Binary classification of dyslipidemia from the waist-to-hip ratio and body mass index: a comparison of linear, logistic, and CART models

BACKGROUND: We sought to improve upon previously published statistical modeling strategies for binary classification of dyslipidemia for general population screening purposes based on the waist-to-hip circumference ratio and body mass index anthropometric measurements. METHODS: Study subjects were participants in WHO-MONICA population-based surveys conducted in two Swiss regions. Outcome variables were based on the total serum cholesterol to high density lipoprotein cholesterol ratio. The other potential predictor variables were gender, age, current cigarette smoking, and hypertension. The models investigated were: (i) linear regression; (ii) logistic classification; (iii) regression trees; (iv) classification trees (iii and iv are collectively known as "CART"). Binary classification performance of the region-specific models was externally validated by classifying the subjects from the other region. RESULTS: Waist-to-hip circumference ratio and body mass index remained modest predictors of dyslipidemia. Correct classification rates for all models were 60–80%, with marked gender differences. Gender-specific models provided only small gains in classification. The external validations provided assurance about the stability of the models. CONCLUSIONS: There were no striking differences between either the algebraic (i, ii) vs. non-algebraic (iii, iv), or the regression (i, iii) vs. classification (ii, iv) modeling approaches. Anticipated advantages of the CART vs. simple additive linear and logistic models were less than expected in this particular application with a relatively small set of predictor variables. CART models may be more useful when considering main effects and interactions between larger sets of predictor variables

In the absence of cancer registry data, is it sensible to assess incidence using hospital separation records?

BACKGROUND: Within the health literature, a major goal is to understand distribution of service utilisation by social location. Given equivalent access, differential incidence leads to an expectation of differential service utilisation. Cancer incidence is differentially distributed with respect to socioeconomic status. However, not all jurisdictions have incidence registries, and not all registries allow linkage with utilisation records. The British Columbia Linked Health Data resource allows such linkage. Consequently, we examine whether, in the absence of registry data, first hospitalisation can act as a proxy measure for incidence, and therefore as a measure of need for service. METHODS: Data are drawn from the British Columbia Linked Health Data resource, and represent 100% of Vancouver Island Health Authority cancer registry and hospital records, 1990–1999. Hospital separations (discharges) with principal diagnosis ICD-9 codes 140–208 are included, as are registry records with ICDO-2 codes C00-C97. Non-melanoma skin cancer (173/C44) is excluded. Lung, colorectal, female breast, and prostate cancers are examined separately. We compare registry and hospital annual counts and age-sex distributions, and whether the same individuals are represented in both datasets. Sensitivity, specificity and predictive values are calculated, as is the kappa statistic for agreement. The registry is designated the gold standard. RESULTS: For all cancers combined, first hospitalisation counts consistently overestimate registry incidence counts. From 1995–1999, there is no significant difference between registry and hospital counts for lung and colorectal cancer (p = 0.42 and p = 0.56, respectively). Age-sex distribution does not differ for colorectal cancer. Ten-year period sensitivity ranges from 73.0% for prostate cancer to 84.2% for colorectal cancer; ten-year positive predictive values range from 89.5% for female breast cancer to 79.35% for prostate cancer. Kappa values are consistently high. CONCLUSION: Claims and registry databases overlap with an appreciable proportion of the same individuals. First hospital separation may be considered a proxy for incidence with reference to colorectal cancer since 1995. However, to examine equity across cancer health services utilisation, it is optimal to have access to both hospital and registry files

The F test for comparing two normal variances: correct and incorrect calculation of the two-sided p-value?

This article illustrates that not all statistical software packages are correctly calculating a p-value for the classical F test comparison of two independent Normal variances. This is illustrated with a simple example, and the reasons why are discussed. Eight different software packages are considered

Identificando hábitats de nidificación potencial para el águila real aplicadas al diseño de 'áreas de importancia para las aves'

Geographic information systems (GIS)-based habitat-suitability modelling is becoming an essential tool in conservation biology. A multi-scale approach has been proposed as a particularly useful way to identify different factors affecting habitat preferences. In this paper, we developed predictive models of potentially suitable habitat for golden eagles Aquila chrysaetos at three spatial scales in a representative Mediterranean area on the Iberian Peninsula. We used logistic regression through a generalized linear model (GLM) to model golden eagle breeding habitat preferences. The best-occurrence GLM models were those that involved topographic factors as independent predictors. Golden eagles seemed to prefer rugged and higher places of the study area for nesting. Climatic factors identified cold temperatures in January and temperate ones in July as the best predictors of eagles’ occurrence. This was also higher in places with less agricultural areas and higher surface of pine forests. The distribution of potentially suitable area matches the distribution of mountain ranges, mainly in inner sectors of the study area. In contrast, potentially suitable nest sites in coastland areas remain unoccupied by golden eagles. Avoidance of coastland places for nesting may be due to the synergistic effects of human avoidance and the occurrence of potential competitors, like the endangered Bonelli’s eagle Hieraaetus fasciatus. When mapped at a fine spatial resolution, the best GLM model identified large areas that fall outside the current network of protected areas. We therefore propose three new important bird areas for the region.Fundación Terra Natur