Testing goodness-of-fit of the logistic regression model in case–control studies using sample reweighting

2004 ◽  
Vol 24 (1) ◽  
pp. 121-130 ◽  
Author(s):  
Nico Nagelkerke ◽  
Jeroen Smits ◽  
Saskia le Cessie ◽  
Hans van Houwelingen
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Goodness Of Fit ◽  
Logistic Regression Model ◽  
Case Control ◽  
Case Control Studies

scholarly journals Sparse estimation for case–control studies with multiple disease subtypes

Biostatistics ◽  
2020 ◽  
Author(s):  
Nadim Ballout ◽  
Cedric Garcia ◽  
Vivian Viallon
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Multinomial Logistic Regression ◽  
Conditional Logistic Regression ◽  
Case Control ◽  
Case Control Studies ◽  
Conditional Logistic Regression Model ◽  
Symmetric Formulation ◽  
Disease Subtypes

Summary The analysis of case–control studies with several disease subtypes is increasingly common, e.g. in cancer epidemiology. For matched designs, a natural strategy is based on a stratified conditional logistic regression model. Then, to account for the potential homogeneity among disease subtypes, we adapt the ideas of data shared lasso, which has been recently proposed for the estimation of stratified regression models. For unmatched designs, we compare two standard methods based on $L_1$-norm penalized multinomial logistic regression. We describe formal connections between these two approaches, from which practical guidance can be derived. We show that one of these approaches, which is based on a symmetric formulation of the multinomial logistic regression model, actually reduces to a data shared lasso version of the other. Consequently, the relative performance of the two approaches critically depends on the level of homogeneity that exists among disease subtypes: more precisely, when homogeneity is moderate to high, the non-symmetric formulation with controls as the reference is not recommended. Empirical results obtained from synthetic data are presented, which confirm the benefit of properly accounting for potential homogeneity under both matched and unmatched designs, in terms of estimation and prediction accuracy, variable selection and identification of heterogeneities. We also present preliminary results from the analysis of a case–control study nested within the EPIC (European Prospective Investigation into Cancer and nutrition) cohort, where the objective is to identify metabolites associated with the occurrence of subtypes of breast cancer.

Assessing the adequacy of the logistic regression model for matched case-control studies

1985 ◽  
Vol 4 (4) ◽  
pp. 425-435 ◽  
Author(s):  
Suresh H. Moolgavkar ◽  
Edward D. Lustbader ◽  
David J. Venzon
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Case Control ◽  
Case Control Studies ◽  
Matched Case

Intuitionistic Fuzzy Partial Logistic Regression Model Using Ridge Methodology

2020 ◽  
Vol 28 (04) ◽  
pp. 527-543
Author(s):  
Gholamreza Hesamian ◽  
Mohammad Ghasem Akbari ◽  
Mehdi Roozbeh
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Smooth Function ◽  
Potential Application ◽  
Goodness Of Fit ◽  
Logistic Regression Model ◽  
Numerical Examples ◽  
Ridge Estimation ◽  
Intuitionistic Fuzzy

This paper applies a ridge estimation approach in an existing partial logistic regression model with exact predictors, intuitionistic fuzzy responses, intuitionistic fuzzy coefficients and intuitionistic fuzzy smooth function to improve an existing intuitionistic fuzzy partial logistic regression model in the presence of multicollinearity. For this purpose, ridge methodology is also involved to estimate the parametric intuitionistic fuzzy coefficients and nonparametric intuitionistic fuzzy smooth function. Some common goodness-of-fit criteria are also used to examine the performance of the proposed regression model. The potential application of the proposed method are illustrated and compared with the intuitionistic partial logistic regression model through two numerical examples. The results clearly indicate the proposed ridge method is quite efficient in model’s performances when there is multicollinearity among the predictors.

scholarly journals Handicraft small enterprises as an instrument for rural economic growth and poverty eradication

2020 ◽  
Vol 18 (4) ◽  
pp. 25-36
Author(s):  
Oluwayemisi A. Abisuga-Oyekunle ◽  
Mammo Muchie
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Goodness Of Fit ◽  
Logistic Regression Model ◽  
Growth Potential ◽  
Small Enterprises ◽  
Potential Contribution ◽  
Goodness Of Fit Test ◽  
Kwazulu Natal ◽  
Two Factors

In South Africa, exploiting economic opportunities in the handicraft sector could create livelihood and employment for ordinary citizens living in rural areas. The potential contribution of handicraft small enterprises to sustainable livelihoods and poverty alleviation is yet to be fully exploited. It is also regarded as a sector with great growth potential, but the degree of support provided to the handicraft sector is low. The study aims to evaluate the socioeconomic factors influencing the viability of handicraft small businesses operating in KwaZulu-Natal. Data collection was drawn from a stratified random sample of 196 handicraft practitioners operating in different areas of KwaZulu-Natal Province with a structured questionnaire. Data analysis was performed with the STATA statistical package. The results obtained from the study have shown that 84 enterprises (42.86%) were not viable, whereas 112 of the 196 handicraft enterprises (57.14%) were viable. The percentage of overall correct classification for this procedure was equal to 77.96%. Percentage sensitivity for the fitted logistic regression model was equal to 60.71%. Percentage specificity for the fitted logistic regression model was equal to 82.14%. The p-value obtained from Hosmer-Lemeshow goodness-of-fit test was equal to 0.0884 > 0.05. This indicates that the fitted logistic regression model is fairly well reliable. The findings from the analysis showed that two factors significantly influenced the viability of handicraft enterprises. These two factors were the belief that handicraft business could sustain the handicraft practitioner, and the level of support for handicraft businesses from non-governmental organizations is decreasing. AcknowledgmentSouth Africa SarChi Chair, Nation Research Fund and Department of Science and Technology, South African, for providing funding for this research.

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