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纳入短期事件时间信息的长期生存 Landmark 预测AI 解读

In recent years, a wide range of markers have become available as potential tools to predict risk or progression of disease. In addition to such biological and genetic markers, short term outcome information may be useful in predicting long term disease outcomes. When such information is available, it would be desirable to combine this along with predictive markers to improve the prediction of long term survival. Most existing methods for incorporating censored short term…

扩展比例优势模型中的回归参数估计AI 解读

The proportional odds model may serve as a useful alternative to the Cox proportional hazards model to study association between covariates and their survival functions in medical studies. In this article, we study an extended proportional odds model that incorporates the so-called "external" time-varying covariates. In the extended model, regression parameters have a direct interpretation of comparing survival functions, without specifying the baseline survival odds…

含协变量测量误差的失效时间线性变换模型AI 解读

In medical studies, patients' biological parameters are often imprecisely measured due to the measuring mechanism or the biological variability. In the presence of covariate measurement error, survival analysis using the Cox model with the observed covariate may yield a biased estimate for the regression parameter. Existing research on this topic has focused on adapting the Cox model to covariates with measurement errors. In this article we generalize linear transformation…