{"id":{"repo_id":"regina","oai_identifier":"oai:uregina.scholaris.ca:10294/6546"},"canonical_url":"https://search.dev.ndltd.org/etd/regina/oai:uregina.scholaris.ca:10294/6546","repository":{"repo_id":"regina","name":"University of Regina","base_url":"https://uregina.scholaris.ca/server/oai/request"},"display":{"title":"Analysis of Longitudinal Data With Missing Responses: A Study of Pain Control Cost","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Yang, Zhenyu"],"institution":"Faculty of Graduate Studies and Research, University of Regina","degree_name":"Master of Science (MSc)","degree_level":"Master's","degree_discipline":"Statistics","degree_department":null,"school":null,"contributors":[],"advisors":["Zhao, Yang"],"committee_chairs":[],"committee_members":["Deng, Dianliang"],"year":2015,"date_issued":"2015-06","date_published":"2015-06","updated_at":"2026-07-24T04:03:29Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.82465/3953"],"render_values":[{"text":"https://doi.org/10.82465/3953","href":"https://doi.org/10.82465/3953","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10294/6546","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Zhao, Yang"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Deng, Dianliang"]},{"key":"dc:creator","label":"Author","values":["Yang, Zhenyu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2015-12-22T19:16:41Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2015-12-22T19:16:41Z"]},{"key":"dc:date.issued","label":"Date","values":["2015-06"]},{"key":"dc:publisher","label":"Institution","values":["Faculty of Graduate Studies and Research, University of Regina"]},{"key":"dc:type","label":"Dc Type","values":["master thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Statistics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master's"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MSc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Faculty of Graduate Studies and Research, University of Regina"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.82465/3953"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10294/6546"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Science in Statistics, University of Regina. x, 58 p."]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["Analysis of Longitudinal Data With Missing Responses: A Study of Pain Control Cost"]}]}],"canonical_facts":{"dc:contributor.advisor":["Zhao, Yang"],"dc:contributor.committeemember":["Deng, Dianliang"],"dc:creator":["Yang, Zhenyu"],"dc:date.accessioned":["2015-12-22T19:16:41Z"],"dc:date.available":["2015-12-22T19:16:41Z"],"dc:date.issued":["2015-06"],"dc:description":["A Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Science in Statistics, University of Regina. x, 58 p."],"dc:description.abstract":["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."],"dc:identifier.doi":["https://doi.org/10.82465/3953"],"dc:identifier.uri":["https://hdl.handle.net/10294/6546"],"dc:language.iso":["en"],"dc:publisher":["Faculty of Graduate Studies and Research, University of Regina"],"dc:title":["Analysis of Longitudinal Data With Missing Responses: A Study of Pain Control Cost"],"dc:type":["master thesis"],"thesis:degree_discipline":["Statistics"],"thesis:degree_level":["Master's"],"thesis:degree_name":["Master of Science (MSc)"],"thesis:institution_name":["Faculty of Graduate Studies and Research, University of Regina"]},"updated_at":"2026-07-24T04:03:29Z"}