Abstract text (incl. figure legends and references)

Objectives: To quantify the individualized treatment effects of major categories of prophylactic treatment in chronic migraine; and quantify the time-to-response of machine prescription and compare it against established heuristic treatment policies.

Methods: Examining a richly phenotyped cohort of 1446 consecutive unselected patients with chronic migraine, we use causal multitask Gaussian process models to estimate individual treatment effects across 10 classes of preventatives. Such modelling enables us to quantify the accessibility of heterogeneous responsiveness to high-dimensional modelling, to infer the likely scale of the underlying causal diversity. We calculate the treatment effects in the overall population, and the conditional treatment effects among those modelled to respond and compare the true response rates between these two groups. Identifying a difference in response rates between the groups supports a diversity of causal mechanisms. Moreover, we propose a data-driven machine prescription policy, estimating the time-to response when sequentially trialling preventatives by individualized treatment effects and comparing it to expert guideline sequences.

Results: We identify significantly higher true response rates among individuals modelled to respond, compared with the overall population (mean difference of 0.034; 95% CI 0.003–0.065; P = 0.033), supporting significant heterogeneity of responsiveness and diverse causal mechanisms. The machine prescription policy yields an estimated 35% reduction in time-to-response (3.750 months; 95% CI 3.507–3.993; P < 0.0001) compared with expert guidelines, with no substantive increase in expense per patient.

Conclusion: We conclude that the highly distributed mode of causation in chronic migraine necessitates high-dimensional modelling for optimal management. Machine prescription should be considered an essential clinical decision-support tool in the future management of chronic migraine.