### Abstract

A method is described for modeling the sensitivity, specificity, and positive and negative predictive values of a diagnostic test. To model sensitivity and specificity, the dependent variable (Y) is defined to be the dichotomous results of the screening test, and the presence or absence of disease, as defined by the "gold standard", is included as a binary explanatory variable (X_{1}), along with variables used to define the subgroups of interest. The sensitivity of the screening test may then be estimated using logistic regression procedures. Modeled estimates of the specificity and predictive values of the screening test may be similarly derived. Using data from a population-based study of peripheral arterial disease, the authors demonstrated empirically that this method may be useful for obtaining smoothed estimates of sensitivity, specificity, and predictive values. As an extension of this method, an approach to the modeling of the relative sensitivity of two screening tests is described, using data from a study of screening procedures for colorectal disease as an example.

Original language | English (US) |
---|---|

Pages (from-to) | 1-7 |

Number of pages | 7 |

Journal | Journal of Clinical Epidemiology |

Volume | 45 |

Issue number | 1 |

DOIs | |

State | Published - Jan 1 1992 |

Externally published | Yes |

### Fingerprint

### Keywords

- Epidemiologic methods
- Mathematical modeling
- Predictive value
- Screening
- Sensitivity
- Specificity

### ASJC Scopus subject areas

- Epidemiology

### Cite this

*Journal of Clinical Epidemiology*,

*45*(1), 1-7. https://doi.org/10.1016/0895-4356(92)90180-U

**The logistic modeling of sensitivity, specificity, and predictive value of a diagnostic test.** / Coughlin, Steven Scott; Trock, Bruce; Criqui, Michael H.; Pickle, Linda W.; Browner, Deirdre; Tefft, Mariella C.

Research output: Contribution to journal › Article

*Journal of Clinical Epidemiology*, vol. 45, no. 1, pp. 1-7. https://doi.org/10.1016/0895-4356(92)90180-U

}

TY - JOUR

T1 - The logistic modeling of sensitivity, specificity, and predictive value of a diagnostic test

AU - Coughlin, Steven Scott

AU - Trock, Bruce

AU - Criqui, Michael H.

AU - Pickle, Linda W.

AU - Browner, Deirdre

AU - Tefft, Mariella C.

PY - 1992/1/1

Y1 - 1992/1/1

N2 - A method is described for modeling the sensitivity, specificity, and positive and negative predictive values of a diagnostic test. To model sensitivity and specificity, the dependent variable (Y) is defined to be the dichotomous results of the screening test, and the presence or absence of disease, as defined by the "gold standard", is included as a binary explanatory variable (X1), along with variables used to define the subgroups of interest. The sensitivity of the screening test may then be estimated using logistic regression procedures. Modeled estimates of the specificity and predictive values of the screening test may be similarly derived. Using data from a population-based study of peripheral arterial disease, the authors demonstrated empirically that this method may be useful for obtaining smoothed estimates of sensitivity, specificity, and predictive values. As an extension of this method, an approach to the modeling of the relative sensitivity of two screening tests is described, using data from a study of screening procedures for colorectal disease as an example.

AB - A method is described for modeling the sensitivity, specificity, and positive and negative predictive values of a diagnostic test. To model sensitivity and specificity, the dependent variable (Y) is defined to be the dichotomous results of the screening test, and the presence or absence of disease, as defined by the "gold standard", is included as a binary explanatory variable (X1), along with variables used to define the subgroups of interest. The sensitivity of the screening test may then be estimated using logistic regression procedures. Modeled estimates of the specificity and predictive values of the screening test may be similarly derived. Using data from a population-based study of peripheral arterial disease, the authors demonstrated empirically that this method may be useful for obtaining smoothed estimates of sensitivity, specificity, and predictive values. As an extension of this method, an approach to the modeling of the relative sensitivity of two screening tests is described, using data from a study of screening procedures for colorectal disease as an example.

KW - Epidemiologic methods

KW - Mathematical modeling

KW - Predictive value

KW - Screening

KW - Sensitivity

KW - Specificity

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UR - http://www.scopus.com/inward/citedby.url?scp=0026556877&partnerID=8YFLogxK

U2 - 10.1016/0895-4356(92)90180-U

DO - 10.1016/0895-4356(92)90180-U

M3 - Article

VL - 45

SP - 1

EP - 7

JO - Journal of Clinical Epidemiology

JF - Journal of Clinical Epidemiology

SN - 0895-4356

IS - 1

ER -