Data management and statistical analyses
Data were extracted from and onto a secure, encrypted SQL server
controlled by NHS Improvement. Data were analysed within this secure
environment using standard statistical software: Microsoft Excel
(Microsoft Corp, Redmond, WA, USA), Stata (Version 13, StataCorp LLC,
College Station, TX, USA) and Alteryx (Alteryx Inc, Irvine, CA, USA).
ITSA was used to investigate changes in practice in response to each
intervention for each recommendation. ITSA allows temporal trends in
data to be examined prior to an intervention, any immediate changes at
the point of the intervention and longer term changes in trend
post-intervention to be examined.6 The itsacommand in Stata was used to conduct the analysis.7The procedure used ordinary least squares to estimate the model
coefficients with Newey–West standard errors used to produce confidence
intervals for the estimates.
Data were categorised into the 30-day periods outlined above for the
analysis. For all three datasets, periods at the start and end of the
time series with fewer than 200 admitted patients per trust were
excluded. Periods with smaller numbers of patients at the start and end
of the time series arose since the trusts visited first had a very long
post-intervention period and the trusts visited last had a very long
pre-intervention period. For the day-case surgery for TURBT dataset this
involved removing 6 (3 at start and end) of 94 time periods. For
treatment for the use of ureteric stents dataset this involved removing
11 (7 at start and 4 at the end) of 94 time periods. For the waiting
times for TURP dataset this involved removing 22 (12 at the start and 10
at the end) of 82 time periods.
Autocorrelation was investigated using the Cumby–Huizinga general test
for autocorrelation and the test implemented using the actestcommand in Stata.8, 9 Autocorrelation was only
detected for the TURBT dataset and was corrected for by a lag of one.
For all other datasets and outcomes studied the lag period was set at
zero.