Evaluation of statistical modeling approaches for epidemiologic studies of low-dose radiation health effects

Abstract

Purpose

A substantial body of epidemiologic literature addresses risks associated with occupational radiation exposure but comparing results between studies is often difficult as different statistical models are commonly used. It is unclear whether different methods produce similar results for estimates of radiation risk when applied to the same data. The goal of this study was to compare the radiation risk estimates for leukemia other than chronic lymphocytic leukemia (non-CLL) and ischemic heart disease (IHD) produced by both Cox and Poisson regression models for time-dependent dose-response analyses of occupational exposure.

Materials and methods

For brevity, this methods paper presents the results from one cohort, the Nuclear Power Plant workers (NPP), though the evaluation considered five cohorts of varying size and exposure as part of the Million Worker Study. Cox Proportional Hazards models, with age as the underlying timescale for hazard, were conducted using three computer software programs: SAS, R, and Epicure. Doses lagged 2 years for non-CLL and 10 years for ischemic heart disease were treated as time-dependent exposures at the annual level and were examined both in categories and as a continuous term. Hazard ratios (HR) and 95% confidence intervals (CI) were reported for each model in SAS and R, while the Peanuts program of Epicure was utilized to produce Excess Relative Risk (ERR) estimates and 95% CI. All models were adjusted for gender and year of birth. Four piece-wise exponential Poisson models (log-linear regression for rate) were developed with varying cutpoints for age strata from very fine to broad categories using both R and the Amfit program in Epicure for ERR estimates.

Results

Comparable estimates of risk (both RR and ERR) were observed from Cox and Poisson models, regardless of software utilized, as long as appropriately narrow categories of age were utilized to control the confounding of age in Poisson models. The ERR estimates produced in Epicure tended to agree very closely with the HR or RR estimates, and the statistical software program used had no impact to risk estimates for the same model.

Conclusions

As computational power is no longer the burden today as it has been in the past, the results of this evaluation support the use of the Cox proportional hazards or the ungrouped Poisson approach to analyzing time-dependent dose-response relationships to ensure that maximum control over the confounding of age is achieved in studies of mortality for cohorts occupationally exposed to radiation.

Keywords:

• Epidemiology
• models of radiation responses
• statistical models
• Cox proportional hazards model
• Poisson regression model

Disclosure statement

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.

Additional information

Funding

This work was supported by the Oak Ridge Associated Universities Directed Research and Development program, an internally funded competitive grant program for ORAU employees and member universities. Additionally, the data analyzed were provided through support by a grant from the U.S. Nuclear Regulatory Commission (NRC-HQ-60-14-G-0011), a grant from the Centers for Disease Control and Prevention (5UE1EH000989), a grant from the National Aeronautics and Space Administration (Grant No. NNX15AU88G), a grant from the National Cancer Institute (Grant No. U01 CA137026), a Discovery Grant from the Vanderbilt-Ingram Cancer Center (Center no. 404-357-9682), and grants from the U.S. Department of Energy (Grant No. DE-SC0008944 and Grant No. DE-AU0000042 awarded to the National Council on Radiation Protection and Measurements, which included interagency support from the U.S. Nuclear Regulatory Commission, the U.S. Environmental Protection Agency and the National Aeronautics and Space Administration).

Notes on contributors

Ashley P. Golden

Ashley P. Golden is a biostatistician and project manager at Oak Ridge Associated Univerities were she directs and supports multi-disciplinary projects in the areas of occupational epidemiology, radiation exposure and dosimetry, medical surveillance, survey analyses, and environmental assessments. She has been a collaborator on the Million Person Study of Low-Dose Health Effects for nearly six years.

Sarah S. Cohen

Sarah S. Cohen is a Principal Epidemiologist at EpidStat Institute where she directs observational research studies in the areas of pharmacoepidemiology, nutritional epidemiology, and occupational epidemiology as well as leads large data management projects and statistical analyses. She is also an Adjunct Assistant Research Professor of Medicine in the Department of Medicine at Vanderbilt University School of Medicine. She has been a collaborator on the Million Person Study of Low-Dose Health Effects for nearly twenty years, providing analytic support as well as co-authoring numerous publications.

Heidi Chen

Heidi Chen is a Research Assistant Professor of Biostatistics in the Vanderbilt Center for Quantitative Sciences. Her Ph.D. is in biomathematics from North Carolina State University and her research focuses on applying mathematical, statistical and computational methods to fit models for biological systems. Since joining the Department of Biostatistics, Vanderbilt University Medical Center in 2003, her research has involved identifying biomarkers and building models to predict disease progression and clinical outcome for cancer patients. Heidi has been a statistical collaborator on the Million Person Study of Low-Dose Health Effects since 2013.

Elizabeth D. Ellis

Elizabeth D. Ellis is an occupational epidemiologist at Oak Ridge Associated Universities. She has been studying the health effects of chronic low dose ionizing radiation on the Department of Energy nuclear workers for over 35 years and has been a collaborator on the Million Person Study of Low-Dose Health Effects for over 10 years. She is a member of an International Commission of Radiation Protection Task Group reviewing the health effects of alpha emitters. She is also active in protection of human participants in research serving on several Institutional Review Boards.

John D. Boice

John D. Boice, Jr. is President of the National Council on Radiation Protection and Measurements and Professor of Medicine at Vanderbilt University. He is an international authority on radiation effects and served on the Main Commission of the International Commission on Radiological Protection and on the United Nations Scientific Committee on the Effects of Atomic Radiation. He directs the Million Person Study of Low-Dose Health Effects.