Sentence examples for both estimation and validation from inspiring English sources
For model specifications please refer to Table 1 ISI class_1 = No clinically significant insomnia; ISI class_2 = Subthreshold insomnia; ISI class_3 = Moderate insomnia; ISI class_4 = Se were insomnia * p < 0.10; ** p < 0.05; *** p < 0.01; **** p < 0.001 Table 4 compares the mean, median, min/max, and range of observed and predicted EQ-5D values for both estimation and validation sample estimates.
Modeling and simulation of CPC purification showed a good performance during both estimation and validation step.
In the AD and E scenarios, universal kriging outperforms genomic BLUP in both estimation and validation set by showing the highest average correlations.
The best fitting models within sample were re-run on three of the four group and applied to the excluded group to ensure in an iterative process until each of the samples had been used as both estimation and validation samples.
Standardised residuals and fitted EQ-5D index values from fitting the final model in both the estimation and validation datasets were plotted against one another.
The Bland-Altman analysis shows reasonable agreement for higher values of the EQ-5D index, but poor agreement for people with EQ-5D index values of approximately 0.4 or less in both the estimation and validation datasets.
Figure 1 shows plots of standardised residuals versus fitted EQ-5D index values in both the estimation and validation datasets, showing evidence of the partly discrete nature of the EQ-5D index at its upper end.
However, using the model to predict the observed EQ-5D index in the validation dataset did not indicate good prediction on average and the Bland-Altman plot showed that the mapping model over-estimated the EQ-5D index for people with observed values of approximately 0.4 and below in both the estimation and validation datasets.
-N/A = Estimates were not available due to the small sample size -MAE = Mean absolute error - Note: For model specifications please refer to Table 1 -Sample size for both the estimation and validation sample was randomly drawn (50%) from the total qualified participants (N = 2,842).
A Bland-Altman analysis was performed, both in the estimation and validation datasets, to see how well the observed and predicted EQ-5D index agreed and if there appeared to be any systematic measurement bias in the predicted index.
