cohort sep 2011.ppt [Kompatibilitetstilstand]publicifsv.sund.ku.dk/~pka/epiE11/coh-SF.pdf ·...

EPIDEMIOLOGYEPIDEMIOLOGY

COHORT STUDIESCOHORT STUDIES

September 2011September 2011September 2011September 2011

Søren FriisSøren Friis

Institut for Epidemiologisk KræftforskningInstitut for Epidemiologisk Kræftforskning

Kræftens BekæmpelseKræftens Bekæmpelse

EPIDEMIOLOGYEPIDEMIOLOGY

COHORT STUDIESCOHORT STUDIES

September 2011September 2011September 2011September 2011

Søren FriisSøren Friis

Institut for Epidemiologisk KræftforskningInstitut for Epidemiologisk Kræftforskning

Kræftens BekæmpelseKræftens Bekæmpelse

”While the individual man is an insoluble puzzle, in the

aggregate he becomes a mathematical certainty. You

can, for example, never foretell what any one man will

do, but you can say with precision what an average do, but you can say with precision what an average

number will be up to”

Arthur Conan Doyle

Sherlock Holmes: The Sign of four

”While the individual man is an insoluble puzzle, in the

aggregate he becomes a mathematical certainty. You

can, for example, never foretell what any one man will

do, but you can say with precision what an average do, but you can say with precision what an average

Arthur Conan Doyle

Sherlock Holmes: The Sign of four

Ideal study of a causal effectIdeal study of a causal effect

”The experience of exposed people is compared

with their experience when not exposed, while

everything else is held constant”

Kenneth Rothman, Modern Epidemiology, 1998

Ideal study of a causal effectIdeal study of a causal effect

”The experience of exposed people is compared

with their experience when not exposed, while

everything else is held constant”

Kenneth Rothman, Modern Epidemiology, 1998

Assignment of exposure

Yes

AnalyticAnalytic epidemiologicalepidemiological

Experimental studies

Randomised/intervention

trials

to

no yes

Random allocation

Community intervention

trials

Assignment of exposure

Non-experimentalstudies

No

epidemiologicalepidemiological studies studies

Non-experimentalstudies

Sampling accordingto exposure status

Sampling according to outcome status

Observational cohort studies

Case-control studies

CohortCohort

ClassicalClassical definitiondefinition

”The delineation of a group of persons who are

distinguished in some specific way from the majority

of the population and observation of them for long of the population and observation of them for long

enough to allow any unusual morbidity or mortality

to be recognised”

studiesstudies

definitiondefinition

”The delineation of a group of persons who are

distinguished in some specific way from the majority

of the population and observation of them for long of the population and observation of them for long

enough to allow any unusual morbidity or mortality

Richard Doll 1964

CohortCohort

Recent definitionRecent definition

� Intervention studies

� Randomised clinical trials

� two (or multiple)-arm, cross

� Field trials

� intervention on single

� Community intervention trials

� intervention on community level

�Observational cohort studies

studiesstudies

Recent definitionRecent definition

Intervention studies

Randomised clinical trials

arm, cross-over

intervention on single-person level

Community intervention trials

intervention on community level

Observational cohort studies

Observational cohort studiesObservational cohort studies

Key characteristicsKey characteristics

�Exposed and non-exposed individuals are not directly comparabledirectly comparable

�Exposure status varies over time

�Vulnerable to bias and confounding

Observational cohort studiesObservational cohort studies

Key characteristicsKey characteristics

exposed individuals are not

Exposure status varies over time

Vulnerable to bias and confounding

From ”The From ”The DailyDaily Telegraph”, 19 August 2011Telegraph”, 19 August 2011Telegraph”, 19 August 2011Telegraph”, 19 August 2011

Observational vs. randomized studiesObservational vs. randomized studies

”Achilles tendon” of observational studies

Observational vs. randomized studiesObservational vs. randomized studies

”Achilles tendon” of observational studies

ClassicalClassical intervention intervention

James Lind (1716James Lind (1716

intervention intervention studystudy

James Lind (1716James Lind (1716--94)94)

James James Lind’sLind’s intervention intervention intervention intervention studystudy

WhyWhy not not alwaysalways conductconduct

�Ethical constrictions

� e.g.: pregnancy, first drug(s) of choice, risk factors

� ”You cannot randomize harmful things”

