The typical approach to identify potentially problematic samples is to calculate summary statistics of genome-wide data and then, by visualizing their distributions across individuals, to manually choose a threshold based on their values for the majority of the data.
The study of AD's secondary effects has proved especially problematic: Past attempts to calculate incremental health costs among informal dementia or AD caregivers have varied widely because study inclusion criteria, control groups and sources of cost data are not consistently constructed [ 8, 9].
The normal-mode entropy is also problematic, because it is expensive to calculate and it lacks information of the conformational entropy.
The most common alternative method to calculate the last "problematic" age range of the life table is extrapolation of death rates based on their change at younger ages in combination with a mortality model (Mathers and Ho 2014; Missov et al. 2016; UN DESA/Population Division 1982, 2017; Wilmoth et al. 2007).
Direct comparison of accuracies between studies can be problematic, considering the diverse methods that are used to calculate DGV accuracy in the scientific literature.
Comparison of results across studies is problematic as there is no universally accepted method to calculate persistency, and results are therefore method-dependent.
All items apart from the prosocial behaviour items are used to calculate a total score (range 0 – 40) with higher scores indicating more problematic behaviour.
The Mitra formula involves the population mean age in the open age interval, ( overline{x} ), an indicator easy to calculate for populations with good-quality data, such as the HMD populations, but problematic for populations with age exaggeration.
We followed their published guidelines [ 27] to calculate maternal or newborn childbirth morbidity, identified from the ICD-9 codes indicating 26 possible problematic outcomes, including bladder lacerations, pelvic hematoma, neonatal birth trauma, and neonatal respiratory problems.
