Multivariate continuation ratio models: Connections and caveats
Multivariate continuation ratio models: Connections and caveats
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DOI：
10.1111/j.0006-341x.2000.00719.x
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发表时间：
2000-09-01
期刊：
影响因子：
1.9
通讯作者：
Zeger, SL
中科院分区：
文献类型：
--
作者：
Heagerty, PJ;Zeger, SL
我们开发了一对回归的半参数估计方法，其特征在于集群离散生存时间的第一和第二时刻。在第一个回归中，我们通过单变量连续指标表示离散的生存时间，其预期使用广义线性模型建模。在第二次回归中，我们使用Clayton-Oakes叉积比（克莱顿，1978，Biometrika 65，141-151; Cakes，1989，Journal of the American Statistical Association 84，487-493）对生存时间的边际成对关联进行建模。这些模型最近由Shih（1998，Biometrics 54，1115-1128）提出。我们将离散生存模型与Heageland和Zeger（1996，Journal of the American Statistical Society 91，1024-1036）中提出的多变量多项式模型相关联，并推导出一个配对估计方程程序，该程序在计算上适用于中型和大型集群。我们扩展了Guo和Lin（1994，Biometrics 50，632-639）和Shih（1998）的工作，允许协方差加权估计方程，并研究加权对渐近相对效率的影响。我们证明，多项式结构时，必须承认采用加权估计方程，并表明一个天真的使用GEE方法可能会导致不一致的参数估计。最后，我们通过分析TenHave和Uttal（1994，Applied Statistics 43，371-384）以及Guo和Lin（1994）之前总结的心理测试数据来说明所提出的方法。
We develop semiparametric estimation methods for a pair of regressions that characterize the first and second moments of clustered discrete survival times. In the first regression, we represent discrete survival times through univariate continuation indicators whose expectations are modeled using a generalized linear model. In the second regression, we model the marginal pairwise association of survival times using the Clayton-Oakes cross-product ratio (Clayton, 1978, Biometrika 65, 141-151; Cakes, 1989, Journal of the American Statistical Association 84, 487-493). These models have recently been proposed by Shih (1998, Biometrics 54, 1115-1128). We relate the discrete survival models to multivariate multinomial models presented in Heagerty and Zeger (1996, Journal of the American Statistical Society 91, 1024-1036) and derive a paired estimating equations procedure that is computationally feasible for moderate and large clusters. We extend the work of Guo and Lin (1994, Biometrics 50, 632-639) and Shih (1998) to allow covariance weighted estimating equations and investigate the impact of weighting in terms of asymptotic relative efficiency. We demonstrate that the multinomial structure must be acknowledged when adopting weighted estimating equations and show that a naive use of GEE methods can lead to inconsistent parameter estimates. Finally, we illustrate the proposed methodology by analyzing psychological testing data previously summarized by TenHave and Uttal (1994, Applied Statistics 43, 371-384) and Guo and Lin (1994).