{"id":{"repo_id":"vcu","oai_identifier":"oai:scholarscompass.vcu.edu:etd-2095"},"canonical_url":"https://search.dev.ndltd.org/etd/vcu/oai:scholarscompass.vcu.edu:etd-2095","repository":{"repo_id":"vcu","name":"Virginia Commonwealth University","base_url":"https://scholarscompass.vcu.edu/do/oai/"},"display":{"title":"A Comparison for Longitudinal Data Missing Due to Truncation","abstract":"Many longitudinal clinical studies suffer from patient dropout. Often the dropout is nonignorable and the missing mechanism needs to be incorporated in the analysis. The methods handling missing data make various assumptions about the missing mechanism, and their utility in practice depends on whether these assumptions apply in a specific application. Ramakrishnan and Wang (2005) proposed a method (MDT) to handle nonignorable missing data, where missing is due to the observations exceeding an unobserved threshold. Assuming that the observations arise from a truncated normal distribution, they suggested an EM algorithm to simplify the estimation.In this dissertation the EM algorithm is implemented for the MDT method when data may include missing at random (MAR) cases. A data set, where the missing data occur due to clinical deterioration and/or improvement is considered for illustration. The missing data are observed at both ends of the truncated normal distribution. A simulation study is conducted to compare the performance of other relevant methods. The factors chosen for the simulation study included, the missing data mechanisms, the forms of response functions, missing at one or two time points, dropout rates, sample sizes and different correlations with AR(1) structure. It was found that the choice of the method for dealing with the missing data is important, especially when a large proportion is missing. The MDT method seems to perform the best when there is reason to believe that the assumption of truncated normal distribution is appropriate.A multiple imputation (MI) procedure under the MDT method to accommodate the uncertainty introduced by imputation is also proposed. The proposed method combines the MDT method with Rubin's (1987) MI method. A procedure to implement the MI method is described.","abstract_html":"Many longitudinal clinical studies suffer from patient dropout. Often the dropout is nonignorable and the missing mechanism needs to be incorporated in the analysis. The methods handling missing data make various assumptions about the missing mechanism, and their utility in practice depends on whether these assumptions apply in a specific application. Ramakrishnan and Wang (2005) proposed a method (MDT) to handle nonignorable missing data, where missing is due to the observations exceeding an unobserved threshold. Assuming that the observations arise from a truncated normal distribution, they suggested an EM algorithm to simplify the estimation.In this dissertation the EM algorithm is implemented for the MDT method when data may include missing at random (MAR) cases. A data set, where the missing data occur due to clinical deterioration and/or improvement is considered for illustration. The missing data are observed at both ends of the truncated normal distribution. A simulation study is conducted to compare the performance of other relevant methods. The factors chosen for the simulation study included, the missing data mechanisms, the forms of response functions, missing at one or two time points, dropout rates, sample sizes and different correlations with AR(1) structure. It was found that the choice of the method for dealing with the missing data is important, especially when a large proportion is missing. The MDT method seems to perform the best when there is reason to believe that the assumption of truncated normal distribution is appropriate.A multiple imputation (MI) procedure under the MDT method to accommodate the uncertainty introduced by imputation is also proposed. The proposed method combines the MDT method with Rubin's (1987) MI method. A procedure to implement the MI method is described.","abstract_has_math":false,"creators":["Liu, Rong"],"institution":null,"degree_name":"Doctor of Philosophy","degree_level":"Dissertation","degree_discipline":"Biostatistics","degree_department":null,"school":null,"contributors":["Dr. Kellie J. Archer","Dr. Viswanathan Ramakrishnan"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2006,"date_issued":"2006-01-01T08:00:00Z","date_published":"2006-01-01T08:00:00Z","updated_at":"2026-07-24T05:54:53Z","subjects":["missing data","data sets","patient dropout","algorithm","simulation","MDT","Biostatistics","Physical Sciences and Mathematics","Statistics and Probability"],"languages":[],"rights":["© The Author"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarscompass.vcu.edu/etd/1096"],"render_values":[{"text":"https://scholarscompass.vcu.edu/etd/1096","href":"https://scholarscompass.vcu.edu/etd/1096","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.25772/NK1C-K533","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Kellie J. 