To understand the effect of the intervention on data-informed decision making, we implemented a second PRISM assessment in 2012 – 4 years after the first PRISM assessment (23).
It has been shown that it is often unwise to base diagnostic, prognostic, preventive, or therapeutic interventions on data from one or few trials [ 1, 9, 10, 26, 75], and our recommendations do not in any way change this.
Intervention time series analysis models the effect of known interventions on data of counts of events collected regularly in time [ 16, 17].
As PrEP gained visibility in the years following its debut, Weinstein and the AHF expressed deep skepticism about the treatment's potential as a community-wide intervention, based on data that showed men who enrolled in early studies had a poor track record of adhering to its once-daily dosing.
Our analysis does not aim to model any specific programme, but rather a 'generic' parenting intervention, drawing on data on a variety of evidence-based programmes that are likely to be implemented in the English context.
** Adherence to hand hygiene measure was defined as disinfection of hands at least 5 times per day during the 5 day after full implementation of the intervention (based on data from daily questionnaire).
Failing this, it is possible to model effects of different interventions, or combinations of interventions, based on data from cross sectional studies.
An experiment often involves introducing an intervention, or assessing the effects of an intervention on a data set.
We illustrate this method with an analysis of 'audit and feedback' interventions, based on data from a Cochrane review.
Identify ways in which infection prevention and antimicrobial stewardship programs can effectively develop interventions based on data such as infection rates, antimicrobial use, and antimicrobial resistance in their hospital.
