Outlier Detection for Healthcare Quality Monitoring – A Comparison of Four Approaches to Over‐Dispersed Proportions

Vidmar, Gaj; Blagus, Rok

doi:10.1002/qre.1581

Cited by 17 publications

(16 citation statements)

References 67 publications

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“…Further work suggested that the statistical approach itself needed some care because the observed distributions of the data differs significantly from the normal distribution, which is used in funnel plots either as the assumed distribution of the data or as the approximation to the binomial distribution. More recent work (Vidmar & Blagus, 2014) demonstrated that when comparing small proportions with large denominator, such as the proportion of pupils with special educational needs in particular placements, the data tends to be overdispersed. The use of standard funnel plots may thus describe virtually all cases as outliers.…”

Section: Monitoring Policiesmentioning

confidence: 99%

The Salamanca Declaration in Practice: Inclusion in Europe 2008-2012

Williams¹,

Vidmar²

2021

Preprint

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Educating all children in local schools (inclusive schooling) was agreed by the Salamanca declaration in 1994. Progress has been uneven with evidence from the USA that rural areas achieve higher rates of inclusion possibly due to socio-economic factors. This paper considers whether individual jurisdictions in Europe are making progress, and which socio-economic factors may be relevant. A novel statistical analysis to identify outliers and economic and Social Progress Index data were used to determine likely sources of variation. There are significant differences across Europe both in the level of inclusion and in the progress towards inclusion. No economic or social indices predicted levels of inclusion. The most likely explanation for the one consistent outlier was a legislative one. The results are discussed with reference to data from the USA, and proposals are made for further analyses that might elucidate the reasons for differing levels of inclusion in education.

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Section: Monitoring Policiesmentioning

confidence: 99%

The Salamanca Declaration in Practice: Inclusion in Europe 2008-2012

Williams¹,

Vidmar²

2021

Preprint

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“…HEXT outputs BS values for each node in each taxon-jackknife tree in a table, which can be used as input for alternative outlier tests (e.g. Vidmar & Blagus 2014).…”

Section: Evaluation Of Bootstrap Support Valuesmentioning

confidence: 99%

hext, a software supporting tree‐based screens for hybrid taxa in multilocus data sets, and an evaluation of the homoplasy excess test

2015

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SummaryThe homoplasy excess test (HET) is a tree-based screen for hybrid taxa in multilocus nuclear phylogenies. Homoplasy between a hybrid taxon and the clades containing the parental taxa reduces bootstrap support in the tree. The HET is based on the expectation that excluding the hybrid taxon from the data set increases the bootstrap support for the parental clades, whereas excluding non-hybrid taxa has little effect on statistical node support. To carry out a HET, bootstrap trees are calculated with taxon-jackknife data sets, that is excluding one taxon (species, population) at a time. Excess increase in bootstrap support for certain nodes upon exclusion of a particular taxon indicates the hybrid (the excluded taxon) and its parents (the clades with increased support).We introduce a new software program, hext, which generates the taxon-jackknife data sets, runs the bootstrap tree calculations, and identifies excess bootstrap increases as outlier values in boxplot graphs. hext is written in r language and accepts binary data (0/1; e.g. AFLP) as well as co-dominant SNP and genotype data.We demonstrate the usefulness of hext in large SNP data sets containing putative hybrids and their parents. For instance, using published data of the genus Vitis (~6,000 SNP loci), hext output supports V. × champinii as a hybrid between V. rupestris and V. mustangensis.With simulated SNP and AFLP data sets, excess increases in bootstrap support were not always connected with the hybrid taxon (false positives), whereas the expected bootstrap signal failed to appear on several occasions (false negatives). Potential causes for both types of spurious results are discussed.With both empirical and simulated data sets, the taxon-jackknife output generated by hext provided additional signatures of hybrid taxa, including changes in tree topology across trees, consistent effects of exclusions of the hybrid and the parent taxa, and moderate (rather than excessive) increases in bootstrap support. hext significantly facilitates the taxon-jackknife approach to hybrid taxon detection, even though the simple test for excess bootstrap increase may not reliably identify hybrid taxa in all applications.

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“…It is an important part of data mining and a very active branch of information science, which has attracted the attention of researchers in many disciplines including data mining, statistics, and information theory [2], [3]. Now, outlier detection is considered as a critical task in many practical applications, such as network intrusion detection [4]- [8], fraud detection [9]- [11], industrial damage detection [12]- [15] and health care monitoring [16]- [18].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Density-Based Local Outlier Detection Approach for Scattered Data

Xiao

Ruan

et al. 2019

IEEE Access

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After the local outlier factor was first proposed, there is a large family of local outlier detection approaches derived from it. Since the existing approaches only focus on the extent of overall separation between an object and its neighbors, and ignore the degree of dispersion between them, the precision of these approaches will be affected by various degrees in the scattered datasets. In addition, the outlier data occupy a relatively small amount in the dataset, but the existing approaches need to perform local outlier factor calculation on all data during the outlier detection, which greatly reduces the efficiency of the algorithms. In this paper, we redefine a local outlier factor called local deviation coefficient (LDC) by taking full advantage of the distribution of the object and its neighbors. And then, we propose a safe nonoutlier objects elimination approach named as rough clustering based on multi-level queries (RCMLQ) to preprocess the datasets to eliminate the non-outlier objects to the utmost. Finally, an efficient local outlier detection approach named as efficient density-based local outlier detection for scattered data (E2DLOS) is proposed based on the LDC and RCMLQ. The RCMLQ greatly reduces the amount of data that needs to be quantified for local outlier factor and the LDC is more sensitive to the degree of anomaly of the scattered datasets, and so the E2DLOS improves the existing local outlier detection approaches in time efficiency and detection accuracy. Experiments show that the LDC can better reflect the true abnormal situations of the data for the scattered datasets. And the RCMLQ can be used in parallel with the traditional methods of improving the efficiency of the nearest neighbor search, which can further improve the efficiency of the E2DLOS algorithm by about 16%.

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Outlier Detection for Healthcare Quality Monitoring – A Comparison of Four Approaches to Over‐Dispersed Proportions

Cited by 17 publications

References 67 publications

The Salamanca Declaration in Practice: Inclusion in Europe 2008-2012

The Salamanca Declaration in Practice: Inclusion in Europe 2008-2012

hext, a software supporting tree‐based screens for hybrid taxa in multilocus data sets, and an evaluation of the homoplasy excess test

An Efficient Density-Based Local Outlier Detection Approach for Scattered Data

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