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Themes and patterns in the data are identified using constant comparative techniques and data are organised into codes and categories to enable theory to be developed at a substantive level.
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Corrected LST and NDVI, which indicate a strong relationship with the land-cover data, were identified using scattergrams.
HUs for the core data were identified using three popular correlations such as Kozeny Carmen (KC) equation, Nooruddin and Hossain equation, and the power law flow unit equation.
Prescription medication data were identified using Medicare Part D. Using fill dates, we identified the date of the initial prescription for antihypertensive therapy (index date).
All patient co-morbidity data was identified using the International Classification of Disease 10th revision (ICD-10) codes, and these were used to calculate the Charlson co-morbidity index for each patient as a separate variable [12].
RSNs and structured artifacts in the data were identified using MELODIC (Multivariate Exploratory Linear Optimized Decomposition into Independent Components [55], FSL's implementation of ICA (Independent Component Analysis [56]).
The base case costing scenario assumed that in the IPTi study cohort all suspected cases of malaria received an RDT at a unit cost of US$0.75 [10] In the absence of data, it is assumed that 50% of all malaria diagnoses recorded in the HMIS data were identified using microscopy at a unit cost of USD0.27 [10] and the other 50% were treated presumptively.
Significant differences between survival data were identified using the log-rank test (Lawless, 1982).
Biological pathways enriched in the data were identified using IPA software (Ingenuity Systems, Redwood City, CA).
Tandem repeats in the genome sequence data were identified using Tandem Repeats Finder [ 43].
In addition, 2 spots that were not identified by PMF data were identified using MS/MS spectra data.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com