Analysis of Longitudinal Data With Missing Responses: A Study of Pain Control Cost

Recent years have seen a major increase of interest in pain control cost studies. Due to rising costs of medical treatment, researchers study factors contributing to cost and appropriate ways to control or reduce the cost of pain control. The first data studied in this research is longitudinal data with cost values completely missing. We fill in the daily cost for minority observations based on the information provided by the price data, and then impute the daily cost for the remaining observations by applying multiple imputation. Multiple sets of complete imputed daily cost data are produced. A generalized estimating equations (GEE) model, is then applied to conduct analysis on each set of data, producing multiple analysis results. The correlation between observations within patients is presented in an AR-1 working correlation structure. The correlation becomes weak with the increase of time lag. Combining six sets of the GEE model results is sufficient to produce an overall result. Two factors, treatment year and types of treatment, are significant to the average daily cost. Overall, it costs the highest for patients in treatment 3, taking mono-drug, dual-drug or triple-drug.

Name thesis:degree_name

Master of Science (MSc)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Statistics

Grantor dc:publisher

Faculty of Graduate Studies and Research, University of Regina

Year dc:date.issued

2015

Author dc:creator

• Yang, Zhenyu

Advisor dc:contributor.advisor

• Zhao, Yang

Committee member dc:contributor.committeemember

• Deng, Dianliang

Language dc:language.iso

en

DOI

https://doi.org/10.82465/3953

OAI identifier oai:identifier

oai:uregina.scholaris.ca:10294/6546