� Logistic issues

� Large expenditures

� May be difficult to control the exposure (e.g. drug use)

� ex: on/off therapy; sporadic therapy; dietary intervention; weight loss

� Generally small and selected patient populations

� Rare outcomes can typically not be studied

� Long-term effects can typically not be studied

conductconduct randomizedrandomized trials?trials?

e.g.: pregnancy, first drug(s) of choice, risk factors

”You cannot randomize harmful things”

May be difficult to control the exposure (e.g. drug use)

ex: on/off therapy; sporadic therapy; dietary intervention; weight loss

Generally small and selected patient populations

Rare outcomes can typically not be studied

term effects can typically not be studied

Populationat risk

Exposed

Non-exposed

Past Present Future

Identify study subjects and assess exposure characteristics

Exposed

exposed

Outcome

+

-

+

-

Censored

Censored

Past Present Future

Follow-up

-

Population at Population at

� Individuals at risk of developing the outcome(s) of interest

� Basis for computation of measures of diseases frequency and effect measures

� Classified according to exposure characteristics

� At baseline� At baseline

� During follow-up

� Censoring at � First outcome (typically)

� Death

� Migration

� Upper age limit, if age restriction

� Other criteria, e.g. shift of exposure shift

Population at Population at riskrisk

Individuals at risk of developing the outcome(s) of interest

Basis for computation of measures of diseases frequency and effect

Classified according to exposure characteristics

Upper age limit, if age restriction

Other criteria, e.g. shift of exposure shift

CohortCohort

� ”Any designated group of individuals who are

followed or traced over a period of time”

� Kenneth Rothman, Modern Epidemiology, 1998

�Can be divided into closed and open populations

CohortCohort

”Any designated group of individuals who are

followed or traced over a period of time”

Kenneth Rothman, Modern Epidemiology, 1998

Can be divided into closed and open populations

ClosedClosed and and OpenOpen

�Closed population

� A population that adds no new members over time

�Open/dynamic population

� A population that may gain members over time or lose members who are still alive

� e.g. drug users within a specific observation period

OpenOpen PopulationsPopulations

A population that adds no new members over time

Open/dynamic population

A population that may gain members over time or lose members who are still alive

e.g. drug users within a specific observation period

StudyStudy

Start

Time

StudyStudy basebase

ClosedClosed populationpopulation

Cohort Cohort entry

Time

populationpopulation

ClosedClosed populationpopulation

limitationslimitations

� Loss to follow-up (censoring)

�Decreasing cohort size�Decreasing cohort size

�Aging of cohort members

�Change in exposure/therapy

�Depletion of susceptibles

populationpopulation

limitationslimitations

up (censoring)

Decreasing cohort sizeDecreasing cohort size

Aging of cohort members

Change in exposure/therapy

Depletion of susceptibles

Open/dynamicOpen/dynamic

StartStart

Time

Open/dynamicOpen/dynamic populationpopulation

SelectionSelection of the of the exposedexposed

� General population� Diet, Cancer & Health cohort, Danish Cancer Society

� Individuals aged 50 to 64 years, follow

� Occupational exposure groups� Nurses Health Study, USA

� Nurses aged 30 to 55 years, follow� Nurses aged 30 to 55 years, follow

� Exposure� ”Special exposure groups”

� Ex.: Workers at the Thule base, Epileptics at Dianalund, individuals exposed to thorotrast

� Drug users

� Registers� General Practice Research Database, UK

� Danish health and administrative registers

exposedexposed populationpopulation

Diet, Cancer & Health cohort, Danish Cancer SocietyIndividuals aged 50 to 64 years, follow-up from 1994 (n ≈ 57,000)

Occupational exposure groups

Nurses aged 30 to 55 years, follow-up from 1976 (n ≈ 120,000)Nurses aged 30 to 55 years, follow-up from 1976 (n ≈ 120,000)

Ex.: Workers at the Thule base, Epileptics at Dianalund, individuals

General Practice Research Database, UK

Danish health and administrative registers

SelectionSelection of the of the comparisoncomparison

� Ideally identical to the exposed group with respect to all other factors that may be related to the disease except the outcome(s) under study

