The Personalised Randomized Controlled Trial: Evaluation of a new trial design

In some clinical scenarios, for example, severe sepsis caused by extensively drug resistant bacteria, there is uncertainty between many common treatments, but a conventional multiarm randomized trial is not possible because individual participants may not be eligible to receive certain treatments. The Personalised Randomized Controlled Trial design allows each participant to be randomized between a personalised randomization list of treatments that are suitable for them. The primary aim is to produce treatment rankings that can guide choice of treatment, rather than focusing on the estimates of relative treatment effects. Here we use simulation to assess several novel analysis approaches for this innovative trial design. One of the approaches is like a network meta-analysis, where participants with the same personalised randomization list are like a trial, and both direct and indirect evidence are used. We evaluate this proposed analysis and compare it with analyses making less use of indirect evidence. We also propose new performance measures including the expected improvement in outcome if the trial's rankings are used to inform future treatment rather than random choice. We conclude that analysis of a personalized randomized controlled trial can be performed by pooling data from different types of participants and is robust to moderate subgroup-by-intervention interactions based on the parameters of our simulation. The proposed approach performs well with respect to estimation bias and coverage. It provides an overall treatment ranking list with reasonable precision, and is likely to improve outcome on average if used to determine intervention policies and guide individual clinical decisions.

Automated summary

The Personalised Randomized Controlled Trial design allows each participant to be randomized between a personalised randomization list of treatments that are suitable for them. It aims to produce treatment rankings that can guide choice of treatment. We used simulation to evaluate different analysis approaches for this innovative trial design, including a network meta-analysis approach. The proposed approach performs well with respect to estimation bias and coverage, providing an overall treatment ranking list with reasonable precision and likely to improve outcome if used for intervention policies and clinical decisions.