Generic Hockey-Stick Model for Estimating Benchmark Dose and Potency: Performance Relative to BMDS and Application to Anthraquinone

Family Socioeconomic Position and Lung Cancer Risk: A Meta-Analysis and a Mendelian Randomization Study

Background

Family socioeconomic position (SEP) in childhood is an important factor to predict some chronic diseases. However, the association between family SEP in childhood and the risk of lung cancer is not clear.

Methods

A systematic search was performed to explore their relationship. We selected education level, socioeconomic positions of parents and childhood housing conditions to represent an individual family SEP. Hazard ratios (HRs) of lung cancer specific-mortality were synthesized using a random effects model. Two-sample Mendelian randomization (MR) was carried out with summary data from published genome-wide association studies of SEP to assess the possible causal relationship of SEP and risk of lung cancer.

Results

Through meta-analysis of 13 studies, we observed that to compared with the better SEP, the poorer SEP in the childhood was associated with the increased lung cancer risk in the adulthood (HR: 1.25, 95% CI: 1.10 to 1.43). In addition, the dose-response analysis revealed a positive correlation between the poorer SEP and increased lung cancer risk. Same conclusion was reached in MR [(education level) OR 0.50, 95% CI: 0.39 to 0.63; P < 0.001].

Conclusion

This study indicates that poor family socioeconomic position in childhood is causally correlated with lung cancer risk in adulthood.

Systematic Review Registration

identifier: 159082.

Logistic Regression Models with Unspecified Low Dose-Response Relationships and Experimental Designs for Hormesis Studies

Hormesis refers to a nonmonotonic (biphasic) dose-response relationship in toxicology, environmental science, and related fields. In the presence of hormesis, a low dose of a toxic agent may have a lower risk than the risk at the control dose, and the risk may increase at high doses. When the sample size is small due to practical, logistic, and ethical considerations, a parametric model may provide an efficient approach to hypothesis testing at the cost of adopting a strong assumption, which is not guaranteed to be true. In this article, we first consider alternative parameterizations based on the traditional three-parameter logistic regression. The new parameterizations attempt to provide robustness to model misspecification by allowing an unspecified dose-response relationship between the control dose and the first nonzero experimental dose. We then consider experimental designs including the uniform design (the same sample size per dose group) and the c -optimal design (minimizing the standard error of an estimator for a parameter of interest). Our simulation studies showed that (1) the c -optimal design under the traditional three-parameter logistic regression does not help reducing an inflated Type I error rate due to model misspecification, (2) it is helpful under the new parameterization with three parameters (Type I error rate is close to a fixed significance level), and (3) the new parameterization with four parameters and the c -optimal design does not reduce statistical power much while preserving the Type I error rate at a fixed significance level.

Determination of Anthraquinone in Some Indonesian Black Tea and Its Predicted Risk Characterization

Anthraquinone (AQ) levels in some Indonesian dried tea leaves samples from different plantation areas and their brewed tea samples were determined by gas chromatography-tandem mass spectrometry methods. The mean lower bound, middle bound, and upper bound of AQ levels in 59 dried tea leaves samples were 82.2, 82.8, and 83.4 μg/kg, respectively, while their 95%th percentile values were identical at 190.3 μg/kg (0.1903 mg/kg). In a transfer rate study, the mean and 95%th AQ levels in 30 dried tea leaves samples with AQ level ≥ LOQ (limit of quantification) were 128.6 and 194.5 μg/kg (0.1945 mg/kg), while those of their corresponding brewed tea samples were 2.1 and 3.4 μg/kg, respectively. The mean and 95%th transfer rates of AQ into brewed tea samples were 51.99 and 88.17%. Using these data and taking into account daily tea consumption, calculated cancer potency slope factor, benchmark dose of 10% effect at lower bound 95% confidence interval of AQ, and average body weight, the risk characterization due to exposure to this compound from tea consumption was calculated and stated as incremental lifetime cancer risk (ILCR) and margin of exposure (MOE). The overall results revealed that AQ levels in dried tea leaves up to the highest level found in the samples lead to an ILCR of not more than 10^-6 and an MOE of not less than 10⁴ and hence was predicted to give sufficient consumer protection.

