DNA adducts, protein adducts, and sister chromatid exchange in cigarette smokers and nonsmokers.

In order to validate markers of internal dose and biologically effective dose of carcinogens, a battery of measurements was made on blood samples from 22 smokers and 24 nonsmokers. The markers included immunoreactivity in an enzyme-linked immunosorbent assay (ELISA) quantified in white blood cells with the use of a polyclonal anti-benzo[a]pyrene diol epoxide-I-DNA antibody, 4-aminobiphenyl hemoglobin (4-ABP-Hb) adducts measured by negative chemical ionization mass spectrometry, sister chromatid exchange (SCE) in cultured lymphocytes, and cotinine in plasma measured by radioimmunoassay. Several blood samples were drawn from each subject. In blood samples 1 and 3 having detectable levels of DNA adducts, mean femtomole-per-microgram levels were consistently higher among smokers compared to nonsmokers. The borderline significance of this difference may be attributable to the small numbers of subjects. Consistently higher adduct levels were seen in females compared to males. In sample 3, adduct levels were significantly correlated with measurements of active smoking in smokers and with passive smoking in nonsmokers. By contrast to the ELISA data, which may reflect cumulative exposure from multiple background sources, the 4-ABP-Hb assay was able to distinguish clearly between smokers and nonsmokers. SCEs were significantly elevated in the smokers compared to nonsmokers. Also observed were significant correlations between 4-ABP-Hb and both cotinine and SCEs, as well as a positive correlation between the 4-ABP-Hb and DNA adduct levels (sample 3) that was highly significant. The correlation between DNA and 4-ABP-Hb adducts was significant in smokers but not nonsmokers (sample 3). These results support the need for batteries of markers to detect and to quantify the carcinogenic dose to humans resulting from both specific and "background" environmental exposures.

Designing for nonparametric Bayesian survival analysis using historical controls.

This paper gives a method for choosing the number of patients N0 out of N available patients to be randomized to current controls om a two-arm study when comparison of nonparametric survival curves is the anticipated method of data analysis. The criterion imposed is that of choosing N0 to minimize the posterior variance of the difference between the current control and experimental survival curves. A nonparametric Bayesian argument incorporating the survival curve of available historical controls establishes the criterion. Formulas and tables which facilitate this computation are presented.

Quantitative risk assessment.

This paper presents a brief survey of current methodology available for quantitative risk assessment of environmental carcinogens. Four current models for low-dose extrapolation are reviewed. Current problems and controversies and possible options in doing quantitative risk assessments based on chronic animal studies are discussed.

Risk assessment for fetal toxicity.

This paper reviews the problem of performing risk assessments using data on fetal toxic effects. It briefly discusses the usual dose-response models and their inappropriateness for application to such data. The paper then considers tests for determining whether the fetal toxic effect is increased over that of the control group. Assuming an increase has been shown, the use of a fetal toxicity, dose-response model for risk assessment is discussed. The paper then applies these methods to data from an experiment using female mice mated with irradiated males. Finally, the paper discusses the need for further statistical research in this important area.

A dose-response model incorporating nonlinear kinetics.

This paper introduces a dose-response model for toxic quantal response data based on hit theory applied to the dose unit as transformed by a nonlinear kinetic equation. When spontaneous background response is included in the model, the resulting dose-response model has four parameters. The maximum likelihood estimators and their large-sample properties are given. Likelihood ratio tests of interest are developed, including one for whether the model is one-hit in the transformed dose and one to check whether nonlinear kinetics is operative. The use of the model for low-dose extrapolation is presented. Finally, the procedures developed are illustrated on data from three animal carcinogenicity bioassays that show, respectively, concave, linear, and convex dose-response curves in the observed data.

A generalized multihit dose-response model for low-dose extrapolation.

A quantal dose-response model based on a multihit theory of toxic response is presented. When spontaneous background toxic response is included, the model involves three unknown parameters. The maximum likelihood estimators for these three parameters are given as the solution of a nonlinear iterative algorithm. The resulting three-dimensional vector of estimators is shown to be asymptotically strongly consistent, asymptotically unique with probability one, and, when suitably normalized, it has asymptotically a trivariate normal distribution. On the basis of these results, a large-sample goodness-of-fit test is given. The use of this model for low-dose extrapolation is indicated. Application of the results is illustrated using three sets of toxicity data.

