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Related Experiment Video

Updated: Jun 3, 2026

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

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

Published on: December 9, 2015

Variable Selection in Multistate Models for Correlated Data With Application in a COVID-19 Vaccination Study.

Jason Mao¹, Yang Li¹, Wanzhu Tu¹

¹Department of Biostatistics and Health Data Science, Indiana University Indianapolis, Indianapolis, Indiana, USA.

Statistics in Medicine

|June 2, 2026

Summary

This study introduces a new statistical method for analyzing patient transitions in health research, particularly for correlated data. The approach improves variable selection and parameter estimation in complex multistate models.

Keywords:

COVID‐19 multistate model regularization variable selection

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Published on: July 24, 2010

Area of Science:

Health Services Research
Epidemiological Research
Biostatistics

Background:

Multistate models (MSM) are crucial for studying patient transitions across clinical states.
MSM complexity poses challenges in parameter estimation and interpretation.
Within-subject correlations in transition times can lead to inefficient estimation and questionable inference.

Purpose of the Study:

To propose a novel method for variable selection in multistate models with correlated data.
To address computational and interpretational challenges in complex MSMs.
To enforce sparsity in multistate models for improved clarity.

Main Methods:

Reparameterization of the likelihood function.
Approximation of the penalty term using a smooth hyperbolic tangent function.
Variable selection for correlated data within MSM framework.

Main Results:

The proposed method demonstrates accuracy in variable selection and parameter estimation through extensive simulations.
The method was successfully applied to analyze care transitions in a COVID-19 vaccine cohort.
Identified key factors influencing transitions among healthy, infection, and hospitalization states.

Conclusions:

The developed method offers a robust approach for variable selection in correlated multistate models.
This facilitates more efficient and reliable inference in health services and epidemiological studies.
Enhances understanding of patient pathways, exemplified by COVID-19 transitions.