Conditional logistic regression models for correlated binary data

Connolly, Margaret A.; Liang, Kung Yee

doi:10.1093/biomet/75.3.501

Cited by 119 publications

(16 citation statements)

References 9 publications

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“…It is the logit of Pr( Y j = 11 Yj,) which is modelled as a linear function of the explanatory variables plus the response for the other eye (Yr) and an interaction of Yr with race. This model is closely related to the conditional logistic model proposed by Rosner (1984) and by Connolly and Liang (1988), and discussed by Neuhaus and Jewell (1990) and Prentice (1988). As a technical aside, we have fitted the conditional model by using GEEI with an independence working correlation matrix.…”

Section: Examplesmentioning

confidence: 94%

Multivariate Regression Analyses for Categorical Data

Liang

Zeger

Qaqish

1992

Journal of the Royal Statistical Society Series B: Statistical Methodology

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479

362

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SUMMARY It is common to observe a vector of discrete and/or continuous responses in scientific problems where the objective is to characterize the dependence of each response on explanatory variables and to account for the association between the outcomes. The response vector can comprise repeated observations on one variable, as in longitudinal studies or genetic studies of families, or can include observations for different variables. This paper discusses a class of models for the marginal expectations of each response and for pairwise associations. The marginal models are contrasted with log‐linear models. Two generalized estimating equation approaches are compared for parameter estimation. The first focuses on the regression parameters; the second simultaneously estimates the regression and association parameters. The robustness and efficiency of each is discussed. The methods are illustrated with analyses of two data sets from public health research.

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Section: Examplesmentioning

confidence: 94%

Multivariate Regression Analyses for Categorical Data

Liang

Zeger

Qaqish

1992

Journal of the Royal Statistical Society Series B: Statistical Methodology

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479

362

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“…This model can be estimated with standard computer programs for logistic regression. CONNOLLY and LIANG (1988) generalized ROSNER's (1984) model to conditional logistic regression models for clusters of binary data, that is a logistic model for one observation conditional on the other observation. The model includes covariates (for each observation) and the parameters of its pseudolikelihood function are estimated using estimating functions.…”

Section: Relation Of the P 2 Model To Other Gl(m)msmentioning

confidence: 99%

p₂: a random effects model with covariates for directed graphs

Duijn¹,

Snijders

Zijlstra

2004

Statistica Neerlandica

187

170

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A random effects model is proposed for the analysis of binary dyadic data that represent a social network or directed graph, using nodal and/or dyadic attributes as covariates. The network structure is reflected by modeling the dependence between the relations to and from the same actor or node. Parameter estimates are proposed that are based on an iterated generalized least‐squares procedure. An application is presented to a data set on friendship relations between American lawyers.

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“…In other words, the joint distribution of any subset of the family has the same form as that of the whole family. Note, however, that the interpretation of the conditional log odds ratios independently developed by Hopper and Derrick [1986] and Connolly and Liang [1988] will vary with family size. The invariance property of our approach is essential for family studies where the sizes differ; see Prentice [1988], Neuhaus and Jewel1 [1990], Liang et al [1991], and Qaqish and Liang [1991] for more detailed discussion.…”

Section: Some Features Of the Modelmentioning

confidence: 99%

Measuring familial aggregation by using odds‐ratio regression models

Liang

Beaty

1991

Genetic Epidemiology

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Detection of familial aggregation of a disease is important for studying possible genetic and environmental factors contributing to disease etiology. Accurate quantification of familial aggregation can provide guidance for subsequent, more sophisticated genetic studies. This article presents a statistical model and method for detecting both inter- and intra-class aggregation of a binary trait with family data. The method used here is based on the logistic regression model which incorporates effects of individual covariates while measuring familial aggregation of risk as the odds ratios among classes of relatives. An estimation equation approach is presented where the joint distribution of binary traits among family members need not be fully specified. Data from a genetic epidemiologic study on liver cancer in Shanghai are analyzed for illustration, and reveal strong aggregation of risk even after adjusting for covariates. Effects of non-random sampling and ascertainment bias are also discussed.

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Conditional logistic regression models for correlated binary data

Cited by 119 publications

References 9 publications

Multivariate Regression Analyses for Categorical Data

Multivariate Regression Analyses for Categorical Data

p₂: a random effects model with covariates for directed graphs

Measuring familial aggregation by using odds‐ratio regression models

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Conditional logistic regression models for correlated binary data

Cited by 119 publications

References 9 publications

Multivariate Regression Analyses for Categorical Data

Multivariate Regression Analyses for Categorical Data

p2: a random effects model with covariates for directed graphs

Measuring familial aggregation by using odds‐ratio regression models

Contact Info

Product

Resources

About

p₂: a random effects model with covariates for directed graphs