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16 We call this the error propensity.
The Orthopaedic Error Index was calculated as a sum of the error propensity and severity.
Error propensity can differ considerably across sites and also depends on the state of the translational machinery.
In parallel, we approximated the total number of orthopaedic procedures carried out in each hospital using data from the national Hospital Episode Statistics (HES; 2009 2010) database, 17 which is a mandatory national database of all patient visits to National Health Service (NHS) hospitals in England, in order to estimate the error propensity.
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Conventional targeting analysis reveals large inclusionary and exclusionary targeting errors; propensity score targeting analysis yields smaller but still large targeting errors.
Unfortunately, the point estimates cannot capture estimation errors (or standard errors) of propensity scores.
18 The OEI comprised the two main domains of error: the propensity of errors (P) and the severity of harm (S).
Conventionally, standard errors of propensity score matching estimates are obtained using bootstrap methods, but with large samples such as those available to this study, it is not feasible to calculate bootstrap standard errors for all estimates.
Therefore, a new matching technique is needed for gauging standard errors of propensity scores.
The present study introduces interval matching using bootstrap confidence intervals for accommodating estimation errors of propensity scores.
Besides accommodating standard errors of propensity scores using confidence intervals, interval matching has another methodologically sound property.
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