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A review and comparison of age-period-cohort models for cancer incidence

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A review and comparison of age-period-cohort models for cancer incidence. / Smith, T.R.; Wakefield, J.
In: Statistical Science, Vol. 31, No. 4, 2016, p. 591-610.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Smith, TR & Wakefield, J 2016, 'A review and comparison of age-period-cohort models for cancer incidence', Statistical Science, vol. 31, no. 4, pp. 591-610. https://doi.org/10.1214/16-STS580

APA

Smith, T. R., & Wakefield, J. (2016). A review and comparison of age-period-cohort models for cancer incidence. Statistical Science, 31(4), 591-610. https://doi.org/10.1214/16-STS580

Vancouver

Smith TR, Wakefield J. A review and comparison of age-period-cohort models for cancer incidence. Statistical Science. 2016;31(4):591-610. doi: 10.1214/16-STS580

Author

Smith, T.R. ; Wakefield, J. / A review and comparison of age-period-cohort models for cancer incidence. In: Statistical Science. 2016 ; Vol. 31, No. 4. pp. 591-610.

Bibtex

@article{dfef9452fd5048049ecb29a029077976,
title = "A review and comparison of age-period-cohort models for cancer incidence",
abstract = "Age-period-cohort models have been used to examine and forecast cancer incidence and mortality for over three decades. However, the fitting and interpretation of these models requires great care because of the well-known identifiability problem that exists; given any two of age, period, and cohort, the third is determined. In this paper, we review the identifiability problem and models that have been proposed for analysis, from both frequentist and Bayesian standpoints. A number of recent analyses that use age-period-cohort models are described and critiqued before data on cancer incidence inWashington State are analyzed with various models, including a new Bayesian approach based on an identifiable parameterization. {\textcopyright} Institute of Mathematical Statistics, 2016.",
keywords = "Age-period-cohort models, Identifiability, Random walk priors",
author = "T.R. Smith and J. Wakefield",
year = "2016",
doi = "10.1214/16-STS580",
language = "English",
volume = "31",
pages = "591--610",
journal = "Statistical Science",
issn = "0883-4237",
publisher = "Institute of Mathematical Statistics",
number = "4",

}

RIS

TY - JOUR

T1 - A review and comparison of age-period-cohort models for cancer incidence

AU - Smith, T.R.

AU - Wakefield, J.

PY - 2016

Y1 - 2016

N2 - Age-period-cohort models have been used to examine and forecast cancer incidence and mortality for over three decades. However, the fitting and interpretation of these models requires great care because of the well-known identifiability problem that exists; given any two of age, period, and cohort, the third is determined. In this paper, we review the identifiability problem and models that have been proposed for analysis, from both frequentist and Bayesian standpoints. A number of recent analyses that use age-period-cohort models are described and critiqued before data on cancer incidence inWashington State are analyzed with various models, including a new Bayesian approach based on an identifiable parameterization. © Institute of Mathematical Statistics, 2016.

AB - Age-period-cohort models have been used to examine and forecast cancer incidence and mortality for over three decades. However, the fitting and interpretation of these models requires great care because of the well-known identifiability problem that exists; given any two of age, period, and cohort, the third is determined. In this paper, we review the identifiability problem and models that have been proposed for analysis, from both frequentist and Bayesian standpoints. A number of recent analyses that use age-period-cohort models are described and critiqued before data on cancer incidence inWashington State are analyzed with various models, including a new Bayesian approach based on an identifiable parameterization. © Institute of Mathematical Statistics, 2016.

KW - Age-period-cohort models

KW - Identifiability

KW - Random walk priors

U2 - 10.1214/16-STS580

DO - 10.1214/16-STS580

M3 - Journal article

VL - 31

SP - 591

EP - 610

JO - Statistical Science

JF - Statistical Science

SN - 0883-4237

IS - 4

ER -