Background stratified Poisson regression analysis of cohort data

Abstract

Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as ‘nuisance’ variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this ‘conditional’ regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models.

Site-specific Solid Cancer Mortality After Exposure to Ionizing Radiation

“…For cancer type j , person-years at risk and deaths were tabulated by categories of the associated organ-specific cumulative dose and other study covariates. We fitted a Poisson regression model of the form shown in expression 1 for each cancer type 20,21 ; an estimate of the coefficient of primary interest, β j , was adjusted to account for the effects of country, attained age (in 5-year intervals), sex, year of birth (in 10-year intervals), socioeconomic status (in five categories, based on job title, for French, US, and UK workers employed by the Atomic Energy Authority and Atomic Weapons Establishment; other UK workers were classified as nonmanual or manual skilled workers, based on employment category), duration of employment or radiation work (in 10-year intervals), and exposure to neutrons (whether a worker had a positive recorded neutron dose, and if so, whether their recorded neutron dose equivalent ever exceeded 10% of their total external radiation dose equivalent). 15,16…”

Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis

“…The EPICURE AMFIT package [ 10 ] implements the conditional Poisson model for stratified survival data under the label background stratified Poisson and this has been used quite extensively in studies of cancer effects of ionizing radiation. Richardson [ 11 ] comments that the AMFIT implementation has an unnecessary limitation in the number of strata, and proposed a method without that limitation using SAS procedure nlp or mlmixed. Xu [ 12 ] presents an approach to fit conditional Poisson models in SAS, but as this is effectively by re-formulating as a conditional logistic model we class this a conditional logistic formulation (discussed below).…”

Impact of physicians’ participation in non-interventional post-marketing studies on their prescription habits: A retrospective 2-armed cohort study in Germany

“…Conditional Poisson regression was used to assess the relative rate of prescriptions for the studied drug in participating physicians versus their respective controls [22]. Coefficients for matching groups were treated as nuisance variables and were eliminated.…”