Age-period-cohort analysis with a constant-relative-variation constraint for an apportionment of period and cohort slopes
Journal
PLoS ONE
Journal Volume
14
Journal Issue
12
Date Issued
2019
Author(s)
Su S.-Y.
Abstract
Age-period-cohort analysis of incidence and/or mortality data has received much attention in the literature. To circumvent the non-identifiability problem inherent in the age-period-cohort model, additional constraints are necessary on the parameters estimates. We propose setting the constraint to reflect the different nature of the three temporal variables: age, period, and birth cohort. There are two assumptions in our method. Recognizing age effects to be deterministic (first assumption), we do not explicitly incorporate the age parameters into constraint. For the stochastic period and cohort effects, we set a constant-relative-variation constraint on their trends (second assumption). The constant-relative-variation constraint dictates that between two stochastic effects, one with a larger curvature gets a larger (absolute) slope, and one with zero curvature gets no slope. We conducted Monte-Carlo simulations to examine the statistical properties of the proposed method and analyzed the data of prostate cancer incidence for whites from 1973–2012 to illustrate the methodology. A driver for the period and/or cohort effect may be lacking in some populations. In that case, the CRV method automatically produces an unbiased age effect and no period and/or cohort effect, thereby addressing the situation properly. However, the method proposed in this paper is not a general purpose model and will produce biased results in many other real-life data scenarios. It is only useful in situations when the age effects are deterministic and dominant, and the period and cohort effects are stochastic and minor. ? 2019 Su, Lee. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Other Subjects
adult; age period cohort analysis; aged; analysis; Article; birth; cancer incidence; cancer risk; Caucasian; cohort slope; constant relative variation constraint; controlled study; groups by age; human; low risk patient; major clinical study; male; mathematical computing; mathematical parameters; methodology; middle aged; Monte Carlo method; parameters; period; period slope; prostate cancer; United States; very elderly; age; cohort analysis; computer simulation; incidence; methodology; prostate tumor; statistical bias; survival rate; Age Factors; Bias; Cohort Studies; Computer Simulation; European Continental Ancestry Group; Humans; Incidence; Male; Monte Carlo Method; Prostatic Neoplasms; Research Design; Survival Rate
Publisher
Public Library of Science
Type
journal article