Randomly and Non-Randomly Missing Renal Function Data in the Strong Heart Study: A Comparison of Imputation Methods.

MedStar author(s):
Citation: PLoS ONE [Electronic Resource]. 10(9):e0138923, 2015.PMID: 26414328Institution: MedStar Health Research InstituteForm of publication: Journal ArticleMedline article type(s): Comparative Study | Journal Article | Research Support, N.I.H., Extramural | Research Support, U.S. Gov't, Non-P.H.S.Subject headings: *Heart/ph [Physiology] | *Kidney/ph [Physiology] | Confidence Intervals | Creatinine/bl [Blood] | Female | Humans | Male | Middle Aged | Proportional Hazards Models | Risk Factors | Time FactorsYear: 2015Local holdings: Available online through MWHC library: 2006 - presentISSN:
  • 1932-6203
Name of journal: PloS oneAbstract: 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.All authors: Howard BV, Lee ET, Shara N, Umans JG, Valaitis E, Wang H, Wang W, Yassin SAFiscal year: FY2016Digital Object Identifier: Date added to catalog: 2016-07-15
Holdings
Item type Current library Collection Call number Status Date due Barcode
Journal Article MedStar Authors Catalog Article 26414328 Available 26414328

Available online through MWHC library: 2006 - present

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.

English

Powered by Koha