Speaker
Description
We consider a two-arm randomized clinical trial in precision oncology with time-to-event endpoint. Patients in the control arm receive standard of care (SOC) treatment whereas patients in the experimental arm are offered personalized treatment, e.g. on the basis of molecular characterization of the disease. However, some patients in the experimental arm will not receive personalized treatment due to various causes; Possible causes are that no personalized treatment is available or paid for. These patients also receive SOC. A goal is to formulate a model that identifies and allows for many variants of the implementation rate of the treatment.
Classical Intention-to-treat analysis compares the outcomes of the two trial arms irrespective of the actual treatment. This is meaningful to evaluate the effect of a policy.
An alternative estimand, which may be more interesting in practice, is the treatment effect in the target population of all patients that would actually implement the personalized treatment, if it was offered to them.
Direct estimation of this effect is not feasible, since information on which patients in the SOC arm would have received personalized treatment if randomized to the experimental arm is missing.
In particular, the survival distribution in the SOC arm is a mixture of distributions for patients who would and patients who would not potentially receive personalized treatment. We applied a nonparametric approach inspired by Patra and Sen [1] combined with isotonic regression to estimate the survival curves of both components in the mixture. The estimated survival curves are then compared using the restricted mean survival time and hypothesis testing is performed via bootstrap resampling. As an alternative, a semi-parametric approach is proposed.
Power and Type I error of the two methods are compared in detailed simulation studies. Both approaches are further evaluated on real clinical trial data.
This work presents a novel rigorous model for the analysis of treatments effects in the presence of mixtures or asymmetric trials. Practical guidelines on when this approach is necessary or appropriate are provided.
[1] Patra, R. K. and Sen B. (2015), Estimation of a Two-component Mixture Model with Applications to Multiple Testing, Journal of the Royal Statistical Society, Vol. 78 (4), 869-893
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