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分组生存数据中 Cox 模型估计回归参数的效率AI 解读

The efficiency of nonparametric survival analysis (Cox, 1972, 1975) relative to parametric analysis is examined for grouped data. This setting avoids the difficulties of partial likelihood and allows standard likelihood methods to apply. The results obtained here are similar to published results for ungrouped data, but are perhaps more easily understood. Some insight is also gained for problems involving time-dependent covariables and for problems with many covariables.

分组生存数据中 Cox 模型回归参数估计的效率AI 解读

The efficiency of nonparametric survival analysis (Cox, 1972, 1975) relative to parametric analysis is examined for grouped data. This setting avoids the difficulties of partial likelihood and allows standard likelihood methods to apply. The results obtained here are similar to published results for ungrouped data, but are perhaps more easily understood. Some insight is also gained for problems involving time-dependent covariables and for problems with many covariables.