Tobacco-cessation program sorts patients by stages (precontemplation, contemplation, etc.). Which risk adjustment?

Comparing variables typically refers to assessing the relationships or differences between different measurements or data points. While this method can provide insights into trends or patterns, it does not specifically categorize patients based on their readiness to quit smoking, which is essential for effective risk adjustment in tobacco-cessation programs.

Bivariate analysis examines the relationship between two variables to identify correlations. Although this statistical method can be useful in research, it does not focus on classifying individuals into stages of change, making it less relevant for risk adjustment in tobacco cessation.

Multiple regression is a statistical technique used to understand the relationship between one dependent variable and several independent variables. While it can help predict outcomes, it does not provide a framework for sorting patients by their stage of readiness to change behavior, which is crucial for effective tobacco-cessation interventions.