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删失数据的平滑秩回归AI 解读

A weighted rank estimating function is proposed to estimate the regression parameter vector in an accelerated failure time model with right censored data. In general, rank estimating functions are discontinuous in the regression parameter, creating difficulties in determining the asymptotic distribution of the estimator. A local distribution function is used to create a rank based estimating function that is continuous and monotone in the regression parameter vector. A…

比例风险回归的一致性概率与区分能力AI 解读

The concordance probability is used to evaluate the discriminatory power and the predictive accuracy of nonlinear statistical models. We derive an analytical expression for the concordance probability in the Cox proportional hazards model. The proposed estimator is a function of the regression parameters and the covariate distribution only and does not use the observed event and censoring times. For this reason it is asymptotically unbiased, unlike Harrell's c-index based on…

带协变量调整的生存分布比较非参数检验AI 解读

Summary The analysis of covariance is a technique that is used to improve the power of a k-sample test by adjusting for concomitant variables. If the end point is the time of survival, and some observations are right censored, the score statistic from the Cox proportional hazards model is the method that is most commonly used to test the equality of conditional hazard functions. In many situations, however, the proportional hazards model assumptions are not satisfied.…

响应变量受删失时回归估计量的比较AI 解读

Several methods of estimating parameters in a linear regression when the response variable is censored are compared. The estimator of Buckley & James (1979) is preferred. Further, it is shown that the strength of the regression, the support and distribution of the censoring variable, and the magnitude of the slope all have an effect on the relative performance of the estimators.

响应变量被删失时回归估计器的比较AI 解读

Several methods of estimating parameters in a linear regression when the response variable is censored are compared. The estimator of Buckley & James (1979) is preferred. Further, it is shown that the strength of the regression, the support and distribution of the censoring variable, and the magnitude of the slope all have an effect on the relative performance of the estimators.