Case-Control Study Chunhua Song 2007.10. Warm up

Case-Control Study

Chunhua Song

2007.10

Warm up

exposed

unexposed

a

b

c

d

Analysis of Cohort Study

CI(Cumulative Incidence)CI(Cumulative Incidence) ID(Incidence Density)ID(Incidence Density)

RR(Relative Risk,Risk RR(Relative Risk,Risk Ratio)Ratio) AR ARP(Attributable Risk AR ARP(Attributable Risk Percent Percent )) PAR PARP(Population Attributable RiskPAR PARP(Population Attributable Risk

PercentPercent))

In fixed cohort (the status of participants

is changeless)

number of new cases of a disease during the follow-up periodCI=CI= number of participants at the initiation of follow-up

in dynamic population (the status of participants is protean)

number of new cases of a disease number of new cases of a disease during the follow-up period during the follow-up period ID=ID= total person-time of observationtotal person-time of observation

RR (Relative Risk)

ratio of the risk (i.e., incidence

rate) in an exposed population

to the risk in an unexposed, but

otherwise similar, population.

Incidence （ exposed ）

RR=

Incidence （ unexposed ）

indicator of the strength

(biological significance) of an

association between an expose

and disease.

RR>1 Research factor is a risk factor

(Positive association)

RR<1 Research factor is a protective

factor

(Negative association )

RR=1 No association between the factor

and the disease. Incidence （ exposed ）

RR=

Incidence （ unexposed ）

AR (Attributable risk)

Numbers of cases among the

exposed that could be

eliminated if the exposure

were removed. AR is an estimate of the amount of risk AR is an estimate of the amount of risk

that is attributable to the risk factor after that is attributable to the risk factor after

all other known causes of the disease all other known causes of the disease

have been taken into accounthave been taken into account AR=Ie-I0

ARP (AR%) (attributable risk percent)

%100

e

oe

I

IIARP

Proportion of disease in the

exposed population that could be

eliminated if exposure were

removed. Among the E group ,what

percentage of the total risk for

disease is due to the exposure

PAR (Population Attributable Risk)

Numbers of cases among the

general population that could be

eliminated if the exposure were

removed.

PAR=It-I0

PARP (PAR%) (population

attributable risk percent)

%100

t

ot

I

IIPARP

Proportion of disease in the

study population that could be

eliminated if exposure were

removed.

In a study of oral contraceptive use and bacteriauria, a total of 2400 women aged from 16 to 49 years were identified as free from bacteriauria. Of these, 400 were OC users at the initiation. 3 years later,20 of the OC users had developed bacteriauria, 50 of the non-OC users had developed bacteriauria. Based on data above, try to evaluate the association between OC and bacteriauria.

Ie( 3-year period CI )=(20/400) ×100%=5.0%Io( 3-year period CI )=(50/2000) ×100%=2.5%RR=Ie/Io=2 contraceptive use is a risk factor to bacteriauria AR=Ie-Io=2.5% Ie-Io 5%-2.5%ARP= ×100%= =50% Ie 5%

(20+50)(20+50)

It = ×100%=2.9 % It = ×100%=2.9 %

24002400

PAR=It-Io=2.9%-2.5%=0.4%PAR=It-Io=2.9%-2.5%=0.4%

It-IoIt-Io

PARP = ×100%=13.8%PARP = ×100%=13.8%

ItIt

Case-Control Study

Postulate of Case-Control Study

Types of Case-Control Study Design of Case-Control Study Analysis of Case-Control Study

Case Control Study: Subjects are selected on the basis of wheth

er they have a particular disease or not .The association between the exposure and the disease is evaluated by comparing the two groups with respect to the proportion having a history of an exposure of interest.

Postulate of Case-Control Study

Case

Control

c

b

c

d

Case

aexposure

Non-exposure

Types of Case-Control Study

Non-matching Case-Control StudyNon-matching Case-Control Study

patient

Control

Case

Sampling

Non patient

Matching Case-Control StudyMatching Case-Control Study

Matching: Definition: In order to exclude the

effect of other factors, control group are required to keep consistent with case group in some aspects.

