Larson M G
Biometrics. 1984 Jun;40(2):459-69.
A general system of log-linear modeling is proposed for analysis of competing-risks data with discrete covariates. The instantaneous cause-specific failure rates, approximated by step-functions, are analyzed by techniques for multidimensional contingency tables. Censored observations are accommodated. Counts of failures of each type, and the amount of follow-up, are summarized in two arrays in which each cell denotes a distinct combination of failure type, time interval and covariate value. Maximum likelihood estimators for the parameters of the model are derived by iterative proportional fitting; the resulting estimates of the number of failures in each cell are used for goodness-of-fit tests. The principal advantages of this approach are its simple display of data, its computational ease for the fitting and comparison of models and its provision of explicit goodness-of-fit tests. Interpretation of the models is facilitated by reference to several alternative models for survivorship and competing risks. The basic model is extended to incorporate stochastic covariates whose values change during follow-up, and to accommodate quantitative covariates.
提出了一种对数线性建模的通用系统，用于分析具有离散协变量的竞争风险数据。通过多维列联表技术分析由阶梯函数近似的瞬时特定原因失效率。考虑了删失观测值。每种类型的失败次数和随访时间在两个数组中进行汇总，其中每个单元格表示失败类型、时间间隔和协变量值的不同组合。通过迭代比例拟合得出模型参数的最大似然估计值；每个单元格中失败次数的所得估计值用于拟合优度检验。这种方法的主要优点是数据显示简单、模型拟合和比较的计算简便，以及提供明确的拟合优度检验。通过参考几种生存和竞争风险的替代模型，便于对模型进行解释。基本模型被扩展以纳入其值在随访期间发生变化的随机协变量，并考虑定量协变量。
