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Cohort Study

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Cohort Study Subodh S Gupta Dr. Sushila Nayar School of Public Health MGIMS, Sewagram * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * The ... – PowerPoint PPT presentation

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Title: Cohort Study


1
Cohort Study
  • Subodh S Gupta
  • Dr. Sushila Nayar School of Public Health
  • MGIMS, Sewagram

2
(No Transcript)
3
Origin of word cohort
  • The word cohort has its origin in the Latin
    cohors
  • cohors (Latin word) Refers to warriors and
    gives notion of a group of persons proceeding
    together in time
  • Group of persons with a common statistical
    characteristic e.g. age, birth date

4
Definition Synonyms
  • Definition
  • The cohort study is an observational
    epidemiological study which, after the manner of
    an experiment, attempts to study the relationship
    between a purported cause (exposure) and the
    subsequent risk of developing disease.
  • Synonyms
  • Follow-up
  • Longitudinal
  • Prospective
  • Incidence study

5
The cohort design
  • Groups are exposure based The group or groups of
    persons to be studied are defined in terms of
    characteristics manifest prior to the appearance
    of the disease under investigation
  • The study is conceptually longitudinal The study
    groups so defined are observed over a period of
    time to determine the frequency of disease among
    them
  • A definite beginning and end

6
The cohort design
  • Efficient for examining
  • When there is good evidence of exposure and
    disease.
  • When exposure is rare but incidence of disease is
    higher among exposed
  • When follow-up is easy, cohort is stable
  • When ample funds are available
  • Common outcomes

7
The cohort design
  • Many different outcomes for same exposure
  • The dynamic nature of many risk factors and their
    relations in time to disease occurrence can be
    captured here (cannot be done in cross-sectional
    study and only with difficulty in case-control
    study)
  • Associations (not cause and effect)
  • Estimate incidence within risk factor groups
  • Cannot estimate prevalence of risk factor

8
Time
Case control study
Direction of enquiry
9
Time
Cohort study
Direction of enquiry
10
Types of cohort study
  • Historical/ Retrospective/ Non-concurrent
  • Prospective/ Concurrent

The distinction between retrospective and
prospective cohort studies is important, not
because of any conceptual difference or
differences in interpretability of findings, but
because of relevance to some practical issues,
mostly the ability to control confounding.
11
Time
Point in time when enquiry begins?
Direction of enquiry
12
Time
Both exposures and outcomes measured prospectively
Direction of enquiry
13
Time
Exposures measured retrospectively and outcomes
prospectively
Direction of enquiry
14
Time
Both exposures and outcomes measured
retrospectively
Direction of enquiry
15
Advantages
  • Direct estimate of risk and rate of disease
    occurrence over time
  • An efficient means of studying rare exposures
  • Assess multiple outcomes of a single exposure
  • Establish temporal relationship between exposure
    and outcome
  • Exposure definitely precedes the outcome
  • Avoids recall bias, survival bias
  • Does not require strict random assignments of
    subjects
  • Can be done with original data or secondary data

Best observational design to establish association
16
Disadvantages
  • Very large sample sizes, especially for rare
    outcomes
  • Expensive and time-consuming
  • Attrition problem (Loss to follow-up)
  • Differences in the quality of measurement of
    exposure or disease b/w the cohorts may introduce
    misclassification (information bias)
  • Can not infer causal relation
  • Very specific finding
  • Complexity of data analysis
  • Ethical issues
  • Study effects

17
Alternate designs and concerns
  • Two separate cohorts exposed and unexposed
    subjects
  • Omission of non-factor group
  • Use of external comparison
  • Use of mortality than morbidity as outcome
  • Event notification arises from routine
    statistics, rather than special observations
  • Comparison of several groups
  • Competing causes of death

18
Cohort Study Steps
19
Steps in conducting cohort study
  • Identification of study population and initial
    steps
  • Measurement of exposure
  • Selection of study and comparison cohorts
  • Follow-up (for outcome measurement)
  • Data analysis

