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Course: EPIB 679-001 Clinical Epidemiology

Date: May 8 to June 28:35 – 11:40

Session 4: Cohort studies

Dr. J. Brophy

TYPES OF STUDIES

DESCRIPTIVE

ANALYTICAL

OVERVIEW OF STUDY DESIGNS IN PHARMACOEPIDEMIOLOGYPOPULATION LEVEL• Ecologic or Correlational

studies

INDIVIDUAL LEVEL• Drug utilization studies• Case reports / series• Cross-sectional surveys

OBSERVATIONAL

EXPERIMENTAL / INTERVENTIONAL

Randomized controlled trials• Prospective• Field

Cohort studies• Prospective vs retrospective• Field vs database studies

Nested case-control studies• Retrospective• Field vs database studies

Case-cohort studies• Retrospective• Field vs database studies

Case-control studies• Prospective vs retrospective• Field vs database studies

Case-crossover studies• Prospective vs Retrospective• Field vs database studies

GOAL

• Hypothesis generating• Resource allocation• Educational needs

GOAL

• Hypothesis testing• Provide evidence to

establish causality

Observational studies

• Distinguishing feature investigator does not control the exposure

• Types of observational studies– Cohort studies– Case-control studies

• Classification– Prospective vs retrospective study– Field vs database study

Definition: Cohort

• From the Latin cohors – warriors, the tenth part of a legion.

Any group of persons (usually sharing some commoncharacteristic) who are followed-up or traced over a period of time.

Schematic of a Cohort Study

Exposure Outcome

Select

Ascertain

Past Present

Populations and General Design

Target populationto which inferencesare drawn

Source populationSource of persons withoutoutcome (sampling frame)

Exposed (dynamic)cohort

Non-exposed (dynamic)or reference population

Assessment of Outcome

Assessment of Outcome

Overarching population (universe) that we would like to make inferences

Time

Participants

Non-participants

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Cohort studies

• Group that shares a common experience

• Subjects classified on the basis of exposure status

• Longitudinal studies followed for a specified period of time until events occur

• Distinguishing feature compare rates of events/outcomes by exposure group

Comparison

Cohort studies

• If rate of event among exposed > rate of event in unexposed = harmful drug

• If rate of event among exposed < rate of event in exposed = protective drug

Timing

Cohort design Cohort studies

• Strengths– Can study rare exposures

– Can study multiple outcomes

– Temporality is assured causality criteria

– Unbiased selection of comparator group

– Retrospective studies are relatively quick and inexpensive … caution re: bias

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Cohort studies

• Limitations– Inefficient for rare events/diseases or outcomes

with long induction periods– If prospective expensive and time consuming

• Sources of bias– Non-participation (selection in)– Losses to follow-up (selection out)– Recall / interviewer bias if retrospective

Potential problems

Definition: Bias

• Bias: Deviation of results or inferences from the truth, or processes leading to such deviation.

1. Systematic variations of measurements from their true values (systematic error; antonym, validity)

2. Variations of statistics from their true values as a result of systematic variation of measurements, other flaws in data collection, or flaws in study design and analysis.

Antonym: Validity

Biases

• Misclassifcation• Selection (chanelling)• Losses to follow-up (correlated to exposure

and disease)• Effect of non-participation

Selection bias Confounding

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Confounding bias

Intervention Outcome

Confounder

• Age• Sex• Stage of disease• Previous treatments• Genetics• Behaviour• Others

Channeling Effect (or Channeling Bias):

• The tendency of clinicians to prescribe treatment based on a patient’s prognosis. As a result of the behavior, comparisons between treated and untreated patients will yield a biased estimate of treatment effect.

Effect modification Key questions for a cohort study

Key questions for a cohort study Comparisons

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Comparisons All patients & hip fractures

Restricted cohort

Key questions for a cohort study Strengths of cohort studies

• Useful if exposure is rare• Can examine multiple effects of a single

exposure• Can elucidate temporal relationship• If prospective, minimizes ascertainment bias• Allows direct measurement of disease

incidence in both exposed and non-exposed groups

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Limitations of cohort studies

• Inefficient for rare diseases• Can be expensive and time consuming if

prospective• If retrospective, need reliable records• Validity affected by losses to follow-up

Cholera in London in the mid-1800s: John Snow and the Beginnings of Epidemiology

Miasmata Theory

• Thought that cholera was brought to Europe from India

• Prevailing theory in the 1880s: airbornepoison arising from unhealthy and unsanitary conditions (“miasmata”)– Miasma: noxious exhalations from putrescent

organic matter; poisonous effluvia or germs infecting the environment

Hypothesis

• Higher rates in the south because water companies drew water from the polluted Thames River

Snow’s Experimentum Crucis

1849 1854Relatively low rates of cholera in London

Water Supply from Polluted Thames River:Southwark & Vauxhall Co.Lambeth Co.

