Missing data: a special challenge in aging research.
Scientific evidence should guide clinical care, but special methodological challenges influence interpretation of the medical literature pertaining to older adults. Missing data, ranging from lack of individual items in questionnaires to complete loss to follow-up, affect the quality of the evidence and are more likely to occur in studies of older adults because older adults have more health and functional problems that interfere with all aspects of data collection than do younger people. The purpose of this article is to promote knowledge about the risks and consequences of missing data in clinical aging research and to provide an organized approach to prevention and management. Although it is almost never possible to achieve complete data capture, efforts to prevent missing data are more effective than analytical "cure." Strategies to prevent missing data include selecting a primary outcome that is easy to determine and devising valid alternate definitions, adapting data collection to the special needs of the target population, pilot testing data collection plans, and monitoring missing data rates during the study and adapting data collection procedures as needed. Key steps in the analysis of missing data include assessing the extent and types of missing data before analysis, exploring potential mechanisms that contributed to the missing data, and using multiple analytical approaches to assess the effect of missing data on the results. Manuscripts should disclose rates of missing data and losses to follow-up, compare dropouts with participants who completed the study, describe how missing data were managed in the analysis phase, and discuss the potential effect of missing data on the conclusions of the study.
Hardy
Susan E
SE
Division of Geriatric Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA. hardys@dom.pitt.edu
Allore
Heather
H
Studenski
Stephanie A
SA
D000375:Aging; D003625:Data Collection; D015340:Epidemiologic Research Design; D016021:Epidemiologic Studies; D006801:Humans; D015233:Models, Statistical; D010352:Patient Dropouts; D012107:Research Design