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The probability of being identified as an outlier with commonly used funnel plot control limits for the standardised mortality ratio
by
Seaton, Sarah E
, Manktelow, Bradley N
in
Analysis
/ Confidence Intervals
/ Control charts
/ Data Interpretation, Statistical
/ Funnel plot
/ Health care industry
/ Health Sciences
/ Humans
/ Medical research
/ Medicine
/ Medicine & Public Health
/ Methods
/ Models, Statistical
/ Mortality
/ Outcome and process assessment (Health Care)
/ Outlier
/ Outliers (Statistics)
/ Poisson
/ Poisson Distribution
/ Probability
/ Research Article
/ Research methodology
/ SMR
/ Statistical process control
/ Statistical Theory and Methods
/ Statistics for Life Sciences
/ Studies
/ Survival Analysis
/ Theory of Medicine/Bioethics
2012
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The probability of being identified as an outlier with commonly used funnel plot control limits for the standardised mortality ratio
by
Seaton, Sarah E
, Manktelow, Bradley N
in
Analysis
/ Confidence Intervals
/ Control charts
/ Data Interpretation, Statistical
/ Funnel plot
/ Health care industry
/ Health Sciences
/ Humans
/ Medical research
/ Medicine
/ Medicine & Public Health
/ Methods
/ Models, Statistical
/ Mortality
/ Outcome and process assessment (Health Care)
/ Outlier
/ Outliers (Statistics)
/ Poisson
/ Poisson Distribution
/ Probability
/ Research Article
/ Research methodology
/ SMR
/ Statistical process control
/ Statistical Theory and Methods
/ Statistics for Life Sciences
/ Studies
/ Survival Analysis
/ Theory of Medicine/Bioethics
2012
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The probability of being identified as an outlier with commonly used funnel plot control limits for the standardised mortality ratio
by
Seaton, Sarah E
, Manktelow, Bradley N
in
Analysis
/ Confidence Intervals
/ Control charts
/ Data Interpretation, Statistical
/ Funnel plot
/ Health care industry
/ Health Sciences
/ Humans
/ Medical research
/ Medicine
/ Medicine & Public Health
/ Methods
/ Models, Statistical
/ Mortality
/ Outcome and process assessment (Health Care)
/ Outlier
/ Outliers (Statistics)
/ Poisson
/ Poisson Distribution
/ Probability
/ Research Article
/ Research methodology
/ SMR
/ Statistical process control
/ Statistical Theory and Methods
/ Statistics for Life Sciences
/ Studies
/ Survival Analysis
/ Theory of Medicine/Bioethics
2012
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The probability of being identified as an outlier with commonly used funnel plot control limits for the standardised mortality ratio
Journal Article
The probability of being identified as an outlier with commonly used funnel plot control limits for the standardised mortality ratio
2012
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Overview
Background
Emphasis is increasingly being placed on the monitoring of clinical outcomes for health care providers. Funnel plots have become an increasingly popular graphical methodology used to identify potential outliers. It is assumed that a provider only displaying expected random variation (i.e. ‘in-control’) will fall outside a control limit with a known probability. In reality, the discrete count nature of these data, and the differing methods, can lead to true probabilities quite different from the nominal value. This paper investigates the true probability of an ‘in control’ provider falling outside control limits for the Standardised Mortality Ratio (SMR).
Methods
The true probabilities of an ‘in control’ provider falling outside control limits for the SMR were calculated and compared for three commonly used limits: Wald confidence interval; ‘exact’ confidence interval; probability-based prediction interval.
Results
The probability of falling above the upper limit, or below the lower limit, often varied greatly from the nominal value. This was particularly apparent when there were a small number of expected events: for expected events ≤50 the median probability of an ‘in-control’ provider falling above the upper 95% limit was 0.0301 (Wald), 0.0121 (‘exact’), 0.0201 (prediction).
Conclusions
It is important to understand the properties and probability of being identified as an outlier by each of these different methods to aid the correct identification of poorly performing health care providers. The limits obtained using probability-based prediction limits have the most intuitive interpretation and their properties can be defined a priori. Funnel plot control limits for the SMR should not be based on confidence intervals.
Publisher
BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
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