For example, between-group comparisons may be difficult when the data-driven analyses identify different time courses for each group.
For example, boundaries between groups of healthcare professionals retarded the spread of potentially valuable innovations such as managing anticoagulation service provision with a computer support system and laparoscopic surgery for inguinal hernia repair in the UK National Health System.
These limitations suggest that agreement measures on colposcopic variables have no absolute significance, and should only be used for internal comparisons for a given test environment (for example: comparisons between groups of colposcopists defined by the number of years of experience, etc).
In the examples below, between-group differences held up even though the cases within each group spanned different topic areas.
Other comparisons, for example, between different groups of respondents, have not been made since there were relatively few studies in some categories.
Current strategies to strengthen primary healthcare concentrate on the establishment of contractual arrangements between primary care providers, for example between physician groups and Regional Health Authorities, and the implementation of integrated systems for the delivery of primary healthcare services [ 53, 55].
This precludes inferences with respect to causality between achieved levels of one outcome, for example, glucose control, and other outcomes, for example, vascular complications between groups.
Differences between urban and rural settings can be seen as a very important example of gaps between groups in a population.
F-contrasts were used to assess whether for example activations differed between groups and/or between 'on' and 'off' l-dopa (e.g. [1 –1 0 0 0 0 0 0; 0 1 –0.5 –0.5 0 0 0 0]); or whether there was a linear and/or quadratic effect of UPDRS on regional brain activation (e.g. [0 0 0 0 1 0 –1 0; 0 0 0 0 0 1 0 –1]).
