Question

ORs and RRs

This assignment will help you analyze the relationship of risk (of an illness) to exposure in public health regression analyses. Both ORs and RRs can be used to demonstrate the relationship between exposure and risk. However, each has its own advantages and disadvantages. While ORs are often used in professional papers, they are also often mistaken for RRs. RRs would often be the better choice as they are less complex than ORs and the interpretation is straightforward.

Using the South University Online Library, the Internet, and your text readings, research the following:

• ORs
• RRs

On the basis of your research, respond to the following:

• Identify and state the definition of an OR that you feel is easy to understand.
• In your own words, explain what an OR is and for what it is used.
• Identify and state the definition of an RR that you feel is easy to understand.
• In your own words, explain what an RR is and for what it is used.
• Compare and contrast ORs and RRs and how they are used for multisample inferences.
• Explain the advantages and disadvantages of using each ratio.

Answer 1

1) definatide of ORs :- The odds ratio (OR) is a statistic defined as the ratio of the odds of A in the presence of B and the odds of A without the presence of B. This statistic attempts to quantify the strength of the association between A and B.

If the OR is greater than 1, then A is considered to be associated with B in the sense that, compared to the absence of B, the presence of B raises the odds of A. Note that this does not establish that B causes A. Often the odds ratio is used to compare the occurrence of some outcome (A) in the presence of some exposure (B), with the occurrence of the outcome (A) in the absence of a particular exposure (absence of B).

2) use of ORs :- the odds ratio is widely used in many fields of medical and social science research. The odds ratio is commonly used in survey research, in epidemiology, and to express the results of some clinical trials, such as in case-control studies. It is often abbreviated "OR" in reports. When data from multiple surveys is combined, it will often be expressed as "pooled OR"

3)risk ratio (RR) or relative riskis the ratio of the probability of an outcome in an exposed group to the probability of an outcome in an unexposed group. It is computed as {\displaystyle I_{e}/I_{u}} ${\displaystyle I_{e}/I_{u}}$ , where {\displaystyle I_{e}} $I_e$ is the incidence in the exposed group, and {\displaystyle I_{u}} $I_{u}$ is the incidence in the unexposed group.^[1] Together with risk difference and odds ratio, risk ratio measures the association between the exposure and the outcome.

4).

Relative Risk (RR) is often used when the study involves comparing the likelihood, or chance, of an event occurring between two groups. Relative Risk is considered a descriptive statistic, not an inferential statistic; as it does not determine statistical significance. Relative Risk utilizes the probability of an event occurring in one group compared to the probability of an event occurring in the other group. It requires the examination of two dichotomous variables, where one variable measures the event (occurred vs. not occurred) and the other variable measures the groups (group 1 vs. group 2). Relative Risk is calculated by dividing the probability of an event occurring for group 1 (A) divided by the probability of an event occurring for group 2 (B). Relative Risk is very similar to Odds Ratio, however, RR is calculated by using percentages, whereas Odds Ratio is calculated by using the ratio of odds. Relative Risk values are greater than or equal to zero. A value of 1 indicates a neutral result: the chance of an event occurring for one group is the same for an event occurring for the other group. However, a value of zero indicates that none of the cases in group 1 had the event occur while x number of cases in group 2 had the event occur; or in other words, the numerator was a zero (A = 0) and the denominator was any number greater than zero (B = x, where x > 0)

5). Advance and disadvance of ORs and RRs:- ORs odds ratio (OR) is among the most commonly used statistical measures of association or effect. It is by far the most commonly reported measure when dependent variables are binary and it is ubiquitous in studies focusing on the association between risk factors and disease outcomes. It is also among the most frequently misunderstood and heavily criticized indices. Unfortunately, even critiques of the OR sometimes overlook important features. All in all, it is challenging to arrive at a balanced and coherent assessment of the OR that takes into account multiple considerations. I will review what I consider to be the primary strengths and limitations of the OR. Relative to commonly cited alternative measures (e.g., the relative risk [RR] and risk difference [RD]), the OR has several desirable features including symmetry and the potential for insensitivity to variations in marginal probabilities, invariance across different reference points, and non-restricted range. Together, these features allow the OR to provide a single, uniform summary of effects across widely varying probabilities. In contrast, particularly when continuous predictors are used, it is often impossible for the RR and RD to demonstrate effects that are constant across all values of other predictors. Ironically, however, the scaling of probabilities that produces such beneficial effects exaggerates small differences at the extremes and makes the OR a less useful and non-intuitive measure that is difficult to understand. In addition, the concise summary of effects provided by the OR can mask a variety of problematic features related to the discrimination of a model, and non-collapsibility renders conclusions based on the OR surprisingly circumscribed. I will conclude by discussing two common misconceptions demonstrated by users based on a recent review of the literature in psychology and allied fields: (1) Mistaking the OR for the RR; and (2) Failing to acknowledge the difference between the multiplicative model associated with the OR and additive models more commonly used in other contexts. In short, conclusions about the nature, functional form, and magnitude of effects derived from the OR may often not generalize to alternatives. Thus, exclusive reliance on the OR can provide a selective or even misleading picture of the phenomen.