Calculating risk and prevalence ratios and differences in R: developing intuition with a hands-on tutorial and code

Area of Science:

• Epidemiology
• Biostatistics
• Public Health Research

Background:

• Epidemiologic research frequently involves binary outcomes, but clear guidance on selecting appropriate measures of association is often lacking.
• Misapplication of statistical measures, such as relying solely on odds ratios, can lead to misleading conclusions in binary outcome research.
• Existing curricula and literature may not adequately cover methods for estimating risk or prevalence ratios.

Purpose of the Study:

• To provide a practical tutorial on selecting and calculating appropriate measures of association for binary outcomes in epidemiologic research.
• To demonstrate the application and comparison of four methods for estimating risk or prevalence ratios (or differences) using annotated R code.
• To guide researchers in understanding when to use specific methods, their strengths, limitations, and interpretation.

Main Methods:

• The study presents a hands-on tutorial using the R statistical programming language.
• Annotated code is provided to illustrate the calculation and comparison of four distinct methods for estimating risk or prevalence ratios.
• Guidance is offered on the appropriate use cases, strengths, and limitations of each method.

Main Results:

• The tutorial enables readers to apply, compare, and understand four methods for estimating risk or prevalence ratios, contrasting them with odds ratios.
• Readers can gain practical experience through annotated R code, facilitating direct implementation in their research.
• The study facilitates a comparative understanding of results obtained from different estimation methods.

Conclusions:

• This resource empowers trainees, public health researchers, and interdisciplinary professionals to confidently implement and interpret risk or prevalence ratios.
• By moving beyond odds ratios, researchers can achieve more accurate and interpretable statistical conclusions for binary outcomes.
• The tutorial fosters a deeper intuition for selecting and applying appropriate statistical measures in epidemiologic studies.