study designs alain moren, epiconcept, june 2006 source: epiet

Study designs

Alain Moren, Epiconcept, June 2006Source: EPIET

Cohort study measuring risk

Exposure Total Cases Risk (%) Risk ratio

Exposed 100 40 40% 4

Unexposed 100 10 10% Reference

Total 200 50 25%

Denominator = those present at beginning Usually short duration = outbreak (attack rate)

Cohort study measuring rate

Exposure Total

Time

Cases Rate

per 100 p.y.

Rate ratio

Exposed 1500 p.y 40 2.7/100 p.y. 2.7

Unexposed 1000 p.y. 10 1.0/100 p.y. Reference

Total 2500 p.y. 50 2.0/100 p.y.

Individuals contribute to different length of time Denominator = sum of times

Cohort study

Currently at risk

Cases exposed CE

Start of study End of study

Currently at risk

Person years at riskof exposed (pyarE)

Person years at risk of unexposed (pyarU)

Initiallyat

RiskNE

Initiallyat

RiskNu

Exposed population (E)

Unexposed population (U)Cases unexposed CU

Still at risk NE - CE

Still at risk Nu - Cu

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

Can we use a sample of the denominator

instead of

the entire denominator?

Cases

E

E

30 10

10 10

E

E

30

10

100

100

Source population

Pop.Cases

.3

.1

IR RR

3

Ref.

30/100

10/100

30/10

10/10

= 3

= 3

Sample

Case control study design

I1 = a / P1

I0 = c /P0

Cases Controls

E

E

a b

c d

E

E

a

c

P1

P0

Source population

Pop.Cases

a / P1 a / (P1 /y)---------- = --------------- c / P0 c / (P0 /y)

E

E

a

c

P1 / y

P0 / y

SampleCases

a / (P1 / y)

c / (P0 / y)

Controls selection

• Controls sampled to mirror the exposure experience in the source population– sampled from source population that gives rise

to cases– representative of exposure in source population

• Sampling independently of exposure status

Cohort study

Currently at risk

Cases exposed CE


Currently at risk


Occurrence of New case


Initiallyat

RiskNE

Initiallyat

RiskNu






Cohort study

Currently at risk

Cases exposed CE


Currently at risk




Initiallyat

RiskNE

Initiallyat

RiskNu






Traditional case control study

Cases

and

Sample of “non cases”

Cases ControlsE CE (NE-CE ) * fNon E CU (NU-CU ) * f

Traditional (exclusive) designMeasure of effect = Odds ratio

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

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

Cohort study

Currently at risk

Cases exposed CE


Currently at risk




Initiallyat

RiskNE

Initiallyat

RiskNu






Case cohort study

Cases

and

Sample of source population

Cases ControlsE CE (NE) * fNon E CU (NU) * f

Case-cohort design Measure of Risk ratio (relative risk)

• Control group to estimate the proportion of the total population that is exposed: may include cases

• In a fixed population controls selected from all individuals at risk at the start of the study

• Controls sampled regardless whether or not they will have developed the disease

• A person selected as a case may also be selected as a control and vice versa

• They are kept in both groups• No need to document disease status among controls• Example: outbreak of gastro-enteritis with 30% attack rate

Cohort study

Currently at risk

Cases exposed CE


Currently at risk




Initiallyat

RiskNE

Initiallyat

RiskNu






Density case controlstudy

Cases

and

Sample of source population still at risk

Cases Controls

E CE (NpyE) * f

Non E CU (NpyU) * f

Density case control (concurrent) design OR estimates the rate ratio

• Controls are selected concurrently from those still at risk when a case occur

• A person selected as a control can later become a case • The opposite not possible: a case no longer at risk • A control who later becomes a case is kept in both groups• Controls represent person years at risk experience among

exposed and unexposed• Match analysis on time of selection is necessary to give

unbiased estimate of rate ratio

Cohort study

Currently at risk

Cases exposed CE


Currently at risk




Initiallyat

RiskNE

Initiallyat

RiskNu






Cohort populations & measures of association

Measure of association Formulation

« Risk ratio» CE/NE

CU/NU

« Rate ratio» CE/pyarE

CU/pyarU

« Odds ratio » CE/(NE- CE)

