Deconstructing the Kaplan-Meier curve: Quantification of treatment effect using the treatment effect process

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.