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

BMC Medical Research Methodology

Fred Paccaud¹  Michael C Costanza² 

[1] Institute of Social and Preventive Medicine, University of Lausanne, Lausanne, Switzerland;Division of Clinical Epidemiology, Geneva University Hospitals, Geneva, Switzerland

关键词: sensitivity and specificity.;    positive and negative predictive values;    dyslipidemia screening;    external validation;    classification and regression trees;    Abdominal obesity;   

Others  :  1143198
DOI  :  10.1186/1471-2288-4-7

 received in 2003-10-27, accepted in 2004-04-06,  发布年份 2004

【 摘 要 】

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.

【 授权许可】

   
2004 Costanza and Paccaud; licensee BioMed Central Ltd. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.

【 图 表 】

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【 参考文献 】

• [1]Han TS, Van Leer EM, Seidell JC, Lean MJ: Waist circumference action levels in the identification of cardiovascular risk factors: prevalence study in a random sample. BMJ 1995, 311:1401-1405.0.
• [2]Reeder BA, Senthiselvan A, Despres JP, Angel A, Liu L, Wand H, Rabkin SW: The association of cardiovascular disease risk factors with abdominal obesity in Canada. Canadian Heart Health Surveys Research Group. CMAJ 1997, 157:S39-S45.
• [3]Shetterly SM, Marshall JA, Baxter J, Hamman RF: Waist-hip-ratio measurement location influences associations with measures of glucose and lipid metabolism. The San Luis Valley Diabetes Study. Ann Epidemiol 1993, 3:295-299.
• [4]Bjorntorp P: Regional patterns of fat distribution. Ann Int Med 1985, 103:994-995.
• [5]Seidell JC, Cigolini M, Charzewska J, Ellsinger BM, di Base G: Fat distribution in European women: a comparison of anthropometric measurements in relation to cardiovascular risk factors. Int J Epidemiol 1990, 19:303-308.
• [6]Pouliot MC, Després JP, Lemieux S, Moorjani S, Bouchard C, Tremblay A, Nadeau A, Lupien PJ: Waist circumference and abdominal sagittal diameter: best simple anthropometric indexes of abdominal visceral adipose tissue accumulation and related cardiovascular risk in men and women. Am J Cardiol 1994, 73:460-468.
• [7]Houmard JA, Wheeler WS, McCammon MR, Holbert D, Israel RG, Barakat HA, Wells JM, Truitt N, Hamad SF: An evaluation of waist to hip ratio measurement methods in relation to lipid and carbohydrate metabolism in men. Int J Obes 1991, 15:181-188.
• [8]Reeder BA, Liu L, Horlick L: Selective screening for dyslipidemia in a Canadian population. J Clin Epidemiol 1996, 49:217-222.
• [9]Paccaud F, Schlüter-Fasmeyer V, Wietlisbach V, Bovet P: Dyslipidemia and abdominal obesity: An assessment in three general populations. J Clin Epidemiol 2000, 53:393-400.
• [10]Chambers JM: Linear models. In In Statistical Models in S. Volume 4. Edited by Chambers JM, Hastie TJ. Wadsworth & Brooks/Cole, Pacific Grove, CA; 1992::95-144.
• [11]Hastie TJ, Pregibon D: Generalized linear models. In In Statistical Models in S. Volume 6. Edited by Chambers JM, Hastie TJ. Wadsworth & Brooks/Cole, Pacific Grove, CA; 1992::195-248.
• [12]Breiman L, Friedman JH, Olshen RA, Stone CJ: Classification and Regression Trees. Wadsworth, Belmont, CA; 1984.
• [13]Clark LA, Pregibon D: Tree-based models. In In Statistical Models in S. Volume 9. Edited by Chambers JM, Hastie TJ. Wadsworth & Brooks/Cole, Pacific Grove, CA; 1992::377-420.
• [14]SAS Institute Inc: SAS OnlineDoc®, Version 8, Cary, North Carolina, USA. 1999.
• [15]Insightful Corp: S-PLUS 2000 Guide to Statistics, Seattle, WA. 1999.
• [16]World Health Organization MONICA Project Principal Investigators: The MONICA Project (MONItoring trends and determinants in CArdiovascular disease): a major international collaboration. J Clin Epidemiol 1988, 41:105-114.
• [17]Venables WN, Ripley BD: Modern Applied Statistics with S-Plus. 3rd edition. Springer, NY; 1999:327.
• [18]Wietlisbach V, Paccaud F, Rickenbach M, Gutzwiller F: Trends in cardiovascular risk factors (1984–1993) in a Swiss region: results of the three population surveys. Prev Med 1997, 26:523-533.
• [19]Terrin N, Schmid CH, Griffith JL, D'Agostino RB Sr, Selker HP: External validity of predictive models: A comparison of logistic regression, classification trees, and neural nerworks. J Clin Epidemiol 2003, 56:721-729.
• [20]Bleeker SE, Moll HA, Steyerberg EW, Donders ART, Derksen-Lubsen G, Grobbee DE, Moons KGM: External validition is necessary in prediction research: A clinical example. J Clin Epidemiol 2003, 56:826-832.
• [21]Sonquist JA, Morgan JN: The detection of interaction effects: A report on a computer program for the selection of optimal combinations of explanatory variables. Monograph 35, University of Michigan, Ann Arbor: Survey Research Center Institute for Social Research 1964.
• [22]Cook EF, Goldman L: Empiric comparison of multivariate analytic techniques: advantages and disadvantages of recursive partitioning analysis. J Chron Dis 1984, 37:721-731.
• [23]Fisher RA: The use of multiple measurements in taxonomic problems. Ann Eugenics 1936, 7:179-188.