20
Types of cohorts
  • Closed or fixed cohorts
  • Fixed group of persons followed from a certain
    point in time until a defined endpoint
  • Starting point - exposure defining event
    Endpoint occurrence of the disease, loss to
    follow-up, death
  • The exposure is an event which occurs only once
  • Open or dynamic cohorts
  • Subjects may enter or leave the study at any time
  • Exposure status may change over time

21
Cohorts
  • General population cohorts population groups
    offering special resources for follow-up or data
    linkage are chosen, and the individuals are
    subsequently allocated according to their
    exposure status
  • Special exposure cohorts Samples chosen on the
    basis of a particular exposure
  • Exposures may be a particular event, a
    permanent state or a reversible state

22
General population cohorts (groups offering
special resources)
  • Groups with readily available health records
  • Certain professional categories
  • Obstetric populations
  • Volunteer groups
  • Geographically identified cohorts
  • Record linkage

23
Special exposure cohorts (groups offering
special resources)
  • Exposed to certain factor or event
  • Occupational groups
  • Based on qualitative characteristics

24
Population-based Cohort Studies
  • Advantages
  • Estimation of distributions and prevalence rates
    of relevant variables
  • Risk factor distributions
  • Ideal setting in which to carry out unbiased
    evaluation of relations

25
Selection of comparison group
  • Internal comparison
  • Only one cohort identified
  • Later on, classified into study and comparison
    cohort based on exposure
  • External comparison
  • More than one cohort identified
  • e.g. Cohort of radiologist compared with
    ophthalmologists
  • Comparison with general population rates
  • If no comparison group is available we can
    compare the rates of study cohort with general
    population
  • Cancer rate of uranium miners with cancer in
    general population

26
Ideal Cohort
  • Stable cohort
  • Cooperative cohort
  • Committed cohort
  • Well informed cohort

27
Exposure measurement
  • Exposures exogenous and/ or endogenous
  • Reference period
  • Frequency of follow-up
  • Challenge of prospective data collection
  • Changes in instrument over time
  • Use of repeated measures
  • Data collection costs

28
Sources of information
  • Records
  • Cohort members self-administered questionnaires,
    interviews, telephone interviews, mailed
    questionnaires,
  • Medical examination biomarkers Clinic
    examinations lab tests
  • Measures of the environment level of air
    pollution, quality of drinking water, airborne
    radiation
  • Multiple methods

29
Follow-up Types of outcomes
  • Discrete events
  • Single events
  • Mortality
  • First occurrence of a disease or health-related
    outcome
  • Multiple occurrences
  • Disease outcome
  • Transition between states of health/ disease
  • Transitions between functional states
  • Level of a marker

30
Examples of short duration Cohort Studies for a
PG dissertation
  • Family conflict, adherence, and glycaemic control
    in youth with Type 1 diabetes
  • Birth cohorts to find out association between
    birth weight and hypertension childhood asthma

31
Exercise 1
  • An investigator wants to discover whether or not
    being overweight in adolescence increases the
    risk of cardiovascular mortality in adulthood.
  • Assuming historical records are available, would
    a prospective or retrospective study be more
    practical?
  • Who would comprise the investigator's cohort
    under study?
  • Who would comprise the investigator's exposed and
    unexposed groups in this cohort?

32
Group Exercise
  • Design a Cohort Study
  • Outline the steps which you will require to do
    for this study
  • Special efforts you may need to do for follow-up
    of the study subjects
  • What care you will need to take to reduce
    measurement bias
  • Calculate the sample size

33
Challenges in conducting Cohort Study
34
Challenge 1 multiple dimensions of time in
cohort study
35
Challenge 2Retaining cohort study members
  • Loss to follow-up
  • Dropouts
  • Can not be traced
  • More concern those who cannot be traced May
    have moved because they have developed the disease

36
Effect of Nonresponse
  • Nonresponse a major problem
  • A differential nonresponse will distrorts the
    true relationship b/w exposure and outcome

37
Nonresponse random or selective?
  • Exposure data find out if nonrespondents are
    different from the respondents
  • Intensive efforts within the study design
  • Follow-up of the nonrespondents as well as
    respondents

38
Challenge 3Large Modern Cohort Studies
  • Huge requirements of resources and manpower
  • Management of huge database
  • Follow-up
  • Exposure information
  • Data quality?
  • Collection of biologic samples?

