Case-control designs in the study of common diseases

& alternative designs

JC Desenclos, F Simón, A Moren

EPIET, Menorca, Spain, October 9, 2006

Case-control studies

• Objective: compare exposure in cases and in population origin of cases– Sample of that population as controls– Representative as for the exposure of interest

• Random sampling, regardless exposure or disease status

• Meaning of OR differs according to different control sampling schemes

Cohort populations origin of cases and controls

Currently at risk

Cases exposed CE

Start of study End of study

Currently at risk

Person years at risk(pyrsE)

Occurrence of New case

Person years at risk (pyrsU)

Initiallyat

RiskNE

Initiallyat

RiskNu

Exposed population (E)

Unexposed population (U)Cases unexposed CU

Still at risk NE - CE

Still at risk Nu - Cu

Rodrigues L et al. Int J Epidemiol. 1990;19:205-13.

Origin of controls and measures of association

Inclusive (case-cohort)

Concurrent (density)

ExcIusive (traditional)No cases at the end

of the study period

People at risk when

the case appears

Total Study Cohort

origin of cases

Origin of Sampled Controls

Alternative Formulation

Formulation

Odds Ratio

Relative Rate

Density Incidence Ratio

Relative Risk

Cumulative

Incidence Ratio

Estimated

Measure of Association

U U

EE

NC

NC

UU

EE

pyrsC

pyrsC

UUU

EEE

CNC

CNC

EU

UE

NC

NC

EU

UE

pyrsC

pyrsC

)CN(C

)CN(C

EEU

UUE

Inclusive design (case-cohort): OR estimates RR

• Controls representative proportion of total population at risk at the beginning of the study period

including cases

• Sampling independent of the exposure and outcome

• A case may also be a control

• No need to asses disease status among controls

• Reasonable if source population is followed up for the same time period (ex: OB of gastro-enteritis)

Concurrent design: OR estimates Relative Rates

• Controls representative proportion of population at risk when the case appears (concurrent selection)

• Represent person-time at risk in exposed and unexposed

• A control can be a case later

• A person can be a control for several cases

• Matched analysis because of time matching

• Example: Prolonged OB of hepatitis C in a dialysis unit selecting 3 controls per case among those at risk of infection at the same time as the case occurs

Traditional design (exclusive)

• Controls sampled from population still at risk at the end of the study period

• ORE of cases to controls = ORD of exposed to non exposed

• OR good estimate of relative risk and relative rate if disease is rare

Example: waterborne OB of gastro-enteritis

Water consumption

Ill Not ill Total

Yes 148 188 336

No 54 319 373

Total 202 507 709

Attack rate = 0,29RR = 3,04

Case

(n = 50)

Control (n = 50)

Yes 37 19

No 13 31

OR = 4,64 (CI 95%: 1.8 – 11.9)

Case

(n = 50)

Control (n = 50)

Yes 37 24

No 13 26

OR = 3,08 (CI 95%: 1.2 – 7.8)

Exclusive design Case cohort design (inclusive)

Which design is best?

• Rear diseases: similar results

• Common diseases:

• Non-recurrent disease with high incidence– Inclusive design (case-cohort): ORRR

• Highly incident and recurrent disease or when probability of exposure changes along time or when the effect of exposure may change along time – Concurrent design: ORRRate

Alternative designs

• « Case to Case »

• « Case - Crossover »

• « Case-time-control»

« Case to case design »

Two listeriosis outbreaks of 2 distinct PFGE patterns, France, 1999-2000

0

1

2

3

4

5

6

7

8

9

10

Outbreak 2 (32 cases)

Outbreak 1 (10 cases)

October November December January February March 1999 2000

Cases

de Valk H et al. Am J Epidemiol 2001;154:944-50

Listeriosis outbreak and sporadic cases by routine PFGE pattern, France, 1999-2000

0

2

4

6

8

10

12

14Sporadic cases

Outbreak 2 (32 cases)

Outbreak 1 (10 cases)

October November December January February March 1999 2000

Cases

de Valk H et al. Am J Epidemiol 2001;154:944-50

Controls selected among sporadic cases for the study of outbreak 2, France, 1999-2000

(Source: InVS-CNR)

0

2

4

6

8

10

12

14 Other sporadic cases

Sporadic cases used as controls (N = 32)

Outbreak 2 (N = 32)

Outbreak 1 (N = 10)

October November December January February March 1999 2000

Cases

de Valk H et al. Am J Epidemiol 2001;154:944-50

Food consumption multivariate analysis on 29 case-patients and 32 control-patients. Outbreak of listeriosis

France, December 1999 - February 2000.