Age Sex BehaviorAge Sex Behavior

Smoke -------Lung cancer

Smoking

Pitman efficiency increase by degrees formula： 1 ： R

X=2R / (R+1) R=1, X=1

R=2, X=1.33

R=4, X=1.6

R=5, X=1.67

Method:

Frequency matching

Individual matching

R=3, X=1.50

Over Matching

But if we let risk factors as matching factor, that is ,control group are required to keep consistent with case group in risk factors. We call it as over matching


Smoking


Drinking

Sex matching and drinking matching

If the persons who If the persons who smoke are all drinkingsmoke are all drinking


The exposure factor (smoking) we The exposure factor (smoking) we study is the same between two study is the same between two groupsgroups

Design of Case-Control Study

1 Ascertain the research intent

2 Define the disease and fix on

the measure method

3 A proper sample size

4 Selection of research subject 4.1 Selection of the case: (1)Hospital-based (2)Community-based

representational

4.2 Selection of the control The same source with case

If control group is come from hospital, the patient in control group should not suffer from disease that have common causal with the disease of interest.

Case lung cancer

Control bronchitis

5 Institute questionnaire5 Institute questionnaire

(1)Selection of variable(1)Selection of variable The factors are relate to the disease The factors are relate to the disease

and perhaps are the causes of and perhaps are the causes of diseasedisease

(2)Definition of variable(2)Definition of variable

(3)Measurement of variable(3)Measurement of variable

6 Collection of the research-related in6 Collection of the research-related informationformation ① Utilize varied routine record

② family interview

③ telephone or correspond inquiry

7 7 Clean-up ,enter & analyze data

Analysis of Case-Control Study

Non-Matching case control study:Non-Matching case control study:

Case Control

Total

exposed

a b a+b

unexposed

c d c+d

Total a+c b+d a+b+c+d=n

1 Test whether difference of exposure proportion in 2 groups.

))()()((

)( 22

dcbadbca

nbcad

Statistics: p-value p<0.05 indicates the likelihood that a study’s findings are due to chance in data analysis

2 Estimate relative risk

bc

adOR

2.1 OR(Odds Ratio)

Ratio of odds in favor of exposure among cases to odds in favor of exposure among controls.

2.2 OR 95%C.I.(confidence interval)

)/96.11( 2

..%95 ORICOR

A case-control study is conducted to reveal the

association between oral contraception and MI.

There are 150 MI patients and 150 Non-MI

patients enrolled in this research. Result is as

bellows: Among the 150 MI patients, 50 once

used OC, and among the 150 Non-MI patients, 30

once used OC. Try to evaluate the association

between OC and MI.

MI Non-MI Total

Exposed to OC

50 30 80

Unexposed

To OC

100 120 220

Total 150 150 300

1 Test the difference of exposure proportion in 2 groups.

82.622080150150

300)1003012050(

))()()((

)(

2

22

dcbadbca

nbcad

P<0.05 . So there is a significant difference of the exposure proportion in 2 groups.

2 Estimate relative risk

0.210030

12050

bc

adOR

2.1 OR(Odds Ratio)

2.2 OR 95%C.I.(confidence interval)

)2,2(2

..%9575.125.0)82.6/96.11(

)96.11( 2

ORICOR

Taking OC is a risk factor to MI

=(1.19, 3.36)

1:1 Matching Case Control Study1:1 Matching Case Control Study

case control exposure ＋＋ a history ＋－ c －＋ b －－ d

1:1 Matching Case Control Study1:1 Matching Case Control Study

Case Total

Exposed Non- exposed

Control

Exposed

Non-exposed

a

c

b

d

a+b

c+d

Total a+c b+d n

b

cOR

cb

cb

)(

)( 22

Comparison of cohort and case-control studies

provide information about a range of effeprovide information about a range of effects related to a single exposurects related to a single exposure

Provide information about one effect thaProvide information about one effect that afflicts the cases selected(studies includt afflicts the cases selected(studies including multiple series of cases are an exceptiing multiple series of cases are an exception)on)


Typically follow-up studies focus on Typically follow-up studies focus on one exposureone exposure

Provide information about a wide raProvide information about a wide range of potentially relevant exposuresnge of potentially relevant exposures


Evaluation of effects on rare Evaluation of effects on rare disease is problematic in disease is problematic in follow-up studies.follow-up studies.

Evaluation of effects on rare Evaluation of effects on rare disease are well suited to case-disease are well suited to case-control studies.control studies.


