Objective Our study aims to evaluate the performance of Chinese risk stratification system for coronary artery bypass grafting (CABG) in the prediction of in-hospital mortality and major postoperative complications af...Objective Our study aims to evaluate the performance of Chinese risk stratification system for coronary artery bypass grafting (CABG) in the prediction of in-hospital mortality and major postoperative complications afterCABG. Methods Clinical information of 9564 consecutive CABG patients was collected in Chinese Coronary Artery Bypass Grafting Registry which recruited 43 centers over China between 2007 and 2008.展开更多
In this paper, we consider an inference method for recurrent event data in which the primary exposure covariate is assessed only in a validation set, while as an auxiliary covariate for the main exposure is available ...In this paper, we consider an inference method for recurrent event data in which the primary exposure covariate is assessed only in a validation set, while as an auxiliary covariate for the main exposure is available for the full cohort. Additive rate model is considered. The existing estimating equations in the absence of primary exposure are corrected by taking use of the validation data and auxiliary information, which yield consistent and asymptotically normal estimators of the regression parameters. The estimated baseline mean process is shown to converge weakly to a zero-mean Gaussian process. Extensive simulations are conducted to evaluate finite sample performance.展开更多
文摘Objective Our study aims to evaluate the performance of Chinese risk stratification system for coronary artery bypass grafting (CABG) in the prediction of in-hospital mortality and major postoperative complications afterCABG. Methods Clinical information of 9564 consecutive CABG patients was collected in Chinese Coronary Artery Bypass Grafting Registry which recruited 43 centers over China between 2007 and 2008.
基金Supported by the National Natural Science Foundation of China(No.11571263,11371299)
文摘In this paper, we consider an inference method for recurrent event data in which the primary exposure covariate is assessed only in a validation set, while as an auxiliary covariate for the main exposure is available for the full cohort. Additive rate model is considered. The existing estimating equations in the absence of primary exposure are corrected by taking use of the validation data and auxiliary information, which yield consistent and asymptotically normal estimators of the regression parameters. The estimated baseline mean process is shown to converge weakly to a zero-mean Gaussian process. Extensive simulations are conducted to evaluate finite sample performance.
