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DOI 10.1177/0272989x12453504

Медицинская библиотека · Medical decision making : an international journal of the Society for Medical Decision Making · 2012

The choice of a noninformative prior on between-study variance strongly affects predictions of future treatment effect.

Gajic-Veljanoski Olga, Cheung Angela M, Bayoumi Ahmed M, Tomlinson George

СтатьяПоддержка исследования: вне правительства СШАПеревод в очереди

Аннотация

PURPOSE: Bayesian random-effects meta-analyses require the analyst to specify the prior distribution for between-study variance of the treatment effect. We assessed the sensitivity of prediction and other outputs of the meta-analysis to the choice of this prior.

METHODS

We reanalyzed 7 published meta-analyses (5-14 trials) with rare (event rates <5%), moderate (15%-50%), and frequent binary outcomes (>50%). We examined 10 noninformative priors: inverse gamma on between-study variance (τ (2)), 2 uniforms on each of the between-study standard deviation (τ) and τ (2), uniform shrinkage on τ (2), DuMouchel shrinkage on τ, half-normal on τ (2), and half-normal priors on τ with large and small variances. For each analysis, we calculated the posterior distributions for τ, the population treatment effect in current studies, and the predicted treatment effect in a future study. We assessed goodness of fit using total residual deviance, the deviance information criterion, and predictive deviance (by cross-validations).

RESULTS

According to total residual deviance, the best-fitting priors were uniform on τ (2). According to predictive deviance, half-normal on τ (2) and the shrinkage priors were optimal. Across analyses with the 10 priors, there were no important differences in the posteriors for the population treatment effect, but there were substantial differences in the posteriors for τ and predictions. The priors that fitted best according to predictive deviance resulted in less uncertainty around predictions of future treatment effect.

CONCLUSIONS

In this sample of Bayesian meta-analyses with binary outcomes, the choice of noninformative prior for between-study variance affected model fit and the predictions of future treatment effect. When the predictive distribution is of interest, we highly recommend examination of multiple prior distributions for between-study variance, especially the half-normal on τ (2) and the shrinkage priors.

Для цитирования: Gajic-Veljanoski Olga, Cheung Angela M, Bayoumi Ahmed M, Tomlinson George. The choice of a noninformative prior on between-study variance strongly affects predictions of future treatment effect.. Medical decision making : an international journal of the Society for Medical Decision Making. 2012. DOI: 10.1177/0272989x12453504.

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