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Activity Number:
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54
- Recent Advances in Categorical Data Analytics
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Type:
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Topic Contributed
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Date/Time:
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Sunday, August 7, 2022 : 4:00 PM to 5:50 PM
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Sponsor:
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Biometrics Section
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Abstract #322286
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Title:
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Bayesian Network Meta-Regression for Aggregate Ordinal Outcomes with Missing Categories
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Author(s):
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Yeongjin Gwon* and Ming-Hui Chen and May Mo and Xun Jiang and Amy Xia and Joseph G Ibrahim
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Companies:
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University of Nebraska Medical Center and University of Connecticut and Amgen and Amgen and Amgen and University of North Carolina
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Keywords:
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Network Meta-Regression;
Bayesian;
Missing category;
Indirect comparison;
MCMC
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Abstract:
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Comparing emerging treatment options is often challenging because of the lack of direct comparison from head-to-head trials and multiple, inconsistencies in outcome measures among published placebo-controlled trials for each involved treatments. One potential solution is to aggregate the different outcome measures into a single ordinal response variable for consistent evaluation. The ordinal response variable will inevitably contain unknown response categories because they cannot be directly derived from published data in the literature. In this talk, we propose a statistical methodology to overcome such common but unresolved issue in the context of network meta-regression model for aggregate ordinal outcome. Specifically, we introduce latent counts and model these counts in Bayesian paradigm. We then develop an efficient Markov chain Monte Carlo sampling algorithm to carry out Bayesian computation. A variation of deviance information criterion is used for the assessment of goodness-of-fit. A case study demonstrating the usefulness of the proposed methodology is carried out using aggregate ordinal outcome data from 17 clinical trials.
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Authors who are presenting talks have a * after their name.
