Analytical Study Design in Medical Research: Measures of risk and disease association

A researcher, while designing any analytical study in medical research, should be aware of few basic terms in epidemiology required to measure disease risk and association. This blog article focuses on defining those terms used for calculating disease risk and association. As mentioned above, there are two different types of measurements: Measures of risk and Measures of association.

Measures of Risk

Risk is defined as the probability of an individual developing a condition or disease over a period of time.

Risk = Chances of something to happen/ Chances of all things to happen

Odds= Chances of something to happen/ Chances of it not happening

Therefore, “Risk” is a proportion, while “Odds” is a ratio.

Incidence: Incidence is a measure of risk which describes the number of cases developed a new condition for a specified period of time. In this context, there is another important term, “Incidence proportion” to be worth mentioning. It is defined as the proportion of the number of cases developed a new condition and total population including the cases with developed condition and no condition in a specified period of time.

For example, among 100 non-diseased persons initially at risk, 20 develop a disease/condition over a period of five years.

Incidence = 20 cases

Incidence proportion = 20 cases per 100 persons i.e., 5%

Incidence rate = 20 cases developed in 100 persons in 5 year means the rate of incidence is equal to 4 per 100 person-years

Prevalence: Prevalence is the proportion of the number of people having a condition at a specific point of time and total population studied. This is specifically called point prevalence. For example, at a certain date, five persons are detected having a condition among 100 people studied. There are two more terms need to be defined in this regard: Period prevalence and Life time prevalence (LTF). The former is defined as the proportion of the number of people having the disease at a certain period of time, say a month or period or a year and the total population studied at that period of time. On the other hand, LTF is defined as the proportion of the number of people having the disease at some point of their life and total population studied.

There is a very subtle difference between incidence and prevalence. Incidence is the frequency of a new event, while prevalence is the frequency of an existing event.

Cumulative Risk: Cumulative risk is defined by the probability of developing a condition over a period of time.

Measures of Association

Association is defined as a statistical measurement between two or more variables.

For measuring the strength of association of a disease for etiological and hypothesis testing, following measurements are important. The terms defined below are used to measure the association between exposure and disease.

Relative risk (RR): The relative risk is measured as a ratio of two risks.

For example, in 100 people consisting of 50 male and 50 female, while 20 male are infected with Tuberculosis, 10 female develop the condition.

Risk in men: 20/50

Risk in women: 10/50

Therefore, relative risk (RR) of developing Tuberculosis in men compared to women is

RR = 20/50 : 10/50 = 2.0

i.e., men are at double risk of developing Tuberculosis as compared to women.

Odd ratio (OR): Odd ratio is measured as the ratio of two odds (odds is defined above).

Continuing the previous example of Tuberculosis in men and women in a total population of 100

Odds in men: 20/30

Odds in women: 10/40

Odd ratio (OR) = 20/30 : 10/40 = 2.67

Therefore, the odds of men getting infected with Tuberculosis are 2.6 times as high as the women developing Tuberculosis.

To measure the impact of   the disease association on public health, following measuerments are important. All these measurements assume that the association between exposure and disease is causal.

Attributable risk (AR): Amount of disease attributed to the exposure i.e., the difference between the incidence of disease in the exposed group (Ie) and the incidence of disease in the unexposed group (Iue).

AR = Ie – Iue

Attributable (risk) fraction (ARF): ARF is the proportion of disease in the exposed population whose disease can be attributed to the exposure.

ARF = Ie – Iue / Ie

Population attributable risk (PAR): The incidence of disease in total population (Ip) that can be attributed to the exposure.

PAR = Ip – Iue

Population attributable (risk) fraction (PARF): PARF is the proportion of the disease in the total population whose disease can be attributed to the exposure.

PARF = Ip – Iue / Ip

 

Bias and Confounding Factors

In an epidemiological study, when association is found between exposure and disease, it is very important to check first whether the association is real. One needs to be cautious if the association is by chance due to non-adequate sample size or it is because of some kind of bias in the design or measurement.

Bias is a systematic error in design, conduct or analysis which results in unreal association of exposure with disease. There are three types of biases possible: (i) Selection bias, (ii) Information bias, and (iii) Confounding.

Selection bias occurs when selection of participants in one group shows different outcome in the selection of other groups. Information bias happens when information is taken differently from two groups.

Confounding occurs when the observed result between exposure and disease differs from the truth due to the influence of a third variable which has not been considered for analysis. For example, a person suffers from headache when he is under stress; however the person eats a lot of junk food especially, when he is in under stress. Therefore, it is hard to predict what actually causes the headache; whether it is lack of sleep, anxiety, gas formation due to indigestion. Therefore, all these variables should be adjusted before associating mental stress with headache.

 

References

1. Health Statistics New South Wales – Definitions. (n.d.). http://www.healthstats.nsw.gov.au/ContentText/Display/Definitions

2. SOURCES OF EPIDEMIOLOGIC DATA – KSU. (n.d.).

http://faculty.ksu.edu.sa/71640/Publications/COURSES/epidemiology-334%20CHS%20%20(70).doc

3. John-Hopkins open courseware. http://ocw.jhsph.edu/courses/fundepiii/lectureNotes.cfm

4. Manuel Bayona M, Chris Olsen, C. Measures in Epidemiology. In The Young Epidemiology Scholars Program (YES)

www.collegeboard.com/prod_downloads/yes/4297_MODULE_09.pdf‎

5. Emily L. Harris EL. Linking Exposures and Endpoints: Measures of Association and Risk

http://www.genome.gov/pages/about/od/opg/epidemiologyforresearchers/3_harris.pdf


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