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No differences in patient characteristics were observed between the estimation dataset and validation dataset.
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There were 2855 records in the estimation dataset and 1887 in the validation dataset.
Age was not correlated with the EQ-5D index in the estimation dataset.
The proportion of responses for the estimation dataset is larger than for the validation dataset to enhance model accuracy with a greater number of responses.
However there is little guidance regarding the choice of algorithm where patient characteristics differ between the estimation and application dataset both in terms of the condition and hence dimensions of health which are likely to be important, and in terms of the severity range of the dataset.
The majority of published algorithms use linear regression by ordinary least squares to estimate the relationship between the QLQ-C30 and EQ-5D where both measures have been used on the same patient population (an estimation dataset).
However, any underlying relationship between the estimation accuracy and genome size may be masked by variation in systematic sequencing error among the datasets tested.
The estimation results for dataset I are listed in Table 1.
It would be also useful to have an idea of how much a different choice of the relative scaling between the two datasets would affect the estimation of microscopic rates.
The other procedures were the same as those applied in the estimation using the combined dataset.
No common SNPs between the HapMap dataset and NIEHS dataset.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com