### Abstract

Original language | English |
---|---|

Pages (from-to) | 164-170 |

Number of pages | 6 |

Journal | Epidemiology |

Volume | 5 |

Issue number | 2 |

Publication status | Published - Mar 1994 |

### Fingerprint

### Keywords

- logistic regression
- differential variability
- nonexposure
- dose response
- case control studies
- models
- data analysis

### Cite this

*Epidemiology*,

*5*(2), 164-170.

}

*Epidemiology*, vol. 5, no. 2, pp. 164-170.

**Some statistical considerations in the analysis of case-control studies when the exposure variables are continuous measurements.** / Robertson, C.; Boyle, P.; Hsieh, C.C.; MacFarlane, G.J.; Maisonneuve, P.

Research output: Contribution to journal › Article

TY - JOUR

T1 - Some statistical considerations in the analysis of case-control studies when the exposure variables are continuous measurements

AU - Robertson, C.

AU - Boyle, P.

AU - Hsieh, C.C.

AU - MacFarlane, G.J.

AU - Maisonneuve, P.

PY - 1994/3

Y1 - 1994/3

N2 - This paper focuses on some statistical considerations in the estimation of dose response in case control studies when the exposure variables are continuous measurements. The first point is that the-effects of differential variability in the exposure distributions over cases and controls cannot be differentiated from a true quadratic risk model. The second point is that when dealing with variables where zero denotes no exposure, it is important to treat the unexposed subjects separately from those who were exposed. Failure to do so can lead to differential variability among cases and controls and the resulting confounding with a quadratic risk model. Both of these points are illustrated by an example.

AB - This paper focuses on some statistical considerations in the estimation of dose response in case control studies when the exposure variables are continuous measurements. The first point is that the-effects of differential variability in the exposure distributions over cases and controls cannot be differentiated from a true quadratic risk model. The second point is that when dealing with variables where zero denotes no exposure, it is important to treat the unexposed subjects separately from those who were exposed. Failure to do so can lead to differential variability among cases and controls and the resulting confounding with a quadratic risk model. Both of these points are illustrated by an example.

KW - logistic regression

KW - differential variability

KW - nonexposure

KW - dose response

KW - case control studies

KW - models

KW - data analysis

UR - http://journals.lww.com/epidem/Abstract/1994/03000/Some_Statistical_Considerations_in_the_Analysis_of.6.aspx

M3 - Article

VL - 5

SP - 164

EP - 170

JO - Epidemiology

JF - Epidemiology

SN - 1044-3983

IS - 2

ER -