1. Measuring associations between exposures and outcomes
Risk assessment measures
Mohammad Aljawadi, PharmD PhD
Nora A. Kalagi, MSc
Aug 2015
PHCL 435

2. Outline • Introduction • Risk estimates • 95% Confidence interval
• Interpretation
• Summary

3. Introduction • Most epidemiological research involves the study of the
relationship of one type of event or characteristic to another.
• Example:
* Does alcohol intake increase the risk of lung cancer?
Alcohol Lung cancer
(exposure) (outcome)
* Does hepatitis B vaccination protect against liver cancer
Hepatitis B vaccine Liver cancer
(exposure) (outcome)

4. Introduction The Exposure
• May be associated with either an increased or a decreased
occurrence of disease or other specified health outcome
• May relate to the environment, lifestyle, inborn or
inherited characteristics.
• The term risk factor is often used to describe an exposure
variable.
The Outcome
• A broad term for any defined disease, state of health,
health-related event or death.
• In some studies, there may be multiple outcomes.

5. Introduction
• The exposures and outcomes are specific to study hypotheses and
should always be clearly defined before the study starts.
• In most instances, it is not sufficient to collect information only on the
exposure and outcome of interest, because of mixed up effect of
another exposure on the same outcome, the two exposures being
correlated. This phenomenon is known as confounding

6. Introduction • Data on the exposures may be obtained through
Personal interviews (either face-to-face or by telephone)
Self-administered questionnaires
Diaries of behavior, reference to records
Biological measurements and measurements in the environment
Family member

7. Definitions Association Risk
A statistical relationship between two or more variables
Risk
 Probability conditional or unconditional of the occurrence of
some event in time
 Probability of an individual developing a disease or change in
health status over a fixed time interval, conditional on the
individual not dying during the same time period 7

8. Definitions Risk measures
Estimates that quantitatively describe the amount of risk
associated with a particular exposure in a sample population
and the development or prevention of disease.
• They can quantify the association between the exposure
to a particular drug and an adverse drug reaction.

9. Risk Estimates
• Risk estimates are part of our daily lives.
• Measures of risk are communicated to patients via newspapers, television, and the Internet daily, while communicated to practitioners via studies published in medical journals.
• These risk measures become important in the clinical
decision-making process for both patients and practitioners.
• It is important to have a clear understanding of risk measures
to appropriately interpret and apply the estimates.

10. Risk measures are difficult to use for many reasons,
1) Conflicting results obtained from different studies. *
• For example, early observational studies have reported a positive
association between calcium channel blocker use and cancer, whereas
more recent observational studies have reported a negative
association.
• When conflicting information pertaining to risk is published, it
becomes difficult for both practitioners and patients to use risk
estimates for clinical decision-making.

11. most reliable risk estimates to use in the midst of
• A clear understanding of study design and the derivation
of risk estimates allows practitioners to determine the
most reliable risk estimates to use in the midst of
conflicting results.

12. 2) Confusion around the actual interpretation of
the results of a study regarding risk estimates.
• Two readers may interpret, communicate, and use the
results of a study very differently.
• Ex. Risk of body weight & death
Newspaper
“Adding pounds 2
Obesity poses
as the years
pass increases
death risk” nd
less of a risk st 1
than thought”
Newspaper

13. risk estimates, we are often faced with conflicting results,
• When we rely on others to summarize study results involving
risk estimates, we are often faced with conflicting results,
depending on the writer's skills and perspective.
• The more adept a pharmacist becomes in evaluating and
interpreting risk estimates, the more effective he/she will be
in helping patients to sort through the conflicting information
they receive.

14. outcomes of association expressed by differences in disease frequency.
Measurement of exposures and
outcomes
• Epidemiologist are often interested in assessing the presence
of association expressed by differences in disease frequency.
• Measuring frequency can be based on either Absolute
differences between measures of disease frequency in groups
being compared (e.g exposed vs unexposed) or Relative
differences or risk.

15. Absolute differences vs. Relative differences or risk.

16. • Measures based on absolute differences are often preferred
when public health main goal is often an absolute reduction in
the risk of undesirable outcome
• In contrast, etiologic studies that are searching disease causes
and determinants usually rely on relative differences in the
occurrence of discrete outcome.

