Search results for ‘Subject term:"older people"’ Sort:
Results 1 - 2 of 2
Comparison of methods for analyzing longitudinal binary outcomes: cognitive status as an example
- Authors:
- KUCHIBHATLA M., FILLENBAUM G. G.
- Journal article citation:
- Aging and Mental Health, 7(6), November 2003, pp.403-408.
- Publisher:
- Taylor and Francis
Longitudinal data generate correlated observations. Ignoring correlation can lead to incorrect estimation of standard errors, resulting in incorrect inferences of parameters. In the example used here, standard logistic regression, a population-averaged (PA) model fit using generalized estimating equations (GEE), and random-intercept models are used to model binary outcomes at baseline, three and six years later. The outcomes indicate cognitive impairment versus no cognitive impairment in a sample of community dwelling elders. The models include both time-invariant (age, gender) and time-varying (time, interactions with time) covariates. The absolute estimates from random-intercept models are larger than those of both standard logistic and GEE models. Compared to the model fit using GEE that accounts for time dependency, standard logistic regression models overestimate standard errors of time-varying covariates (such as time, and time by problems with activities of daily living), and underestimate the standard errors of time-invariant covariates (such as age and gender). The standard errors from the random-intercept model are larger than those from logistic regression and GEE models. The choice of models, GEE or random-intercept, depends on the research question and the nature of the covariates. Population-averaged methods are appropriate when between-subjects effects are of interest, and random-effects are useful when subject-specific effects are important.
Alternative statistical approaches to identifying dementia in a community-dwelling sample
- Authors:
- KUCHIBHATLA M., FILLENBAUM G. G.
- Journal article citation:
- Aging and Mental Health, 7(5), September 2003, pp.383-389.
- Publisher:
- Taylor and Francis
Little attention has been paid to examining the extent to which alternative statistical models may facilitate identification of persons with dementia. Using a sub-sample of the Duke Established Populations for Epidemiologic Studies of the Elderly, two analytical approaches were compared: logistic regression (which focuses on identifying specific characteristics predictive here of dementia), and recursive partitioning methods using tree-based models (which permit identification of the characteristics of those groups with high dementing disorder). In the stepwise multiple logistic regression model which included as potential predictors, gender, age, history of chronic health conditions, scales of basic and instrumental activities of daily living (IADL), and cognitive status, only IADL and cognitive status were significant predictors, with cognitive status the single most important factor. The classification tree approach, which permits identification of the characteristics of those groups with particularly high dementia rates, identified cognitive status as the most important criterion for dementia (as did logistic regression analysis). Among those without cognitive impairment, older age was a risk factor, confirming findings consistently reported in the literature. Among the cognitively impaired, IADL was an important risk factor. Those with five or more IADL problems were further classified into two risk groups, based on number of ADL problems. While classification tree analysis encourages identification of groups at risk, logistic regression encourages targeting of specific characteristics.