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I Deltour

Publications and source records attributed to I Deltour.

2 recordsLinked to original sources

Projection of cancer risks from the Japanese atomic bomb survivors to the England and Wales population taking into account uncertainty in risk parameters.

Generalized relative risk models, with adjustments to the relative risk for time after exposure and age at exposure and incorporating a linear-quadratic dose response, were fitted to the latest (Life Span Study Report 12) Japanese atomic bomb survivor cancer mortality data using Bayesian Markov Chain Monte Carlo methods, taking account of random errors in the DS86 dose estimates. The resulting uncertainty distributions in the relative risk model parameters were used to derive uncertainties in population cancer risks for a current UK population. Following an assumed administered dose of 1 Sv, leukaemia mortality risks were estimated to be 1.93x10(-2) Sv(-1) (95% CI 1.14, 3.38), or 0.44 years of life lost Sv(-1) (95% CI 0.22, 0.94). Following an assumed administered dose of 1 Sv, solid cancer mortality risks were calculated to be 10.36x10(-2) Sv(-1) (95% CI 8.41, 12.42), or 1.38 years of life lost Sv(-1) (95% CI 1.11, 1.68). In general, solid cancer risks were very similar to those predicted by classical likelihood-based methods; however, leukaemia risks were somewhat higher, by 10-35%, than those predicted by classical likelihood-based methods. This is so in both cases, irrespective of whether or not adjustments are made in these likelihood-based fits for the effects of measurement errors, and the discrepancy for leukaemia tends to be greater at higher doses. Overall, cancer risks predicted by Bayesian Markov Chain Monte Carlo methods are similar to those derived by classical likelihood-based methods and which form the basis of established estimates of radiation-induced cancer risk.

Bayes Theorem↗

Stochastic algorithms for Markov models estimation with intermittent missing data.

Multistate Markov models are frequently used to characterize disease processes, but their estimation from longitudinal data is often hampered by complex patterns of incompleteness. Two algorithms for estimating Markov chain models in the case of intermittent missing data in longitudinal studies, a stochastic EM algorithm and the Gibbs sampler, are described. The first can be viewed as a random perturbation of the EM algorithm and is appropriate when the M step is straightforward but the E step is computationally burdensome. It leads to a good approximation of the maximum likelihood estimates. The Gibbs sampler is used for a full Bayesian inference. The performances of the two algorithms are illustrated on two simulated data sets. A motivating example concerned with the modelling of the evolution of parasitemia by Plasmodium falciparum (malaria) in a cohort of 105 young children in Cameroon is described and briefly analyzed.

Algorithms↗