17. Risk Estimates 1) Odd Ratio (OR) Attributable risk (AR) 2)
Measures of association based on ratios 
Odd Ratio (OR)
Relative Risk (RR)
Attributable risk (AR)
Measures 2)
of association based on absolute
differences:

18. Risk Estimates • RR, OR and AR can be estimated from a 2 x 2
contingency table in cohort, case control studies and
clinical trials.

19. intervals? What about confidence
• The confidence intervals determine the reliability of the
risk estimate obtained in the sample.
• confidence intervals are defined as the range within which
the true effect lies with a certain degree of assurance.
Point Estimate ± Confidence Coefficient *Standard Error
• A large CI indicates a low level of precision of the RR or OR,
whereas a small CI indicates a higher precision of the RR or
OR.

20. • 95% confidence interval means that, if a study were
• In practice, the 95% CI is often used as a proxy for the
presence of statistical significance if it does not overlap the
null value (e.g. OR=1).
• 95% confidence interval means that, if a study were
conducted 100 times, the true measure of association (e.g.,
relative risk) would fall within the intervals 95 times.
• In other words, the investigator may have 95% assurance,
or confidence, that the true measure of association (e.g.,
relative risk) lies within the confidence interval.

21. Relative risk (RR) group relative to the unexposed group;
• Is the likelihood of developing the disease in the exposed
group relative to the unexposed group;
 i.e. A measure of association between the exposure and
the outcome.
• Risk of an event = probability that the event occurs
• Relative Risk= probability that event occurs in the exposed
probability that event occurs in the unexposed Or

22. Relative risk (RR)

24. Confidence Interval for Relative Risk
• Constructing a Confidence Interval for the Relative Risk is
similar to constructing a CI for the Odds Ratio
• A Relative Risk = 1.0 indicates ‘no association’ between the
exposure and the disease.
• If the 95% confidence interval for the RR does not contain
1.0 we can conclude that there is a statistically significant*
association between the exposure and the disease.
* at the significance level
• If the 95% confidence interval for the RR contains 1.0, the
association is not significant at the 0.05 level.

25. Relative risk • Interpretation of RR
• The null hypothesis in a comparison of two groups states that the
proportion of subjects with the outcome of interest is equal in the exposed
and the unexposed groups.
• Ho: RR= 1
• Interpretation of RR
Null hypothesis
Protective effect

26. Example Q….What is The relative risk for developing asthma for
college X students living in the city compared with college
X students living in the country. Using the 2x2
contingency table.

27. Answer….
 Students in college X who live in the city have times the risk of
developing asthma than students in college X who live in the country.

28. Example • Consider the results of three different studies. The
relative risk and the corresponding 95% confidence
interval for each study were as follows:

29. Odd Ratio (OR) • Odds of an event = probability that the event occurs
• the odds ratio is used to estimate the relative risk in a case-control
study.
• Represents the odds that an outcome will occur given a particular
exposure, compared to the odds of the outcome occurring in the
absence of that exposure..
• Odds of an event = probability that the event occurs
probability that the event does not occur
• Odds Ratio= odds of event for occurrence in exposed
odds of event occurrence in non exposed

30. Odds vs. Risk:
Among 5 persons one is sick:
Risk=1/5=0.2  20%
Odds=1/4=0.25  25%

33. OR, when is it used?
• Odds ratios are used to compare the relative odds of the
occurrence of the outcome of interest (e.g. disease or
disorder), given exposure to the variable of interest (e.g.
health characteristic, aspect of medical history).
• The odds ratio can also be used to determine whether a
particular exposure is a risk factor for a particular outcome,
and to compare the magnitude of various risk factors for that
outcome.

34. Interpretation of 95% Confidence Interval of OR
• An Odds Ratio = 1 indicates ‘no association’ between the
exposure and the disease.
• If the 95% confidence interval for the OR does not contain 1.0
we can conclude that there is a statistically significant*
association between the exposure and the disease. * at the
0.05 significance level
• If the 95% confidence interval for the OR contains 1.0, the
association is not significant at the 0.05 level.

35. Odd Ratio
• The null hypothesis when using the odds ratio states,
Ho: OR = 1.

36. Example Q…. What is The Odd ratio for developing asthma for college
X students living in the city compared with college X students
living in the country. Using the 2x2 contingency table.

37. Answer….

39. clinical trials, but not in case-control study.
Relative risk and case control design:
Relative risk can be used to measure the association
between exposure and outcome in cohort studies and
clinical trials, but not in case-control study.
• Why?

