Modeling heteroscedastic age-period-cohort cancer data

The extent to which cancer will be a burden on the Canadian health-care system will be determined by future cancer rates and future population levels in the high-risk age groups. Parametric models of incidence and mortality rates for various cancers may be used to obtain medium-term forecasts of rates, which then can be used in conjunction with population projections to obtain forecasts of total incidence and mortality. Age-period-cohort cancer data often exhibit marked heteroscedasticity, which complicates the modeling of the data. Methods to allow for the effects of this heteroscedasticity on residual processes are developed and discussed in the context of modeling Canadian female breast-cancer incidence data.     

Age-period-cohort analysis: an illustration of the problems in assessing interaction in one observation per cell data

This paper discusses the specific problems of age-period-cohort (A-P-C) analysis within the general framework of interaction assessment for two-way cross-classified data with one observation per cell. The A-P-C multiple classification model containing the effects of age groups (rows), periods of observation (columns), and birth cohorts (diagonals of the two-way table) is characterized as one of a special class of models involving interaction terms assumed to have very specific forms. The so-called A-P-C identification problem, which results from the use of a particular interaction structure for detecting cohort effects, is shown to manifest itself in the form of an exact linear dependency among the columns of the design matrix. The precise relationship holding among these columns is derived, as is an explicit formula for the bias in the parameter estimates resulting from an incorrect specification of an assumed restriction on the parameters required to solve the normal equations. Current methods for modeling A-P-C data are critically reviewed, an illustrative numerical example is presented, and one potentially promising analysis strategy is discussed. However, gien the large number of possible sources for error in A-P-C analyses, it is strongly recommended that the results of such analyses be interpreted with a great deal of caution.     

Gambling on Electronic Gaming Machines in Germany: An Age-Period-Cohort Analysis

To provide insights into etiological factors of gambling at the population level, it is critical to document the separate contributions of biological aging, period influences, and birth cohorts on observed temporal trends in gambling. This study investigated age, period, and cohort effects on prevalence rates of electronic gaming machine (EGM) gambling in Germany. We used data from a series of repeated cross-sectional surveys from the Federal Centre for Health Education covering the period 2007–2015. A total of 53,005 participants were surveyed about their past-year participation in EGMs. Using the intrinsic estimator, we disentangled the separate effects of age (16–17 to 64–65 years), period (2007, 2009, 2011, 2013, 2015), and birth cohort (1941–42 to 1997–98). Age effects were highest for those aged 18–33 (p < 0.001). Period effects were highest in 2013 and lowest in 2015 (p < 0.005). A significant increasing birth cohort effect was evident in cohorts born between 1985 and 1998 (p < 0.005). Implications of these results are discussed with regard to the vulnerability of younger ages to gambling, period changes in the regulatory framework of Germany, and differential cohort vulnerability of the Millennial generation due to early-life video gaming experiences.