Age-period-cohort models of chronic disease rates. II: Graphical approaches.
In a companion article we have reviewed a number of available modelling approaches employed in estimating the influence of age, period and cohort effects on chronic disease rates. Here we review some of the graphical methods for displaying disease rates with a view to extracting information about the separate and joint effects of age, period and cohort. The more traditional displays such as line charts are compared to approaches based on smoothing and two- and three-dimensional plots which have recently been proposed. Other graphical techniques which are principally concerned with displaying interactions, such as biplots and correspondence analysis, are also considered. These techniques are illustrated with examples to compare the techniques revealing their strengths and weaknesses. It is clear that graphical approaches can be useful tools in understanding the behaviour of chronic disease time trends.
Robertson
C
C
Division of Epidemiology and Biostatistics, European Institute of Oncology, Milan, Italy.
Boyle
P
P
D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D001943:Breast Neoplasms / Q000453:epidemiology / Q000401:mortality; D002908:Chronic Disease / Q000453:epidemiology*; D015331:Cohort Studies; D003196:Computer Graphics; D004739:England / Q000453:epidemiology; D005260:Female; D006801:Humans; D015994:Incidence; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D012606:Scotland / Q000453:epidemiology; D013736:Testicular Neoplasms / Q000453:epidemiology / Q000401:mortality; D013997:Time Factors; D002583:Uterine Cervical Neoplasms / Q000453:epidemiology; D014852:Wales / Q000453:epidemiology