The adjusted. OR in this case is the same as the crude OR

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Thanks dear professor

Thanks dear professor

Example: Calculating Adjusted Odds Ratios

Suppose we are interested in understanding whether a mother’s age affects the probability of having a baby with a low birthweight.

To explore this, we can perform logistic regression using age as a predictor variable and low birthweight (yes or no) as a response variable.

Suppose we collect data for 300 mothers and fit a logistic regression model. Here are the results:

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To obtain the odds ratio for age, we simply need to exponentiate the coefficient estimate from the table: e0.173 = 1.189.

This tells us that an increase of one year in age is associated with an increase of 1.189 in the odds of a baby having low birthweight. In other words, the odds of having a baby with low birthweight are increased by 18.9% for each additional yearly increase in age.

This odds ratio is known as a “crude” odds ratio or an “unadjusted” odds ratio because it has not been adjusted to account for other predictor variables in the model since it is the only predictor variable in the model.

But suppose we were interested in understanding whether a mother’s age and her smoking habits affect the probability of having a baby with a low birthweight.

To explore this, we can perform logistic regression using age and smoking (yes or no) as predictor variables and low birthweight as a response variable.

Suppose we collect data for 300 mothers and fit a logistic regression model. Here are the results:

📷

You mean that univariate produces unadjusted OR

While multivariate produces adjusted OR

The adjusted odds ratio is simply calculated in the multivariate logistic model where you include several variables simultaneously (two or more independent variables). This means that the adjusted odds ratio in the multivariate model is calculated for one independent variable by controlling for other independent variables in their reference value. Whereas, in the univariate model we calculate the crude odds ratio to study the causality between a single independent variable and our dichotomous variable (dependent variable). Moreover, the univariate model is built before the multivariate model because from the univariate model and based on the probability (p-value