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Often the dropout is nonignorable and the missing mechanism needs to be incorporated in the analysis. The methods handling missing data make various assumptions about the missing mechanism, and their utility in practice depends on whether these assumptions apply in a specific application. Ramakrishnan and Wang (2005) proposed a method (MDT) to handle nonignorable missing data, where missing is due to the observations exceeding an unobserved threshold. Assuming that the observations arise from a truncated normal distribution, they suggested an EM algorithm to simplify the estimation.In this dissertation the EM algorithm is implemented for the MDT method when data may include missing at random (MAR) cases. A data set, where the missing data occur due to clinical deterioration and/or improvement is considered for illustration. The missing data are observed at both ends of the truncated normal distribution. A simulation study is conducted to compare the performance of other relevant methods. The factors chosen for the simulation study included, the missing data mechanisms, the forms of response functions, missing at one or two time points, dropout rates, sample sizes and different correlations with AR(1) structure. It was found that the choice of the method for dealing with the missing data is important, especially when a large proportion is missing. The MDT method seems to perform the best when there is reason to believe that the assumption of truncated normal distribution is appropriate.A multiple imputation (MI) procedure under the MDT method to accommodate the uncertainty introduced by imputation is also proposed. The proposed method combines the MDT method with Rubin's (1987) MI method. A procedure to implement the MI method is described."]},{"key":"dc:title","label":"Title","values":["A Comparison for Longitudinal Data Missing Due to Truncation"]}]}],"canonical_facts":{"dc:contributor":["Dr. Kellie J. Archer","Dr. Viswanathan Ramakrishnan"],"dc:creator":["Liu, Rong"],"dc:date.available":["2014-07-09T07:00:00Z"],"dc:description.abstract":["Many longitudinal clinical studies suffer from patient dropout. Often the dropout is nonignorable and the missing mechanism needs to be incorporated in the analysis. The methods handling missing data make various assumptions about the missing mechanism, and their utility in practice depends on whether these assumptions apply in a specific application. Ramakrishnan and Wang (2005) proposed a method (MDT) to handle nonignorable missing data, where missing is due to the observations exceeding an unobserved threshold. Assuming that the observations arise from a truncated normal distribution, they suggested an EM algorithm to simplify the estimation.In this dissertation the EM algorithm is implemented for the MDT method when data may include missing at random (MAR) cases. A data set, where the missing data occur due to clinical deterioration and/or improvement is considered for illustration. The missing data are observed at both ends of the truncated normal distribution. A simulation study is conducted to compare the performance of other relevant methods. The factors chosen for the simulation study included, the missing data mechanisms, the forms of response functions, missing at one or two time points, dropout rates, sample sizes and different correlations with AR(1) structure. It was found that the choice of the method for dealing with the missing data is important, especially when a large proportion is missing. The MDT method seems to perform the best when there is reason to believe that the assumption of truncated normal distribution is appropriate.A multiple imputation (MI) procedure under the MDT method to accommodate the uncertainty introduced by imputation is also proposed. The proposed method combines the MDT method with Rubin's (1987) MI method. A procedure to implement the MI method is described."],"dc:identifier":["https://doi.org/10.25772/NK1C-K533","https://scholarscompass.vcu.edu/etd/1096"],"dc:rights":["© The Author"],"dc:subject":["missing data","data sets","patient dropout","algorithm","simulation","MDT","Biostatistics","Physical Sciences and Mathematics","Statistics and Probability"],"dc:title":["A Comparison for Longitudinal Data Missing Due to Truncation"],"thesis:degree_discipline":["Biostatistics"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy"]},"updated_at":"2026-07-24T05:54:53Z"}