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删失数据下纵向替代指标的稳健评估AI 解读

Abstract The development of statistical methods to evaluate surrogate markers is an active area of research. In many clinical settings, the surrogate marker is not simply a single measurement but is instead a longitudinal trajectory of measurements over time, e.g. fasting plasma glucose measured every 6 months for 3 years. In general, available methods developed for the single-surrogate setting cannot accommodate a longitudinal surrogate marker. Furthermore, many of the…

无模型方法量化替代标志物解释的治疗效应比例AI 解读

Summary In randomized clinical trials, the primary outcome, $Y$, often requires long-term follow-up and/or is costly to measure. For such settings, it is desirable to use a surrogate marker, $S$, to infer the treatment effect on $Y$, $\Delta$. Identifying such an $S$ and quantifying the proportion of treatment effect on $Y$ explained by the effect on $S$ are thus of great importance. Most existing methods for quantifying the proportion of treatment effect are model based and…

随机临床试验中生存率与治疗效应的界标估计AI 解读

In many studies with a survival outcome, it is often not feasible to fully observe the primary event of interest. This often leads to heavy censoring and thus, difficulty in efficiently estimating survival or comparing survival rates between two groups. In certain diseases, baseline covariates and the event time of nonfatal intermediate events may be associated with overall survival. In these settings, incorporating such additional information may lead to gains in efficiency…

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…