CU/(NU- CU)

Cohort design3 measures of association

Currently at risk

Cases exposed CE


Currently at risk




Initiallyat

RiskNE

Initiallyat

RiskNu





Cohort populations & measures of association

Measure of association Formulation Alternative

formulation


CU/NU

CE/CU

NE/NU


CU/pyarU

CE/CU

pyarE/pyarU


CU/(NU- CU)

CE/CU

(NE- CE) /(NU- CU)

Measures of association

Currently at risk

Cases exposed CE


Currently at risk




Initiallyat

RiskNE

Initiallyat

RiskNu





Cohort populations and estimate each measure of association

Measure of association Formulation Alternative

formulation


CU/NU

CE/CU

NE/NU


CU/pyarU

CE/CU

pyarE/pyarU


CU/(NU- CU)

CE/CU

(NE- CE) /(NU- CU)

Numerator = ratio of exposed to non-exposed cases Denominator = ratio of exposed to unexposed: persons at risk at start of the study person years at risk for the duration of the study persons still disease free at the end of study

Currently at risk

Cases exposed CE


Currently at risk




Initiallyat

RiskNE

Initiallyat

RiskNu





Cohort populations and controls selection for the OR to estimate each measure of association

Numerator = ratio of exposed to non-exposed cases Denominators = ratio of exposed to non-exposed persons at risk at start of the study person years at risk for the duration of the study persons still disease free at the end of study


Alternative

formulation

Controls to be sampled from

«Risk ratio» CE/NE

CU/NU

CE/CU

NE/NU

Total study population

regardless of past or future

disease status

«Rate ratio» CE/pyarE

CU/pyarU

CE/CU

pyarE/pyarU

People currently at risk


CU/(NU- CU)

CE/CU

(NE- CE) /(NU- CU)People disease-free

throughout study period

Currently at risk

Cases exposed CE


Currently at risk

Person years at riskOf exposed (pyarE)



Initiallyat

RiskNE

Initiallyat

RiskNu





How to select controls to estimate the respective measure of association


Alternative

formulation


«Risk ratio» CE/NE

CU/NU

CE/CU

NE/NU



disease status

«Rate ratio» CE/pyarE

CU/pyarU

CE/CU

pyarE/pyarU



CU/(NU- CU)

CE/CU



Cohort populations, control selection to estimate each measure of association &

corresponding designs


Alternative

formulation


«Risk ratio» or «cumulative

incidence ratio »

CE/NE

CU/NU

CE/CU

NE/NU



disease status

«Rate ratio » or incidence

density ratio »

CE/pyarE

CU/pyarU

CE/CU

pyarE/pyarU



CU/(NU- CU)

CE/CU



Case-cohort design

Density case control design

Traditional case control design

Currently at risk

Cases exposed CE


Currently at risk

Person years at riskOf exposed (pyarE)



Initiallyat

RiskNE

Initiallyat

RiskNu





What design and when?Traditional case control

- rare diseaseCase cohort

- frequent disease- same denominator over time- non recurrent outcome

Density case control- rare or frequent disease- exposure changes over time- non or recurrent outcome

OR in case-control studies: the rare disease assumption

• Case control study very efficient for rare diseases

• Initially used for testing significant differences in exposure without attempting to quantify the risk associated with exposure :« Statistically do more lung cancer patients have a history of smoking than controls ?» rather than « by how many times does smoking increase the risk of lung cancer ? »

• Cornfield (1961): if disease is rare : OR ~ RR

• Used more and more for common diseases

• Miettinen (1976), Greenland (1981), Smith (1984) : if controls chosen appropriately, no rare disease assumption is needed for the OR to estimate the relative risk or rate !

Total Cases Non C. Rate RR

E 28010 41 27969 14.6 1.9

E 19017 15 19002 7.9 Ref.