Food consumed

Adjusted Odds ratio*

95% CI

p

Pork tongue in jelly 75,5 4,7 – 1216,0 0,002

Cooked ham 7,1 0,7 – 71,8 0,1

Pâté de campagne 8,9 1,7 – 46,1 0,009

*adjusted for underlying condition, pregnancy status and date of interview by logistic regression

de Valk H et al. Am J Epidemiol 2001;154:944-50

« Case-to-case » study design

• Controls = patient with non epidemic subtypes

– from same source population

– same susceptibility (underlying diseases)

– included as cases if they had the OB strain

– Information readily available

• Reduces the information (recall) bias

• Food-exposure collected before status is known

« Case-Crossover design »

September October November December January

Community cases

Hospital 1

Cases Hospital 2

8 Hospital 3

7 Hospital 4

6 Hospital 5

5 Nursing home4

3

2

1

37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 01 Week

Alert

Haegebaert S et al. Epidemiol infect 2003;130,1-5

Hospital and community OB of S. Typhimurium

Case-Crossover design

• Same person taken as control (matched design)• Compare exposure in a «risk period» to a prior «control

period» of the same duration • Matched analysis (discordant periods)• Evaluates exposures that

– vary from time to time within a person– triggering a short term effect, with abrupt onset

• Key issue : the definition of the risk period

« Case crossover » design applied to a prolonged S. Typhimurium outbreak

Control period

72 hours

“Wash out”period

48 hoursRisk period 72 hours

Discordant pair ( 1,0 )

Concordant pair ( 1,1 )

Discordant pair ( 0,1 )

Concordant pair ( 0,0 )

Exposure

Onset

Haegebaert S et al. Epidemiol infect 2003;130,1-5

Food exposures from menu information in the risk and control periods and matched OR for 17 nosocomial cases

Foods Riskperiod

Control period Matched

OR 95% C.I. Exposed (%) Exposed (%)

Veal 5 (29) 1 (6) 5 0,6 - 236,5 Pork 4 (23) 6 (35) 0,6 0,1 - 3,1 Hamburgers 13 (77) 5 (29) 5 1,1 - 46,9 Ham 6 (35) 5 (29) 1,5 0,2 - 17,9 Pâté 2 (12) 2 (12) 1 0,01 - 78,5 Chicken 2 (12) 3 (18) 1 0,01 - 78,5 Turkey 11 (65) 6 (35) 2,67 0,7 - 15,6 “Cordon bleu” 0 (0) 2 (12) undefined - Lamb sausages 2 (12) 0 (0) - Poultry sausages 2 (12) 0 (0) -

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Haegebaert S et al. Epidemiol infect 2003;130,1-5

Case-Crossover design

• For extended source outbreaks• No need of a control group• One to several control-periods per risk period• Controls for «between-persons» confounding• Very sensitive to recall bias unless data have been

collected prior to onset (administrative databases) • May be biased by time trend in exposure: between-

period confounding– «Case-time-control»

«Case-time control design»

Between period confounding

ORa/ORb = OR of exposure adjusted for time trend

Cyclical variation of exposure

Control period Risk period

onset

Cases : ORa for the exposure and the time trend

Controls: ORb for the time trend

Folic acid antagonists (FAA) in pregnancy and congenital cardiovascular defects (CCD)

• Case: Woman who had a child with CCD (N=3870)• Control: Woman who had a child without CCD (N=8387)

• Exposure: FAA during 2nd & 3rd month of pregnancy

• Case-crossover study for cases and controls independently

OR=1.0 (0.5-2.0)

OR= 0.3 (0.2-0.6)

Case-time control OR = 1/0.3 = 2.9 (1.2-7.2)

-2 -1 1 2 3 4 5 6 7 8 9Cases:

-2 -1 1 2 3 4 5 6 7 8 9Controls:

ORcrude=2.3 (1.4-3.9)

Controlperiod

Riskperiod

Delivery

Hernandez-Diaz S. Am J Epidemiol 2003;158:385-391

Conclusions

• If you do not need that OR estimates correctly the RR then: “traditional design”

• Otherwise, if you need OR RR, identify the best design for each situation

• If you can not find or want to avoid controls– Case to case– Case-crossover

Find the foot fitting the glass slipper

References

1. Rodrigues L et al. Int J Epidemiol 1990;19:205-13

2. de Valk H et al. Am J Epidemiol 2001;154:944-50

3. Haegebaert S et al. Epidemiol infect 2003;131,809-813

4. Hernandez-Diaz S et al. Am J Epidemiol 2003;158:385-391

5. Rothman KJ; Epidemiology: an introduction. Oxford University Press 2002, 73-93