ArticlePloS one2012
Specifying the probability characteristics of funnel plot control limits: an investigation of three approaches.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Article
- Individualized empirical null estimation for exact tests of healthcare quality.Statistics in medicine · 2024Article
- [Three-year revision rate of certified centres for joint replacement according to EndoCert : Risk-adjusted analysis of outcome quality and comparison with other quality assurance systems].Orthopadie (Heidelberg, Germany) · 2023Article
- Test-specific funnel plots for healthcare provider profiling leveraging individual- and summary-level information.Health services & outcomes research methodology · 2023Article
- Guidelines on constructing funnel plots for quality indicators: A case study on mortality in intensive care unit patients.Statistical methods in medical research · 2018Article
- Variation in rates of ICU readmissions and post-ICU in-hospital mortality and their association with ICU discharge practices.BMC health services research · 2017Article
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Authors and funding
2 authors.
Funding
Abstract
backgroundEmphasis is increasingly being placed on the monitoring and comparison of clinical outcomes between healthcare providers. Funnel plots have become a standard graphical methodology to identify outliers and comprise plotting an outcome summary statistic from each provider against a specified 'target' together with upper and lower control limits. With discrete probability distributions it is not possible to specify the exact probability that an observation from an 'in-control' provider will fall outside the control limits. However, general probability characteristics can be set and specified using interpolation methods. Guidelines recommend that providers falling outside such control limits should be investigated, potentially with significant consequences, so it is important that the properties of the limits are understood.
methodsControl limits for funnel plots for the Standardised Mortality Ratio (SMR) based on the Poisson distribution were calculated using three proposed interpolation methods and the probability calculated of an 'in-control' provider falling outside of the limits. Examples using published data were shown to demonstrate the potential differences in the identification of outliers.
resultsThe first interpolation method ensured that the probability of an observation of an 'in control' provider falling outside either limit was always less than a specified nominal probability (p). The second method resulted in such an observation falling outside either limit with a probability that could be either greater or less than p, depending on the expected number of events. The third method led to a probability that was always greater than, or equal to, p.
conclusionThe use of different interpolation methods can lead to differences in the identification of outliers. This is particularly important when the expected number of events is small. We recommend that users of these methods be aware of the differences, and specify which interpolation method is to be used prior to any analysis.
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