Introduction: In medical studies, we are often confronted with data that have been collected longitudinal or cluster. The generalized Linear mixed models that had been developed from the generalized linear models and linear mixed models- are useful methods for analyzing such data. In this paper, we introduced this models and their estimation methodologies using examples of their application in the medical field.
Methods and Materials: The data of this study were related to 8525 lung cancer patients that mixed logistic regression model was used for the analysis with R software version 3.0.1 by Laplace method.
Results: Regression analysis showed that age, level of experience of doctors and cancer stage were factors affecting on recovery of patients. Individual and not measured factors covered 4.03 variation of response variable.
Conclusion: Generalized linear mixed models include a wide range of data, but many researchers ignored the random effects due to the lack of familiarity with these models. This mistakenly leads to some parameters have significant meanings. Correct use of these models leads to prevent many false results.