To use control charts in practice, the in‐control state usually has to be estimated. This estimation has a detrimental effect on the performance of control charts, which is often measured by the false alarm probability or the average run length. We suggest an adjustment of the monitoring schemes to overcome these problems. It guarantees, with a certain probability, a conditional performance given the estimated in‐control state. The suggested method is based on bootstrapping the data used to estimate the in‐control state. The method applies to different types of control charts, and also works with charts based on regression models. If a non‐parametric bootstrap is used, the method is robust to model errors. We show large sample properties of the adjustment. The usefulness of our approach is demonstrated through simulation studies.

The adjusted incidence rate, seasonal distribution and mortality rate was stable. PPU frequently presents outside regular work-hours. Increase in incidence and mortality occurs with older age.

ERCP is a procedure with considerable risk for complications. Morbidity and mortality are related to patient age and co-morbidity, as well as hospital volume of ERCP procedures and the type of intervention.

Headache, cognitive dysfunction and mood disorders are common in both diseases, but otherwise there are distinct differences in NP involvement, with cerebrovascular diseases more prevalent in SLE and neuropathies more common in PSS. This indicates that some NP disease mechanisms are shared while others differ between the two diseases.