Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 427823
Title Estimating net transition probabilities from cross-sectional data wit application to risk factors in chronic disease modeling
Author(s) Kassteele, J. van de; Hoogenveen, R.T.; Engelfriet, P.M.; Baal, P.H. van; Boshuizen, H.C.
Source Statistics in Medicine 31 (2012)6. - ISSN 0277-6715 - p. 533 - 543.
DOI https://doi.org/10.1002/sim.4423
Department(s) Biometris (WU MAT)
Chair Nutrition and Disease
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2012
Keyword(s) health
Abstract A problem occurring in chronic disease modeling is the estimation of transition probabilities of moving from one state of a categorical risk factor to another. Transitions could be obtained from a cohort study, but often such data may not be available. However, under the assumption that transitions remain stable over time, age specific cross-sectional prevalence data could be used instead. Problems that then arise are parameter identifiability and the fact that age dependent cross-sectional data are often noisy or are given in age intervals. In this paper we propose a method to estimate so-called net annual transition probabilities from cross-sectional data, including their uncertainties. Net transitions only describe the net inflow or outflow into a certain risk factor state at a certain age. Our approach consists of two steps: first, smooth the data using multinomial P-splines, second, from these data estimate net transition probabilities. This second step can be formulated as a transportation problem, which is solved using the simplex algorithm from linear programming theory. A sensible specification of the cost matrix is crucial to get meaningful results. Uncertainties are assessed by parametric bootstrapping. We illustrate our method using data on body mass index. We conclude that this method provides a flexible way of estimating net transitions and that the use of net transitions has implications for model dynamics, for example when modeling interventions
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