Lockhart A M, Piegorsch W W, Bishop J B
Computer Sciences Corporation, Research Triangle Park, NC.
Mutat Res. 1992 Aug;272(1):35-58. doi: 10.1016/0165-1161(92)90007-9.
In dominant lethal studies the primary variables of interest are typically expressed as discrete counts or proportions (e.g., live implants, resorptions, percent pregnant). Simple statistical sampling models for discrete data such as binomial or Poisson generally do not fit this type of data because of extra-binomial or extra-Poisson departures from variability predicted under these simple models. Extra-variability in the fetal response may originate from parental contributions. These can lead to over- or under-dispersion seen as, e.g., extra-binomial variability in the proportion response. Utilizing a large control database, we investigated the relative impact of extra-variability from male or female contributions on the endpoints of interest. Male-related effects did not seem to contribute to overdispersion in our database; female-related effects were, however, evidenced. Various statistical methods were considered to test for significant treatment differences under these forms of sampling variability. Computer simulations were used to evaluate these methods and to determine which are most appropriate for practical use in the evaluation of dominant lethal data. Our results suggest that distribution-free statistical methods such as a nonparametric permutation test or rank-based tests for trend can be recommended for use.
在显性致死研究中，主要关注的变量通常表示为离散计数或比例（例如，存活植入数、吸收数、妊娠百分比）。对于离散数据的简单统计抽样模型，如二项式或泊松模型，通常不适用于这类数据，因为在这些简单模型下预测的变异性存在超二项式或超泊松偏差。胎儿反应中的额外变异性可能源于亲代的贡献。这些可能导致过度离散或欠离散，例如在比例反应中表现为超二项式变异性。利用一个大型对照数据库，我们研究了来自雄性或雌性贡献的额外变异性对感兴趣终点的相对影响。在我们的数据库中，雄性相关效应似乎对过度离散没有贡献；然而，雌性相关效应是有证据的。考虑了各种统计方法来检验在这些抽样变异性形式下显著的处理差异。使用计算机模拟来评估这些方法，并确定哪些方法最适合在显性致死数据评估中实际使用。我们的结果表明，可以推荐使用无分布统计方法，如非参数置换检验或基于秩的趋势检验。
