TY - JOUR
T1 - Prior distributions for variance parameters in a sparse-event meta-analysis of a few small trials
AU - Pateras, Konstantinos
AU - Nikolakopoulos, Stavros
AU - Roes, Kit C.B.
N1 - Funding Information:
The author(s) were supported by the EU FP7 HEALTH. April 2, 2013-3 project Advances in Small Trials dEsign for Regulatory Innovation and eXcellence (Asterix): Grant 603160. The authors would like to thank all anonymous reviewers whose feedback improved this manuscript and Romin Pajouheshnia for proofreading the manuscript.
Funding Information:
The author(s) were supported by the EU FP7 HEALTH. April 2, 2013‐3 project Advances in Small Trials dEsign for Regulatory Innovation and eXcellence (Asterix): Grant 603160.
Publisher Copyright:
© 2020 The Authors. Pharmaceutical Statistics published by John Wiley & Sons Ltd.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - In rare diseases, typically only a small number of patients are available for a randomized clinical trial. Nevertheless, it is not uncommon that more than one study is performed to evaluate a (new) treatment. Scarcity of available evidence makes it particularly valuable to pool the data in a meta-analysis. When the primary outcome is binary, the small sample sizes increase the chance of observing zero events. The frequentist random-effects model is known to induce bias and to result in improper interval estimation of the overall treatment effect in a meta-analysis with zero events. Bayesian hierarchical modeling could be a promising alternative. Bayesian models are known for being sensitive to the choice of prior distributions for between-study variance (heterogeneity) in sparse settings. In a rare disease setting, only limited data will be available to base the prior on, therefore, robustness of estimation is desirable. We performed an extensive and diverse simulation study, aiming to provide practitioners with advice on the choice of a sufficiently robust prior distribution shape for the heterogeneity parameter. Our results show that priors that place some concentrated mass on small τ values but do not restrict the density for example, the Uniform(−10, 10) heterogeneity prior on the log(τ2) scale, show robust 95% coverage combined with less overestimation of the overall treatment effect, across varying degrees of heterogeneity. We illustrate the results with meta-analyzes of a few small trials.
AB - In rare diseases, typically only a small number of patients are available for a randomized clinical trial. Nevertheless, it is not uncommon that more than one study is performed to evaluate a (new) treatment. Scarcity of available evidence makes it particularly valuable to pool the data in a meta-analysis. When the primary outcome is binary, the small sample sizes increase the chance of observing zero events. The frequentist random-effects model is known to induce bias and to result in improper interval estimation of the overall treatment effect in a meta-analysis with zero events. Bayesian hierarchical modeling could be a promising alternative. Bayesian models are known for being sensitive to the choice of prior distributions for between-study variance (heterogeneity) in sparse settings. In a rare disease setting, only limited data will be available to base the prior on, therefore, robustness of estimation is desirable. We performed an extensive and diverse simulation study, aiming to provide practitioners with advice on the choice of a sufficiently robust prior distribution shape for the heterogeneity parameter. Our results show that priors that place some concentrated mass on small τ values but do not restrict the density for example, the Uniform(−10, 10) heterogeneity prior on the log(τ2) scale, show robust 95% coverage combined with less overestimation of the overall treatment effect, across varying degrees of heterogeneity. We illustrate the results with meta-analyzes of a few small trials.
KW - Bayesian
KW - heterogeneity
KW - meta-analysis
KW - rare diseases
KW - rare events
UR - http://www.scopus.com/inward/record.url?scp=85089082617&partnerID=8YFLogxK
U2 - 10.1002/pst.2053
DO - 10.1002/pst.2053
M3 - Article
C2 - 32767452
AN - SCOPUS:85089082617
SN - 1539-1604
VL - 20
SP - 39
EP - 54
JO - Pharmaceutical Statistics
JF - Pharmaceutical Statistics
IS - 1
ER -