Regression is a type of statistical analysis used to investigate the relationship between two or more variables. Biomedical researchers often use regression to investigate the association between a particular disease and its risk factors or to predict the outcomes of certain treatments. This blog post outlines the different types of regression and what each can be used for.<\/p>\n\n\n\n
Linear regression is the most basic and commonly used type of regression analysis. It is used to study the relationship between a continuous dependent variable and one or more independent variables. In biomedical research, linear regression can be used to study the relationship between a particular disease and a specific risk factor, such as levels of X biomarker.<\/p>\n\n\n\n
Multiple regression is an extension of linear regression that allows for the analysis of the relationship between a dependent variable and two or more independent variables. This type of regression is commonly used in biomedical research to study the effects of multiple risk factors on a disease or health outcome, such as age and body mass index.<\/p>\n\n\n\n
Stepwise regression is a method that automatically selects the most important independent variables to include in a multiple regression model. It is often used in biomedical research when there are a large number of potential independent variables to consider, for example, to identify determinants of quality of life in a certain patient population.<\/p>\n\n\n\n
Logistic regression is a type of regression analysis used to study the relationship between a binary dependent variable (i.e., one that can take on only two values, such as disease presence or absence) and one or more independent variables. This type of regression is commonly used in biomedical research to study the risk factors for a disease or to predict the likelihood of disease occurrence.<\/p>\n\n\n\n
Cox regression is a type of regression analysis used to study the relationship between a time-to-event dependent variable (i.e., the time from a specific event, such as diagnosis, to a particular outcome, such as death) and one or more independent variables. This type of regression is commonly used in biomedical research to study the factors that affect the prognosis or survival of patients.<\/p>\n\n\n\n
Poisson regression is a type of regression analysis used to study the relationship between a count-dependent variable and one or more independent variables. This type of regression is commonly used in biomedical research to study the occurrence of events, such as the number of hospitalizations or the number of infections.<\/p>\n\n\n\n