title: Randomly and Non-Randomly Missing Renal Function Data in the Strong Heart Study: A Comparison of Imputation Methods.
abstract: Kidney and cardiovascular disease are widespread among populations with high prevalence of diabetes, such as American Indians participating in the Strong Heart Study (SHS). Studying these conditions simultaneously in longitudinal studies is challenging, because the morbidity and mortality associated with these diseases result in missing data, and these data are likely not missing at random. When such data are merely excluded, study findings may be compromised. In this article, a subset of 2264 participants with complete renal function data from Strong Heart Exams 1 (1989-1991), 2 (1993-1995), and 3 (1998-1999) was used to examine the performance of five methods used to impute missing data: listwise deletion, mean of serial measures, adjacent value, multiple imputation, and pattern-mixture. Three missing at random models and one non-missing at random model were used to compare the performance of the imputation techniques on randomly and non-randomly missing data. The pattern-mixture method was found to perform best for imputing renal function data that were not missing at random. Determining whether data are missing at random or not can help in choosing the imputation method that will provide the most accurate results.
authors.affiliation: MedStar Health Research Institute, Hyattsville, Maryland, United States of America; Georgetown-Howard Universities Center for Clinical and Translational Science, Washington, District of Columbia, United States of America.
authors.forename: Nawar
authors.initials: N
authors.lastname: Shara
authors.affiliation: MedStar Health Research Institute, Hyattsville, Maryland, United States of America.
authors.forename: Sayf A
authors.initials: SA
authors.lastname: Yassin
authors.affiliation: Department of Mathematics and Statistics, American University, Washington, District of Columbia, United States of America.
authors.forename: Eduardas
authors.initials: E
authors.lastname: Valaitis
authors.affiliation: MedStar Health Research Institute, Hyattsville, Maryland, United States of America; Georgetown-Howard Universities Center for Clinical and Translational Science, Washington, District of Columbia, United States of America.
authors.forename: Hong
authors.initials: H
authors.lastname: Wang
authors.affiliation: MedStar Health Research Institute, Hyattsville, Maryland, United States of America; Georgetown-Howard Universities Center for Clinical and Translational Science, Washington, District of Columbia, United States of America.
authors.forename: Barbara V
authors.initials: BV
authors.lastname: Howard
authors.affiliation: College of Public Health, University of Oklahoma, Oklahoma City, Oklahoma, United States of America.
authors.forename: Wenyu
authors.initials: W
authors.lastname: Wang
authors.affiliation: College of Public Health, University of Oklahoma, Oklahoma City, Oklahoma, United States of America.
authors.forename: Elisa T
authors.initials: ET
authors.lastname: Lee
authors.affiliation: MedStar Health Research Institute, Hyattsville, Maryland, United States of America; Georgetown-Howard Universities Center for Clinical and Translational Science, Washington, District of Columbia, United States of America.
authors.forename: Jason G
authors.initials: JG
authors.lastname: Umans
mesh_terms: D016001:Confidence Intervals; D003404:Creatinine / Q000097:blood; D005260:Female; D006321:Heart / Q000502:physiology*; D006801:Humans; D007668:Kidney / Q000502:physiology*; D008297:Male; D008875:Middle Aged; D016016:Proportional Hazards Models; D012307:Risk Factors; D013997:Time Factors
chemical_list: D003404:Creatinine