Terms Of Mean Square Difference Research Articles

Valuation of EQ-5D-3l Health States in Slovenia: VAS Based and TTO Based Value Sets.

IntroductionThe two primary objectives of this paper were (a) to develop first logically consistent TTO based EQ-5D-3L value sets for Slovenia and (b) to revisit earlier developed VAS based EQ-5D-3L value sets.MethodsBetween September 2005 and April 2006, face-to-face interviews with 225 individuals in Slovenia were conducted. Protocols from the Measurement and Value of Health study were followed closely. Each respondent valued 15 health states out of a total of 23. Model selection was informed by the criteria monotonicity/logical consistency. Predictive accuracy was assessed in terms of mean square difference between out-of-sample predictions and corresponding observed means, as well as Lin’s Concordance Correlation Coefficient.ResultsModelling was based on 2,717 VAS and 2,831 TTO values elicited from 225 respondents. A 6-parameter constrained regression model with a supplementary power term was selected for VAS and TTO value sets, as it produces monotonic values, and proved superior in terms of out-of-sample predictive accuracy over the tested alternatives.ConclusionThis is the first EQ-5D-3L TTO based value set in Slovenia and the second in Central and Eastern Europe (besides Poland). It is also the first monotonic and logically consistent VAS value set in Central and Eastern Europe. Comparisons with Polish and UK TTO values show considerable differences, mostly due to mobility with having a substantially greater weight in Slovenia. The UK value set generally produces lower values and the Polish value set higher values for mild states.

A practical comparison of two K-Means clustering algorithms

BackgroundData clustering is a powerful technique for identifying data with similar characteristics, such as genes with similar expression patterns. However, not all implementations of clustering algorithms yield the same performance or the same clusters.ResultsIn this paper, we study two implementations of a general method for data clustering: k-means clustering. Our experimentation compares the running times and distance efficiency of Lloyd's K-means Clustering and the Progressive Greedy K-means Clustering.ConclusionBased on our implementation, not just in processing time, but also in terms of mean squared-difference (MSD), Lloyd's K-means Clustering algorithm is more efficient. This analysis was performed using both a gene expression level sample and on randomly-generated datasets in three-dimensional space. However, other circumstances may dictate a different choice in some situations.