Incidence of breast cancer after radiation


Random

Total Cases Non C. Rate RR Contr. OR

28010 41 27969 14.6 1.9 280 1.9

19017 15 19002 7.9 Ref. 190 Ref.


NonRandom Cases.

Total Cases Non C. Rate RR Contr. OR Contr. OR

28010 41 27969 14.6 1.9 280 1.9 279 1.9

19017 15 19002 7.9 Ref. 190 Ref. 190 Ref.

Cases

E

E 4

SourcePopulation

60

40

Outbreak of food borne disease in a nursing home100 residents, 40 cases

36

RR = 6

24

36

Non cases

Cohort

CasesNon cases

E

E

12

4 18

SourcePopulation

60

40


36

RR = 6 OR = 13.5

24

36

Non cases

Potential control groupsCohort

CasesNon cases

E

E

12

4 18

SourcePopulation

60

40


36

SourcePopulation

30

20

RR = 6 OR = 13.5 OR = 6

24

36

Non cases

Potential control groupsCohort

Rare disease assumption = wrong issue

Issue = selection of controls

Alternative designs

• « Case-to-case »

• « Case-crossover »

« Case-to-case approach »

Source: Jean Claude Desenclos, Jet De Valk

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)


October November December January February March 1999 2000

Cases

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

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

0

2

4

6

8

10

12

14Sporadic cases




Cases


Controls selected among sporadic cases for the case to case control study, listeriosis 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)


Cases


Food consumption of case-patients and control-subjects, multivariate analysis on 29 case-patients

and 32 control-subjects. 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


« Case-to-case » control study

• Possible if disease can be classified in subgroups that have specific risk factors

• May be the case for infectious agents subtypes?• Controls = cases with non epidemic subtypes

– from same source population? – same susceptibility (underlying diseases)– included as cases if they had the outbreak strain– readily available

• Reduces the information (recall) bias• Food-exposure collected before status is known

The case-crossover design

The case-crossover design

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

« control period » of the same duration • No control group needed• Only pairs of period discordant for the exposure of

interest used in the analysis • Acute diseases & exposures that change overtime• Transient exposures (drug adverse events…)• Key issue : the definition of the risk period

Reference period

“Wash out”

period

Currentperiod

Exposure Onset

Cases Matched pairs

1 Discordant 0, 1

2 Discordant 1, 0

3 Concordant 1, 1

4 Concordant 0,0

« Case crossover » design applied to a prolonged Salmonella 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 period 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) -

undefined

undefined

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

Case-crossover design

• No need of a control group• One to several control-periods per risk period• Controls for « between-persons » confounding• Need of data collected prior to onset

(administrative source), • If exposure collected by interview

then very sensitive to recall bias• May be biased by time trend in exposure:

between-period confounding• « Case-time-control design »

References1. Rodrigues L et al. Int J Epidemiol 1990;19:205-132. de Valk H et al. Am J Epidemiol 2001;154:944-503. Haegebaert S et al. Epidemiol infect 2003;130,1-54. Hernandez-Diaz S et al. Am J Epidemiol 2003;158:385-3915. Rothman KJ; Epidemiology: an introduction. Oxford University Press 2002,

73-936. Suisa S. The case-time-control design. Epidemioogy. 1995;6:248-253.7. Greenland S. Confounding and exposure trends in Case-cross-over and

case-time-control designs. Epidemiology. 1996; 7231-239.8. Mittleman, Maclure, Robins. Control sampling strategies for case cross-

over studies: An assessment or relative effectiveness. A J Epidemiol. 142;1:91-98.

Cohort study

Currently at risk

Cases exposed CE


Currently at risk




Initiallyat

RiskNE

Initiallyat

RiskNu






Surveillance of human listeriosis, France

Lm human isolates

Routine extended

food questionnaire

Clusterdetection

Mandatory notification

Routine ongoingPFGE typing

>3 isolates with same

PFGE patternin 10 weeks

Investigation

InVSDDASSClinicians

Laboratories NRC

• Timely detection of clusters• Food history readily available when cluster recognised• “Case to case” case-control studies

study designs alain moren, epiconcept, june 2006 source: epiet

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