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Mapping can only model and capture the overlap between two measures, in this case, the MSWS-12 and the EQ-5D and important details will certainly be lost in the process.
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8While the tf-idf here coincides with betweenness, the mild correlation between the two measures in Table 5 reveals this is not always the case.
The ICC indicates the concordance between two measures and is expressed in a score ranging from 0 (no agreement) to 1 (perfect agreement).
The correlation coefficient between the two measures in our sample is 0.75, significant at 1 %.
The Bland and Altman plot (Figure 3a, b, c) confirmed excellent agreement between the two measures in all body positions, with a calculated bias and precision of -0.06 and 0.6 when supine and 0.006 and 0.5 at 30°.
As seen in Figure 2 there is a high positive correlation (r = 0.76) between the two measures in initial screens.
There was also a significant correlation between these two measures in the control group (r = 0.33, p < 0.0005).
Due to the availability of a repeated measure for 11 909 subjects in the Czech cohort (99.3%), we could evaluate the agreement between the two measures in this database.
The correlation of Comt expression with two indices of morphine effect suggested a possible relationship between these two measures in mice.
However, a divergence between the two measures in the mid range values suggests that a ratio such as the CNR may be useful.
Preliminary regression analyses showed collinearity between the indicators exploring national public expenditure on health care and national per capita health expenditure, suggesting considerable overlap between the two measures in terms of their relationship with maternal mortality.
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