose of the Study:

To present a computationally simpler adjustment to the Cox proportional hazards model for recurrent event gap time analysis.
To demonstrate that this simplified method yields parameter estimates comparable to more intensive techniques.
To provide a practical illustration using a biological dataset.

Main Methods:

A modified Cox proportional hazards model approach is proposed.
Partial likelihood contributions are weighted by the inverse of observed gap times per individual.
A working independence correlation matrix is assumed.

Main Results:

The proposed method provides parameter estimates similar to computationally intensive resampling techniques.
The adjustment offers a more efficient alternative for analyzing recurrent event gap times.
The method's applicability is demonstrated with an example of recurrent mammary tumors in rats.

Conclusions:

A simplified Cox proportional hazards model adjustment effectively analyzes recurrent event gap times.
This method offers a computationally feasible alternative to complex resampling techniques.
The approach is valuable for researchers dealing with recurrent event data, enhancing statistical efficiency.