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Designing a New Study:

II. Cross-sectional and Case-

control Studies

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A cohort study: the sequence of making the measurements

is the same as the chronology of cause and effect.

Cross-sectional

(prevalence) study

All measurements on a

single occasion.

Determine predictor and

and outcome after the data

collection

Estimate prevalence

Case-control study

Begin with the outcome,

then identify the predictor

Explore the potential

association

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CROSS-SECTIONAL STUDIES

Well suited to the goal of describing variables and

their distribution patterns

Structure Similar to cohort study (except that measurements are

made at once)

Can examine associations based on the investigators

hypothesis, not based on the study design. e.g., age, race---usually predictors

blood lead level and hyperactivity ---->misleading

(historic information on the time course)

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Cross-sectional Study

Risk factor;

Disease

Risk factor;

No disease

No risk factor;

Disease

No risk factor;

No Disease

Population

Sample

Steps:

1. Select a sampling from the population2. Measure predictor and outcome variables

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CROSS-SECTIONAL STUDIES

Designing

Settle on the research question

Specify criteria for the target and accessible populations Establish the design for drawing the sample

Decide the phenomena to study

Define the approach to measuring appropriate variables.

Example 8.1: Sexually transmitted disease and the use

of oral contraceptives

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Statistics for expressing disease frequency in observational studies

Type of Study Statistics Definition

Cross-sectional Prevalence

Cohort Incidence

# of people who have the disease at one point in time

# of people at risk at that point

# of new cases of disease over a period of time

# of people at risk during that period

* Relative prevalence = Relative risk prevalence/incidence bias

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Cross-sectional Studies

strength Relatively fast and

inexpensive

No waiting time to see the

outcome

No loss to follow-up

Provide the prevalence of a

disease or a risk factor

Convenient for examining

networks of causal links

alcohol intake and HDL-

cholesterol

First step for investigations

weakness Difficult to establish causalrelationship

Impractical for the study of rare

diseases or risk factors from a

general population e.g., 1 in 10,000 in a general

population for a stomach

cancer in 45-59 year old men

Susceptible to

prevalence/incidence bias

e.g., Kids with HLA-A2 were

at increased risk factor for the

incidence of leukemia ???

[truth was that HLA-A2 kids

live longer]

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Case series =~ cross sectional studies for relatively rare

diseases

the sample from a diseased population not from a general

population

suitable to describe the characteristics of the disease than to

analyzing differences between these patients and healthy people

e.g., Of the first 1000 patients with AIDS, for example, 727 were homosexual

or bisexual males and 236 were I.V. drug abusers. Furthermore, within a

sample of patients with a disease there may be association of interest, the higher

risk of Kaposi sarcoma among AIDS patients who are homosexual than among

AIDS patients who are I.V. drug abusers.

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Serial Surveys

To draw inferences about changing patterns over time.

e.g., census data

Not a cohort study (i.e., following a single group of people)

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Case-Control Studies

Structure

the prevalence of the risk factor in subjects with the

disease (cases) can be compared with the prevalence insubjects without the disease (controls).

Retrospective

house red

more modest and a little riskier than the otherselections, but much less expensive and sometimes

surprisingly good!

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Case-control design

THE PAST OR PRESENT THE PRESENT

Risk factor

present

Risk

factor

absent

Risk

factor

present

Risk factor

absent

Disease

No disease

Sample

of cases

Sampleof controls

Much larger population

without disease (controls)

Population

with disease

(cases)

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Steps:

1. Select a sample from a population of people with the

disease (cases)2. Select a sample from a population at risk that is free of

the disease (controls)

3. Measure predictor variables

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Designing a case-control study


Specify criteria for the target and accessible

populations (the cases and the controls) Establish the design for drawing the sample


Define the variables and measurement approaches,and establishes the hypotheses to be tested.

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Whether there is an association between use ofaspirin and the development of Reyes syndrome

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Specify criteria for the target and accessible

populations (the cases and the controls)

The cases: Children with a viral infection followed by

Reyes syndrome

The controls: Children with a viral infection but noReyes syndrome

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Establish the design for drawing the sample(cases)

all 30 patients with Reyes syndrome who are

accessible to him for study

Establish the design for drawing the

sample(controls)

60 patients drawn from the much larger

population of accessible patients who have had

minor viral illnesses without Reyes syndrome

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Define the variables and measurement approaches,

and establishes the hypotheses to be tested.ask the subjects in both groups about their use of

aspirin.

approximate relative risk can be computed

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Cannot yield estimates of the incidence or prevalence

of a disease, because the proportion of study subjects

who have the disease is determined by how many

cases and how many controls the investigator choosesto sample, rather than by their proportions in the

population.

Can yield some descriptive information on thecharacteristics of the cases and an estimate of the

strength of the association between each predictor

variable and the presence or absence of the disease

(odds ratio).

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Odds ratio and relative risk

Odds ratio in a cross

sectional studies

ad/cb

Risk factor present

Risk factor absent

Disease No disease

a b

c d

ad/cd =a/b

c/d

[a/(a + b) c/(c + d)]..

= a ( c + d)

c (a + b)

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Strengths

Efficiency for rare

outcomes

high yield of informationfrom relatively few

subjects

Usefulness for generating

hypotheses

Weaknesses

no incidence

no prevalence

no attributable or excess

risk

Sampling bias, and how to

control it

randomization is near

impossible (pts with a

diagnosed)

misdiagnosed or

misdiagnosed are omitted

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Weaknesses

selection of cases ---relativelystraightforward

selection of controls---?? Sampling the cases and

controls in the same way

Matching

Using two or more control

groups Using a population-based

sample

Differential measurement

bias, and how to control it

Use of data recorded

before the outcome

occurred

Blinding

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