Excess Risk Models Research Articles

Of Men and Mice: Using Terrestrial Radiation Epidemiology Methods to Inform Analysis of Animal Models for Space Radiation Risk Assessment.

Prediction of cancer risk from space radiation exposure is critical to ensure spaceflight crewmembers are adequately informed of the risks they face when accepting assignments to ambitious long-duration exploratory missions. Although epidemiological studies have assessed the effects of exposure to terrestrial radiation, no robust epidemiological studies of humans exposed to space radiation exist to support estimates of the risk from space radiation exposure. Mouse data derived from recent irradiation experiments provides valuable information to successfully develop mouse-based excess risks models for assessing relative biological effectiveness for heavy ions that can provide information to scale unique space radiation exposures so that excess risks estimated for terrestrial radiation can be adjusted for space radiation risk assessment. Bayesian analyses were used to simulate linear slopes for excess risk models with several different effect modifiers for attained age and sex. Relative biological effectiveness values for all-solid cancer mortality were calculated from the ratio of the heavy-ion linear slope to the gamma linear slope using the full posterior distribution and resulted in values that were substantially lower than what is currently applied in risk assessment. These analyses provide an opportunity to improve characterization of parameters used in the current NASA Space Cancer Risk (NSCR) model and generate new hypotheses for future animal experiments using out-bred mouse populations.

Comparison and multi-model inference of excess risks models for radiation-related solid cancer

In assessments of detrimental health risks from exposures to ionising radiation, many forms of risk to dose–response models are available in the literature. The usual practice is to base risk assessment on one specific model and ignore model uncertainty. The analysis illustrated here considers model uncertainty for the outcome all solid cancer incidence, when modelled as a function of colon organ dose, using the most recent publicly available data from the Life Span Study on atomic bomb survivors of Japan. Seven recent publications reporting all solid cancer risk models currently deemed plausible by the scientific community have been included in a model averaging procedure so that the main conclusions do not depend on just one type of model. The models have been estimated with different baselines and presented for males and females at various attained ages and ages at exposure, to obtain specially computed model-averaged Excess Relative Risks (ERR) and Excess Absolute Risks (EAR). Monte Carlo simulated estimation of uncertainty on excess risks was accounted for by applying realisations including correlations in the risk model parameters. Three models were found to weight the model-averaged risks most strongly depending on the baseline and information criteria used for the weighting. Fitting all excess risk models with the same baseline, one model dominates for both information criteria considered in this study. Based on the analysis presented here, it is generally recommended to take model uncertainty into account in future risk analyses.

Excess cumulative incidence estimation for matched cohort survival studies.

We suggest a regression approach to estimate the excess cumulative incidence function (CIF) when matched data are available. In a competing risk setting, we define the excess risk as the difference between the CIF in the exposed group and the background CIF observed in the unexposed group. We show that the excess risk can be estimated through an extended binomial regression model that actively uses the matched structure of the data, avoiding further estimation of both the exposed and the unexposed CIFs. The method naturally deals with two time scales, age and time since exposure and simplifies how to deal with the left truncation on the age time-scale. The model makes it easy to predict individual excess risk scenarios and allows for a direct interpretation of the covariate effects on the cumulative incidence scale. After introducing the model and some theory to justify the approach, we show via simulations that our model works well in practice. We conclude by applying the excess risk model to data from the ALiCCS study to investigate the excess risk of late events in childhood cancer survivors.

Risk of Leukemia Associated with Protracted Low-Dose Radiation Exposure: Updated Results from the National Registry for Radiation Workers Study.

While the link between risk of leukemia and acute radiation exposure is well established for large doses received acutely, uncertainty remains around the translation of these risk estimates to occupational exposure scenarios where the doses are low and accumulated over time, possibly over many years. We present leukemia incidence and mortality radiation risk estimates derived from the National Registry for Radiation Workers, which is a large cohort of occupationally exposed workers from the United Kingdom (UK). The cohort comprised 173,081 workers from the UK who were monitored for occupational exposure to radiation. The cohort was followed for a total of 5.3 million person-years and the incidence and mortality due to leukemia was identified through to the end of follow-up in 2011. Poisson regression was used to investigate the relationship between cumulative radiation dose and leukemia mortality and incidence rates using excess relative risk (ERR) and excess additive risk (EAR) models. The results of this work showed a collective dose of 4,414 person-Sv accumulated by the cohort with an average cumulative dose of 25.5 mSv. Among male workers both the ERR and EAR models showed evidence of increased leukemia risk (excluding chronic lymphatic leukemia) associated with increasing cumulative dose. The ERR was 1.38 per Sv (90% CI: 0.04; 3.24) and EAR was 1.33 per 10,000 person-year-Sv (90% CI: 0.04; 2.89) when a linear model was used. These excess risks were driven by increased risks for chronic myeloid leukemia [ERR/Sv = 6.77 (90% CI: 2.14; 15.44)]. In conclusion, this study provides further evidence that leukemia risks may be increased by low-dose and protracted external radiation exposure. The risks are generally consistent with those observed in the atomic bomb survivor studies, as well as with risk coefficients on which international radiation safety standards, including the dose limits and constraints used to control exposures, are based.

