𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Logistic regression model to estimate the risk of unbalanced offspring in reciprocal translocations

✍ Scribed by Christine Cans; Olivier Cohen; Christian Lavergne; Marie-Ange Mermet; Jacques Demongeot; Pierre Jalbert


Publisher
Springer
Year
1993
Tongue
English
Weight
676 KB
Volume
92
Category
Article
ISSN
0340-6717

No coin nor oath required. For personal study only.

✦ Synopsis


The aim of this study was to estimate the risk of viable unbalanced offspring for a parental carrier of reciprocal translocation. On a large computerized database of reciprocal translocations we used logistic regression to model this risk. The status of the progeny is the outcome variable. Explanatory covariates are cytogenetic characteristics of the translocation, age and sex of the parental carrier, and potential viability of the gametes. The results obtained by the logistic model demonstrate the important role of certain variables such as the sex of the parental carrier and the R band length of the translocated segments. Within the group of lower risk (risk of viable unbalanced offspring less than 5%), 97% of the individuals are correctly classified with this model. For this group, the choice prenatal diagnosis can be best discussed by considering both the risk for viable unbalanced offspring and the risk of induced abortion following prenatal diagnosis.


πŸ“œ SIMILAR VOLUMES


Recombination in a male carrier of two r
✍ Anna Soler; Aurora SΓ‘nchez; Ana CarriΓ³; CΓ¨lia Badenas; Montserrat MilΓ ; Ester Ma πŸ“‚ Article πŸ“… 2005 πŸ› John Wiley and Sons 🌐 English βš– 238 KB πŸ‘ 2 views

## Abstract We report an unusual case of a familial complex chromosome rearrangement (CCR), ascertained through prenatal diagnosis. The fetus carried an apparently balanced CCR with a recombinant 3‐segment chromosome derived from two paternal reciprocal translocations involving both homologs of chr

The breakdown behavior of the maximum li
✍ Christophe Croux; CΓ©cile Flandre; Gentiane Haesbroeck πŸ“‚ Article πŸ“… 2002 πŸ› Elsevier Science 🌐 English βš– 145 KB

In this note we discuss the breakdown behavior of the maximum likelihood (ML) estimator in the logistic regression model. We formally prove that the ML-estimator never explodes to inΓΏnity, but rather breaks down to zero when adding severe outliers to a data set. An example conΓΏrms this behavior.