39
Challenge 4Long term follow-up
  • Operational problems
  • Cumulative risk getting closer to one

40
Nested case control Case cohort study
  • Nested Case Control Study
  • Case Cohort Study
  • Can be done in both population-based and
    non-population based cohort settings
  • Meets the assumptions cases and controls come
    from the same population
  • Avoids problems related to recall bias

41
Cohort Study Analysis
42
Standard 2 X 2 table(Relation between exposure
and outcome)
43
Two types of measures for rate
  • Cumulative incidence Proportion of study
    subjects getting the outcome during the study
    period
  • Incidence rate New cases/ Person-time under
    observation

44
1. Cumulative incidence rate
  • Number of new cases of disease occurring over a
    specified period of time in a
    population at risk.

45
EXAMPLE
  • A surveillance system for Hospital acquired
    infection among the post-operative patients in a
    month.

46
Example
9 6 14 14 24 19 14 4 5 19 21 6
0 5 10
15 20 25
30
47
2. Incidence density
  • Number of new cases of disease occurring over a
    specified period of time in a population at risk
    throughout the interval.

48
  • Incidence density requires us to add up the
    period of time each individual was present in the
    population, and was at risk of becoming a new
    case of disease.
  • Incidence density characteristically uses as the
    denominator person-years at risk. (Time period
    can be person-months, days, or even hours,
    depending on the disease process being studied.)

49
USES OF INCIDENCE DENSITY AND CUMULATIVE INCIDENCE
  • Incidence density gives the best estimate of
    the true risk of acquiring disease at any moment
    in time.
  • Cumulative incidence gives the best estimate of
    how many people will eventually get the disease
    in an enumerated population.

50
Standard 2 X 2 table(Relation between exposure
and outcome)
51
l X 2 table(Relation between exposure and
outcome)
52
Comparing risks in different groups
  • Relative risk OR Risk ratio (RR)
  • Attributable risk OR Risk difference (AR)
  • Attributable risk percent (AR)
  • Population attributable risk (PAR)
  • Population attributable risk percent (PAR)
  • Odds Ratio (OR)

53
Relative risk OR Risk ratio
  • Ratio of the risk among exposed to the risk among
    unexposed Risk (Exp) / Risk (Unexp)
  • Risk of disease among exposed a/ a b)
  • Risk of disease among unexposed c/ c d)
  • RR a/ a b) / c/ c d)
  • For null hypothesis, Risk ratio will equal one
  • SE

54
Risk difference vs. Relative risk
22
Relative risk
Absolute risk
1
55
Attributable risk OR Risk difference (Absolute
differences in risks or rates)
  • Also known as attributable risk
  • Risk (Exp) Risk (Unexp)
  • Risk of disease among exposed a/ a b)
  • Risk of disease among unexposed c/ c d)
  • Risk difference a/ a b) - c/ c d)
  • For null hypothesis, Risk difference will equal
    zero

56
Risk difference vs. Relative risk
Risk difference
Absolute risks(Exp Unexp)
57
Attributable risk percent among exposed
  • Among exposed, what percent of the total risk for
    disease is due to the exposure
  • AR (Exposed)
  • Risk (Exp) Risk (Unexp)/ Risk (Exp) X 100
  • (RR 1)/ RR X 100
  • (OR 1)/ OR X 100 (if risk is small)

58
Attributable Risk Percent
22
risk due to exposure
Relative risk
Absolute risks (Exp)
risk due to background
1
59
Attributable Risk Percent
p0RR
Relative risk
p0RR
p0(RR-1)
p0
1
Attributable risk Percent (RR-1)/ RR 100
60
Population attributable risk
  • In the general population, how much of the total
    risk for disease is due to the risk factor
  • Risk (Total) Risk (Unexp)
  • Risk (Total)
  • Proportion population Exp X Risk (Exp)
  • Proportion population Unexp X Risk
    (Unexp)

