Abstract
The question of whether two disorders cluster together, or coaggregate, within families often arises. This paper considers how to analyze familial aggregation of two disorders and presents two multivariate logistic regression methods that model both disorder outcomes simultaneously. The first, a proband predictive model, predicts a relative's outcomes (the presence or absence of each of the two disorders) by using the proband's disorder status. The second, a family predictive model derived from the quadratic exponential model, predicts a family member's outcomes by using all of the remaining family members' disorder statuses. The models are more realistic, flexible, and powerful than univariate models. Methods for estimation and testing account for the correlation of outcomes among family members and can be implemented by using commercial software.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 500-505 |
| Number of pages | 6 |
| Journal | American Journal of Epidemiology |
| Volume | 153 |
| Issue number | 5 |
| DOIs | |
| State | Published - Mar 1 2001 |
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Keywords
- Family
- Family characteristics
- Generalized estimating equation
- Logistic models
ASJC Scopus subject areas
- Epidemiology
Cite this
Multivariate logistic regression for familial aggregation of two disorders. I. Development of models and methods. / Hudson, James I.; Laird, Nan M.; Betensky, Rebecca.
In: American Journal of Epidemiology, Vol. 153, No. 5, 01.03.2001, p. 500-505.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Multivariate logistic regression for familial aggregation of two disorders. I. Development of models and methods
AU - Hudson, James I.
AU - Laird, Nan M.
AU - Betensky, Rebecca
PY - 2001/3/1
Y1 - 2001/3/1
N2 - The question of whether two disorders cluster together, or coaggregate, within families often arises. This paper considers how to analyze familial aggregation of two disorders and presents two multivariate logistic regression methods that model both disorder outcomes simultaneously. The first, a proband predictive model, predicts a relative's outcomes (the presence or absence of each of the two disorders) by using the proband's disorder status. The second, a family predictive model derived from the quadratic exponential model, predicts a family member's outcomes by using all of the remaining family members' disorder statuses. The models are more realistic, flexible, and powerful than univariate models. Methods for estimation and testing account for the correlation of outcomes among family members and can be implemented by using commercial software.
AB - The question of whether two disorders cluster together, or coaggregate, within families often arises. This paper considers how to analyze familial aggregation of two disorders and presents two multivariate logistic regression methods that model both disorder outcomes simultaneously. The first, a proband predictive model, predicts a relative's outcomes (the presence or absence of each of the two disorders) by using the proband's disorder status. The second, a family predictive model derived from the quadratic exponential model, predicts a family member's outcomes by using all of the remaining family members' disorder statuses. The models are more realistic, flexible, and powerful than univariate models. Methods for estimation and testing account for the correlation of outcomes among family members and can be implemented by using commercial software.
KW - Family
KW - Family characteristics
KW - Generalized estimating equation
KW - Logistic models
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UR - http://www.scopus.com/inward/citedby.url?scp=0035283435&partnerID=8YFLogxK
U2 - 10.1093/aje/153.5.500
DO - 10.1093/aje/153.5.500
M3 - Article
VL - 153
SP - 500
EP - 505
JO - American Journal of Epidemiology
JF - American Journal of Epidemiology
SN - 0002-9262
IS - 5
ER -