Excess risk estimation for matched cohort survival data.

We present an excess risk regression model for matched cohort data, where the occurrence of some events for individuals with a disease is compared to that of healthy controls that are matched at the onset-of-disease by various factors. By using the matched structure, we show how to estimate the excess risk and its dependence on covariates on both proportional and additive form. We remove the individual effects on background mortality related to matching factors by considering differences. The model handles two different time scales, namely attained age and follow-up time. First, we solve estimating equations for the non-parametric and parametric components of the excess risk model, providing large sample properties for the suggested estimators. Next, we report results from a simulation study. Lastly, we describe an application of the method on childhood cancer data, to study the excess risk of cardiovascular events in adults' life among childhood cancer survivors.

Measurements of electronic cigarette-generated particles for the evaluation of lung cancer risk of active and passive users

Electronic cigarettes (ECs) are perceived to be safer than traditional tobacco cigarettes because of the absence of combustion processes. The use of these devices, however, exposes the users (“vapers”) and passive “vapers” to possible high concentration of fine and ultrafine particles (UFPs) which can deliver toxic and carcinogenic compounds. In the present work, an experimental campaign was carried out using dedicated instrumentation in order to characterize both the aerosol emitted from ECs and the exposure to second hand EC smoke in a typical indoor microenvironment in terms of particle number and surface area concentrations. Thus, the potential carcinogenic effects due to the inhalation of EC-generated aerosol was evaluated by means of an ad-hoc Excess Lifetime Cancer Risk (ELCR) model able to take into account for the contribution of both sub-micron and super-micron particles, referring to the particle surface area, evaluated on the basis of their solid core only, by heating the aerosol at 300°C. To this end, literature data of toxic compounds deposited on EC-generated particles (both with and without nicotine) and typical smoking behaviours of male and female Italian vapers were considered. The results showed that the particle number concentrations in EC mainstream aerosol (2.23–2.34×108 part. cm−3, mode at 34nm) are higher than that in mainstream smoke of traditional cigarettes, while surface area concentrations in mainstream EC aerosol (2.48–3.35×1010nm2cm−3, at 300°C)) are lower than that in traditional mainstream cigarettes smoke. The corresponding ELCR value of mainstream EC aerosol (6.11–7.26×10−6) is 5 orders of magnitude lower than that of mainstream traditional cigarettes smoke, and also lower than the guideline values defined by EPA and WHO. Particle number concentrations equal to 6.30–9.08×103 part. cm−3 with bi-modal distribution (at 30nm and 90nm) and surface area concentrations of 5.16–5.90×107nm2cm−3 (at 300°C), respectively, were measured in second-hand aerosol of ECs, leading to extremely low values of ELCR due to the exposure to second-hand EC aerosol (1.24–2.70×10−8).

Quantifying risk over the life course - latency, age-related susceptibility, and other time-varying exposure metrics.

Identification of the latency period and age-related susceptibility, if any, is an important aspect of assessing risks of environmental, nutritional, and occupational exposures. We consider estimation and inference for latency and age-related susceptibility in relative risk and excess risk models. We focus on likelihood-based methods for point and interval estimation of the latency period and age-related windows of susceptibility coupled with several commonly considered exposure metrics. The method is illustrated in a study of the timing of the effects of constituents of air pollution on mortality in the Nurses' Health Study. Copyright © 2016 John Wiley & Sons, Ltd.

