Isbn: 9789283221357 - methods for investigating localized clustering of disease (iarc scientific publication, band 135) (5 Ergebnisse)

Zustand: Good. 1st Edition. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Paperback. 247 S. Good condition. Reading pages are clean and without marks. Retired library exemplar, with the usual marking. Lbel and marking on frotnt cover. Corners slightly rubbed or bumped. Still good copy. 9789283221357 Sprache: Englisch Gewicht in Gramm: 544.

Zustand: New. In English.

Taschenbuch. Zustand: Neu. Neuware - Since the beginning of this century, 'clusters' of certain forms of cancer--particularly leukemia in children and Hodgkin's disease--have been reported around locations of specific environmental hazards. Identification of such clusters is not an easy task, since there is no exact definition of what a cluster is. This monograph describes the variety of statististical techniques cuurently in use, and their application to simulated data-sets chosen to represent a range of clustering scenarios. The scientists who developed these techniques were invited to apply their methodology to these data-sets and to share their conclusions in this volume. In addition, these researchers describe in complete detail how they proceeded with the analysis, since an element of subjectivity figures prominently in the application and interpretation of some of these methods. The identification and analysis of disease clusters can yield significant clues in epidemiologic research, and as such will continue to be an important subject of cancer research and epidemiology for the foreseeable future.…

Zustand: New. InhaltsverzeichnisForeword Introduction 1. Historical aspects of leukaemia clusters 2. The simulated data -sets 3. Analysing the spatial distribution of disease using a method of constructing geographical areas of approximately equal.