Natural experiment:In 1852, Lambeth changed its source to a less polluted part of The Thames 1854 epidemic: Snow determined no. of homes

served by each companyCollected death reports and classified deaths by water companyCalculated ratios of deaths to no. of homes, by water company

Epidemiology

• Unit of observation is mixed:1. Numerator - the individual: fact, date, cause of

death, and water companyWater company obtained from detailed inquiry ortest of water for concentrations of NaCl

2. Denominator – the number of homes (not individuals) served by each company

• Statistic: Ratio=Numerator/Denominator (unit: persons/homes)– not a proportion (unitless)

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Epidemiology

• Exposures to the “causal agent”: inferred to be related to the water supply– Thus, the company that supplied the water is a surrogate

variable• Use of “company” is referred to as an “ecological”

variable– Every individual and home so classified is assumed to have

the same exposure (homogeneity of exposure)• This study would probably now be referred to as an

“ecological study”

Deaths from Cholera per 10,000 Homes, by Source of Water Supply, London, 1854

+259.9

-18.0

0

Difference

5.7315.41,26340,046Southwark & Vauxhall

0.737.59826,107Lambeth

155.51,422256,423Rest of London

RatioDeaths/10,000 homes

Deaths from cholera

Number of homes served

Company

Estimate of average no. of deaths per home ratio=8.4

Broad Street Pump Episode

• Another detailed cluster investigation by Snow

• Occurred at the end of August 1854• Attributed source: polluted well water

contaminated from an adjoining cesspool which was contaminated with water from a young girl who apparently had the cholera

Broad Street Pump - Number of Deaths in 1854

0

20

40

60

80

100

120

140

Date (from Aug 31, 1854)

No.

of d

eath

s

Pump handleremoved (Sept 8)

Total deaths=573

Pump Handle Removed

Contingency Table: Mortality from Cholera in the Broad Street, Aug. 31-Sept. 2 (Whitehead’s observations: Shephard, p. 224)

436299137Total

33627957No cholera

1002080Cholera

TotalDid not drink water

Drank water

Acknowledged to have:

Relative Risk as a Measure of Association

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Odds Ratio as a Measure of Association RRs and ORs

• The OR>>RR because the disease is not rare– i.e., the risk of dying is 100/436=23%

• For small risks (∼≤0.05), OR~RR

Classic cohort studies

• British Doctors Cohort• Framingham• Harvard Nurses’ Health Study

Example: British Doctors Cohort Study

Design

1951 1957 1966 1972 time

More quests

Questionnaires on smoking habits to 59,600

male & femalephysicians - 34,440

responded 1st quest.

Response~69%

Follow-up for mortality

British Doctors Cohort British Doctors Cohort

• Overarching population (universe): entire population

• Target population: Men and women, age >20, in 1951

• Source population: British MDs, age >20, in 1951– Sampling frame: Medical register of MDs

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British Doctors Cohort

• Exposure: Smoking information from subjectsbased on a short postal questionnaire– Current smokers

• Age started smoking• Amount consumed currently• Method of smoking

– Past smokers• Same as above• Date stopped smoking

– Never smoked regularly (<1 cigarette/year for one year)

British Doctors Cohort

• Outcome:– Mortality ascertained by looking-up death

certificates– Cause of death is filled in by a physician or the

coroner• Analysis:

– Compare rates of death according to level of self-reported smoking

Typical Questions about Smoking

• Type of smoking (cigarettes, cigars, pipes)• Have you ever smoked regularly?• How old were you when you started to smoke?• How many cigarettes per day do you smoke

now?• If you stopped completely, how long ago was

this?

Metrics of Exposure to Tobacco Smoke

• The following indices can be estimated:– Type of smoking (cigarettes, cigars, pipes)– Duration (time since starting)– Time since quiting– Average Intensity (e.g., no. of cigarettes/day)– Frequency (e.g., percent time smoked in a week)– Current smoking status

Metrics of Exposure

• Cumulative exposure: frequency of smoking x intensity x duration– E.g., 1 pack per day x 20 cigarettes/pack x 365

days/year x 30 years= 219,000 cigarette-days=30 pack-years

• Lagged cumulative exposure (e.g., excluding last 10 years of smoking)

Definitions: Exposure and Dose

• Exposure: The presence of a substance in the environment external to the subject (external/environmental)

• Dose: The amount of a substance that reaches susceptible targets in the body (internal)

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British Doctors

• Amount smoked at time of administration of firstquestionnaire:

Non-smokersCurrent: 1-14 cigs/day

15-24 ≥ 25

• These groups represent sub-cohorts defined by exposure at time of entry into the study

• However, information obtained during follow-up can change exposure status, so these sub-cohorts would not be fixed

