title: Modelling infectious disease transmission with complex exposure pattern and sparse outcome data.
abstract: We present a regression modelling framework to analyse infectious disease transmission during a time period where extensive exposure data are available, but where the outcome data are sparse. A latent variable model is used for each exposure time, allowing a straight-forward accumulation of risk for a collection of exposures for which outcome data are available. We describe an analysis of HIV infection from blood products among a cohort of haemophiliacs in Ireland between 1980 and 1985. The analysis provides estimates of the time pattern and batch effects; we show how analytical complexity such as smoothly varying coefficients or random coefficient models can be accommodated by the model. Finally, we discuss other problems where the model is applicable.
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. marie.reilly@meb.ki.se
authors.forename: Marie
authors.initials: M
authors.lastname: Reilly
authors.forename: Agus
authors.initials: A
authors.lastname: Salim
authors.forename: Emer
authors.initials: E
authors.lastname: Lawlor
authors.forename: Owen
authors.initials: O
authors.lastname: Smith
authors.forename: Ian
authors.initials: I
authors.lastname: Temperley
authors.forename: Yudi
authors.initials: Y
authors.lastname: Pawitan
mesh_terms: D015331:Cohort Studies; D003141:Communicable Diseases / Q000635:transmission*; D018562:Disease Transmission, Infectious; D015658:HIV Infections / Q000635:transmission; D006467:Hemophilia A / Q000628:therapy; D006801:Humans; D007494:Ireland; D008954:Models, Biological; D015233:Models, Statistical; D065227:Transfusion Reaction