Background: Frailty constitutes an important risk factor for adverse outcomes among older adults. In longitudinal studies on frailty, selective sample attrition may threaten the validity of results. Objective: To assess the impact of sample attrition on frailty index trajectories and gaps related to socio-economic status (education) therein among older adults in Europe. Methods: A total of 64,143 observations from 21,044 respondents (50+) from the Survey of Health, Ageing and Retirement in Europe across 12 years of follow-up (2004–2015) and subject to substantial sample attrition (59%) were analysed. We compared results of a standard linear mixed model assuming missing at random (MAR) sample attrition with a joint model assuming missing not at random sample attrition. Results: Estimated frailty trajectories of both the mixed and joint models were identical up to an age of 80 years, above which modest underestimation occurred when a standard linear mixed model was used rather than a joint model. The latter effect was larger for men than women. Substantial education-based inequality in frailty continued throughout old age in both the mixed and joint models. Conclusion: Linear mixed models assuming MAR sample attrition provided good estimates of frailty trajectories up until high age. Thus, the validity of existing studies estimating frailty trajectories based on standard linear mixed models seems not threatened by substantial sample attrition.

1.
Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K: Frailty in elderly people. Lancet 2013; 381: 752–762.
2.
Mitnitski AB, Song X, Rockwood K: The estimation of relative fitness and frailty in community-dwelling older adults using self-report data. J Gerontol A Biol Sci Med Sci 2004; 59: 627–632.
3.
Rockwood K, Mitnitski AB: Frailty in relation to the accumulation of deficits. J Gerontol A Biol Sci Med Sci 2007; 62: 722–727.
4.
Yang Y, Lee LC: Dynamics and heterogeneity in the process of human frailty and aging: evidence from the U.S. older adult population. J Gerontol B Psychol Sci Soc Sci 2010; 65B: 246–255.
5.
Romero-Ortuno R, Kenny RA: The frailty index in Europeans: association with age and mortality. Age Ageing 2012; 41: 684–689.
6.
Hoogendijk EO, van Hout HPJ, Heymans MW, van der Horst H, Frijters DHM, van Groenou MIB, Deeg DJ, Huisman M: Explaining the association between educational level and frailty in older adults: results from a 13-year longitudinal study in the Netherlands. Ann Epidemiol 2014; 24: 538–544.
7.
Marshall A, Nazroo J, Tampubolon G, Vanhoutte B: Cohort differences in the levels and trajectories of frailty among older people in England. J Epidemiol Community Health 2015; 69: 316–321.
8.
Stolz E, Mayerl H, Waxenegger A, Rásky É, Freidl W: Impact of socioeconomic position on frailty trajectories in 10 European countries: evidence from the Survey of Health, Ageing and Retirement in Europe (2004–2013). J Epidemiol Community Health 2017; 71: 73–80.
9.
Stolz E, Mayerl H, Waxenegger A, Freidl W: Explaining the impact of poverty on old-age frailty in Europe: material, psychosocial and behavioural factors. Eur J Public Health 2017; 27: 1003–1009.
10.
Hoogendijk EO, Heymans MW, Deeg DJH, Huisman M: Socioeconomic inequalities in frailty among older adults: results form a 10-year longitudinal study in the Netherlands. Gerontology 2018; 64: 1–8.
11.
Yu R, Wong M, Chong KC, Chang B, Lum CM, Auyeung TW, Lee J, Lee R, Woo J: Trajectories of frailty among Chinese older people in Hong Kong between 2001 and 2012: an age-period-cohort analysis. Age Ageing 2018; 47: 254–261.
12.
Jones M, Mishra GD, Dobson A: Analytical results in longitudinal studies depended on target of inference and assumed mechanism of attrition. J Clin Epidemiol 2015; 68: 1165–1175.
13.
Kelfve S, Fors S, Lennartsson C: Getting better all the time? Selective attrition and compositional changes in longitudinal and life-course studies. Longit Life Course Stud 2017; 8: 104–119.
14.
Zajacova A, Burgards SA: Healthier, wealthier, and wiser: a demonstration of compositional changes in aging cohorts due to selective mortality. Popul Res Policy Rev 2013; 32: 311–324.
15.
Van Beijsterveldt CEM, van Boxtel MPJ, Bosma H, Houx H, Buntinx PJ, Jolles J: Predictors of attrition in a longitudinal cognitive aging study: the maastricht aging study (MAAS). J Clin Epidemiol 2002; 55: 216–223.
16.
