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Theory and Methods: Estimation in Regressive Logistic Regression Analyses of Familial Data with Missing Outcomes
Author(s) -
FitzGerald Patrick E.B.,
Knuiman Matthew W.
Publication year - 1998
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/1467-842x.00035
Subject(s) - missing data , imputation (statistics) , logistic regression , statistics , mathematics , econometrics , estimation , binary data , regression , autoregressive model , regression analysis , binary number , engineering , arithmetic , systems engineering
This paper examines a number of methods of handling missing outcomes in regressive logistic regression modelling of familial binary data, and compares them with an EM algorithm approach via a simulation study. The results indicate that a strategy based on imputation of missing values leads to biased estimates, and that a strategy of excluding incomplete families has a substantial effect on the variability of the parameter estimates. Recommendations are made which depend, amongst other factors, on the amount of missing data and on the availability of software.