A dose-response model for teratological experiments involving quantal responses.

This paper introduces a dose-response model for teratological quantal response data where the probability of response for an offspring from a female at a given dose varies with the litter size. The maximum likelihood estimators for the parameters of the model are given as the solution of a nonlinear iterative algorithm. Two methods of low-dose extrapolation are presented, one based on the litter size distribution and the other a conservative method. The resulting procedures are then applied to a teratological data set from the literature.

Assessing the power and quality of epidemiologic studies of asbestos-exposed populations.

This paper briefly discusses criteria for evaluating epidemiologic studies for risk assessment purposes, using asbestos as an example. Asbestos is one of the few carcinogens for which substantial data exist on exposures to humans. However, there are major difficulties in using these data for conducting risk assessments. In particular, exposure data are often incomplete, and risk assessments usually involve extrapolating from the higher exposures of the occupational environments to the lower levels typically encountered in the nonoccupational environment. The term "asbestos" refers to the fibrous form of several minerals, and levels of exposures to these fibers are not easily assessed. Criteria for evaluating epidemiologic studies used in an Ontario Royal Commission report on asbestos are discussed. The importance of considering the statistical power of studies to detect an excess risk is examined using as examples major cohort studies of asbestos-exposed workers, as summarized in a report by the U.S. National Research Council.

Race, poverty, and survival in multiple myeloma.

To identify possible interracial differences in the behavior of multiple myeloma, the records of 52 black myeloma patients at Harlem Hospital Center (HHC) and 46 black and 46 white patients at Columbia-Presbyterian Medical Center (CPMC) were reviewed. In addition to clinical variables such as tumor burden, azotemia, and hypoalbuminemia, the effect of poverty on prognosis was examined, using socioeconomic indices from the United States census block group data of each patient. The median survival of CPMC black and white patients was comparable (34 and 29 months, respectively) whereas that of the HHC group was 12 months (Breslow test, P less than 0.0001). Overcrowding and hypoalbuminemia were the most significant prognostic factors by multivariate regression analysis on all 144 patients (P = 0.001); for HHC patients, overcrowding was the single significant variable affecting survival (P = 0.004). By all socioeconomic indices, HHC patients were more impoverished than CPMC patients (P less than 0.001); they also presented with more advanced disease. Race is not a significant prognostic factor in myeloma, whereas the effect of socioeconomic status on survival appears to equal that of previously described clinical features.

Endogenous synthesis of volatile nitrosamines: model calculations and risk assessment.

A kinetic model for estimating the gastric synthesis of N-nitroso compounds and a method for estimating the risk from exposure to volatile nitrosamines are described. A tentative calculation of the possible risks for human gastric cancer, based on extrapolating from literature data on the induction of tumours in rats, suggests that the endogenous formation of non-volatile N-nitroso compounds is more important than that of volatile nitrosamines.

A comparison of statistical methods for low dose extrapolation utilizing time-to-tumor data.

The assessment of health risks due to low levels of exposure to potential environmental hazards based on the results of toxicological experiments necessarily involves extrapolation of results obtained at relatively high doses to the low dose region of interest. In this paper, different statistical extrapolation procedures which take into account both time-to-response and the presence of competing risks are compared using a large simulated data base. The study was designed to cover a range of plausible dose response models as well as to assess the effects of competing risks, background response, latency and experimental design on the performance of the different extrapolation procedures. It was found that point estimates of risk in the low dose region may differ from the actual risk by a factor of 1000 or more in certain situations, even when precise information on the time of occurrence of the particular lesion of interest is available. Although linearized upper confidence limits on risk can be highly conservative when the underlying dose response curve is sublinear in the low dose region, they were found not to exceed the actual risk in the low dose region by more than a factor of 10 in those cases where the underlying dose response curve was linear at low doses.