Changes in surrogate outcomes can be translated into clinical outcomes using a Monte Carlo model

Abstract 

Objective

To meaningfully interpret trials using surrogate outcomes, one must translate changes in the surrogate outcome to changes in the clinical outcome. Formulae to do this are uncommon because they require primary data from multiple randomized trials that measure both the surrogate and clinical outcome.

Study Design and Setting

We developed a model to translate changes in anticoagulation control (the surrogate outcome) into hemorrhagic and thromboembolic event rates (the clinical outcome). The model used Monte Carlo simulation and association measures between the surrogate and the clinical outcome from a meta-analysis. In randomized trials having interventions that improved anticoagulation control, we used the model to predict and statistically compare event rates between the study groups.

Results

Seven randomized trials found significantly improved anticoagulation control (mean increase in proportion of time in therapeutic range: 8.4%; range: 1.8–18%). These improvements in anticoagulation control translated to small decreases in hemorrhagic and thromboembolic events (mean: 0.66%/yr; range: 0.13–1.42%). These changes were never statistically significant.

Conclusion

Monte Carlo modeling can be used to translate surrogate outcomes into clinical outcomes. Statistically significant changes in anticoagulation control did not translate to significant differences in clinical outcomes. This methodology could be applied to other areas in medicine to assess surrogate outcomes.

Keywords: Surrogate outcomes, Randomized trials, Monte Carlo simulation, Oral anticoagulation, Minimal important difference, Time in therapeutic range