Here’s a breakdown of the details provided, focusing on the statistical analysis described in the text:
Key Findings & Methods:
Correlation Analysis: The study initially examined the correlation between antibiotic resistance rates of Serratia marcescens and the Defined Daily Doses (DDDs) of various antibiotics (Table 4).
Multiple Linear Regression: Further analysis used multiple linear regression models to explore independent correlations. This means they looked at how multiple antibiotic ddds together predicted resistance rates.
Model Validation: The regression models were rigorously checked for:
Normality of Residuals: The errors (residuals) of the models were normally distributed. This is a key assumption of linear regression.
Homoscedasticity: The variance of the errors was constant across all levels of the predicted values. Another notable assumption. Low multicollinearity: The predictor variables (antibiotic DDDs) were not too highly correlated wiht each other. High multicollinearity can make it difficult to interpret the individual effects of each predictor.
Regression Diagnostic Plots: Figures 1, 2, and 3 show diagnostic plots for three different regression models (Model 1, Model 2, and Model 3).These plots visually assess the normality of residuals (histograms and Q-Q plots) and homoscedasticity (scatterplot of residuals vs. fitted values).
Robustness Checks: The models were “triple validated” and consistently showed specific negative correlations:
Imipenem resistance was negatively correlated with gentamicin usage.
Cefoxitin resistance was negatively correlated with piperacillin-tazobactam usage.
Tables & Figures:
Table 4: Shows the correlation coefficients between Serratia marcescens resistance rates and antibiotic DDDs.
Table 5: Presents the results of the linear regression analysis, likely including coefficients, p-values, and R-squared values.
Figures 1-3: Provide visual assessments of the regression model assumptions (normality and homoscedasticity) for three different models.In essence, the study used statistical modeling to investigate the relationship between antibiotic use and resistance in Serratia marcescens*, and took steps to ensure the reliability of its findings.
