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

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• 作者：Deborah R. Zucker ; Robin Ruthazer ; Christopher H. Schmid
• 刊名：Journal of Clinical Epidemiology
• 出版年：2010
• 出版时间：December 2010
• 年：2010
• 卷：63
• 期：12
• 页码：1312-1323
• 全文大小：268 K

文摘

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.