In linear regression analysis, we can include more parameters in an effort to find factors that better predict an outcome. This analysis is known as multiple linear regression analysis. Additionally, multiple linear regression analysis can be used to adjust for confounders. In randomized controlled trials, confounding is minimized by randomization, but weak confounding might still be present because of small imbalances of outcome predictors.

In a multiple regression model, we can add a mixture of continuous and categorical predictors as well as interaction terms among categorical predictors, or among categorical and continuous predictors, to assess simultaneously the combined effect of those parameters on the outcome.

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