# Application of Regression in Medical: A Quick Guide (2024)

The application of Regression cannot be ignored, as regression is a powerful statistical tool widely used in medical research to understand the relationship between variables. It helps identify risk factors, predict outcomes, and optimize treatment strategies.

Considering the application of regression analysis in medical sciences, Chan et al. (2006) used multiple linear regression to estimate standard liver weight for assessing adequacies of graft size in live donor liver transplantation and remnant liver in major hepatectomy for cancer. Standard liver weight (SLW) in grams, body weight (BW) in kilograms, gender (male=1, female=0), and other anthropometric data of 159 Chinese liver donors who underwent donor right hepatectomy were analyzed. The formula (fitted model)

$SLW = 218 + 12.3 \times BW + 51 \times gender$

was developed with a coefficient of determination $R^2=0.48$.

These results mean that in Chinese people, on average, for each 1-kg increase of BW, SLW increases about 12.3 g, and, on average, men have a 51-g higher SLW than women. Unfortunately, SEs and CIs for the estimated regression coefficients were not reported. Using Formula 6 in their article, the SLW for Chinese liver donors can be estimated if BW and gender are known. About 50% of the variance of SLW is explained by BW and gender.

The regression analysis helps in:

• Identifying risk factors: Determine which factors contribute to the development of a disease (For example, gender, age, smoking, and blood pressure for heart disease).
• Predicting disease occurrence: Estimate the likelihood of a patient developing a disease based on specific risk factors. for example, logistic regression is used to predict the risk of diabetes based on factors like BMI, age, and family history.

The following types of regression models are widely used in medical sciences:

• Linear regression: Used when the outcome variable is continuous (e.g., blood pressure, cholesterol levels).
• Logistic regression: Used when the outcome variable is binary (e.g., disease present/absent, survival/death).
• Cox proportional hazards regression: Used for survival analysis (time to event data)

#### Reference of Article

• Chan SC, Liu CL, Lo CM, et al. (2006). Estimating liver weight of adults by body weight and gender. World J Gastroenterol 12, 2217–2222.

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