Matthews FE, Chatfield M, Freeman C, McCracken C, Brayne C: Attrition and bias in the MRC cognitive function and ageing study: an epidemiological investigation. BMC Public Health 2004; 4: 12.
17.
Rubin DB: Inference and missing data. Biometrika 1976; 63: 581–592.
18.
Enders CK: Applied Missing Data Analysis. New York, The Guilford Press, 2010, pp 86–112.
19.
McLean RR, Hannan MT, Epstein BE, Bouxein ML, Cupples LA, Murabito J, Kiel DP: Elderly cohort study subjects unable to return for follow-up have lower bone mass than those who can return. Am J Epidemol 2000; 51: 689–692.
20.
Chatfield MD, Brayne CE, Matthews FE: A systematic literature review of attrition between waves in longitudinal studies in the elderly shows a consistent pattern of dropout between differing studies. J Clin Epidemiol 2005; 58: 13–19.
21.
Raudenbush S, Bryk A: Hierarchical Linear Models. Thousand Oaks, SAGE Publications, 2002, pp 3–94.
22.
Rizopoulos D: Joint Models for Longitudinal and Time-To-Event Data. New York, CRC Press, 2012, pp 51–98.
23.
Gewa D, Shahar D, Harris T, Tepper S, Molenberghs G, Friger M: Snapshots of statistical methods used in geriatric cohort studies: how do we treat missing data in publications? Int J Stat Med Res 2013; 2: 289–296.
24.
Wu M, Carrol R: Estimation and comparison of changes in the presence of informative right censoring by modelling the -censoring process. Biometrics 1988; 44: 175–188.
25.
Pinheiro JC, Bates DM: Mixed-Effects Models in S and S-PLUS. New York, Springer, 2000, p 152.
26.
R Core Team: R: A Language and Environment for Statistical Computing, Version 3.4.2, 2017.
27.
Pinheiro JC, Bates DM, DebRoy S, Sarkar D: nlme: Linear and Nonlinear Mixed Effects Models, Version 3.1-131, 2017.
28.
Rizopolous D: JM: Joint Modelling of Longitudinal and Survival Data, Version 1.4-7, 2017.
29.
Schöllgen I, Huxhold O, Tesch-Römer C: Socioeconomic status and health in the second half of life: findings from the German ageing survey. Eur J Ageing 2010; 7: 17–28.
30.
Leopold L, Engelhartdt H: Education and physical health trajectories in old age. Evidence from the Survey of Health, Ageing and Retirement in Europe (SHARE). Int J Public Health 2013; 58: 23–31.
31.
Leopold L, Leopold T: Education and health across lives and cohorts: a study of cumulative (dis)advantage and its rising importance in Germany. J Health Soc Behav 2018;59:94–112.
32.
O’Rand AM, Henretta JC: Age and Inequality. Diverse Pathways Through Later Life. Colorado, Westview Press, 1999, vol 69, pp 9–11.
33.
Herd P, Goesling B, House JS: Socioeconomic position and health: the differential effects of education versus income on the onset versus progression of health problems. J Health Soc Behav 2007; 48: 223–238.
34.
Kristman V, Manno M, Cote P: Loss to follow-up in cohort studies: how much is too much? Eur J Epidemiol 2004; 19: 751–760.
35.
Feng D, Silverstein M, Giarrusso R, McArdel JJ, Bengston VL: Attrition of older adults in longitudinal surveys: detection and correction of sample selection bias using multigenerational data. J Gerontol B Psychol Sci Soc Sci 2006; 61; 323–328.
36.
Kristman V, Manno M, Cote P: Methods to account for attrition in longitudinal data: do they work? A simulation study. Eur J Epidemiol 2005; 20: 657–662.
37.
Suddel M, Kolamunnage-Dona R, Tudur-Smith C: Joint models for longitudinal and time-to-event data: a review of reporting quality with a view to meta-analysis. BMC Med Res Methodol 2016; 16: 168.
38.
Salthouse T: Selectivity of attrition in longitudinal studies of cognitive functioning. J Gerontol B Psychol Sci Soc Sci 2013; 69: 567–574.
39.
Baeten S, Van Ourti T, van Doorslaer E: The socioeconomic health gradient across the life cycle: what role for selective mortality and institutionalization? Soc Sci Med 2013; 97: 66–74.
40.
Hedeker D, Gibbons RD: Application of random-effects pattern-mixture models for missing data in longitudinal studies. Psychol Methods 1997; 2: 64–78.
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