Joint Modeling of Longitudinal Change and Survival: An Investigation of the Association Between Change in Memory Scores and Death (original) (raw)
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Joint Modeling of Longitudinal Change and Survival
GeroPsych: The Journal of Gerontopsychology and Geriatric Psychiatry, 2011
Joint longitudinal-survival models are useful when repeated measures and event time data are available and possibly associated. The application of this joint model in aging research is relatively rare, albeit particularly useful, when there is the potential for nonrandom dropout. In this article we illustrate the method and discuss some issues that may arise when fitting joint models of this type. Using prose recall scores from the Swedish OCTO-Twin Longitudinal Study of Aging, we fitted a joint longitudinal-survival model to investigate the association between risk of mortality and individual differences in rates of change in memory. A model describing change in memory scores as following an accelerating decline trajectory and a Weibull survival model was identified as the best fitting. This model adjusted for random effects representing individual variation in initial memory performance and change in rate of decline as linking terms between the longitudinal and survival models. Memory performance and change in rate of memory decline were significant predictors of proximity to death. Joint longitudinal-survival models permit researchers to gain a better understanding of the association between change functions and risk of particular events, such as disease diagnosis or death. Careful consideration of computational issues may be required because of the complexities of joint modeling methodologies.
Joint modelling of survival and cognitive decline in the Australian Longitudinal Study of Ageing
Journal of the Royal Statistical Society Series C, 2011
The paper describes the use of a longitudinal tobit model to characterize cognitive decline over a 13-year period in a cohort of 2087 elderly Australians. Use of a tobit formulation allows for the so-called 'ceiling effect' wherein many subjects achieve perfect test scores. A Bayesian hierarchical joint model is presented that allows for random subject-specific intercepts and slopes, as well as for informative dropout. Results suggest several potential areas of intervention. For example, there is a clear dose-response effect of exercise whereby increasing levels of exercise are associated with higher cognitive scores.
Frontiers in Psychology, 2021
With aging populations worldwide, there is growing interest in links between cognitive decline and elevated mortality risk—and, by extension, analytic approaches to further clarify these associations. Toward this end, some researchers have compared cognitive trajectories of survivors vs. decedents while others have examined longitudinal changes in cognition as predictive of mortality risk. A two-stage modeling framework is typically used in this latter approach; however, several recent studies have used joint longitudinal-survival modeling (i.e., estimating longitudinal change in cognition conditionally on mortality risk, and vice versa). Methodological differences inherent to these approaches may influence estimates of cognitive decline and cognition-mortality associations. These effects may vary across cognitive domains insofar as changes in broad fluid and crystallized abilities are differentially sensitive to aging and mortality risk. We compared these analytic approaches as app...
Frontiers in public health, 2014
Longitudinal data on aging, health, and longevity provide a wealth of information to investigate different aspects of the processes of aging and development of diseases leading to death. Statistical methods aimed at analyses of time-to-event data jointly with longitudinal measurements became known as the "joint models" (JM). An important point to consider in analyses of such data in the context of studies on aging, health, and longevity is how to incorporate knowledge and theories about mechanisms and regularities of aging-related changes that accumulate in the research field into respective analytic approaches. In the absence of specific observations of longitudinal dynamics of relevant biomarkers manifesting such mechanisms and regularities, traditional approaches have a rather limited utility to estimate respective parameters that can be meaningfully interpreted from the biological point of view. A conceptual analytic framework for these purposes, the stochastic process...