� ”Internal” comparison

� general population/large occupational cohort� general population/large occupational cohort

� frequent exposure

� ”External” comparison

� General population (rates)

� Standardised incidence rate ratio (SIR)

� Standardised mortality rate ratio (SMR)

comparisoncomparison groupgroup

Ideally identical to the exposed group with respect to all other factors that may be related to the disease except the outcome(s) under study

general population/large occupational cohortgeneral population/large occupational cohort

Standardised incidence rate ratio (SIR)

Standardised mortality rate ratio (SMR)

Data Data sourcessources

Exposure

� Existing data

� registers

� medical records

� bio-banks� bio-banks

� Questionnaires

� interview

� self-administered

� Ad hoc measurements

� clinical parametes

� biological samples

sourcessources

Outcome

� Registers

� Clinical examination

� Information from study � Information from study subjects

� interview

� questionnaire

� Information from next-of-kin

� Mortality data

MeasuresMeasures of of diseasedisease

Definitions

What is the case?

What is the study period?

What is the population at risk?

diseasedisease frequencyfrequency

What is the population at risk?

MeasuresMeasures of of diseasedisease frequencyfrequency

� Incidence proportion (IP)

� Proportion of population that develops the outcome of interest during a specified time

� Can be measured only in closed populations

� ”Average risk” for a population

� Incidence rate (IR)

� Number of new cases of the outcoperson-time in the base population

� Can be measured in both open and closed populations

� Most often restricted to include a maximum of one event per person

� Prevalence proportion (PP)

� Proportion of population that has t

frequencyfrequency, summary, summary

Proportion of population that develops the outcome of interest during a

Can be measured only in closed populations

tcome of interest divided by the amount of time in the base population

Can be measured in both open and closed populations

Most often restricted to include a maximum of one event per person

s the outcome of interest at given instant

Exposure + Outcome

+ a

- c

a+c

Effect measures in cohort studiesEffect measures in cohort studies

IP+ = a/a+b

IP- = c/c+d

RR = IP+/IP-

Attributable risk (AR) = IP

Attributable proportion (AP) = AR/IP

- Outcome

b a + b

d c + d

b+d N

Effect measures in cohort studiesEffect measures in cohort studies

IP+ - IP-

Attributable proportion (AP) = AR/IP+ = (RR-1)/RR

IncidenceIncidence proportion proportion

ConditionsConditions

� All persons should be followeduntil end of study with respect to the outcome(s) of interest

� Problems:� Problems:

� Open/dynamic population (t0?)

� Competing risks of death

� Censoring

� Is usually not directly observable,

� Computation of incidence rates

proportion proportion

ConditionsConditions

All persons should be followed-up from start of study (t0) until end of study with respect to the outcome(s) of

?)

Is usually not directly observable, solution:

Computation of incidence rates

Non-exposed

Exposed

Time dimensionTime dimension

Person-time in study

Problem: Exposure status changes over time (episodical, sporadical)

Solution: Allow persons to contribute person

cases

Time dimensionTime dimension

cases

Problem: Exposure status changes over time (episodical, sporadical)

Solution: Allow persons to contribute person-time to multiple exposure categories

