Background stratified Poisson regression analysis of cohort data.

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.

Hierarchical latency models for dose-time-response associations.

Exposure lagging and exposure-time window analysis are 2 widely used approaches to allow for induction and latency periods in analyses of exposure-disease associations. Exposure lagging implies a strong parametric assumption about the temporal evolution of the exposure-disease association. An exposure-time window analysis allows for a more flexible description of temporal variation in exposure effects but may result in unstable risk estimates that are sensitive to how windows are defined. The authors describe a hierarchical regression approach that combines time window analysis with a parametric latency model. They illustrate this approach using data from 2 occupational cohort studies: studies of lung cancer mortality among 1) asbestos textile workers and 2) uranium miners. For each cohort, an exposure-time window analysis was compared with a hierarchical regression analysis with shrinkage toward a simpler, second-stage parametric latency model. In each cohort analysis, there is substantial stability gained in time window-specific estimates of association by using a hierarchical regression approach. The proposed hierarchical regression model couples a time window analysis with a parametric latency model; this approach provides a way to stabilize risk estimates derived from a time window analysis and a way to reduce bias arising from misspecification of a parametric latency model.

Fitting general relative risk models for survival time and matched case-control analysis.

Cox proportional hazards regression analysis of survival data and conditional logistic regression analysis of matched case-control data are methods that are widely used by epidemiologists. Standard statistical software packages accommodate only log-linear model forms, which imply exponential exposure-response functions and multiplicative interactions. In this paper, the authors describe methods for fitting non-log-linear Cox and conditional logistic regression models. The authors use data from a study of lung cancer mortality among Colorado Plateau uranium miners (1950-1982) to illustrate these methods for fitting general relative risk models to matched case-control control data, countermatched data with weights, d:m matching, and full cohort Cox regression using the SAS statistical package (SAS Institute Inc., Cary, North Carolina).

Are nested case-control studies biased?

It has been recently asserted that the nested case-control study design, in which case-control sets are sampled from cohort risk sets, can introduce bias ("study design bias") when there are lagged exposures. The bases for this claim include a theoretical and an "empirical evaluation" argument. We examined both of these arguments and found them to be incorrect. We describe an appropriate empirical evaluation method to explore the performance of nested case-control study designs and analysis methods from an existing cohort. This empirical evaluation approach relies on simulating case-control outcomes from risk sets in the cohort from which the case-control study is to be performed. Because it is based on the underlying cohort structure, the empirical evaluation can provide an assessment that is tailored to the specific characteristics of the study under consideration. The methods are illustrated using samples from the Colorado Plateau uranium miners cohort.

Using tensor product splines in modeling exposure-time-response relationships: application to the Colorado Plateau Uranium Miners cohort.

An adequate depiction of exposure-time-response relationships is important in assessing public health implications of an occupational or environmental exposure. Recent advances have focused on flexible modeling of the overall shape of latency. Methods are needed to allow for varying shapes of latency under different exposure profiles. A tensor product spline model is proposed for describing exposure-response relationships for protracted time-dependent occupational exposure histories in epidemiologic studies. The methods use flexible multi-dimensional techniques to jointly model age, latency and exposure-response effects. In analyzing data from the Colorado Plateau Uranium Miners cohort, a model that allows for varying exposure-dependent latency shapes is found to be superior to models that only allowed for an overall latency curve. Specifically, the model suggests that, at low exposure levels risk increased at short latencies followed by a slow decline for longer latency periods. On the other hand, risk was higher but did not change much by latency for higher exposure levels. The proposed methodology has the advantage of allowing for latency functions that vary by exposure levels and, conversely, exposure-response relationships that are influenced by the latency structure.

Effect of systemic adjuvant treatment on risk for contralateral breast cancer in the Women's Environment, Cancer and Radiation Epidemiology Study.

BACKGROUND: Results from randomized trials indicate that treatment with tamoxifen or chemotherapy for primary breast cancer reduces the risk for contralateral breast cancer. However, less is known about how long the risk is reduced and the impact of factors such as age and menopausal status. METHODS: The study included 634 women with contralateral breast cancer (case patients) and 1158 women with unilateral breast cancer (control subjects) from the Women's Environment, Cancer and Radiation Epidemiology Study. The women were younger than age 55 when they were first diagnosed with breast cancer during 1985-1999. Rate ratios (RRs) and 95% confidence intervals (CIs) for contralateral breast cancer after treatment with chemotherapy or tamoxifen were assessed by multivariable adjusted conditional logistic regression analyses. RESULTS: Chemotherapy was associated with a lower risk for contralateral breast cancer (RR = 0.57, 95% CI = 0.42 to 0.75) than no chemotherapy. A statistically significant association between chemotherapy and reduced risk for contralateral breast cancer persisted up to 10 years after the first breast cancer diagnosis and was stronger among women who became postmenopausal within 1 year of the first breast cancer diagnosis (RR = 0.28, 95% CI = 0.11 to 0.76). Tamoxifen use was also associated with reduced risk for contralateral breast cancer (RR = 0.66, 95% CI = 0.50 to 0.88) compared with no use, and the association was statistically significant for 5 years after the first diagnosis. CONCLUSION: The associations between chemotherapy and tamoxifen treatment and reduced risk for contralateral breast cancer appear to continue for 10 and 5 years, respectively, after the initial breast cancer is diagnosed. Ovarian suppression may have a role in the association between chemotherapy and reduced risk for contralateral breast cancer.