TY - JOUR
T1 - Robust Respondents and Lost Limitations
T2 - The Implications of Nonrandom Missingness for the Estimation of Health Trajectories
AU - Jackson, Heide
AU - Engelman, Michal
AU - Bandeen-Roche, Karen
N1 - Funding Information:
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Institute on Aging (T32AG000247, NIA P30 AG17266, R24AG045061); and the National Institute of Child Health and Human Development (P2C HD047873).
Publisher Copyright:
© The Author(s) 2017.
PY - 2019/4/1
Y1 - 2019/4/1
N2 - Objective: We offer a strategy for quantifying the impact of mortality and attrition on inferences from later-life health trajectory models. Method: Using latent class growth analysis (LCGA), we identify functional limitation trajectory classes in the Health and Retirement Study. We compare results from complete case and full information maximum likelihood (FIML) analyses, and demonstrate a method for producing upper- and lower-bound estimates of the impact of attrition on results. Results: LCGA inferences vary substantially depending on the handling of missing data. For older adults who die during the follow-up period, the widely used FIML approach may underestimate functional limitations by up to 20%. Discussion: The most commonly used approaches to handling missing data likely underestimate the extent of poor health in aging populations. Although there is no single solution for nonrandom missingness, we show that bounding estimates can help analysts to better characterize patterns of health in later life.
AB - Objective: We offer a strategy for quantifying the impact of mortality and attrition on inferences from later-life health trajectory models. Method: Using latent class growth analysis (LCGA), we identify functional limitation trajectory classes in the Health and Retirement Study. We compare results from complete case and full information maximum likelihood (FIML) analyses, and demonstrate a method for producing upper- and lower-bound estimates of the impact of attrition on results. Results: LCGA inferences vary substantially depending on the handling of missing data. For older adults who die during the follow-up period, the widely used FIML approach may underestimate functional limitations by up to 20%. Discussion: The most commonly used approaches to handling missing data likely underestimate the extent of poor health in aging populations. Although there is no single solution for nonrandom missingness, we show that bounding estimates can help analysts to better characterize patterns of health in later life.
KW - latent class growth analysis
KW - longitudinal analysis
KW - mortality and attrition bias
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U2 - 10.1177/0898264317747079
DO - 10.1177/0898264317747079
M3 - Article
C2 - 29254422
AN - SCOPUS:85063202672
SN - 0898-2643
VL - 31
SP - 685
EP - 708
JO - Journal of Aging and Health
JF - Journal of Aging and Health
IS - 4
ER -