Weller E A, Ryan L M
Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA.
Biometrics. 1998 Jun;54(2):762-73.
Among the tests that can be used to detect dose-related trends in count data from toxicological studies are nonparametric tests such as the Jonckheere-Terpstra and likelihood-based tests, for example, based on a Poisson model. This paper was motivated by a data set of tumor counts in which conflicting conclusions were obtained using these two tests. To define situations where one test may be preferable, we compared the small and large sample performance of these two tests as well as a robust and conditional version of the likelihood-based test in the absence and presence of a dose-related trend for both Poisson and overdispersed Poisson data. Based on our results, we suggest using the Poisson test when little overdispersion is present in the data. For more overdispersed data, we recommend using the robust Poisson test for highly discrete data (response rate lower than 2-3) and the robust Poisson test or the Jonckheere-Terpstra test for moderately discrete or continuous data (average responses larger than 2 or 3). We also studied the effects of dose metameter misspecification. A clear effect on efficiency was seen when the 'wrong' dose metameter was used to compute the test statistic. In general, unless there is strong reason to do otherwise, we recommend the use of equally spaced dose levels when applying the Poisson or robust Poisson test for trend.
在可用于检测毒理学研究计数数据中剂量相关趋势的检验中，有非参数检验，如琼克尔-特普斯特拉检验，以及基于似然性的检验，例如基于泊松模型的检验。本文的动机源于一组肿瘤计数数据集，在使用这两种检验时得出了相互矛盾的结论。为了确定在哪种情况下一种检验可能更可取，我们比较了这两种检验以及基于似然性检验的稳健和条件版本在泊松数据和过度分散泊松数据不存在和存在剂量相关趋势时的小样本和大样本性能。根据我们的结果，我们建议当数据中几乎不存在过度分散时使用泊松检验。对于过度分散程度更高的数据，对于高度离散的数据（反应率低于2 - 3），我们建议使用稳健泊松检验；对于中度离散或连续的数据（平均反应大于2或3），我们建议使用稳健泊松检验或琼克尔-特普斯特拉检验。我们还研究了剂量测量指标错误设定的影响。当使用“错误”的剂量测量指标来计算检验统计量时，对效率有明显影响。一般来说，除非有充分理由不这样做，我们建议在应用泊松或稳健泊松趋势检验时使用等距剂量水平。
