Bayesian projections: what are the effects of excluding data from younger age groups?
Bayesian age-period-cohort models are used increasingly to project cancer incidence and mortality rates. Data for younger age groups for which rates are low are often discarded from the analysis. The authors explored the effect of excluding these data, in terms of the precision and accuracy of projections, for selected cancer mortality data sets. Projections were made by using a generalized Bayesian age-period-cohort model. Smoothing was applied to each time scale to reduce random variation between adjacent parameter estimates. The sum of squared standardized residuals was used to assess the accuracy of projections, and 90% credible intervals were calculated to assess precision. For the data sets considered, inclusion of all age groups in the analysis provided more precise age-standardized and age-specific projections as well as more accurate age-specific projections for younger age groups. An overall improvement in the accuracy of age-standardized rates was demonstrated for males but not females, which may suggest that analysis of the full data set is beneficial when projecting cancer rates with strong cohort effects.
Baker
A
A
School of Mathematics and Statistics, University of Plymouth, Plymouth, United Kingdom.
Bray
I
I
D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D000368:Aged; D001499:Bayes Theorem; D002648:Child; D002675:Child, Preschool; D005060:Europe / Q000453:epidemiology; D005260:Female; D006801:Humans; D015994:Incidence; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D008875:Middle Aged; D009369:Neoplasms / Q000209:etiology / Q000401:mortality*; D009656:North America / Q000453:epidemiology; D011379:Prognosis; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012307:Risk Factors; D017678:Sex Distribution; D012907:Smoking / Q000009:adverse effects* / Q000453:epidemiology