Concern is in the follow-upConcern is in the follow-up Concern is in the determination Concern is in the determination

of a correct exposure of a correct exposure classificationclassification


Exposure status is determined Exposure status is determined before the presence of disease. before the presence of disease. No possibility for the disease No possibility for the disease outcome to influence exposure outcome to influence exposure classificationclassification

Exposure information comes from Exposure information comes from the subject (or proxies) after the subject (or proxies) after disease onset. Knowledge of disease onset. Knowledge of disease could affect exposure disease could affect exposure data. Greater possibility of biasdata. Greater possibility of bias


Large, expensive, take timeLarge, expensive, take time Smaller, less expensive, quickSmaller, less expensive, quick

Section 3: Calculate and answer questions:Section 3: Calculate and answer questions: 1. In order to evaluate the hypothesized association between the 1. In order to evaluate the hypothesized association between the

infection of HBV and liver cancer, a case–control study was infection of HBV and liver cancer, a case–control study was conducted. 100 patients with liver cancer and 100 patients with conducted. 100 patients with liver cancer and 100 patients with diabetes were enrolled into the research. The results of the study diabetes were enrolled into the research. The results of the study were following: 80 patients with liver cancer have had the infection were following: 80 patients with liver cancer have had the infection of HBV, and only 10 patients with diabetes have had the infection of of HBV, and only 10 patients with diabetes have had the infection of HBV. HBV.

Questions:Questions: (1) Fill in the data into a 2×2 table.(1) Fill in the data into a 2×2 table. (2) Is there a significant difference of the proportion of the (2) Is there a significant difference of the proportion of the

infections of HBV between this two groups?infections of HBV between this two groups? (3) Try to calculate the strength of the association between the (3) Try to calculate the strength of the association between the

infection of HBV and liver cancer.infection of HBV and liver cancer.

What can be wrong in the study?

Random errorRandom error

Results in Results in low precision low precision of the epidemiological of the epidemiological measure measure measure is measure is not precisenot precise, but true, but true

1 Imprecise measuring1 Imprecise measuring

2 Too small groups2 Too small groups

Systematic errors(= bias)

Results in low validity of the epidemiological measure measure is not true

1 Selection bias

2 Information bias

3 Confounding

Random errors

target practice

Systematic errors

Random error Low precision because ofLow precision because of

Imprecise measuringImprecise measuring Too small groupsToo small groups

Decreases with increasing group sizeDecreases with increasing group size

Can be quantified by confidence intervalCan be quantified by confidence interval

Errors in epidemiological studies

Bias in epidemiology1 Concept of bias

2 Classification and controlling of bias

2.1 selective bias

2.2 information bias

2.3 confounding bias

Overestimate?

Underestimate?

Random error ：

Definition

Deviation of results and inferences

from the truth, occurring only as a

result of the operation of chance.

Definition: Systematic, non-random deviation of results and inferences from the truth.

Bias:

2 Classification and controlling of bias

Assembling subjects

collecting data

analyzing data

Selection bias

Information bias

Confounding bias

Time

2.1 Selection bias2.1.1 definition

Due to improper assembling method or limitation, research population can not represent the situation of target population, and deviation arise from it.

2.1.2 several common Selection biases

（ 1 ） Admission bias （ Berkson’s bias)

There are 50,000 male citizen aged 30-50 years old in a community. The prevalence of hypertension and skin cancer are considerably high. Researcher A want to know whether hypertension is a risk factor of lung cancer and conduct a case-control study in the community .

case control sum

Hypertension 1000 9000 10000

No hypertension 4000 36000 40000

sum 5000 45000 50000 χ2 =0

OR=(1000×36000)/(9000 ×4000)=1

No association between hypertension and chronic gastritis

Researcher B conduct another case-control study in hospital of the community.(chronic gastritis patients as control) .

admission rate

Lung cancer & hypertension 20%

Lung cancer without hypertension 20%

chronic gastritis & hypertension 20%

chronic gastritis without hypertension 20%

case control sum

hypertension 200 (1000) 200 (2000) 400

No hypertension 800 (4000) 400 (8000) 1200

sum 1000 (5000) 600 (10000) 1600

case control sum hypertention 40 100 140

No hypertention 160 200 360

sum 200 300 500

χ2 =10.58 P<0.01

OR=(40×200)/(100×160)=0.5

2.2 Information Biasrecalling bias

2 ） report bias

3 ） diagnostic/exposure suspicion bias

Measurement bias

2.3 Confounding bias

Definition:

The apparent effect of the exposure of interest is distorted because the effect of an extraneous factor is mistaken for or mixed with the actual exposure effect.

Properties of a Confounder:

• A confounding factor must be a risk factor for the disease.

• The confounding factor must be associated with the exposure under study in the source population.

• A confounding factor must not be affected by the exposure or the disease.

The confounder cannot be an intermediate step in the causal path between the exposure and the disease.

2.3.2 Control of confounding bias

1 ) restriction

2) randomization

3) matching

1 In designing phase

2 In analysis phase

1) Stratified analysis (Mantal-Hazenszel’s method)2) Standardized

3) logistic analysis

Documents

Case-Control Study Chunhua Song 2007.10. Warm up