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A comparison of statistical methods for prenatal screening for Down syndrome

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  • Christopher J. Williams
  • Sauchi Stephen Lee
  • Rachel A. Fisher
  • Lois H. Dickerman

Abstract

Currently, prenatal screening for Down Syndrome (DS) uses the mother's age as well as three biochemical markers for risk prediction. Risk calculations for the biochemical markers use a quadratic discriminant function. In this paper we compare several classification procedures to quadratic discrimination methods for biochemical‐based DS risk prediction, based on data from a prospective multicentre prenatal screening study. We investigate alternative methods including linear discriminant methods, logistic regression methods, neural network methods, and classification and regression‐tree methods. Several experiments are performed, and in each experiment resampling methods are used to create training and testing data sets. The procedures on the test data set are summarized by the area under their receiver operating characteristic curves. In each experiment this process is repeated 500 times and then the classification procedures are compared. We find that several methods are superior to the currently used quadratic discriminant method for risk estimation for these data. The implications of these results for prenatal screening programs are discussed.

Suggested Citation

  • Christopher J. Williams & Sauchi Stephen Lee & Rachel A. Fisher & Lois H. Dickerman, 1999. "A comparison of statistical methods for prenatal screening for Down syndrome," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 15(2), pages 89-101, April.
    • Handle: RePEc:wly:apsmbi:v:15:y:1999:i:2:p:89-101
    DOI: 10.1002/(SICI)1526-4025(199904/06)15:23.0.CO;2-K
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    1. Omid Rahmati & Ali Haghizadeh & Hamid Reza Pourghasemi & Farhad Noormohamadi, 2016. "Gully erosion susceptibility mapping: the role of GIS-based bivariate statistical models and their comparison," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 82(2), pages 1231-1258, June.
    2. F. Falaschi & F. Giacomelli & P. Federici & A. Puccinelli & G. D’Amato Avanzi & A. Pochini & A. Ribolini, 2009. "Logistic regression versus artificial neural networks: landslide susceptibility evaluation in a sample area of the Serchio River valley, Italy," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 50(3), pages 551-569, September.

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