Identification of patient subgroups with enhanced treatment response in RCTs with time-to-event outcomes: use of counterfactual restricted survival times.

Identification of patient subgroups with enhanced treatment response in RCTs with time-to-event outcomes: use of counterfactual restricted survival times.

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Background
Therapeutic evaluation through RCTs is commonly focusing on difference in the average outcome between the experimental and control groups. However, a treatment is unlikely to be equally effective for all patients.
As a consequence, there is an increased interest in identifying subgroups that receive additional benefit from the experimental treatment. Identifying characteristics associated with improved response is critical to subsequently allow making treatment decisions using patient profiles. This paradigm - stratified medicine - is especially promising for cancer treatment.
The counterfactual model is gaining in importance as a way to identify those subgroups. The underlying principle would be to estimate the outcome of a patient under the experimental treatment, when the patient was actually allocated to the control group, and the other way round. The use of counterfactual models for treatment selection has been proposed for binary and continuous outcomes (1-4).
In cancer trials, time-to-event outcomes (eg, time to progression, relapse, failure, or death) are common. For a given patient, a causal effect would be a measure of the discrepancy between the times to event under the experimental and control treatments (only one being observed). In absence of censoring, simple sample averages could be used to estimate the mean time to event of the two groups. However, in presence of right-censoring, the average causal effect estimate may not be identifiable.
An alternative is to use the restricted mean survival times (RMST), i.e. the expectation of survival time restricted to some time horizon. If the time horizon is 12 months, the RMST measures the average number of months survived over one year. Thus, the difference in RMST measures the gain in one-year life expectancy associated with the experimental group. Restricted survival times have been advocated as a relevant survival outcome for RCTs (5,6).
Objectives
This proposal involves a methodological study done by our research team. We develop a statistical method to identify and characterize patient subgroups with an improved treatment response in RCTs with time-to-event outcomes. This method relies on a counterfactual model for the restricted survival time of a given patient under each treatment; it will allow estimating the benefit of the experimental treatment as the difference in counterfactual restricted survival times between the experimental and the control treatment for this particular patient. Data from the selected studies for which we request access will allow illustrating the application of this novel method and will provide additional information for the methodological study we are conducting.
Expected results
The potential benefit of this methodological development for public health could be substantial. If responsive subpopulations are identified, it would inform the treatment selection process and influence clinical decision-making and clinical practice.

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