Model Averaging with AIC Weights for Hypothesis Testing of Hormesis at Low Doses

For many dose-response studies, large samples are not available. Particularly, when the outcome of interest is binary rather than continuous, a large sample size is required to provide evidence for hormesis at low doses. In a small or moderate sample, we can gain statistical power by the use of a parametric model. It is an efficient approach when it is correctly specified, but it can be misleading otherwise. This research is motivated by the fact that data points at high experimental doses have too much contribution in the hypothesis testing when a parametric model is misspecified. In dose-response analyses, to account for model uncertainty and to reduce the impact of model misspecification, averaging multiple models have been widely discussed in the literature. In this article, we propose to average semiparametric models when we test for hormesis at low doses. We show the different characteristics of averaging parametric models and averaging semiparametric models by simulation. We apply the proposed method to real data, and we show that P values from averaged semiparametric models are more credible than P values from averaged parametric methods. When the true dose-response relationship does not follow a parametric assumption, the proposed method can be an alternative robust approach.

Reassessment of MTBE cancer potency considering modes of action for MTBE and its metabolites

A 1999 California state agency cancer potency (CP) evaluation of methyl tert-butyl ether (MTBE) assumed linear risk extrapolations from tumor data were plausible because of limited evidence that MTBE or its metabolites could damage DNA, and based such extrapolations on data from rat gavage and rat and mouse inhalation studies indicating elevated tumor rates in male rat kidney, male rat Leydig interstitial cells, and female rat leukemia/lymphomas. More recent data bearing on MTBE cancer potency include a rodent cancer bioassay of MTBE in drinking water; several new studies of MTBE genotoxicity; several similar evaluations of MTBE metabolites, formaldehyde, and tert-butyl alcohol or TBA; and updated evaluations of carcinogenic mode(s) of action (MOAs) of MTBE and MTBE metabolite's. The lymphoma/leukemia data used in the California assessment were recently declared unreliable by the U.S. Environmental Protection Agency (EPA). Updated characterizations of MTBE CP, and its uncertainty, are currently needed to address a variety of decision goals concerning historical and current MTBE contamination. To this end, an extensive review of data sets bearing on MTBE and metabolite genotoxicity, cytotoxicity, and tumorigenicity was applied to reassess MTBE CP and related uncertainty in view of MOA considerations. Adopting the traditional approach that cytotoxicity-driven cancer MOAs are inoperative at very low, non-cytotoxic dose levels, it was determined that MTBE most likely does not increase cancer risk unless chronic exposures induce target-tissue toxicity, including in sensitive individuals. However, the corresponding expected (or plausible upper bound) CP for MTBE conditional on a hypothetical linear (e.g., genotoxic) MOA was estimated to be ∼2 × 10(-5) (or 0.003) per mg MTBE per kg body weight per day for adults exposed chronically over a lifetime. Based on this conservative estimate of CP, if MTBE is carcinogenic to humans, it is among the weakest 10% of chemical carcinogens evaluated by EPA.

Estimation of a benchmark dose in the presence or absence of hormesis using posterior averaging

U.S. Environment Protection Agency benchmark doses for dichotomous cancer responses are often estimated using a multistage model based on a monotonic dose-response assumption. To account for model uncertainty in the estimation process, several model averaging methods have been proposed for risk assessment. In this article, we extend the usual parameter space in the multistage model for monotonicity to allow for the possibility of a hormetic dose-response relationship. Bayesian model averaging is used to estimate the benchmark dose and to provide posterior probabilities for monotonicity versus hormesis. Simulation studies show that the newly proposed method provides robust point and interval estimation of a benchmark dose in the presence or absence of hormesis. We also apply the method to two data sets on carcinogenic response of rats to 2,3,7,8-tetrachlorodibenzo-p-dioxin.