Combining single patient (N-of-1) trials to estimate population treatment effects and to evaluate individual patient responses to treatment

Abstract

When treating individual patients, physicians may face difficulties using the evidence from center-based randomized control trials (RCTs) due to limitations in these studies generalizability. Therefore, they often perform their own "informal" tests of treatment effectiveness. Single patient ("N-of-1") trials provide a structured design for more rigorous assessment of medical treatments of chronic diseases, but are applied only to the index patient. We present a hierarchical Bayesian random effects model to combine N-of-1 studies to obtain an estimate of treatment effectiveness for the population and to use this population information to aid in the evaluation of an individual patient's trial results. The model's treatment effect estimates are adjustments between the population estimate and the individual's observed results. This adjustment is based upon the within-patient and between-patient heterogeneity. We demonstrate this patient-focused method using published data from 23 N-of-1 trial results comparing amitriptyline and placebo for the treatment of fibromyalgia.