# Logistic regression yields adjusted odds ratios

## Adjusted odds ratios are easier generalized to clinical situations

**adjusted odds ratios with 95% confidence intervals**. Medicine, as a science, often uses categorical outcomes to research causal effects. It is important to

**assess clinical outcomes**(measured at the dichotomous categorical level)

**within the context of various predictor, clinical, prognostic, demographic, and confounding variables**.

__is the statistical method used to understand the associations between the aforementioned variables and__

**Logistic regression****dichotomous categorical outcomes**.

Logistic regression yields

**adjusted odds ratios with 95% confidence intervals**, rather than the more prevalent

**used in 2x2 tables. The odds ratios in logistic regression are "**

__unadjusted odds ratios__**adjusted**" because their associations to the dichotomous categorical outcome are "

**controlled for**" or "

**adjusted**" by the other variables in the model. The

**95% confidence interval**is used as the

**primary inference**with adjusted odds ratios, just like with unadjusted odds ratios. If the 95% confidence interval crosses over 1.0, then there is a non-significant association with the outcome variable.

Adjusted odds ratios are important in medicine because

**very few physiological or medical phenomena are bivariate in nature**. Most disease states or physiological disorders are understood and detected within the context of many different factors or variables. Therefore, to truly understand treatment effects and clinical phenomena,

**.**

__multivariate__adjustment must occur to properly account for clinical, prognostic, demographic, and confounding variables