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Binary logistic regression model (forward stepwise method) was used to estimate the coefficient of predictors (physico-chemical characteristics) in association with V. cholerae.
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*Estimated coefficient of predictor on dependent variable and result from Wald test of statistical significance.
Abbreviations: CI confidence interval, β coefficient of predictor variables, CPZeq Chlorpromazine Dose Equivalencies.
Abbreviations: C.I confidence interval, β coefficient of predictor variables, S.E.
Abbreviations: CI confidence interval, β coefficient of predictor variables, DM diabetes mellitus, SGA Second Generation Antipsychotic, CPZeq Chlorpromazine Dose Equivalencies.
In these models the estimated coefficient of predictor variables is interpreted as the ratio of means in the outcome variable comparing either unit changes (for continuous variables) or categories compared to the reference category (for categorical variables), adjusted for other covariates [ 24].
Preliminary models were fitted with random variables included also in the regression coefficients of predictors, but the improvements due to these additional parameters were marginal.
Random parameter models allow the coefficients of predictors to vary across the countries/regions per an analyst-specified continuous distribution (such as the normal distribution in this case) (Anastasopoulos and Mannering 2011; Venkataraman et al. 2013; Mannering et al. 2016).
The major reasons for leveraging the random parameter modeling technique in our research are summarized as follows: 1. Random parameter models allow the coefficients of predictors to vary across the countries/regions per an analyst-specified continuous distribution (such as the normal distribution in this case) (Anastasopoulos and Mannering 2011; Venkataraman et al. 2013; Mannering et al. 2016).
Confounders were retained if they changed the beta coefficients of predictors by greater than 10%.
Where numbers allow, we will explore coefficients of predictors of outcome.
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