Age

40

45

50

55

Y

30-year-old man is

enrolled in a cohort

study of drug X in

relation to disease Y

in 1980 and followed

free of Y through

Calendar time

1970 20001985 1990 1995 2005

30

35

40

X

Non-X

2005

35-year-old man is

enrolled in 1980 and

followed until

occurrence of Y in

1993

Contribution from the two study subjects

Exp. to drug X Non-exp. to drug X

Age PY Disease Y PY Disease Y

30-34 y 0 0 5 0

35-39 y 5 0 5 0

Calendar time

2005

35-39 y 5 0 5 0

40-44 y 10 0 0 0

45-49 y 8 1 0 0

50-54 y 0 0 5 0

”Crude” 23 1 15 0

cases

cases

Non-exposed

Exposed

Effect measures in cohort studiesEffect measures in cohort studies

Person-time in study

cases

Incidence rate = cases / person

Incidens Rate Ratio (IRR) = IR

A

PYC

PY

Cases Person-time

Exposure

Yes

No

A = Exposed cases

Effect measures in cohort studiesEffect measures in cohort studies

Incidence rate = cases / person-time

Incidens Rate Ratio (IRR) = IR+ / IR-

A = Exposed cases

C = Non-exposed cases

Exposure Outcome

+ a

- c

a+c

Effect measures in cohort studiesEffect measures in cohort studies

IR+ = a/PY+

IR- = c/PY-

Incidence rate ratio (IRR) = IR

Incidence rate difference

AP = IRD/IR+ = (IR+-IR-)/IR

a+c

Person-time

PY+

PY-

N

Effect measures in cohort studiesEffect measures in cohort studies

Incidence rate ratio (IRR) = IR+/IR-

= IRD (≈AR) = IR+ - IR-

)/IR+ = (IRR-1)/IRR

N

Indirect Standardisation

� Do more outcomes occur in the studied population than would be expected if the risk prevailing was the same as in the general population?

Effect measures in cohort studiesEffect measures in cohort studies

� Estimation of expected number of outcomes

� Number of person-years at risk x incidence rate

� PYage,period,sex x incidence

� Observed number/expected number

� Standardised incidence ratio (SIR)

Do more outcomes occur in the studied population than would be expected if the risk prevailing was the same as in the general population?

Effect measures in cohort studiesEffect measures in cohort studies

Estimation of expected number of outcomes

years at risk x incidence rate

x incidenceage,period,sex

Observed number/expected number ≈ RR

Standardised incidence ratio (SIR)

SIR

Calendar time

SIR = Observed number of outcomes/

expected number of outcomes

= Obs/IRpop x PYexp

= (Obs/PYexp) / IRpop

= IRexp / IRpop

≈ IRexp / IR0

= IRR (RR)

IR>32/IR<19 = 393/84,522 / 577/230,899 =

IR25.0-26.9/IR<19 = 512/196,254 / 577/230,899 =

= 393/84,522 / 577/230,899 = 1.86

= 512/196,254 / 577/230,899 = 1.04Comment?

MansonManson et al.et al. NEJM 1995; 333: 677NEJM 1995; 333: 677--8585

IR>30/IR18.5-21.9 = 45/30,966 / 349/660,583

IR22-24.9/IR18.5-21.9 = 140/222,722 / 349/660,583

Van Dam et al. Ann Intern Med 2006; 145: 9 Van Dam et al. Ann Intern Med 2006; 145: 9

349/660,583 = 2.75

349/660,583 = 1.18

Van Dam et al. Ann Intern Med 2006; 145: 9 Van Dam et al. Ann Intern Med 2006; 145: 9 --97 97

Comment?

Absolute vs. relative disease measuresAbsolute vs. relative disease measures

�Avoid confusing measures of frequency with

measures of association (effect measures)

Ex:

� A RR=10 is described as a high risk, or a population for

whom RR=10 is said to be at higher risk than a

population in which RR=5

� A RR=10 may be described as a high

Absolute vs. relative disease measuresAbsolute vs. relative disease measures

Avoid confusing measures of frequency with

measures of association (effect measures)

A RR=10 is described as a high risk, or a population for

whom RR=10 is said to be at higher risk than a

A RR=10 may be described as a high relative risk

RiskRisk of of deepdeep veinvein

ThirdThird vs. vs. secondsecond generation oral generation oral

� RR ≈ 1.7 (1.4-1.7)

� AR ≈ 1.5 per 10 000 person

� Mortality of DVT

Kemmeren et al. BMJ 2001; 323: 131

thrombosisthrombosis (DVT)(DVT)

generation oral generation oral contraceptivescontraceptives

1.7)

1.5 per 10 000 person-years

Mortality of DVT ≈ 3%

Kemmeren et al. BMJ 2001; 323: 131-4

VioxxVioxx (rofecoxib) and (rofecoxib) and

APPROVeAPPROVe

� 2,586 patients randomised to rofecoxib (Vioxx) (25 mg daily; n=1287) or placebo (n=1299) during a 3year study period

� 1.50 CVE per 100 py (46 events; 3,059 py) � 1.50 CVE per 100 py (46 events; 3,059 py)

� 0.78 CVE per 100 py (26 events; 3,327 py)

� RR = 1.92 (1.19-3.11)

