Sentence examples for mapping formulas from inspiring English sources

New mapping formulas were analysed, including PAC-QOL total and subscale scores.

The statistic used to test the fit of the mapping formulas was the root mean square error (RMSE).

The statistics used to test the fit of the mapping formulas were the RMSE and the adjusted R2 as well as predicted versus observed plots.

Two mapping formulas were analysed: the relationship between the EQ-5D-3 L and the PAC-QOL total score (as in Parker et al. [10]); and the relationship between the EQ-5D-3 L and PAC-QOL subscale scores.

The mapping formulas directly estimated in this study performed slightly better than the formula provided by Parker et al. However, these formulas still performed poorly compared to other published mapping studies [8, 16], as shown by the high RMSE and low R2 scores.

Three mapping formulas were presented: one formula using only the summary PAC-QOL score as an independent variable, and two formulas using the PAC-QOL score and the PAC-SYM score (a different questionnaire, the Patient Assessment of Constipation Symptoms) as independent variables.

The mapping formula given by Parker et al. [10] is: Formula 1: mathrm{E}mathrm{Q}hbox 5mathrm{D}hbox 3mathrm{L} = 0.977 hbox 0.098 times PACmathit{hboxQOL.

The Fermi-Dirac beam model was chosen as the distribution function of the flattop beam in this paper, the mapping formula of the input Gaussian beam and the output flattop beam was establish, the surface coefficient of aspheric was given.

Although there was an association between high PAC-QOL scores and lower EQ-5D-3 L utilities, it was not possible to reliably map the PAC-QOL on to the EQ-5D-3 L; the mapping formula provided by Parker et al. proved unreliable in our study population [10].

Finally, while the mapping formula of Parker et al. appeared to perform better in their population of CIC (as demonstrated by a lower RMSE statistic), other important fit criteria such as the R2 and predicted versus observed plots were not presented.

We are aware of two papers which have compared both the QALY gain and incremental cost per QALY predicted on the basis of different mapping formulae [ 38, 39].