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Predictive models for nodal metastasis, lymphovascular invasion (LVI) and distant metastasis were constructed using a multilevel reverse logistic regression model.
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In the first step, a reverse stepwise logistic regression model was performed using CpGs in each gene as independent variables.
A multilevel reverse stepwise logistic regression model was constructed using individual CpGs to identify genes associated with disease progression: LVI, lymph node metastasis and distant metastasis (Table S2, supporting information).
The threshold for significance was set at P < 0·100; significant CpGs were taken forward into the next stage, where these CpGs were entered into a reverse stepwise logistic regression model as independent variables, with the significance threshold for removal from the model set at P < 0·100.
After adjusting for destination, age, and month in the multivariate logistic regression model, the risks were reversed with a significantly higher OR in males (1.17; 95 % CI 1.11 1.23).
Table 13 Logistic regression model.
(Source: Ordinal logistic regression model: single predictors).
A spatially-explicit logistic regression model was developed for Kampala.
The data were analysed using a mixed logistic regression model.
Secondly, the ordinal logistic regression model was considered.
The logistic regression model was used to determine binary outcomes.
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