Bonner Simon J, Morgan Byron J T, King Ruth
Department of Statistics, University of British Columbia, Vancouver, BC, Canada.
Biometrics. 2010 Dec;66(4):1256-65. doi: 10.1111/j.1541-0420.2010.01390.x.
Time varying, individual covariates are problematic in experiments with marked animals because the covariate can typically only be observed when each animal is captured. We examine three methods to incorporate time varying, individual covariates of the survival probabilities into the analysis of data from mark-recapture-recovery experiments: deterministic imputation, a Bayesian imputation approach based on modeling the joint distribution of the covariate and the capture history, and a conditional approach considering only the events for which the associated covariate data are completely observed (the trinomial model). After describing the three methods, we compare results from their application to the analysis of the effect of body mass on the survival of Soay sheep (Ovis aries) on the Isle of Hirta, Scotland. Simulations based on these results are then used to make further comparisons. We conclude that both the trinomial model and Bayesian imputation method perform best in different situations. If the capture and recovery probabilities are all high, then the trinomial model produces precise, unbiased estimators that do not depend on any assumptions regarding the distribution of the covariate. In contrast, the Bayesian imputation method performs substantially better when capture and recovery probabilities are low, provided that the specified model of the covariate is a good approximation to the true data-generating mechanism.
在对有标记动物进行的实验中，随时间变化的个体协变量存在问题，因为通常只有在捕获每只动物时才能观察到协变量。我们研究了三种方法，将生存概率的随时间变化的个体协变量纳入标记重捕-恢复实验的数据分析中：确定性插补、基于对协变量和捕获历史的联合分布进行建模的贝叶斯插补方法，以及仅考虑相关协变量数据被完全观察到的事件的条件方法（三项式模型）。在描述了这三种方法之后，我们比较了它们应用于分析体重对苏格兰希拉岛索艾羊（Ovis aries）生存影响的结果。然后基于这些结果进行模拟以作进一步比较。我们得出结论，三项式模型和贝叶斯插补方法在不同情况下表现最佳。如果捕获和恢复概率都很高，那么三项式模型会产生精确、无偏的估计量，且不依赖于关于协变量分布的任何假设。相比之下，当捕获和恢复概率较低时，只要协变量的指定模型能很好地近似真实的数据生成机制，贝叶斯插补方法的表现会好得多。