61
Population attributable risk percent
  • Among the general population, what percent of the
    total risk for disease is due to the risk factor
  • PAR
  • Risk (Total) Risk (Unexp)/ Risk (Total) X
    100
  • Pe (RR 1)/ 1 Pe (RR 1) X 100

62
Population attributable risk percent
RR
(RR-1)(1-Pe)
Pe(RR-1)
(1-Pe)
Pe
1
Population Attributable risk Percent
Pe (RR 1)/ 1 Pe (RR 1) X 100
63
Risk Reduction
  • Risk (T/t) a/(ab)
  • Risk (Exp) c/(cd)
  • RR Risk (T/t)/ Risk (Exp)
  • ARR Risk (Exp) Risk (T/t)
  • RRR Risk (Exp) Risk (T/t) / Risk (Exp)
  • 1-Risk(T/t)/Risk(Exp)
  • 1-RR
  • NNT 1/ARR
  • 1/Risk(Exp)RRR
  • NNH

64
Analytical considerations
  • Concurrent follow-up
  • Varying follow-up dates
  • Moving baseline dates
  • Withdrawals
  • Competing causes of death

65
(No Transcript)
66
Analytical considerations
  • Concurrent follow-up
  • Simple risk-based analyses
  • Survival analysis
  • Varying follow-up dates
  • Simple risk analysis for all events up to, but
    not exceeding, the minimum elapsed time
  • Survival analysis
  • Moving baseline dates
  • Ignore and measure elapsed time since recruitment
  • Survival analysis
  • Withdrawals
  • Competing causes of failure

67
Advanced methods
  • Standardization
  • Stratification
  • Life Tables
  • Multivariate analysis and Cox regression

68
Exercise 2
  • A cohort study to explore the relationship
    between visual impairment and the risk of
    injuries from falls among the elderly.
  • A total of 400 visually impaired (VI) persons gt70
    yrs are compared against 400 controls without VI.
  • Over a 5-year follow-up period, 80 VI persons and
    20 non-VI persons have injuries from falls.
  • Construct a 2x2 table from the information above
  • Calculate the followings with their CI
  • Cumulative Incidence rate for exposed and
    unexposed
  • Relative risk
  • Attributable risk Attributable risk percent

69
Exercise 2
  • A cohort study to explore the relationship
    between visual impairment and the risk of
    injuries from falls among the elderly.
  • A total of 400 visually impaired (VI) persons gt70
    yrs are compared against 400 controls without VI.
  • Over a 5-year follow-up period, 80 VI persons and
    20 non-VI persons have injuries from falls.
  • Construct a 2x2 table from the information above
  • Calculate the followings with their CI
  • Cumulative Incidence rate for exposed and
    unexposed
  • Relative risk
  • Attributable risk Attributable risk percent

70
Exercise 3
  • A retrospective cohort study to explore the
    relationship between perimenopausal exogenous
    estrogen use and the risk of coronary heart
    disease (CHD).
  • A total of 5000 exposed and 5000 unexposed women
    are enrolled and followed for 15 years for the
    development of myocardial infarction (MI).
  • A total of 200 estrogen users and 300 nonusers
    had MIs.

71
Exercise 3 (Contd.)
  • The risk (CI) of a MI among estrogen users
  • The risk (CI) of a MI among nonusers of estrogen
  • The relative risk (CIR) for MI
  • Based on the results of this study is estrogen
    use a causative or protective factor for MI?

72
Exercise 4
  • Shaper et. al. (1988)
  • A random sample of 7729 middle-aged British men
  • Each man asked, at baseline, his alcohol
    consumption
  • Next 7.5 years, death certificates collected for
    any subject who died

73
Exercise 4 (Contd.)
  • Calculate the risk and the relative risk for each
    alcohol consumption group.
  • Why might the conclusion based on the above table
    may be misleading? Given adequate funding,
    describe how?