Longitudinal Data with Follow-up Truncated by Death: Match the Analysis Method to Research Aims
Statistical Science, 2009
Diverse analysis approaches have been proposed to distinguish data missing due to death from nonresponse, and to summarize trajectories of longitudinal data truncated by death. We demonstrate how these analysis approaches arise from factorizations of the distribution of longitudinal data and survival information. Models are illustrated using cognitive functioning data for older adults. For unconditional models, deaths do not occur, deaths are independent of the longitudinal response, or the unconditional longitudinal response is averaged over the survival distribution. Unconditional models, such as random effects models fit to unbalanced data, may implicitly impute data beyond the time of death. Fully conditional models stratify the longitudinal response trajectory by time of death. Fully conditional models are effective for describing individual trajectories, in terms of either aging (age, or years from baseline) or dying (years from death). Causal models (principal stratification) as currently applied are fully conditional models, since group differences at one timepoint are described for a cohort that will survive past a later timepoint. Partly conditional models summarize the longitudinal response in the dynamic cohort of survivors. Partly conditional models are serial cross-sectional snapshots of the response, reflecting the average response in survivors at a given timepoint rather than individual trajectories. Joint models of survival and longitudinal response describe the evolving health status of the entire cohort. Researchers using longitudinal data should consider which method of accommodating deaths is consistent with research aims, and use analysis methods accordingly.
Joint Modelling of Longitudinal and Survival Data: A comparison of Joint and Independent Models
2011
In recent years, the interest in longitudinal data analysis has grown rapidly through the development of new methods and the increase in computational power to aid and further develop this field of research. One such method is the joint modelling of longitudinal and survival data. It is commonly found in the collection of medical longitudinal data that both repeated measures and time-to-event data are collected. These processes are typically correlated, where both types of data are associated through unobserved random effects. Due to this association, joint models were developed to enable a more accurate method to model both processes simultaneously. When these processes are correlated, the use of independent models can cause biased estimates [3, 6, 7], with joint models resulting in a reduction in the standard error of estimates. Thus, with more accurate parameter estimates, valid inferences concerning the effect of covariates on the longitudinal and survival processes can be obtai...
Psychology and Aging, 2003
This longitudinal study examined memory loss in a sample of 391 initially nondemented older adults. Analyses decomposed observed memory loss into decline associated with preclinical dementia, study attrition, terminal decline, and chronological age. Measuring memory as a function of only chronological age failed to provide an adequate representation of cognitive change. Disease progression accounted for virtually all of the memory loss in the 25% of the sample that developed diagnosable dementia. In the remainder of the sample, both chronological age and study attrition contributed to observed memory loss. These results suggest that much of memory loss in aging adults may be attributable to the progression of preclinical dementia and other nonnormative aging processes that are not captured by chronological age.
European Journal of Ageing, 2019
Longitudinal studies examining changes in physical functioning with advancing age among very old people are plagued by high death rates, which can lead to biased estimates. This study was conducted to analyse changes in physical functioning among the oldest old with three distinct methods which differ in how they handle dropout due to death. The sample consisted of 3992 persons aged 90 or over in the Vitality 90+ Study who were followed up on average for 2.5 years (range 0–13 years). A generalized estimating equation (GEE) with independent ‘working’ correlation, a linear mixed-effects (LME) model and a joint model consisting of longitudinal and survival submodels were used to estimate the effect of age on physical functioning over 13 years of follow-up. We observed significant age-related decline in physical functioning, which furthermore accelerated significantly with age. The average rate of decline differed markedly between the models: the GEE-based estimate for linear decline am...
Psychology and Aging, 2011
This study examined competing substantive hypotheses about dynamic (i.e., time-ordered) links between memory and functional limitations in old age. We applied the Bivariate Dual Change Score Model to 13-year longitudinal data from the Asset and Health Dynamics Among the Oldest Old Study (AHEAD; N ϭ 6,990; ages 70 -95). Results revealed that better memory predicted shallower increases in functional limitations. Little evidence was found for the opposite direction that functional limitations predict ensuing changes in memory. Spline models indicated that dynamic associations between memory and functional limitations were substantively similar between participants aged 70 -79 and those aged 80 -95. Potential covariates (gender, education, health conditions, and depressive symptoms) did not account for these differential lead-lag associations. Applying a multivariate approach, our results suggest that late-life developments in two key components of successful aging are intrinsically interrelated. Our discussion focuses on possible mechanisms why cognitive functioning may serve as a source of age-related changes in health both among the young-old and the old-old.