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Books > Science & Mathematics > Biology, life sciences
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Multi-State Survival Models for Interval-Censored Data (Paperback)
Loot Price: R1,568
Discovery Miles 15 680
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Multi-State Survival Models for Interval-Censored Data (Paperback)
Series: Chapman & Hall/CRC Monographs on Statistics and Applied Probability
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Multi-State Survival Models for Interval-Censored Data introduces
methods to describe stochastic processes that consist of
transitions between states over time. It is targeted at researchers
in medical statistics, epidemiology, demography, and social
statistics. One of the applications in the book is a three-state
process for dementia and survival in the older population. This
process is described by an illness-death model with a dementia-free
state, a dementia state, and a dead state. Statistical modelling of
a multi-state process can investigate potential associations
between the risk of moving to the next state and variables such as
age, gender, or education. A model can also be used to predict the
multi-state process. The methods are for longitudinal data subject
to interval censoring. Depending on the definition of a state, it
is possible that the time of the transition into a state is not
observed exactly. However, when longitudinal data are available the
transition time may be known to lie in the time interval defined by
two successive observations. Such an interval-censored observation
scheme can be taken into account in the statistical inference.
Multi-state modelling is an elegant combination of statistical
inference and the theory of stochastic processes. Multi-State
Survival Models for Interval-Censored Data shows that the
statistical modelling is versatile and allows for a wide range of
applications.
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