� AR ≈ 72 pr. 10 000 py

Bresalier et al. N Engl J Med 2005; 352: 1092

(rofecoxib) and (rofecoxib) and cardiovascularcardiovascular diseasedisease

APPROVeAPPROVe trialtrial

2,586 patients randomised to rofecoxib (Vioxx) (25 mg daily; n=1287) or placebo (n=1299) during a 3-

1.50 CVE per 100 py (46 events; 3,059 py) vs.1.50 CVE per 100 py (46 events; 3,059 py) vs.

0.78 CVE per 100 py (26 events; 3,327 py)

Bresalier et al. N Engl J Med 2005; 352: 1092-1102

AttributableAttributable

� What proportion of the disease among the exposed is attributable to the exposure (AP

APexp = IR+-IR0 / IR+ = AR / IR

� What proportion of the disease in the total study population of exposed and non-exposed individuals is attributable to the exposed and non-exposed individuals is attributable to the exposure (APpop)?

APpop = IRpop-IR0 / IRpop

= AR x pe / IRpop (pe = exp. prevalence in population)

= APexp x pc (pc = exp. prevalence among cases)

= [(RR-1) x pe] / [(RR-1) x p

proportionsproportions

What proportion of the disease among the exposed is attributable to the exposure (APexp)?

= AR / IR+ = (RR-1)/RR

What proportion of the disease in the total study population of exposed individuals is attributable to the exposed individuals is attributable to the

= exp. prevalence in population)

= exp. prevalence among cases)

1) x pe - 1]

Hu et al. Hu et al. Diet, lifestyle, and the risk of type 2 diabetes mellitus. NEJM 2001;3 45: 790Diet, lifestyle, and the risk of type 2 diabetes mellitus. NEJM 2001;3 45: 790Diet, lifestyle, and the risk of type 2 diabetes mellitus. NEJM 2001;3 45: 790Diet, lifestyle, and the risk of type 2 diabetes mellitus. NEJM 2001;3 45: 790--77

AttributableAttributable

IncidenceIncidence rates of head and rates of head and

”Non-smoker”

”Non-drinker” 1

� Among drinking smokers, what proportion of head and neck cancer is caused by smoking?

� Among drinking smokers, what proportion of head and neck cancer is caused by drinking?

”Drinker” 3

AttributableAttributable proportionproportion

rates of head and rates of head and neckneck cancer per 100,000 py cancer per 100,000 py

smoker” ”Smoker”

4

Among drinking smokers, what proportion of head and neck cancer is caused by smoking?

Among drinking smokers, what proportion of head and neck cancer is caused by drinking?

12

AttributableAttributable

IncidenceIncidence rates of head and rates of head and

”Non-smoker”

”Non-drinker” 1

� Among drinking smokers, what proportion of HNC is caused by smoking?

� AP = IRD/IR+S+A = (IR+S+A-IR

”Drinker” 3

AttributableAttributable proportionproportion

rates of head and rates of head and neckneck cancer per 100,000 py cancer per 100,000 py

smoker” ”Smoker”

4

Among drinking smokers, what proportion of HNC is caused

IR-S+A)/IR+S+A = (12-3)/12 = 75%

12

AttributableAttributable

IncidenceIncidence rates of head and rates of head and

”Non-smoker”

”Non-drinker” 1

� Among drinking smokers, whadrinking?

� AP = IRD/IR+S+A = (IR+S+A-IR

”Drinker” 3

AttributableAttributable proportionproportion

rates of head and rates of head and neckneck cancer per 100,000 py cancer per 100,000 py

smoker” ”Smoker”

4

hat proportion of HNC is caused by

IR+S-A)/IR+S+A = (12-4)/12 ≈ 67%

12

Study population

NSAID users

Non-users of NSAID 100,000

In total 120,000

A A hypotheticalhypothetical population population consistsconsists

antianti--inflammatoryinflammatory drugs (drugs (NSAIDsNSAIDs

NSAID. The NSAID. The studystudy subjectssubjects areare followedfollowed

occurrenceoccurrence of upper gastrointestinal (GI) of upper gastrointestinal (GI)

Calculation of the following measures of frequency and risk:

