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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

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Published on: September 17, 2019

Using generalized estimating equations to analyze longitudinal data in nursing research.

Shan Liu¹, Jane Dixon, Guang Qiu

¹The Nethersole School of Nursing of The Chinese University of Hong Kong, Hong Kong. shanliu2002@yahoo.com.cn

Western Journal of Nursing Research

|June 13, 2009

Summary

This summary is machine-generated.

This study demonstrates Generalized Estimating Equations (GEE) for analyzing longitudinal nursing data. GEE effectively analyzes changes in gynecological cancer symptoms over time and identifies associated factors.

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Area of Science:

Nursing Research
Biostatistics
Oncology

Background:

Longitudinal data analysis in nursing research often relies on traditional methods.
Generalized Estimating Equations (GEE) offer a more robust approach for analyzing repeated measures data.
This study addresses the underutilization of GEE in nursing research.

Purpose of the Study:

To illustrate the application of GEE for analyzing longitudinal symptom data in women with gynecological cancer.
To demonstrate how GEE can address research questions regarding symptom changes and influencing factors.
To provide practical guidance for implementing GEE in nursing research.

Main Methods:

Utilized a dataset of gynecological cancer patients with symptom data collected over 6 months at eight time points.
Employed Generalized Estimating Equations (GEE) to analyze longitudinal changes in symptom number and individual symptoms.
Investigated associations between psychosocial/disease variables and individual symptoms using GEE.

Main Results:

GEE was effectively applied to analyze symptom changes in gynecological cancer patients over a 6-month period.
The study identified significant changes in the number and specific types of symptoms post-surgery.
Key psychosocial and disease variables were found to be associated with individual symptom experiences.

Conclusions:

GEE provides a valuable statistical framework for analyzing complex longitudinal data in nursing.
The findings highlight the dynamic nature of symptoms in gynecological cancer survivors.
This methodology can enhance the understanding of symptom trajectories and inform supportive care interventions.