Conditional Logistic Regression Models for Correlated Binary Data

Connolly, Margaret A.; Liang, Kung‐Yee

doi:10.2307/2336600

Cited by 33 publications

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“…This confirms the findings of Connolly and Liang [1988] that the pseudolikelihood approach with appropriate adjustment for correlation is nearly fully efficient relative to the full-likelihood approach. Note also that the analytic approximation is quite accurate, except when the design calls for all case families (r¼1).…”

Section: Numerical Resultssupporting

confidence: 87%

“…This is due to the presence of a normalization constant, which is a summation over the 2 n possible outcome vectors for each family of size n. However, if regression modelling of the canonical conditional parameters is of primary interest, as it typically is in family studies, computationally feasible approaches are available. Connolly and Liang [1988] showed that fitting the QEM via a pseudolikelihood based on derived logistic regression models is highly efficient relative to the full likelihood approach. Hudson et al [2001a,b] and Betensky et al [2001] used these derived logistic regression models, with adjustment for correlation via the generalized estimating equation methodology, for the analysis of two disease outcomes within family members.…”

Section: Introductionmentioning

confidence: 99%

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Power calculations for familial aggregation studies

Rabbee

Betensky

2004

Genetic Epidemiology

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Family studies are frequently undertaken as the first step in the search for genetic determinants of disease. Significant familial aggregation of disease is suggestive of a genetic etiology for the disease, and may lead to more focused genetic analyses. Many methods have been proposed in the literature for the analysis of family studies. One model that is appealing for its simplicity of computation and the conditional interpretation of its parameters is the quadratic exponential model (e.g., Zhao and Prentice [1990] Biometrika 77:642-648; Betensky and Whittemore [1996] Appl. Stat. 45:422-429; Hudson et al. [2001a] Am. J. Epidemiol. 153:500-514). However, a limiting factor in its application, as well as that of the other proposed methods, is that power and sample size calculations have not been derived. These calculations are essential for investigators who are designing family studies. Here we derive analytic approximations for power for testing for familial aggregation, for both randomly sampled and nonrandomly sampled families. We also present simulation studies of power for both single- and two-disease cases, both under random and nonrandom sampling.

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Section: Numerical Resultssupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Power calculations for familial aggregation studies

Rabbee

Betensky

2004

Genetic Epidemiology

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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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“…The family predictive models are based on a general class of multivariate models introduced by Connolly and Liang [8]. The strong dependence model is also a variant of the second-order log-linear model for n-way (n-dimensional) contingency tables (reference [9], p. 42).…”

Section: Likelihood Approachesmentioning

confidence: 99%

Regression methods for assessing familial aggregation of disease

Laird

Cuenco

2003

Statistics in Medicine

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This paper reviews methods for assessing familial aggregation of disease based on simple logistic regression models. Studies are based on a case-control sampling design, where the disease status of the first degree relatives of both cases and controls are obtained. Both 'proband predictive' and 'family predictive' models are discussed, and an example is given using a case-control sample from a lung cancer study in non-smokers. The methods are extended to characterize co-aggregation of two disorders, that is, presence of one disorder in the proband increases the risk of a second disorder in the relative. An example involving eating disorders and depression is given.

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Conditional Logistic Regression Models for Correlated Binary Data

Cited by 33 publications

References 0 publications

Power calculations for familial aggregation studies

Power calculations for familial aggregation studies

Measuring familial aggregation by using odds‐ratio regression models

Regression methods for assessing familial aggregation of disease

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