The phrase "adjustment methodologies" is correct and usable in written English.
It can be used in contexts related to processes or techniques for making changes or modifications in various fields, such as research, finance, or engineering.
Example: "The study outlines several adjustment methodologies to improve the accuracy of the data analysis."
Alternatives: "modification techniques" or "calibration methods."
Of note, the persistent age related differences found in the present study suggest that any risk adjustment methodologies stratify or adjust outcomes using age 85 reflecting that this group of oldest old have more positive outcomes for some kinds of measures (e. g. lower hospitalization rate for patients with COPD) and less positive outcomes for other kinds.
The first stage was devoted to producing the description of different adjustment methodologies and assessing their merits.
Methylation data were adjusted for plate effects via ComBat (Johnson et al. 2007), which performs effectively compared with competing adjustment methodologies.
The challenges of data availability, comparability, and adjustment methodologies limit the usefulness of simple health systems comparisons for identifying successful strategies for transfer.
There are differences in how the population is defined (i.e. who is included and excluded) and how the risk adjustment methodologies are applied [ 5].
Thus, the use of a data mining approach could identify the dependencies and interactions that influence outcomes so that risk adjustment methodologies can be improved in accuracy and actual benchmarks could be established.
For 13 of the 19 indicators that have risk adjustment, after applying the risk adjustment methodology, the difference between the adjusted rates in the two regions was reduced.
A previous simulation study has confirmed that the risk adjustment methodology in these reports introduced significant bias into the calculation of risk adjusted mortality rates.
In the second stage a practical benchmark exercise was defined in order to test the reliability of the nuclear data adjustment methodology.
This study assessed the Kaiser Permanente inpatient risk adjustment methodology for hospital mortality in a patient population distinct from that used for its derivation.
By taking into account the risk factors for a covered population, prospective risk adjustment methodologies--commonly referred to as "predictive models" (PMs --can be helpful for health budget planning and case profiling [ 3].
