The EQ-5D is a five-item self-administered questionnaire and one of the most widely used generic preference-based measures for estimating health utilities.
However, the majority of clinical trials do not use 'preference based' measures opting instead for disease specific measure that are not preference-based so cannot be used to estimate QALYs directly.
One study has assessed the agreement of 15 creatinine based formulas for estimating GFR with measured GFR values (iohexol plasma clearance) in 600 patients with type 2 diabetes with a baseline mGFR of 101 ml/min/1.73 m in a cross-sectional analysis and in a longitudinal analysis for 449 patients that had serial mGFR measurements over 4 years (rate of GFR decline -3.37 ml/min/1.73 m).
2) Creatinine based formulae for estimating renal function In this population renal function was estimated, not measured.
This definition of entropy is based on Shannon's entropy, which is a standard measure for estimating the uncertainty of a given signal in Information Theory (Schneider 2010).
Its distinctive feature is a similarity measure for estimating the population diversity, which is based on the angle between the objective vectors.
The results demonstrate that similarity measures based on seasonality indexes are effective for estimating extreme flow quantiles for the study area.
A new approach based on the universal generating function technique is suggested for estimating boundary points of several reliability measures for such systems.
Recent efforts, for example, have developed frameworks for estimating "brain age" based on morphological features from inherently qualitative structural MRI scans, quantitative scans, and measures of functional connectivity.
The USC CHS framework employed a hierarchical approach for estimating exposure, ranging from the coarsest spatial estimates based on community pollutant levels measured at a single central monitor per community, to the finest spatial-scale estimates based on integrated models for individual exposure assessment.
This study proposes a methodology for estimating rear-end crash potential, as a probabilistic measure, in real time based on the analysis of vehicular movements.
