Demystifying standardized coefficients: Understanding their importance in clinical research
Imagine you’re investigating how age and diet affect cholesterol levels. You collect data on these variables and run a regression analysis, and yes, both these independent variables appear to have an effect. But, you’re measuring age in years and diet by using scores on a scale. How do you know which has a stronger effect on cholesterol levels? Here’s where standardized coefficients come in handy.
What Are Standardized Coefficients?
Standardized coefficients, also known as beta coefficients, are a way to measure the strength and direction of the relationship between variables when they are expressed in different units or scales. By standardizing the coefficients, we bring all variables to a common scale, making comparisons much easier.
Why Do We Need Standardized Coefficients?
In clinical research, we often encounter situations where the predictor variables (e.g., WBC count and race) and outcome variables (e.g., blood pressure) are measured using different units. It becomes challenging to interpret the results accurately without standardization.
Calculating Standardized Coefficients
Calculating standardized coefficients is quite straightforward. For each predictor variable (X), we divide the unstandardized coefficient (B) obtained from regression by the standard deviation of that predictor variable (SDx):
β = B / SDx
The resulting value (β) represents the change in the outcome variable (Y) associated with a one-standard-deviation change in the predictor variable (X).
Interpreting Standardized Coefficients
Interpreting standardized coefficients is more intuitive than dealing with raw regression coefficients. For each one-standard-deviation increase in the predictor variable, the outcome variable changes by β standard deviations.
Benefits of Standardized Coefficients in Clinical Research
Standardized coefficients offer several valuable benefits in clinical research:
Comparability: Researchers can now compare the impact of different predictor variables on the outcome variable within the same model, regardless of their original units. This makes it easier for us to identify which variable has the strongest or weakest influence.
Variable Importance: Standardized coefficients help researchers understand which predictors have a more substantial impact on the outcome variable, aiding in prioritizing relevant factors. So if we’re examining, for example, factors affecting the incidence of malaria, we can use standardized coefficients to determine which factors are the most influential and include them in interventions to prevent malaria.
Meta-Analysis Facilitation: Combining studies with different measurement scales becomes more straightforward using standardized coefficients, which is essential for conducting meta-analyses.
Conclusion
Standardized coefficients play a crucial role in biomedical research, allowing us to understand relationships between variables in a more meaningful and comparable way. When predictor and outcome variables are measured using different units, standardized coefficients come to the rescue, making our analyses more insightful and reliable.
Our expert biostatisticians can help you run a wide variety of analyses, to explore relationships between the variables you’re studying. Check out Editage’s Statistical Analysis & Review Services.
Comments
You're looking to give wings to your academic career and publication journey. We like that!
Why don't we give you complete access! Create a free account and get unlimited access to all resources & a vibrant researcher community.
Subscribe to Conducting Research
