Objective: To compare different statistical models for combining N-of-1 trials to estimate a population treatment effect.
Study design and setting: Data from a published series of N-of-1 trials comparing amitriptyline (AMT) therapy and combination treatment (AMT+fluoxetine [FL]) were analyzed to compare summary and individual participant data meta-analysis; repeated-measure models; Bayesian hierarchical models; and single-period, single-pair, and averaged outcome crossover models.
Results: The best-fitting model included a random intercept (response on AMT) and fixed treatment effect (added FL). Results supported a common, uncorrelated within-patient covariance structure that is equal between treatments and across patients. Assuming unequal within-patient variances, a random-effect model was favored. Bayesian hierarchical models improved precision and were highly sensitive to within-patient variance priors.
Conclusion: 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.
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