Memorial Sloan Kettering Cancer Center, New York, USA.
Department of Statistical Science, University College London, UK.
Contemp Clin Trials. 2023 Feb;125:107043. doi: 10.1016/j.cct.2022.107043. Epub 2022 Dec 5.
In studies of survival and its association with treatment and other prognostic variables, elapsed time alone will often show itself to be among the strongest, if not the strongest, of the predictor variables. Kaplan-Meier curves will show the overall survival of each group and the general differences between groups due to treatment. However, the time-dependent nature of treatment effects is not always immediately transparent from these curves. More sophisticated tools are needed to spotlight the treatment effects. An important tool in this context is the treatment effect process. This tool can be potent in revealing the complex myriad of ways in which treatment can affect survival time. We look at a recently published study in which the outcome was relapse-free survival, and we illustrate how the use of the treatment effect process can provide a much deeper understanding of the relationship between time and treatment in this trial.
在研究生存及其与治疗和其他预后变量的关系时，单独的时间往往是最强的预测变量之一，如果不是最强的话。Kaplan-Meier 曲线将显示每个组的总体生存率以及由于治疗导致的组间总体差异。然而，从这些曲线上并不总是能够立即看出治疗效果的时变性质。需要更复杂的工具来突出治疗效果。在这种情况下，一个重要的工具是治疗效果过程。该工具可以有效地揭示治疗影响生存时间的复杂方式。我们来看一个最近发表的研究，该研究的结果是无复发生存率，我们将说明如何使用治疗效果过程来更深入地了解该试验中时间和治疗之间的关系。