Comparable survival for young rectal cancer patients, despite unfavourable morphology and more advanced-stage disease

BackgroundYoung patients with rectal cancer tend to present with more advanced-stage disease and unfavourable tumour morphology. The effects of these tumour characteristics on survival in this particular patient group are unclear. MethodsPopulation-based data from the Netherlands Cancer Registry (NCR) were used. Data from patients diagnosed with rectal cancer between 1989 and 2010 were selected. Younger patients (⩽40years) were compared with middle-aged patients (41–70years) with respect to disease stage, tumour characteristics, treatment and outcomes. Patients aged older than 70years were excluded. Relative excess risk (RER) models were used to perform uni- and multivariate survival analyses. FindingsA total of 37.056 patients were included (⩽40years n=1.102). Compared with middle-aged patients, young patients were more likely to have stage III (33.8% versus 27.8%) and stage IV (24.3% versus 19.6%) disease (p<0.001). Young patients also presented more frequently with mucinous tumours (10.8% versus 9.0%), signet cell carcinomas (2.6% versus 0.6%) and poorly differentiated tumours (16.6% versus 12.3%) (p=0.001). The treatment of stage I–III patients did not differ between the two groups, except regarding adjuvant chemotherapy, which was more often given to young patients (24.3% versus 14.4%, p<0.001). Young age was a prognostic factor for better survival in stage I–III patients (RER 0.82 CI 0.71–0.94). Adjuvant chemotherapy was associated with improved survival in stage I–III patients (RER 0.76, 95%CI 0.70–0.83). In an exploratory analysis, adjuvant chemotherapy in young stage III and pN1 patients was associated with improved survival. Concluding statementYoung patients present with more advanced disease and have more unfavourable tumour characteristics compared with middle-aged patients. Despite these characteristics, survival rates are equal, and young age is a prognostic factor for better survival. Although the use of adjuvant chemotherapy is controversial, a positive correlation with survival was found in this study.

Do Female Cancer Patients Display Better Survival Rates Compared with Males? Analysis of the Korean National Registry Data, 2005–2009

BackgroundSex differences have been reported in the prognosis of certain cancers. In this study, we investigated whether Korean females display better survival rates compared with male patients for solid tumor sites.MethodsWe analyzed data from the Korean National Cancer Incidence Database from 599,288 adult patients diagnosed with solid cancers between 2005 and 2009. Patients were followed until December 2010. We applied a relative excess risk (RER) model adjusting for year of follow-up, age at diagnosis, and stage at diagnosis.ResultsFor all solid cancer sites combined, women displayed an 11% lower risk of death compared to men (RER 0.89; 95% CI 0.88–0.90) after adjusting for year of follow-up, age, stage, and case mix. Women showed significantly lower RERs for the following sites: head/neck, esophagus, small intestine, liver, nasal cavities, lung, bone/cartilages, melanoma of skin, soft tissue, brain and CNS, and thyroid. In contrast, women displayed a poorer prognosis than did men for colorectal, laryngeal, kidney and bladder cancer. However, the survival gaps between men and women narrowed by increase in age; female patients over 75 years of age displayed a 3% higher RER of death compared with males in this age group.ConclusionsFemale cancer patients display an improved survival for the majority of solid tumor sites, even after adjustment for age and stage. Age at diagnosis was the major contributor to the women’s survival advantage.

Lung Cancer Mortality (1950–1999) among Eldorado Uranium Workers: A Comparison of Models of Carcinogenesis and Empirical Excess Risk Models

Lung cancer mortality after exposure to radon decay products (RDP) among 16,236 male Eldorado uranium workers was analyzed. Male workers from the Beaverlodge and Port Radium uranium mines and the Port Hope radium and uranium refinery and processing facility who were first employed between 1932 and 1980 were followed up from 1950 to 1999. A total of 618 lung cancer deaths were observed. The analysis compared the results of the biologically-based two-stage clonal expansion (TSCE) model to the empirical excess risk model. The spontaneous clonal expansion rate of pre-malignant cells was reduced at older ages under the assumptions of the TSCE model. Exposure to RDP was associated with increase in the clonal expansion rate during exposure but not afterwards. The increase was stronger for lower exposure rates. A radiation-induced bystander effect could be a possible explanation for such an exposure response. Results on excess risks were compared to a linear dose-response parametric excess risk model with attained age, time since exposure and dose rate as effect modifiers. In all models the excess relative risk decreased with increasing attained age, increasing time since exposure and increasing exposure rate. Large model uncertainties were found in particular for small exposure rates.

