Promotion time models with time-changing exposure and heterogeneity: application to infectious diseases

Biometrical Journal. Biometrische Zeitschrift
Maud Tournoud, René Ecochard

Abstract

Promotion time models have been recently adapted to the context of infectious diseases to take into account discrete and multiple exposures. However, Poisson distribution of the number of pathogens transmitted at each exposure was a very strong assumption and did not allow for inter-individual heterogeneity. Bernoulli, the negative binomial, and the compound Poisson distributions were proposed as alternatives to Poisson distribution for the promotion time model with time-changing exposure. All were derived within the frailty model framework. All these distributions have a point mass at zero to take into account non-infected people. Bernoulli distribution, the two-component cure rate model, was extended to multiple exposures. Contrary to the negative binomial and the compound Poisson distributions, Bernoulli distribution did not enable to connect the number of pathogens transmitted to the delay between transmission and infection detection. Moreover, the two former distributions enable to account for inter-individual heterogeneity. The delay to surgical site infection was an example of single exposure. The probability of infection was very low; thus, estimation of the effect of selected risk factors on that probability obtained w...Continue Reading

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Citations

Nov 17, 2009·The Journal of Hospital Infection·H CharvatR Ecochard
Sep 18, 2009·Computer Methods and Programs in Biomedicine·Mário de CastroJosemar Rodrigues
Jul 3, 2009·Biometrical Journal. Biometrische Zeitschrift·Mário de CastroJosemar Rodrigues
Jan 27, 2015·Statistics in Medicine·Edwin M M OrtegaVicente G Cancho
Dec 25, 2012·Statistical Methods in Medical Research·Patrick BorgesNarayanaswamy Balakrishnan
Sep 29, 2019·Statistical Methods in Medical Research·Diego I GallardoMário de Castro
Oct 31, 2015·Asian Pacific Journal of Cancer Prevention : APJCP·Ahmad Reza BaghestaniKimiya Gohari

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