1. Incidence rate (IR) for GI bleeding in each exposure group

2. Incidence rate ratio (IRR) for the association between NSAID and upper GI bleeding

3. Incidence rate difference (IRD≈AR) between NSAID users and non

4. Attributable proportion (APexp) among users of NSAIDs

5. Attributable proportion (APpop) in the total population

(Censoring in the risk population should be ignored)

N GI bleeding

20,000 100

100,000 100

120,000 200

consistsconsists of 20.000 users of of 20.000 users of nonnon--steroidsteroid

NSAIDsNSAIDs) og 100.000 non) og 100.000 non--users of users of

followedfollowed for for oneone yearyear for the for the

of upper gastrointestinal (GI) of upper gastrointestinal (GI) bleedingbleeding

Calculation of the following measures of frequency and risk:

1. Incidence rate (IR) for GI bleeding in each exposure group

2. Incidence rate ratio (IRR) for the association between NSAID and upper GI

3. Incidence rate difference (IRD≈AR) between NSAID users and non-users

) among users of NSAIDs

) in the total population

(Censoring in the risk population should be ignored)

Study population

NSAID users

Non-users of NSAID

In total

IRNSAID = 100/20000 = 0.005 = 5 per 1000 person

IRo = 100/100000 = 0.001 = 1 per 1000 person

IR = 200/120000 = 0.00167 = 1.67 per 1000 person

AR = IRD = IRNSAID–IRo = 5

IRR = IRNSAID/IRo = 5/1 = 5

IRpop = 200/120000 = 0.00167 = 1.67 per 1000 person

APexp = AR/IRNSAID = 4 per 1000/5 per 1000 = 0.80 or 80%

ARpop = IRpop–IRo = 1.67 – 1 = 0.67 per 1000 person

APpop = ARpop /IRpop = 0.67/1.67

N GI bleeding

20,000 100

100,000 100

120,000 200

= 100/20000 = 0.005 = 5 per 1000 person-years

= 100/100000 = 0.001 = 1 per 1000 person-years

= 0.00167 = 1.67 per 1000 person-years

= 5-1 = 4 per 1000 person-years

= 5/1 = 5

= 0.00167 = 1.67 per 1000 person-years

= 4 per 1000/5 per 1000 = 0.80 or 80%

1 = 0.67 per 1000 person-years

= 0.67/1.67 ≈ 0.40 or 40%

CohortCohort

� Can examine

� multiple effects of a single exposure

� rare exposures

AdvantagesAdvantages

� rare exposures

� Exposures with certainty precede outcomes (if prospective)

� Allows direct measurement of incidence (IR, IP) of outcomes

studiesstudies

� Can elucidate temporal relationship between exposure and outcome

� Allow study subjects to

AdvantagesAdvantages

� Allow study subjects to contribute person-time to multiple exposure categories

� Biological material can be collected prior to outcome

� If prospective, minimizes bias in the ascertainment of exposure

CohortCohort

� Is inefficient for the evaluation of rare diseases

� If prospective, can be

DisadvantagesDisadvantages

� If prospective, can be very expensive and time consuming

� If retrospective, requires the availability of adequate records for both exposure and outcome

studiesstudies

� If prospective, cannot provide quick answers

� If retrospective, precise classification of exposure

DisadvantagesDisadvantages

classification of exposure and outcome may be difficult

� Validity of the results can be seriously affected by losses to follow-up

Planning a Planning a cohortcohort

� Definition of the scientific question(s)

� Important considerations

� Possibilities for collection of detailed information on exposure(s), confounders and outcome(s)

� Precise/consistent definition of exposure(s) and outcome(s)

� Evaluation of the empirical vs. theoretical definition

� Size of study

� Sample size calculations

� Prevalence of exposure

� Incidence of outcome

cohortcohort studystudy

Definition of the scientific question(s)

Possibilities for collection of detailed information on exposure(s),

Precise/consistent definition of exposure(s) and outcome(s)

Evaluation of the empirical vs. theoretical definition

PlanningPlanning a a cohortcohort

� Time dimension

� Historical cohort study, available data on both exposure and outcome

� Prospective study, continuous update of exposure, confounder and outcome dataconfounder and outcome data

� Potentially ambi-directional

� Selection of study population

� Representative of population in study base?

cohortcohort studystudy (2)(2)

Historical cohort study, available data on both exposure and

Prospective study, continuous update of exposure, confounder and outcome dataconfounder and outcome data

Selection of study population

Representative of population in study base?

