Incorporating Historical Control Data When Comparing Tumor Incidence Rates

Abstract

Animal carcinogenicity studies, such as those conducted by the U.S. National Toxicology Program (NTP), focus on detecting trends in tumor rates across dose groups. Over time, the NTP has compiled vast amounts of data on tumors in control animals. Currently, this information is used informally, without the benefit of statistical tests for carcinogenicity that directly incorporate historical data on control animals. This article proposes a survival-adjusted test for detecting doserelated trends in tumor incidence rates, which incorporates data on historical control rates and formally accounts for variation in these rates among studies. An extensive simulation, based on a wide range of realistic situations, demonstrates that the proposed test performs well in comparison to the current NTP test, which does not incorporate historical control data. In particular, our test can aid in interpreting the occurrence of a few tumors in treated animals that are rarely seen in controls. One such example, which motivates our work, concerns the analysis of histiocytic sarcoma in the NTP's 2-year cancer bioassay of benzophenone. Whereas the occurrence of three histiocytic sarcomas in female rats was not significant according to the current NTP testing procedure (P = 0.074), it was highly significant (P = 0.004) when control data from six recent historical studies were included and our test was applied to the combined data.

Comparing Tumor Rates in Current and Historical Control Groups in Rodent Cancer Bioassays

“…We considered three values for the shape of the incidence curve ( γ 1 = 1.5, 3, and 6), ranging from early-onset to late-onset tumors, and four values for the mean tumor rate among the historical control groups ( π h = 0.01, 0.05, 0.15, and 0.30), ranging from rare to common tumors. For given values of γ 1 , γ 2 , and ψ 2 , we used equation (1) in Peddada et al [5] to determine what value of ψ 1 , the baseline incidence scale parameter for an “average” control group ( Z = 0), yields the desired value of π h . We examined the homogeneous case ( τ = 0) and a heterogenous case based on a value of τ that made the variance of the tumor rates 20% larger than in the homogeneous case.…”

“…This issue was addressed by Bieler and Williams [6]; their variance estimator is widely used in this context and we apply it here. Following the approach of Peddada et al [5], we allow the variance of π̂ h to have two components; namely, the variability within each historical control group and also the variability between historical control groups.…”

“…Peddada et al [5] developed a method based on order-restricted inference to evaluate the dose-response in the current study while formally incorporating historical control tumor data. Toxicologists, however, expressed an interest in also comparing the tumor rate in the current control group with that among the historical control groups.…”

Glyphosate toxicity and carcinogenicity: a review of the scientific basis of the European Union assessment and its differences with IARC

“…In addition to best practices (Greim et al 2003; Keenan et al 2009), graphical visualisations (Elmore and Peddada 2009) and statistical approaches (Dinse and Peddada 2011; Peddada et al 2007) have been developed, although direct comparison with the historical control range in the test laboratory around the time of the study is the approach mostly used in the regulatory context, and preferred in the EU assessment. This approach was considered for malignant lymphomas and haemangiosarcomas in mice when the studies reported the historical range for the test laboratory.…”

Linear Mixed Effects Models under Inequality Constraints with Applications

“…For instance, toxicologists are typically interested in detecting dose-related trends in mean response such as trends in tumor incidence as the dose of a toxin increases [1]–[2]. In cell and circadian biology researchers are often interested in the phase order of genes participating in cell cycle or the circadian clock [3]–[10].…”