British Doctors Cohort: Men

NA

NA

NA

NA

18,963

40,637 (69%)

N/A

N/A

1st Quest

362445369Other

2240372Not found

1026336Refused

216531Too ill

5071156508Reasons for nonresponse

23,299 (97.9%)26,163 (96.4%)30,810 (98.4%)Replied

238062713931318Presumably alive

1063473013122Known to have died

4th Quest3rd Quest2nd QuestSurvey period

British Doctors Study: Lung Cancer in Men among Current Smokers from Data Obtained at Last

Questionnaire

25.1251>25

12.712715-24

7.8

8.2

5.8 (=58/10)

14 (=140/10)

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Mortality Rate Ratio

781-14

Cigarettes only (No. per day)

82Mixed

58Pipe &/or cigars

140Cigarettes only

10Non smokers

Age-standardized death rate (10-5)

Nested Case-Control Studies

• Sub-study that is based on an explicit cohort• Motivation:

– Computational ease for large datasets– Require additional information not already

collected• To reduce costs, a sample of subjects from the original

cohort is taken

Synonyms

• Case-control-within-cohort studies• Incidence density sampling studies• Synthetic case-control studies

• Case-control studies are also referred to as case-referent studies

Incidence Density Sampling

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Time

No.12345678

Time for 1st failure

Time for 2nd failureRisk set for 1st failure

Risk set for 2nd failure

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Incidence Density Sampling

1. For each failure time (T) of each case, define all subjects who at that time are still at risk of developing the outcome

– The complete set of such subjects is called the risk set for the case

– Will exclude all subjects who before T were:• Censored• Failed

Incidence Density Sampling

2. Randomly select without replacement a sample of “controls” from the risk set

• These subjects are therefore “matched” to the case by time of event

• Other matching variables can be used so that the sampling is stratified; e.g., select only a random sample of women

• If a potential control eventually becomes a case, he is still at-risk at the time of the event

• A fixed number of controls can be selected; that number can vary from risk set to risk set

Incidence Density Sampling

3. The analysis of these data is similar to the stratified analysis used in the M-H procedure for rates

4. The strata are now defined as each selected risk set.

Incidence Density Sampling

5. The measure of association is the odds ratio. With this sampling strategy and a matched analysis, it provides an unbiased estimate of the rate ratio.

• A matched analysis is one that accounts explicitly for the matching during the fieldwork

Incidence Density Sampling

6. The estimated OR will have more variability than the full M-H cohort analysis because fewer subjects are included

7. There is no need to calculate person-years in this analysis. It is subsumed automatically in the sampling.

Incidence Density Sampling

8. Odds ratios in each risk set are not calculated; rather a summary estimate across all risk sets is obtained.

• This assumes that the rate ratio does not vary by time (proportional hazards assumption). Equivalently, the OR across strata (matched subjects) are ~ equal (homogeneous).

9. Only risk sets that are discordant on exposure contribute information

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Examples

Background

• Stenting common Rx for CAD symptoms• Statin therapy improves survival in secondary

prevention in conservatively treated patients• Is the same benefit present following

stenting?

Methods

• 4,520 patients < 80• Examined 1 year mortality• 3,585 with statins on discharge• 935 no statins on discharge

Results

• Mortality 2.6% statins, 5.6% no statins• Unadjusted OR 0.46 (95% 0.33 – 0.65)• Adjusted OR 0.51 (95% 0.36 – 0.71)• Methods included propensity analysis for

statin prescription and Cox PH model with a substantial number of clinical covariates

NEJM 1998;339:1349-57

Typical RCT

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So, what’s the problem?

51% reduction in mortality observed in 12 months

24% reduction in mortality observed in 72 months

Red Flag

• If it looks too good to be true, it probably is too good to be true

Potential Biases

• Channeling (selection bias in pharmacoepistudies)

• Misclassification (exposure is not time independent)

1 2 3 4 5 6 7 8 9 10 11 12

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7

6

5

4

3

2

1

X

X

X

Time (Months)

Statin Group

No Statin Group

RR = 1/4 / 2/4 =.5

RR = 1/ 42 person-months / 2 /42 pm = 0.5

1 2 3 4 5 6 7 8 9 10 11 12

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7

6

5

4

3

2

1

X

X

X

Time (Months)

Statin Group

No Statin Group

D/C @ 1 month - 11 months non-statin exposureX

Start statin @ 1 month - 6 months -statin exposureX

Person-time

Statin = 2 / 37 Non statin = 1 / 47

RR = 2.4

A different approach

Results: Decrease in mortality of 34% 95%CI (4-55%) after 36 months)

(Am Heart J 2005;150:282- 7.)

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Message

• Vital to consider the time dependency of drug exposure

• Another relatively easy method is to perform a nested case control study that matches on cohort entry

• Assure equal follow-up time