Managerial Epidemiology: Assignment Week 4

Chapter 9

Measures of Effect

Learning Objectives

• Explain the meeting of absolute and relative

effects

• Calculate and interpret the following

measures: risk difference, population risk

difference, etiologic fraction, and population

etiologic fraction

• Discuss the role of statistical tests in

epidemiologic research

• Apply Hill’s criteria for evaluation of

epidemiologic associations

Effect Measure

• A quantity that measures the effect of

a factor on the frequency or risk of a

health outcome

Three Effect Measures

• Attributable Fractions

– Measure the fraction of cases due to a

factor.

• Risk and Rate Differences

– Measure the amount a factor adds to

the risk or rate of a disease.

• Risk and Rate Ratio

– Measure the amount by which a factor

multiplies the risk or rate of disease.

Absolute vs. Relative Effects

• Absolute

– Attributable risk is also known as a rate

difference or risk difference.

– Population risk difference

• Relative

– Relative risk

– Etiologic fraction

– Population etiologic fraction

Risk Difference (Attributable

Risk)

• Risk difference--the difference

between the incidence rate of

disease in the exposed group (Ie)

and the incidence rate of disease in

the nonexposed group (Ine).

• Risk difference = Ie - Ine

Calculation of Risk Difference

• For women younger than age 75, the incidence (Ie) of hip fractures per 100,000 person-days was highest in the winter (0.41), and the incidence (Ine) was lowest in the summer (0.29). The risk difference between the two seasons (Ie - Ine) was 0.41 - 0.29, or 0.12 per 100,000 person-days.

Population Risk Difference

• Measures the benefit to the

population derived by modifying a

risk factor.

Etiologic Fraction

• Defined as the proportion of the rate

in the exposed group that is due to

the exposure.

• Also termed attributable proportion or

attributable fraction.

Population Etiologic Fraction

• Provides an indication of the effect of

removing a particular exposure on the

burden of disease in the population.

• Also termed attributable fraction in the

population.

Statistical Measures of Effect

• Significance tests

• The P value

• Confidence interval

Null Hypothesis

• Underlying all statistical tests is a null

hypothesis, which states that there is

no difference among the groups being

compared.

• The parameters may consist of the

prevalence or incidence of disease in

the population.

Significance Tests

• Used to decide whether to reject or fail to reject

a null hypothesis.

• Involves computation of a test statistic, which is

compared with a critical value obtained from

statistical tables.

• The critical value is set by the significance level

of the test.

• The significance level is the chance of rejecting the null hypothesis when, in fact, it is true.

The P Value

• Indicates the probability that the

findings observed could have

occurred by chance alone.

• However, a nonsignificant difference

is not necessarily attributable to

chance alone.

The P Value (cont’d)

• Possible meaning of nonsignificant

differences: For studies with a small

sample size the sampling error may

be large, which can lead to a

nonsignificant test even if the

observed difference is caused by a

real effect.

Confidence Interval (CI)

• A computed interval of values that, with a

given probability, contains the true value

of the population parameter.

• The degree of confidence is usually stated

as a percentage; commonly the 95% CI is

used.

• Influenced by variability of the data and

sample size.

Clinical vs. Statistical

Significance

• While small differences in disease frequency or

low magnitudes of relative risk (RR) may be

significant, they may have no clinical

significance.

• Conversely, with small sample sizes, large

differences or measures of effect may be

clinically important and worthy of additional

study.

Statistical Power

• The ability of a study to demonstrate

an association if one exists.

• Determined by:

– Frequency of the condition under study.

– Magnitude of the effect.

– Study design.

– Sample size.

Evaluating Epidemiologic

Associations

• Five key questions to be asked:

– Could the association have been observed by

chance?

• Determined through the use of statistical tests.

– Could the association be due to bias?

• Bias refers to systematic errors, i.e., how samples

were selected or how data was analyzed.

Evaluating Epidemiologic

Associations (cont’d)

• Could other confounding variables have

accounted for the observed relationship?

• To whom does this association apply?

– Representativeness of sample

– Participation rates

• Does the association represent a cause-

and-effect relationship?

– Considers criteria of causality.

Types of Associations between

Factors and Outcomes

• Not statistically associated

(independent)

• Statistically associated

Statistical Association

• When a factor and outcome are

statistically associated, the

relationship can be:

– Non-causal

– Causal

• Indirect

• Direct

Multiple Causality

• Also referred to as multifactorial

etiology.

• “…requirement that more than one factor be present for disease to

develop…”

Models of Multiple Causality

• Epidemiologic triangle

• Web of causation, e.g., in avian

influenza

• Wheel model, e.g., childhood lead

poisoning

• Pie model, e.g., lung cancer