Estimates of the potential risk of radiation-related cancer from screening in the UK

Estimates of the potential risk of radiation-related cancer from screening in the UK

A coupled simulation and fuzzy-rule-based method for assessing health risks at a petroleum-contaminated site

Risk assessment of contaminated sites is crucial for evaluating adverse health impacts on local communities, and provides effective decision support for site-remediation practices. In this study, a coupled simulation and fuzzy-rule-based method was proposed for health risk assessment of groundwater contamination, and applied to a petroleum contaminated site. Benzene concentrations under two remediation scenarios (i.e., natural attenuation and pump-and-treat remediation) at different time intervals (1, 10, 20, 30, and 60 years later) were examined. The obtained benzene concentrations were then used to quantify the health risks through the excess lifetime cancer risk (ELCR) model. The study results indicated that the proposed method was useful for assessing potential human health effects when the groundwater was used for drinking water purpose and could effectively identify variation of risk levels under different remediation scenarios. The study also found that the dissolved oxygen was the most thermodynamically favoured electron accepter used in the biodegradation of petroleum hydrocarbons. When implementing remediation actions, we can inject oxygen to subsurface to strengthen aerobic biodegradation process (i.e., air sparging).

Do women with cancer have better survival as compared to men after adjusting for staging distribution?

Gender aspects in medicine are receiving increasing attention, namely also in oncology. For this reason, we decided to investigate whether for solid cancer sites women have better survival outcome than do men in the population of Tyrol, Austria. We conducted an observational population-based study in Tyrol. All solid cancer sites excluding non-melanoma skin cancer and sex-specific sites were analysed in total and all specific sites with more than 500 patients in the analysis. By the end of 2006, follow-up was ended. We applied a relative excess risk model, thus correcting for differences in life expectancy between women and men. For all cancer sites combined, after adjusting for case mix, women had a relative excess risk of 0.95 (95% CI 0.91-0.99). For the following sites our analysis resulted in a relative excess risk statistically different from 1, namely for women as compared to men: head and neck without larynx 0.72 (95% CI 0.56-0.93), stomach 0.86 (95% CI 0.75-0.97) and lung 0.82 (95% CI 0.75-0.90). In a healthcare system with free access to diagnostics and therapy, after adjusting for staging distribution female cancer patients have a lesser excess mortality risk than do men for lung, stomach and head and neck cancer and also for all cancer sites combined after adjusting for case mix.

Estimation of the hazard function in a semiparametric model with covariate measurement error

We consider a failure hazard function, conditional on a time-independent covariate Z, given by . The baseline hazard function and the relative risk both belong to parametric families with . The covariate Z has an unknown density and is measured with an error through an additive error model U = Z + ε where ε is a random variable, independent from Z, with known density . We observe a n-sample (Xi, Di, Ui), i = 1, ..., n, where Xi is the minimum between the failure time and the censoring time, and Di is the censoring indicator. Using least square criterion and deconvolution methods, we propose a consistent estimator of θ0 using the observations n-sample (Xi, Di, Ui), i = 1, ..., n. We give an upper bound for its risk which depends on the smoothness properties of and as a function of z, and we derive sufficient conditions for the -consistency. We give detailed examples considering various type of relative risks and various types of error density . In particular, in the Cox model and in the excess risk model, the estimator of θ0 is -consistent and asymptotically Gaussian regardless of the form of .

Decision analyses for prophylactic replacement of the Björk-Shiley convexo-concave heart valve: an evaluation of assumptions and estimates.

Björk-Shiley convexo-concave (BScc) mechanical heart valves have a risk of outlet strut fracture. Decision-analytic models may support decision making regarding prophylactic replacement to avert the disastrous consequences of fracture. Assumptions and estimates from previous analyses were evaluated to develop an accurate new decision model, incorporating updated follow-up experience from 2,263 patients with BScc valves implanted between 1979 and 1985 in The Netherlands. The authors focus on estimation of fracture risks (50 events) and survival (883 deaths, excluding fractures). In previous analyses, fracture risk was estimated with widely varying combinations of patient, valve, and production characteristics. Two analyses assumed a constant fracture hazard during follow-up, while data from the present study suggest that risk decreases with increasing age during follow-up. An additive excess-risk model was applied in two analyses to estimate survival. The assumption of a constant additive excess risk was not supported by the Dutch data, which suggest that the life expectancies of patients currently alive with BScc valves may be shorter than previously believed. Based on the revised decision model, over 90% of the currently alive Dutch BScc valve patients do not benefit from replacement, since the fracture risk causes only a minor reduction of remaining life expectancy. The variation in essential assumptions of previous decision analyses directly affected the indication for prophylactic replacement. This study shows how detailed statistical analyses may guide modeling choices in a decision analysis.

Relative risk and excess rate models in exposure-response analysis.

