Individual (N-of-1) trials can be combined to give population comparative treatment effect estimates: methodologic considerations

Optimal models for combining N-of-1 trials need to consider goals, data sources, and relative within- and between-patient variances and Bayesian hierarchical models improved precision and were highly sensitive to within-patient variance priors.

Abstract

Optimal models for combining N-of-1 trials need to consider goals, data sources, and relative within- and between-patient variances. Without sufficient patients, between-patient variation will be hard to explain with covariates. N-of-1 data with few observations per patients may not support models with heterogeneous within-patient variation. With common variances, models appear robust. Bayesian models may improve parameter estimation but are sensitive to prior assumptions about variance components. With limited resources, improving within-patient precision must be balanced by increased participants to explain population variation.