40. Relative risk • In case-control studies, subjects are chosen based on
disease status and then compared for rates of exposure.
• So the investigator cannot estimate the incidence of
disease. Subjects either have the disease or do not have
the disease at the onset of a case-control study.
• Because the relative risk is merely a ratio of two
incidences, the relative risk is not useful in the context
of a case-control study. Instead, the odds ratio is used
to estimate the relative risk in a case-control study.

41. Attributable Risk or (risk difference)
• Another measure of risk used in studies.
• The attributable risk provides information on the absolute
effect of the exposure, describes the excess risk of disease
in those exposed compared with those who were
unexposed.

42. • Attributable risk allows the investigator to determine how
morbidity and mortality are affected by removing the
exposure.
• The null hypothesis for using the attributable risk states, Ho:
AR = 0. Means there is no association between exposure and
outcome.
• The attributable risk provides information on the type of
effect that can be achieved by decreasing or eliminating the

43. Example Q….The attributable risk for college X students? eliminated.
 The excess occurrence of asthma attributable to living in the city
is cases per 100,000.
 In other words, if living in the city causes asthma, then cases of
asthma per 100,000 subjects could be eliminated if living in the city were
eliminated.

44. Attributable Risk Percent
• The attributable risk percent provides an estimate of
the proportion of the disease among the exposed that
is attributable to the exposure.
AR % = (RR-1)/RR * 100

45. Example Q….The attributable risk percent for college X students
If living in the city causes asthma, then approximately 46% of asthma
among subjects living in the city can be attributed to living in the city and
could, therefore, be eliminated if the subjects did not live in the city.

46. Number need to treat primarily in pharmacoeconomical studies.
• The number needed to treat (NNT) is an estimate used
primarily in pharmacoeconomical studies.
• This estimate represents the number of patients one would
need to treat to prevent one clinical event.
• Like the attributable risk, the number needed to treat is
used by administrators to allocate health resources.

47. Example
Q…. If AR= , NNT?

48. studies • RRR • Relative Risk • Absolute Risk (RD)
Measuring risk association in RCT
studies
• Absolute Risk
When comparing 2 risks the absolute difference between them is
referred to as the absolute risk reduction (ARR) or the risk difference
(RD)
• Relative Risk
• the proportion of the original risk that is still present when patients
receive the experimental treatment
• The ratio of risk in the treated group to the risk in the control group
(RR) is known as the relative risk
• RRR
Is an estimate of the proportion of baseline risk that is removed by the
therapy.

49. • EER is the experimental event risk= outcome/ Treatment
• CER is the control event risk= Outcome/Control
• Relative risk (RR) compares the risk of outcome in the
intervention group (EER) with the risk of outcome in the
control group (CER)
• Absolute risk reduction (ARR) calculated by subtracting the
EER from the CER.

50. dividing the ARR by the CER or subtracting the RR from 1.
• Number needed to treat (NNT) is the reciprocal of the ARR
• Relative risk reduction (RRR) can be calculated by either
dividing the ARR by the CER or subtracting the RR from 1.

51. Example  Control event rate (CER): 20/100 = 20%.
 Experimental event rate (EER): 15/100 = 15%.
 Absolute risk reduction or risk difference: CER – EER, 20% – 15% = 5%.
 Relative risk: EER/CER = (15/100)/(20/100) × 100% = 75%.
 Relative risk reduction: 1 – (EER/CER) × 100% = 1 – 75% = 25%.

52. Summary • In conclusion, measures of risk allow the quantification of
degree of risk associated with any number of exposures.
• Risk estimates are point estimates, the estimates obtained in
the particular study population. Therefore, may or may not
represent the true, or actual, risk that exists in the general
population.
• When evaluating risk estimates, it is important to consider the
baseline risk of developing the disease and the confounding
variables.

53. association (or relationship) between two nominal variables.
• Both Odds Ratio and Relative Risk are measures of
association (or relationship) between two nominal
variables.
• The Odds Ratio is typically estimated from data
collected in a Case-Control study.
• The Relative Risk can be estimated from data
collected in a Cohort study.

54. • If there is no association between the two variables,
the OR or RR = 1
• An OR or RR > 1.0 or < 1.0 indicates a possible
statistical relationship (or association) between the
two variables.
• Hypothesis tests for the OR and RR are not used to
determine statistical significance of the association
• Instead, Confidence intervals of OR or RR are
constructed and used to determine whether or not
the association is statistically significant.