General population General population General population General population cohortcohort

� Establishment of cohort

� Population cohort

• General population, e.g. ”Diet, Cancer and Health study”, ”Mother/child study”

PlanningPlanning a a cohortcohort

”Mother/child study”

• Sub-population, e.g., ”Nurses Health Study”

� Identification based on exposure

• Special exposure groups, e.g., painters

• Specific exposure(s), e.g., drugs

General population, e.g. ”Diet, Cancer and Health study”,

cohortcohort studystudy (3)(3)

population, e.g., ”Nurses Health Study”

Identification based on exposure

Special exposure groups, e.g., painters

Specific exposure(s), e.g., drugs

� Choice of comparison group(s)

� Internal comparison, population cohorts

� External comparison group

• General population sample

PlanningPlanning a a cohortcohort

• General population sample

• Other population group

• Occupational/special exposure group

• Drug users

• etc.

� Whole population

� Indirect standardization approach

Choice of comparison group(s)

Internal comparison, population cohorts

cohortcohort studystudy (4)(4)

Occupational/special exposure group

Indirect standardization approach

� Ascertainment of exposure(s) and outcome(s)

� Instrument

� Methods of ascertainment similar for each study group?

� Evaluate methods to reduce bias

PlanningPlanning a a cohortcohort

� Evaluate methods to reduce bias

� Knowledge about hypothesis and the other study axis (exposure/outcome)?

• Study subject

• Observer

� Register data (primary or secondary data source)

Ascertainment of exposure(s) and outcome(s)

Methods of ascertainment similar for each study group?

Evaluate methods to reduce bias

cohortcohort studystudy (5)(5)

Evaluate methods to reduce bias

Knowledge about hypothesis and the other study axis

Register data (primary or secondary data source)

�Historical cohort studies

�Comparison with general population (rates)

CohortCohort

MethodsMethods for for reductionreduction

�Comparison with general population (rates)

�Register studies

�Nested case-control studies

Historical cohort studies

Comparison with general population (rates)

studiesstudies

reductionreduction of of costscosts and time and time

Comparison with general population (rates)

control studies

Register studies in DKRegister studies in DKRegister studies in DKRegister studies in DK

Register studies in DKRegister studies in DKRegister studies in DKRegister studies in DK

Register studies in DKRegister studies in DK

Frank L. Science 2000;287: 2398Frank L. Science 2000;287: 2398--99

Register studies in DKRegister studies in DK

Register studies in DKRegister studies in DK

CPR RegisterIDA Register(socioeconomic

Cancer Registry

National Hospital Register

(socioeconomic variables)

Register studies in DKRegister studies in DK

CPR Register

National Death Files

Birth RegisterBirth Register

Prescription Databases

Thygesen et al. Scandinavian Journal of Public Health, 2011 Thygesen et al. Scandinavian Journal of Public Health, 2011 Thygesen et al. Scandinavian Journal of Public Health, 2011 Thygesen et al. Scandinavian Journal of Public Health, 2011 SupplSuppl, Vol. 39 , Vol. 39 IssueIssue 77

AdvantagesAdvantages withwith recordrecord

Data Data specificityspecificity

recordrecord linkagelinkage studiesstudies

and and sensitivitysensitivity

Register studiesRegister studies

�Registers are highly valuable data sources,

�Difficulties in interpretation due to incomplete data on competing risk factorson competing risk factors

� Life-style factors, socioeconomic factors, comorbidity, medical treatment

�Other potential biases

� Misclassification, non-

Register studiesRegister studies

Registers are highly valuable data sources, BUT

Difficulties in interpretation due to incomplete data on competing risk factorson competing risk factors

, socioeconomic factors, comorbidity, medical treatment

-compliance, etc.

Risk Risk windowwindow

Exposure

Often unknown

windowwindow

Often unknown

Relevant exposure?Relevant exposure?

Ex Ex Ex

Ex Ex Ex

ExExEx

Ex Ex Ex

Ex Ex Ex

Relevant exposure?Relevant exposure?

1-3 days?

10-15 days?

100-150 days?

years?

