OBSERVATIONAL STUDY

Dr. Partha Sarkar (PGT, 2nd Yr)

Department of Pharmacology Medical College, Kolkata

STUDY DESIGN

Observational

Analytical Cross sectional Cohort Case control

Descriptive Case report Case series surveys

Experimental RCT QUASI

Study Design

Observational vs. Experimental Study

Observational studies The population is observed without any interference by the investigator

Experimental studies The investigator tries to control the environment in which the hypothesis is tested (the randomized, double-blind clinical trial is the gold standard)

Observational Study

• Non-experimental• Observational because there is no individual intervention• Treatment and exposures occur in a “non- controlled” environment• Individuals can be observed prospectively, retrospectively, or currently

Limitation of observational research: confounding

Confounding: Risk factors don’t happen in isolation, except in a controlled experiment.

Example Breastfeeding has been linked to higher IQ in infants, but the association could be due to confounding by socioeconomic status. Women who breastfeed tend to be better educated and have better prenatal care, which may explain the higher IQ in their infants.

Cause and Effect?

AtherosclerosisDepression in

elderly

?Biological changes

?Lack of exercise Poor Eating

Advancing Age

Why Observational Studies?

Cheaper Faster Can examine long-term effects Hypothesis-generating Sometimes, experimental studies are not ethical (e.g- Randomizing subjects to smoke)

Descriptive Studies

Good descriptive reporting answers five basic W questions: Who, what, why, when, where

Case report Case-series reports Surveillance studies

And a sixth: so what ?

Who has the disease in question ? What is the condition or disease being studied ? Why did the condition or disease arise ? Where does or does not the condition arise?

Descriptive studies

Case Report

Case Series

DescriptiveEpidemiology Study

One case of unusual findings

Multiple cases of findings

Population-based cases with denominator

Descriptive studies

Case Reports

Detailed presentation of a single case or handful of cases

Generally report a new or unique finding

e.g-

• previous undescribed diseas• unexpected link between diseas• unexpected new therapeutic effect• adverse events

Case Series

Experience of a group of patients with a similar diagnosis Assesses prevalent disease Cases may be identified from a single or multiple sources Generally report on new/unique condition May be only realistic design for rare disorders

Advantages• Useful for hypothesis generation• Informative for very rare disease with few established risk factors• Characterizes averages for disorder

Disadvantages• Cannot study cause and effect relationships• Cannot assess disease frequency

Case Series

Analytical Studies

Look to link exposure and disease What is the exposure? Who are the exposed? What are the potential health effects? What approach will you take to study the relationship

between exposure and effect?

Basic Question in Analytic Epidemiology

Basic Question in Analytic Epidemiology

Are exposure and disease linked?

Exposure Disease

Analytical Stdies

Analytical Stdies

Cross-sectional studies

An “observational” design that surveys exposures and disease status at a single point in time (a cross-section of the population)

timeStudy only exists at this point in time

Cross-sectional Design

time

Study only exists at this point in time

Studypopulation

No Disease

Disease

factor present

factor absent

factor present

factor absent

Cross-sectional Studies

Often used to study conditions that are relatively frequent with long duration of expression (nonfatal, chronic conditions) It measures prevalence, not incidence of disease Example: community surveys Not suitable for studying rare or highly fatal diseases or a disease with short duration of expression

Cross-sectional studies

Disadvantages Weakest observational design,(it measures prevalence,

not incidence of disease). Prevalent cases are survivors The temporal sequence of exposure and effect may be

difficult or impossible to determine Usually don’t know when disease occurred Rare events a problem. Quickly emerging diseases a

problem

Cross-sectional study

Relationship between atherosclerosis and late-life depression (Tiemeier et al. Arch Gen Psychiatry, 2004).

Methods Researchers measured the prevalence of coronary artery calcification (atherosclerosis) and the prevalence of depressive symptoms in a large cohort of elderly men and women in Rotterdam (n=1920).

Coronary calc >500 539

Coronary calc <=500 1381

81 1839 1920

Any depression

None

28 511

53 1328

2.19) (0.86, CI 95% ;37.1038.052.

RR

Risk Ratio

Interpretation: those with coronary calcification are 37% more likely to have depression (not significant).

Key difference

WHO IS BEING COMPARED?

COHORT: EXPOSED VS. UNEXPOSED

CASE-CONTROL: DISEASED VS. NON-DISEASED

Cohort studies

Sample on exposure status and track disease development (for rare exposures)

Marginal probabilities (and rates) of developing disease for exposure groups are valid.

Timeframe of Studies

Prospective Study Looks forward, looks to the future, examines future events, follows a condition, concern or disease into the future

time

Study begins here

Cohort Studies

Target population

Exposed

Not Exposed

Disease-free cohort

Disease

Disease-free

Disease

Disease-free

TIME

Types of cohort study

The Framingham Heart Study

The Framingham Heart Study was established in 1948, when 5209 residents of Framingham, Mass, aged 28 to 62 years, were enrolled in a prospective epidemiologic cohort study.

Health and lifestyle factors were measured (blood pressure, weight, exercise, etc.).

Interim cardiovascular events were ascertained from medical histories, physical examinations, ECGs, and review of interim medical record.

Measuring Risk

Cohort StudyWhat is the probability of getting diseased if you are

exposed as compared to unexposed?

Case-Control StudyWhat is the probability of having been exposed if

you have the disease compared to not having the disease?