74
Exercise 5
  • In a cohort study of 34387 menopausal women in
    Iowa, intakes of certain vitamins were assessed
    in 1986. In the period up to the end of 1992,
    879 of these women were newly diagnosed with
    breast cancer. The table below shows data for two
    vitamins, classified according to ranked
    categories of intake.

75
Exercise 5 (Contd.)
  • For each vitamin, calculate the relative rates
    (with 95 confidence intervals) taking the
    low-consumption group as the base. Do your
    results suggest any beneficial (or otherwise)
    effect of additional vitamin C or E intake?

76
Types of bias
  • Selection bias
  • Follow-up bias
  • Information bias
  • Confounding bias
  • Post hoc bias

77
Selection bias
  • Group studied does not reflect the same
    distribution of factors (such as age, sex, SES,
    behavior etc.) as occurs in the general
    population
  • Effect of volunteering
  • Whole spectrum of independent variables not
    represented in the study group
  • Presence of incipient disease
  • Distribution of covariates
  • Survival cohorts cohorts ascertained long after
    exposure

78
Example bias with survival cohort
Observed improvement
TRUE COHORT
True improvement
Measure outcome Improved 75 Not improved
75
Assemble Cohort (N150)
50
50
SURVIVAL COHORT
Assemble patients
Begin Follow-up (N50)
Measure outcome Improved 40 Not improved 10
50
80
Not observed (N100)
Dropouts Improved 35 Not improved 65
79
Follow-up bias
  • Also known as Migration Bias
  • In nearly all large studies some members of the
    original cohort drop out of the study
  • If drop-outs occur randomly, such that
    characteristics of lost subjects in one group are
    on an average similar to those who remain in the
    group, no bias is introduced
  • But ordinarily the characteristics of the lost
    subjects are not the same

80
Example of lost to follow-up
EXPOSURE irradiation
EXPOSURE irradiation
30
30
DISEASE cataract
RR 30/4000 30/8000 2
RR 50/10000 100/20000 1
81
Example. healthy worker effect
  • Question association b/w formaldehyde exposure
    and eye irritation
  • Subjects factory workers exposed to formaldehyde
  • Bias those who suffer most from eye irritation
    are likely to leave the job at their own request
    or on medical advice
  • Result remaining workers are less affected
    association effect is diluted

82
Measurement / (Mis) classification
  • Exposure misclassification occurs when exposed
    subjects are incorrectly classified as unexposed,
    or vice versa
  • Disease misclassification occurs when diseased
    subjects are incorrectly classified as
    non-diseased, or vice versa

83
Misclassification bias due to measurement errors
  • Systematic bias
  • Measurement errors
  • Non-differential observed relative risk biased
    towards the null hypothesis
  • Differential This can lead to study results,
    which can not be interpreted because the observed
    relative risk may be biased towards the null,
    away from the null, or cross over the null value
    compared with the true relative risk

84
Sources of measurement errors
  • Selection/ design of the instrument to measure
    the exposure
  • Omissions in the protocol for use of the
    instrument
  • Poor execution of the study protocol
  • Inherent subject characteristics
  • Drift in accuracy of exposure measures over time
  • Data processing and creation of exposure variables

85
Reassignment to exposure category
  • Changes in dichotomous exposure, if not taken
    into consideration will tend to make the strength
    of an observed association lower than that which
    actually existed
  • Latency is likely to be short
  • Exposure accumulates over time during the study
  • Very accurate results desirable
  • Reassignment may not be possible
  • Close cohort as a rule
  • Latency is very long
  • Duration of follow-up is very long

Separate examination of outcome in those who
changed exposure status during the study
86
Confounding bias
  • Other factors which are associated with both
    outcome and exposure variables do not have the
    same distribution in the exposed and unexposed
    group

87
Examples confounding
HEART DISEASE
COFFEE DRINKING
(Smoking increases the risk of heart ds)
(Coffee drinkers are more likely to smoke)
SMOKING
88
Resolving Confounding Bias
  • Standardization
  • Stratification
  • Multivariate adjustment

89
Post hoc bias
  • Use of data from a cohort study to make
    observations that were not part of original study
    intent.

90
Thank you
91
Internal External validity
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