TY - JOUR
T1 - Goodness-of-fit tests for GEE with correlated binary data
AU - Pan, Wei
PY - 2002/3
Y1 - 2002/3
N2 - The marginal logistic regression, in combination with GEE, is an increasingly important method in dealing with correlated binary data. As for independent binary data, when the number of possible combinations of the covariate values in a logistic regression model is much larger than the sample size, such as when the logistic model contains at least one continuous covariate, many existing chi-square goodness-of-fit tests either are not applicable or have some serious drawbacks. In this paper two residual based normal goodness-of-fit test statistics are proposed: the Pearson chi-square and an unweighted sum of residual squares. Easy-to-calculate approximations to the mean and variance of either statistic are also given. Their performance, in terms of both size and power, was satisfactory in our simulation studies. For illustration we apply them to a real data set.
AB - The marginal logistic regression, in combination with GEE, is an increasingly important method in dealing with correlated binary data. As for independent binary data, when the number of possible combinations of the covariate values in a logistic regression model is much larger than the sample size, such as when the logistic model contains at least one continuous covariate, many existing chi-square goodness-of-fit tests either are not applicable or have some serious drawbacks. In this paper two residual based normal goodness-of-fit test statistics are proposed: the Pearson chi-square and an unweighted sum of residual squares. Easy-to-calculate approximations to the mean and variance of either statistic are also given. Their performance, in terms of both size and power, was satisfactory in our simulation studies. For illustration we apply them to a real data set.
KW - Generalized estimating equations
KW - Logistic regression
KW - Pearson's chi-square
KW - Unweighted sum of squares
UR - http://www.scopus.com/inward/record.url?scp=0036005421&partnerID=8YFLogxK
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U2 - 10.1111/1467-9469.00091
DO - 10.1111/1467-9469.00091
M3 - Article
AN - SCOPUS:0036005421
SN - 0303-6898
VL - 29
SP - 101
EP - 110
JO - Scandinavian Journal of Statistics
JF - Scandinavian Journal of Statistics
IS - 1
ER -