Expocicion No Experimental Experimental y Cuasi Experimental
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Transcript of 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