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Relative risk, Risk difference and Odds ratio

When the data to be analyzed consist of counts in a cross-classification of two groups (or conditions) and two outcomes, the data can be represented in a fourfold table as follows:

 Group 1Group 2Total
Number with positive outcomeaca+c
Number with negative outcomebdb+d
Totala+bc+da+b+c+d

Several statistics can be calculated such as relative risk and risk difference, relevant in prospective studies, and odds ratio, relevant in retrospective case controls studies.

How to calculate Relative Risk

The relative risk (RR), its standard error and 95% confidence interval are calculated as follows (Altman, 1991).

The relative risk or risk ratio is given by

Formula for relative risk$$ RR = \frac {a/(a+b) } { c/(c+d) } $$

with the standard error of the log relative risk being

Formula for standard error of relative risk$$ \operatorname{SE} \left \{ \operatorname{ln}\left(RR\right) \right \} = \sqrt { \frac {1}{a} + \frac {1}{c} - \frac {1}{a+b} - \frac {1}{c+d} } $$

and 95% confidence interval

Formula for confidence interval of relative risk$$ \operatorname{95\%\text{ } CI} = \operatorname{exp} \Big( \text{ } \operatorname{ln}\left(RR\right) - 1.96 \times \operatorname{SE} \left \{ \operatorname{ln}\left(RR\right) \right \} \text{ } \Big) \quad \text{ to }\quad \operatorname{exp} \Big(\text{ } \operatorname{ln}\left(RR\right) + 1.96 \times \operatorname{SE} \left \{ \operatorname{ln}\left(RR\right) \right \} \text{ }\Big)$$

Risk difference

The risk difference (RD) and its 95% confidence interval are calculated according to Newcombe & Altman (2000)

Formula for risk difference$$ RD = \frac {a} {a+b} - \frac {c} {c+d} $$

The recommended method for the calculation of the risk difference, which is a difference between proportions, requires the calculation of the confidence intervals of the two proportions separately. MedCalc calculates exact binomial confidence intervals for proportions (Armitage et al., 2002). With l1 to u1 being the 95% CI of the first proportion p1 and l2 to u2 being the 95% CI of the second proportion p2, the 95% confidence interval for the difference is given by

Formula for confidence interval of risk difference$$ \operatorname{95\%\text{ } CI} = RD - \sqrt { (p_1-l_1)^2 + (u_2-p_2)^2 } \quad \text{ to }\quad RD + \sqrt { (p_2-l_2)^2 + (u_1-p_1)^2} $$

In the context of meta-analysis, the standard error and 95% confidence interval are calculated according to Deeks & Higgins (2010), where the standard error is defined as

Formula for standard error of risk difference (meta-analysis context)$$ \operatorname{SE} \left \{ RD \right \} = \sqrt { \frac {a \times b}{ \left ( a+b \right )^3} + \frac {c\times d}{\left (c+d\right )^3} } $$

and 95% confidence interval

Formula for confidence interval of risk difference (meta-analysis context)$$ \operatorname{95\%\text{ } CI} = RD - 1.96 \times \operatorname{SE} \left \{ RD \right \} \quad \text{ to }\quad RD + 1.96 \times \operatorname{SE} \left \{ RD \right \} $$

How to calculate Odds Ratio

The odds ratio (OR), its standard error and 95% confidence interval are calculated as follows (Altman, 1991).

The formula for odds ratio is:

Odds ratio formula$$ \begin{align} OR & = \frac {a/b} {c/d} \\ & = \frac {a \times d } { b \times c} \end{align}$$

with the standard error of the log odds ratio being

Formula for standard error of odds ratio$$ \operatorname{SE} \left \{ \operatorname{ln}\left(OR\right) \right \} = \sqrt { \frac {1}{a} + \frac {1}{b} + \frac {1}{c} + \frac {1}{d} } $$

and 95% confidence interval

Formula for confidence interval of odds ratio$$ \operatorname{95\%\text{ } CI} = \operatorname{exp} \Big( \text{ } \operatorname{ln}\left(OR\right) - 1.96 \times \operatorname{SE} \left \{ \operatorname{ln}\left(OR\right) \right \} \text{ }\Big) \quad \text{ to }\quad \operatorname{exp} \Big(\text{ } \operatorname{ln}\left(OR\right) + 1.96 \times \operatorname{SE} \left \{ \operatorname{ln}\left(OR\right) \right \} \text{ }\Big)$$

Notes

Where zeros cause problems with computation of effects or standard errors, 0.5 is added to all cells (a, b, c, d) (Pagano & Gauvreau, 2000; Deeks & Higgins, 2010).

In meta-analysis for relative risk and odds ratio, studies where a=c=0 or b=d=0 are excluded from the analysis (Higgins & Thomas, 2021).

Literature

Recommended book

Statistics in Epidemiology: Methods, Techniques and Applications
Hardeo Sahai, Anwer Khurshid

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Epidemiologic studies provide research strategies for investigating public health and scientific questions relating to the factors that cause and prevent ailments in human populations. Statistics in Epidemiology: Methods, Techniques and Applications presents a comprehensive review of the wide range of principles, methods and techniques underlying prospective, retrospective and cross-sectional approaches to epidemiologic studies. Written for epidemiologists and other researchers without extensive backgrounds in statistics, this new book provides a clear and concise description of the statistical tools used in epidemiology. Emphasis is given to the application of these statistical tools, and examples are provided to illustrate direct methods for applying common statistical techniques in order to obtain solutions to problems.

Statistics in Epidemiology: Methods, Techniques and Applications goes beyond the elementary material found in basic epidemiology and biostatistics books and provides a detailed account of technique.