Projects per year
Abstract
This paper considers the problem in aggregate data meta‐analysis of studies reporting multiple competing binary outcomes, and of studies using different summary formats for those outcomes. For example, some may report numbers of patients with at least one of each outcome while others may report the total number of such outcomes. We develop a shared parameter model on hazard ratio scale accounting for different data summaries and competing risks. We adapt theoretical arguments from the literature to demonstrate that the models are equivalent if events are rare. We use constructed data examples and a simulation study to find an event rate threshold of approximately 0.2 above which competing risks and different data summaries may bias results if no adjustments are made. Below this threshold, simpler models may be sufficient. We recommend analysts consider the absolute event rates and only use a simple model ignoring data types and competing risks if all of underlying events are rare (below our threshold of approximately 0.2). If one or more of the absolute event rates approaches or exceeds our informal threshold, it may be necessary to account for data types and competing risks through a shared parameter model in order to avoid biased estimates.
Original language | English |
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Number of pages | 14 |
Journal | Research Synthesis Methods |
Early online date | 22 Aug 2019 |
DOIs | |
Publication status | E-pub ahead of print - 22 Aug 2019 |
Keywords
- competing risks
- network meta-analysis
- meta-analysis
- shared parameter models
- different data summaries
- rare events
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Projects
- 2 Finished
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No Pfizer: Calibration of multiple treatment comparisons using individual patient data
1/03/17 → 29/02/20
Project: Research
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