Kristine J Rosenberger1, Aiwen Xing1, Mohammad Hassan Murad2, Haitao Chu3, Lifeng Lin4. 1. Department of Statistics, Florida State University, Tallahassee, FL, 32306, USA. 2. Evidence-Based Practice Center, Mayo Clinic, Rochester, MN, USA. 3. Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA. 4. Department of Statistics, Florida State University, Tallahassee, FL, 32306, USA. linl@stat.fsu.edu.
Abstract
BACKGROUND: Network meta-analysis (NMA) is a popular tool to compare multiple treatments in medical research. It is frequently implemented via Bayesian methods. The prior choice of between-study heterogeneity is critical in Bayesian NMAs. This study evaluates the impact of different priors for heterogeneity on NMA results. METHODS: We identified all NMAs with binary outcomes published in The BMJ, JAMA, and The Lancet during 2010-2018, and extracted information about their prior choices for heterogeneity. Our primary analyses focused on those with publicly available full data. We re-analyzed the NMAs using 3 commonly-used non-informative priors and empirical informative log-normal priors. We obtained the posterior median odds ratios and 95% credible intervals of all comparisons, assessed the correlation among different priors, and used Bland-Altman plots to evaluate their agreement. The kappa statistic was also used to evaluate the agreement among these priors regarding statistical significance. RESULTS: Among the selected Bayesian NMAs, 52.3% did not specify the prior choice for heterogeneity, and 84.1% did not provide rationales. We re-analyzed 19 NMAs with full data available, involving 894 studies, 173 treatments, and 395,429 patients. The correlation among posterior median (log) odds ratios using different priors were generally very strong for NMAs with over 20 studies. The informative priors produced substantially narrower credible intervals than non-informative priors, especially for NMAs with few studies. Bland-Altman plots and kappa statistics indicated strong overall agreement, but this was not always the case for a specific NMA. CONCLUSIONS: Priors should be routinely reported in Bayesian NMAs. Sensitivity analyses are recommended to examine the impact of priors, especially for NMAs with relatively small sample sizes. Informative priors may produce substantially narrower credible intervals for such NMAs.
BACKGROUND: Network meta-analysis (NMA) is a popular tool to compare multiple treatments in medical research. It is frequently implemented via Bayesian methods. The prior choice of between-study heterogeneity is critical in Bayesian NMAs. This study evaluates the impact of different priors for heterogeneity on NMA results. METHODS: We identified all NMAs with binary outcomes published in The BMJ, JAMA, and The Lancet during 2010-2018, and extracted information about their prior choices for heterogeneity. Our primary analyses focused on those with publicly available full data. We re-analyzed the NMAs using 3 commonly-used non-informative priors and empirical informative log-normal priors. We obtained the posterior median odds ratios and 95% credible intervals of all comparisons, assessed the correlation among different priors, and used Bland-Altman plots to evaluate their agreement. The kappa statistic was also used to evaluate the agreement among these priors regarding statistical significance. RESULTS: Among the selected Bayesian NMAs, 52.3% did not specify the prior choice for heterogeneity, and 84.1% did not provide rationales. We re-analyzed 19 NMAs with full data available, involving 894 studies, 173 treatments, and 395,429 patients. The correlation among posterior median (log) odds ratios using different priors were generally very strong for NMAs with over 20 studies. The informative priors produced substantially narrower credible intervals than non-informative priors, especially for NMAs with few studies. Bland-Altman plots and kappa statistics indicated strong overall agreement, but this was not always the case for a specific NMA. CONCLUSIONS: Priors should be routinely reported in Bayesian NMAs. Sensitivity analyses are recommended to examine the impact of priors, especially for NMAs with relatively small sample sizes. Informative priors may produce substantially narrower credible intervals for such NMAs.
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