The cumulative dose of an environmental exposure and age are usually correlated. It is shown that if an excess rate model is the true relationship, a relative risk model based on standardized mortality ratios (SMR) may show a biased and even contradictory dose-response relationship if the incidence rate in the reference population increases rapidly by age. The choice of model should be based on biological plausibility rather than on available software, and in many cases it may be reasonable to analyze the data both according to a relative and to an excess risk model as the prior knowledge rarely favors one of these models. In a review of a volume of this journal, there were eight cohort studies analyzing dose-response relationship by comparing SMRs without reporting or analyzing excess rates. Thus, there seems to be some lack of awareness of the possible bias in analyzing exposure-disease associations by SMRs.

Risk Assessment for Aflatoxin B1: A Modeling Approach

The data generated by Yeh et al. on hepatitis B virus, aflatoxin, and primary hepatocellular carcinoma (PHC) in Southern Guangxi, China was used to evaluate the cancer potency of aflatoxin. We examined model fits to these data to explore whether hepatitis B virus (HBV) and aflatoxin intake act together to affect PHC rates in an additive, multiplicative, or interactive fashion, using relative and excess risk model forms. Purely additive models fit the data poorly. Fitted models were checked for plausibility by comparing predictions for the U.S. population with actual PHC incidence rates in the United States, and parameter stability was evaluated. The multiplicative relative risk and the interactive excess risk models provided satisfactory descriptions of the Yeh et al. data and U.S. PHC rates. There is about an eight-fold difference in the potency estimate for aflatoxin under the multiplicative relative risk (5.7 (mg/kg-day)-1) and interactive excess risk models (45.6 mg/kg-day)-1). The assumptions and limitations of the various models are discussed.

Nonparametric tests of the Markov model for survival data

SUMMARY Jones & Crowley (1989) introduced a general class of nonparametric statistics for right-censored survival data that includes many of the existing procedures involving a single covariate. This paper considers this class in the framework of a Markov model and suggests some tests especially appropriate for the Markov setting. Application to left-truncated and right-censored data is also given. Small sample properties and asymptotic relative efficiencies for several statistics of this class are investigated for both relative and excess risk models for the hazard.

Analysis of Time and Age Patterns in Cancer Risk for A-Bomb Survivors

This report has two aims: (1) to describe and analyze the age/time patterns of excess cancer risk in the atomic bomb survivor cohort followed up by the Radiation Effects Research Foundation (RERF), and (2) to describe statistical methods which are used in RERF's analyses of data on mortality and morbidity in the cohort. In contrast to previous analyses of the cohort cancer mortality data, substantial use is made of Japanese national cancer rates for the purpose of investigation of the age/time variations in excess risk. This analysis considers mortality from all cancers except leukemia as a group. Primary attention is given to description in terms of the age-specific excess relative risk, but the importance of appropriate descriptions of the absolute excess risk is also emphasized. When models for the excess risk allow variation with age and time, both constant relative and absolute excess risk models provide similar fits to the data. Previous reports have indicated that for a given age at exposure and sex, the excess age-specific relative risk is remarkably constant throughout the current follow-up period. Statistical analysis here indicates that for those less than about 35 years of age at exposure there is no departure from this pattern, beyond ordinary sampling variation. For those over about 35 years of age at exposure, there is modest evidence of an increasing trend in the excess relative risk, which could plausibly be attributed to effects related to minimal latent period. Some brief consideration is given to modeling the absolute excess risk as the product of an age-at-exposure and time-since-exposure effect. Interpretation of these results, particularly in regard to projections beyond the current follow-up, is discussed.

Asymptotic Properties of a General Class of Nonparametric Tests for Survival Analysis

Many of the popular nonparametric test statistics for censored survival data used in two-sample, $s$-sample trend and single continuous covariate situations are special cases of a general statistic, differing only in the choice of covariate-based label and weight function. Formulated in terms of counting processes and martingales this general statistic, standardized by the square root of its consistent variance estimator, is shown to be asymptotically normal under the null hypothesis and under a sequence of contiguous hazard alternatives that includes both relative and excess risk models. As an application to some specific cases of the general statistic, the asymptotic relative efficiencies of the Brown, Hollander and Korwar modification of the Kendall rank statistic, the Cox score statistic and the generalized logrank statistic of Jones and Crowley are investigated under relative and excess risk models. Finally, an example is given in which the Cox score test is not as efficient as the generalized logrank test in the presence of outliers in the covariate space.