Risk in Cohort Studies

Relative Risk (RR)

R R

A A BC C D

p ro b ab ility o f d isea se g iv en e x p o sedp ro b ab ility o f d isea se g iv en u n ex p o sed

/ ( )/ ( )

Disease Non-DiseasedExposed A B A+B

Unexposed C D C+DA+C B+D

400 400

1100 2600

0.23000/4001500/400 RR

Hypothetical Data

Normal BP

CHF

No CHF

1500 3000

High Systolic BP

Cohort Studies-Advantages/Limitations

Advantages Allows you to measure true rates and risks of disease for

the exposed and the unexposed groups. Temporality is correct (easier to infer cause and effect). Can be used to study multiple outcomes. Prevents bias in the ascertainment of exposure that may

occur after a person develops a disease.Disadvantages

Can be lengthy and costly! 60 years for Framingham. Loss to follow-up is a problem (if non-random) Selection Bias: Participation may be associated with

exposure status for some exposures

Case-Control Studies

Sample on disease status and ask retrospectively about exposures (for rare diseases)Marginal probabilities of exposure for cases and controls

are valid.

Doesn’t require knowledge of the absolute risks of disease

For rare diseases, can approximate relative risk

Timeframe of Studies

• Retrospective Study “to look back”, looks back in time to study events that have already occurred

time

Study begins here

Target population

Exposed in past

Not exposed

Exposed

Not Exposed

Case-Control Studies

Disease(Cases)

No Disease(Controls)

Case-control example

A study of the relation between body mass index and the incidence of age-related macular degeneration.

Methods Researchers compared 50 Iranian patients with confirmed age-related macular degeneration and 80 control subjects with respect to BMI, smoking habits, hypertension, and diabetes. The researchers were specifically interested in the relationship of BMI to age-related macular degeneration.

Results

Comparison of BMI in case and control groups

Case n = 50(%) Control n = 80 (%) p Value

Lean BMI <20 7 (14) 6 (7.5) NS

Normal 20 BMI <25 16 (32) 20 (25) NS

Overweight 25 BMI <30 21 (42) 36 (45) NS

Obese BMI 30 6 (12) 18 (22.5) NS

NS, not significant.

Overweight Normal

ARMD 27 23

No ARMD 54 26

What is the risk ratio here?

50

80

There is no risk ratio, because we cannot calculate the risk of disease!!

Corresponding 2x2 Table

Odds vs. Risk

We cannot calculate a risk ratio from a case-control study.

BUT, we can calculate a measure called the odds ratio…

Odds vs. Risk

If the risk is… Then the odds are…

½ (50%)

¾ (75%)

1/10 (10%)

1/100 (1%)

An odds is always higher than its corresponding probability, unless the probability is 100%

1:13:1

1:9

1:99

The proportion of cases and controls are set by the investigator; therefore, they do not represent the risk (probability) of developing disease.

bcad

dcba

dcddccbabbaa

ORDEP

DEPDEP

DEP

)/()/()/()/(

)~/(~)~/()/(~

)/(

Exposure (E) No Exposure (~E)

Disease (D) a b

No Disease (~D) c d

a+b=cases

c+d=controls

Odds of exposure in the cases

Odds of exposure in the controls

Odds Ratio

dbca

dcba

bcadOR

Exposure (E) No Exposure (~E)

Disease (D) a b

No Disease (~D) c d

Odds of disease for the exposed

Odds of exposure for the controls

Odds of exposure for the cases

Odds of disease for the unexposed

Odds Ratio

57.54*2326*27

26542327

OR

Overweight Normal

ARMD 27 23

No ARMD 54 26

Can be interpreted as: Overweight people have a 43% decrease in their ODDS of age-related macular degeneration. (not statistically significant here)

Odds Ratio

RROR

If the disease is rare (affecting <10% of the population)

WHY?

If the disease is rare, the probability of it NOT happening is close to 1, and the odds is close to the risk. Eg:

50.10:120/1

474.9/1

19/1

RR

OR

Odds Ratio

Good approximation of the risk ratio if the disease is rare

The Rare Disease Assumption

RROR EDPEDP

EDPEDPEDP

EDP

)~/()/(

)~/(~)~/()/(~

)/(

1

1

When a disease is rare: P(~D) = 1 - P(D) 1

The odds ratio vs. the risk ratio

1.0 (null)

Odds ratio

Risk ratio Risk ratio

Odds ratio

Odds ratio

Risk ratio Risk ratio

Odds ratio

Rare Outcome

Common Outcome

1.0 (null)

When is the OR is a good approximation of the RR?

General Rule of Thumb

“OR is a good approximation as long as the probability of the outcome in the unexposed is less than 10%”

Prevalence of age-related macular degeneration is about 6.5% in people over 40 in the US (according to a 2011 estimate). So, the OR is a reasonable approximation of the RR.

Case-control studiesAdvantages/Limitations:

• Advantages– Cheap and fast– Efficient for rare diseases

• Disadvantages– Getting comparable controls is often tricky– Temporality is a problem (did risk factor cause disease or

disease cause risk factor?– Recall bias

Nested case-control studies

A case-control study nested within a cohort study

Ideal for predictor variables that are expensive to measure and that can be assessed at the end of the study on subjects who develop the outcome during the study (cases) and on a sample of those who do not (controls)

Because the number of cases is probably fairly small, can match multiple controls to a given case to increase the power.

Why use a nested case-control study?

Removes recall bias because data collected before development of disease.

Allows for the time element to be included in the case- control. Therefore, if abnormal biologic characteristics were found years before the disease developed, these findings could now be attributed to risk factors for the disease rather than potential developments of early, subclinical disease. Often more cost-effective than a cohort. Not all samples

collected are tested. Rather they are stored until the disease has developed at which time analysis begins.

Table Size Test or measures of association

2x2 Risk ratio (cohort or cross-sectional studies)Odds ratio (case-control studies)Chi-squareDifference in proportionsFisher’s Exact test (cell size less than 5)

RxC Chi-squareFisher’s Exact test (expected cell size >5)

Summary of statistical tests for contingency tables

THANK YOU