HazardHazard functionfunction

Outcome Theoretical association

functionfunction

Theoretical association

Exposure

HazardHazard functionsfunctions

Outcome

functionsfunctions

Exposure

Assumption: minimum induction time of X years

The follow-up) time for an exposed person

Defining followDefining follow

The follow-up) time for an exposed person

should begin X years after that person becomes

exposed

Assumption: minimum induction time of X years

up) time for an exposed person

Defining followDefining follow--up timeup time

up) time for an exposed person

should begin X years after that person becomes

� Hypothesis:

� Jobs involving heavy work activity protects against cardiovascular disease (CVD)

� Let us suppose that heavy activity has no effect unless

DefiningDefining followfollow

� Let us suppose that heavy activity has no effect unless 10 years of such activity has accumulated. Let us suppose further that the protective effect persists for 5 years beyond the end of the heavy activity

� How should incidence rates for CVD among workers with heavy activity be calculated?

Jobs involving heavy work activity protects against cardiovascular disease (CVD)

Let us suppose that heavy activity has no effect unless

followfollow--up timeup time

Let us suppose that heavy activity has no effect unless 10 years of such activity has accumulated. Let us suppose further that the protective effect persists for 5 years beyond the end of the heavy activity

How should incidence rates for CVD among workers with heavy activity be calculated?

Defining follow

0 105 15

A = period of heavywork

B = period of

0 105 15

Start ofheavy work

Protection begins

A B

Defining follow-up time

2520

B = period of protection

2520

Time (yrs)

End of heavy work

End of protection

NSAID NSAID cohortcohort

� Population: Saskatchewan – provinhabitants

� A study of the association between use of NSAIDs and risk of gastrointestinal (GI) bleeding included all 228,392 individuals who had redeemed one og more prescriptions for NSAIDs. The study subjects were followed during the period 1982hospitalization due to upper GI bleedinghospitalization due to upper GI bleeding

� From the paper: .. Entered our cohort upon the first receipt of a prescription for diclofenac, indomethacin, naproxen, piroxicam or sulindac. Person-time contributed by this person continued until the earliest of: 1) hospitalization due to UGB, 2) death, 3) departure from Saskatchewan or 4) end of study

� Note!: No control group of ’non-

Garcia Rodriguez et al. NSAIDs and GI-hospitalizations in Saskatchewan: A cohort study.

Epidemiology 1992;3:337-42

cohortcohort studystudy

rovince in Canada with appr. 1.1 mill.

A study of the association between use of NSAIDs and risk of gastrointestinal (GI) bleeding included all 228,392 individuals who had redeemed one og more prescriptions for NSAIDs. The study subjects were followed during the period 1982-1986 for hospitalization due to upper GI bleedinghospitalization due to upper GI bleeding

.. Entered our cohort upon the first receipt of a prescription for diclofenac, indomethacin, naproxen, piroxicam or

time contributed by this person continued until the earliest of: 1) hospitalization due to UGB, 2) death, 3) departure from Saskatchewan or 4) end of study

-exposed’

hospitalizations in Saskatchewan: A cohort study.

Current user Recent past user Old past user

Day 0 30 60 150

The person time of the study subjects was categorized according to time since last prescription

Current user Current user Current user

# 1

# 2

1. Rx

Day 0 30 30 30 30 60

1.Rx 3.Rx2.Rx

Current user Recent past user

Person 1 30 30

Person 2 120 30

Old past user Non-user

0 30 60 150

The person time of the study subjects was categorized according to time since last prescription

Current user Recent past userCurrent user

0 30 30 30 30 60

4.Rx

Recent past user Old past user Nonuser

90 >90

- -

Incidence rate ratios of GI-hospitalisations of NSAID users

Current users Recent past users (0-30 days)

Diclofenac 3.9

Indomethacin 4.0

Modified from Garcia Rodriguez et al. NSAIDS and GI

Saskatchewan: A cohort study. Epidemiology 1992;3:337

Naproxen 3.8

Nonusers

hospitalisations of NSAID users

Recent past users Old past users

(30-60 days) (60-150 days)

2.2 1.3

1.7 1.4

Modified from Garcia Rodriguez et al. NSAIDS and GI-hospitalizations in

Saskatchewan: A cohort study. Epidemiology 1992;3:337-42

2.3 1.4

1.0