Missing data is an unavoidable issue in controlled clinical trials and public health research and practice. Presence of missing data and applying inappropriate methods of analysis generates biased estimates and reduces power of study. It is very important for investigators to use appropriate methods of analysis to deal with missing data in order to maintain internal (power of study) and external (generalization of sample results to larger population) validity of study. The focus of this dissertation is to compare different methods to deal with missing data in controlled clinical trials and public health research and practice. In addition, this dissertation also discusses that current approaches to deal with missing data might not produce valid inferences and may affect internal and external validity of results. Furthermore, emphasis is put on demonstrating how well multiple imputation works to deal with missing data under Missing at Random (MAR) mechanism with monotonic and non-monotonic missing data patterns for a range of percent missing under both normal and non-normal distributions. The results of this dissertation showed that multiple imputation is an efficient technique to obtain valid inferences compared to single imputation methods. In addition estimates obtained from multiple imputation also preserve the internal validity of study.
","abstract_html":"<p>Missing data is an unavoidable issue in controlled clinical trials and public health research and practice. Presence of missing data and applying inappropriate methods of analysis generates biased estimates and reduces power of study. It is very important for investigators to use appropriate methods of analysis to deal with missing data in order to maintain internal (power of study) and external (generalization of sample results to larger population) validity of study. The focus of this dissertation is to compare different methods to deal with missing data in controlled clinical trials and public health research and practice. In addition, this dissertation also discusses that current approaches to deal with missing data might not produce valid inferences and may affect internal and external validity of results. Furthermore, emphasis is put on demonstrating how well multiple imputation works to deal with missing data under Missing at Random (MAR) mechanism with monotonic and non-monotonic missing data patterns for a range of percent missing under both normal and non-normal distributions. The results of this dissertation showed that multiple imputation is an efficient technique to obtain valid inferences compared to single imputation methods. In addition estimates obtained from multiple imputation also preserve the internal validity of study.</p>","abstract_has_math":false,"creators":["Garg, Priyanka"],"institution":null,"degree_name":"Doctor of Public Health in Biostatistics (Dr.P.H.)","degree_level":"Dissertation (open access)","degree_discipline":"Department of Biostatistics (COPH)","degree_department":null,"school":null,"contributors":["Hani M. Samawi"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-04-01T07:00:00Z","date_published":"2013-04-01T07:00:00Z","updated_at":"2026-07-24T02:26:17Z","subjects":["ETD","Monotone missing data pattern","Non-monotone missing data pattern","Sensitivity","Specificity","Public Health"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.georgiasouthern.edu/etd/35","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hani M. Samawi"]},{"key":"dc:creator","label":"Author","values":["Garg, Priyanka"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2013-07-19T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Department of Biostatistics (COPH)"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation (open access)"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Public Health in Biostatistics (Dr.P.H.)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["ETD","Monotone missing data pattern","Non-monotone missing data pattern","Sensitivity","Specificity","Public Health"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.georgiasouthern.edu/etd/35"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Missing data is an unavoidable issue in controlled clinical trials and public health research and practice. Presence of missing data and applying inappropriate methods of analysis generates biased estimates and reduces power of study. It is very important for investigators to use appropriate methods of analysis to deal with missing data in order to maintain internal (power of study) and external (generalization of sample results to larger population) validity of study. The focus of this dissertation is to compare different methods to deal with missing data in controlled clinical trials and public health research and practice. In addition, this dissertation also discusses that current approaches to deal with missing data might not produce valid inferences and may affect internal and external validity of results. Furthermore, emphasis is put on demonstrating how well multiple imputation works to deal with missing data under Missing at Random (MAR) mechanism with monotonic and non-monotonic missing data patterns for a range of percent missing under both normal and non-normal distributions. The results of this dissertation showed that multiple imputation is an efficient technique to obtain valid inferences compared to single imputation methods. In addition estimates obtained from multiple imputation also preserve the internal validity of study.
"]},{"key":"dc:title","label":"Title","values":["Robustness of Multiple Imputation under Missing at Random (MAR) Mechanism: A Simulation Study"]}]}],"canonical_facts":{"dc:contributor":["Hani M. Samawi"],"dc:creator":["Garg, Priyanka"],"dc:date.available":["2013-07-19T07:00:00Z"],"dc:description.abstract":["Missing data is an unavoidable issue in controlled clinical trials and public health research and practice. Presence of missing data and applying inappropriate methods of analysis generates biased estimates and reduces power of study. It is very important for investigators to use appropriate methods of analysis to deal with missing data in order to maintain internal (power of study) and external (generalization of sample results to larger population) validity of study. The focus of this dissertation is to compare different methods to deal with missing data in controlled clinical trials and public health research and practice. In addition, this dissertation also discusses that current approaches to deal with missing data might not produce valid inferences and may affect internal and external validity of results. Furthermore, emphasis is put on demonstrating how well multiple imputation works to deal with missing data under Missing at Random (MAR) mechanism with monotonic and non-monotonic missing data patterns for a range of percent missing under both normal and non-normal distributions. The results of this dissertation showed that multiple imputation is an efficient technique to obtain valid inferences compared to single imputation methods. In addition estimates obtained from multiple imputation also preserve the internal validity of study.
"],"dc:identifier":["https://digitalcommons.georgiasouthern.edu/etd/35"],"dc:subject":["ETD","Monotone missing data pattern","Non-monotone missing data pattern","Sensitivity","Specificity","Public Health"],"dc:title":["Robustness of Multiple Imputation under Missing at Random (MAR) Mechanism: A Simulation Study"],"thesis:degree_discipline":["Department of Biostatistics (COPH)"],"thesis:degree_level":["Dissertation (open access)"],"thesis:degree_name":["Doctor of Public Health in Biostatistics (Dr.P.H.)"]},"updated_at":"2026-07-24T02:26:17Z"}