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lüll Individual (N-of-1) trials can be combined to give population comparative treatment effect estimates: methodologic considerations Zucker DR; Ruthazer R; Schmid CHJ Clin Epidemiol 2010[Dec]; 63 (12): 1312-23OBJECTIVE: 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.|*Models, Statistical[MESH]|Amitriptyline/therapeutic use[MESH]|Antidepressive Agents/therapeutic use[MESH]|Bayes Theorem[MESH]|Chronic Disease[MESH]|Clinical Trials as Topic/*standards[MESH]|Drug Therapy, Combination[MESH]|Fluoxetine/therapeutic use[MESH]|Humans[MESH]|Meta-Analysis as Topic[MESH]|Outcome Assessment, Health Care/*standards[MESH]|Precision Medicine/*statistics & numerical data[MESH]|Research Design/*standards[MESH] |