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A parametric approach to modeling changes in general health and cognition: developing a stochastic model of aging

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If you have a question about this talk, please contact Nikolaos Demiris.

I will discuss a novel approach which is being developed by our group at Dalhousie University, Halifax, to summarizing health transitions as a stochastic process of changes in health states. Instead of analyzing health transitions separately in each direction (improvement, worsening and death) we suggest a general parametric model (e.g., based on a Poisson density function) which allows estimating the probabilities of changes (including adverse outcomes) as a function of the current state.

The basic model has four population specific parameters, two of which represent health transitions of survivors; the other two represent the probability of death. I will show how covariates can be incorporated in the model. The approach is illustrated in representative Canadian populations for: (i) transitions in general health status (deficits accumulation count/the frailty index), (ii) transitions in cognitive states (represented by the errors in the Modified Mini Mental State Examination Score), and (iii) transitions between cognitive impairments of different degrees. The performances of the models are always very high, R2 ranging from 0.89 to 0.99, and robust across settings.

I will also discuss the general stochastic mechanism of organism-environment interactions, coupled with a decrease of stress-resistance, to explain the observed patterns of how health state changes with age. This allows an understanding the origin of health deficit (and similarly, cognitive deficits) accumulation, can be used to classify different populations. This approach has the potential for a wide range of applications in analyzing age-related health trajectories.

This talk is part of the MRC Biostatistics Unit Seminars series.

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