Assessing the Fit of the Logistic Regression Model to Individual Matched Sets of Case-Control Data

Bedrick, Edward J.; Hill, Joe R.

doi:10.2307/2533139

Cited by 9 publications

(9 citation statements)

References 13 publications

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“…Diagnostic tests for detecting the influence of outliers or influential pairs on matched case-control analysis as proposed by Pregibon [5], Moolgavar et al [4,6], Bedrick and Hill [7], and Hosmer and Lemeshow [2] do not have the ability to test overall model adequacy. Arbogast and Lin [13] have developed methodology for assessing the adequacy of the functional form of the covariates, the logistic link function, as well as the overall model fit for matched case-control data.…”

Section: Conditional Logistic Regression Modelmentioning

confidence: 99%

“…Thus the only option for many analysts is an ad-hoc approach that is done by creating a data set containing the difference variables and using standard logistic regression diagnostics [2]. The approaches currently available in the literature mainly focus on diagnostic methods that test for influential pairs and outliers [2,4–7], and do not have the ability to test the overall model adequacy. We are interested in testing whether the functional form of the covariates is correctly specified.…”

Section: Introductionmentioning

confidence: 99%

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Nonparametric Diagnostic Test for Conditional Logistic Regression

Goodman

2012

J Biomet Biostat

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The use of conditional logistic regression models to analyze matched case-control data has become standard in statistical analysis. However, methods to test the fit of these models has primarily focused on influential observations and the presence of outliers, while little attention has been given to the functional form of the covariates. In this paper we present methods to test the functional form of the covariates in the conditional logistic regression model, these methods are based on nonparametric smoothers. We assess the performance of the proposed methods via simulation studies and illustrate an example of their use on data from a community based intervention.

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Section: Conditional Logistic Regression Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonparametric Diagnostic Test for Conditional Logistic Regression

Goodman

2012

J Biomet Biostat

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“…We present a new exact sampling method based on conditional‐Poisson sampling. The matching criteria can be treated as variables as in Hirji, Mehta, and Patel (1988) and Bedrick and Hill (1996), which leads back to logistic regression. When the matching variables are the matched sets themselves, then the group labels are strata or factor levels, and the tables are very sparse.…”

Section: Introductionmentioning

confidence: 99%

Sampling for Conditional Inference on Case–Control Data

2007

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The problem of exact conditional inference for discrete multivariate case-control data has two forms. The first is grouped case-control data, where Monte Carlo computations can be done using the importance sampling method of Booth and Butler (1999, Biometrika86, 321-332), or a proposed alternative sequential importance sampling method. The second form is matched case-control data. For this analysis we propose a new exact sampling method based on the conditional-Poisson distribution for conditional testing with one binary and one integral ordered covariate. This method makes computations on data sets with large numbers of matched sets fast and accurate. We provide detailed derivation of the constraints and conditional distributions for conditional inference on grouped and matched data. The methods are illustrated on several new and old data sets.

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“…Britton (1997) proposes a t,est to detect within-family clustering of infected individuals for the susceptible-infective-removed epidemic model. Recent work in modeling the risk of disease as a function of family histories based on logistic regression include Pregibon (1984), Wong and Mason (1985), Bonney (1987), Piegorsch and Casella (1996), Bedrick and Hill (1996), Betensky and Whittemore (1996), FitzGerald and Knuiman (1998), and Ten Have et al (1998. Grimson (1993) and Grimson and Odeh Frequencies of 31 cases of childhood cancer in the siblings of 24 probands described by Li et al (1988).…”

Section: Introductionmentioning

confidence: 99%

Statistical Inference for Familial Disease Clusters

Yu¹,

Zelterman²

2002

Biometrics

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In many epidemiologic studies, the first indication of an environmental or genetic contribution to the disease is the way in which the diseased cases cluster within the same family units. The concept of clustering is contrasted with incidence. We assume that all individuals are exchangeable except for their disease status. This assumption is used to provide an exact test of the initial hypothesis of no familial link with the disease, conditional on the number of diseased cases and the distribution of the sizes of the various family units. New parametric generalizations of binomial sampling models are described to provide measures of the effect size of the disease clustering. We consider models and an example that takes covariates into account. Ascertainment bias is described and the appropriate sampling distribution is demonstrated. Four numerical examples with real data illustrate these methods.

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Assessing the Fit of the Logistic Regression Model to Individual Matched Sets of Case-Control Data

Cited by 9 publications

References 13 publications

Nonparametric Diagnostic Test for Conditional Logistic Regression

Nonparametric Diagnostic Test for Conditional Logistic Regression

Sampling for Conditional Inference on Case–Control Data

Statistical Inference for Familial Disease Clusters

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