title: Detecting a Local Cohort Effect for Cancer Mortality Data Using a Varying Coefficient Model.
abstract: BACKGROUND
  Cancer mortality is increasing with the aging of the population in Japan. Cancer information obtained through feasible methods is therefore becoming the basis for planning effective cancer control programs. There are three time-related factors affecting cancer mortality, of which the cohort effect is one. Past descriptive epidemiologic studies suggest that the cohort effect is not negligible in cancer mortality.
  
  
  METHODS
  In this paper, we develop a statistical method for automatically detecting a cohort effect and assessing its statistical significance for cancer mortality data using a varying coefficient model.
  
  
  RESULTS
  The proposed method was applied to liver and lung cancer mortality data on Japanese men for illustration. Our method detected significant positive or negative cohort effects. The relative risk was 1.54 for liver cancer mortality in the cohort born around 1934 and 0.83 for lung cancer in the cohort born around 1939.
  
  
  CONCLUSIONS
  Cohort effects detected using the proposed method agree well with previous descriptive epidemiologic findings. In addition, the proposed method is expected to be sensitive enough to detect smaller, previously undetected birth cohort effects.
authors.affiliation: Faculty of Management and Information Systems, Prefectural University of Hiroshima.
authors.forename: Tetsuji
authors.initials: T
authors.lastname: Tonda
authors.forename: Kenichi
authors.initials: K
authors.lastname: Satoh
authors.forename: Ken-ichi
authors.initials: K
authors.lastname: Kamo
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015988:Cohort Effect; D006801:Humans; D007564:Japan / Q000453:epidemiology; D008113:Liver Neoplasms / Q000401:mortality; D008175:Lung Neoplasms / Q000401:mortality; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009026:Mortality / Q000639:trends; D009369:Neoplasms / Q000401:mortality*; D015203:Reproducibility of Results