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Cohort studiesFollowing groups of subject over time
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Classifications of research
designLaboratory, clinical, community
Basic vs. applied
Translational research (from bench tobed)
Descriptive vs. analytic
Observational vs. interventional
Prospective vs. retrospective
Quantitative vs. qualitativeNot mutually exclusive: basic research
may be descriptive or analytic
Clinical research may be observational
or experimental, etc
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Basic study designs
Observational
Case reportsCase series
Cross-sectional
studiesCase-control studies
Cohort studies
Meta-analysis
Interventional
Labexperiments
Clinical trials
Field trials
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Experimental StudyA study in which the investigator
influences the exposure status of
individual subjects (independent
variable) and then monitors the
subjects outcome (dependentvariable)
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Cohort = ancient Roman term = a
group of soldiers that marched
together into battle
In clinical study = a group of person
followed up together over time
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Cohort studies
Descriptive: To describe incidence of
certain outcome over time (absoluterisk)
Analytic: To analyze association
between risk factors and outcomes(relative risk)
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Prospective cohort
Select a sample from the population
Measure predictor variable (present ofabsent)
Follow-up the cohort
Measure outcome (present or absent)
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Disadvantages of cohort studies
1. Generally require large samples
2. Not useful for rare outcomes3. As an observational study, can never be
assumed to be free of confounding and bias
4. Must usually control for potential confounding
in the analysis, though can control in the
design
5. Prone to loss to follow up / drop outs
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Retrospective cohort
Basically the same with prospective
Basic measures, follow up, outcome all
in the past
Only possible if all data are complete
and prepared for other purpose
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Retrospective cohort
Assemble cohort in the
past
Measure risk factors
Follow-up
Measure outcomes
Analyze
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Retrospective cohort: strength
Much less costly and efficient
Less time consumingAll subjects (assumed) from same
population
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Retrospective cohort; weakness
Secondary dataMay not include necessary
data
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When to use cohort design
Accurately describing incidence
May be the only way to establish temporal
sequence of risk and outcomes
Malnutrition in chronic diarrhea may be the result
rather than a cause
Only way to study certain rapidly fatal diseases
Avoiding survivor bias
Allow investigator to study multiple outcomes /
ever increasing outcomes
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Choosing among cohort designs
Retrospective
Quick
Economical
Prospective Rapidly occurring outcome, e.g. discharge
from nursery after bone facture
When less expensive design fail to answer
research question properly
When case control studies give conflictingresults
When key measurements must be performedbefore outcomes occur
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Selecting subjects
Define group of subjects at the
beginning of the study (inception
cohort), e.g., cervical cancer, stage 1-2Select samples with rapidly occurring
outcomes, e.g., hip fracture elderly
womanAdequate sample size
Control in double cohort should be
selected by random sampling
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Measuring predictor and
confounding variables
The quality of the result depend largely on
the accuracy of measuring predictor andoutcome variables
The validity of the result (cause-effect
relationship) also depends on themeasurement and control of confounding
variables
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Following subjects
Important: minimize loss to follow up!!! Strategies:
During design: Restriction: exclude those likely to loss
Moving
Unwilling to return
Planning for future tracking Address, telephone, mobile, fax etc
During follow-up Periodic contact
Phone, visits, etc
Other relevant measures
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Analyzing results
Descriptive: Incidence rate, mean,
proportions, SD, etc
Analytic: Relative risk
Other analysis:
Survival analysis
Multivariate analysis as appropriate
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Cohort study
Analysis
Incidence
Relative/incidence risk
Relative risk (RR) = the ratio between the
incidence of an effect in the exposed
group to that in the non-exposed
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Cohort study: example
Disease -
Exp. +
Disease +
50
50
45
5
20
30
Exp. -
Disease -
Disease +
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Cohort study: analysis
Exp + 20 30 50
Exp - 5 45 50
Disease + Disease -
20Relative risk = incidence in expose/incidence in non-exp
RR = 20/50 : 5/50 = 4
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RR = 4
The probability of developing the disease
in exposed group is 4 x the probability of
developing the disease in non-exposed
group
Exposed individuals are 4 x more likely to
develop the disease compared with non-
exposed
CI is recommended; if CI include 1, then
statistically not significant (the probability
that the result is caused by chance is high)
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Famous cohort studies
Population-based
1. Cardiovascular2. Child health
3. Special exposures
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1. Cardiovascular
disease Framingham, Ma
Tecumseh, Mi Evans county, Ga (biracial)
Muscatine, IA
Bogalusa, LA (children)
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2. Child health
National birthday trust studies One week of births in England and Wales in 1946,1958 and 1970
Project on premature infants All births < 1,500 g or < 32 weeks in Holland in 1983
The national childrens studyhttp://www.Nichd.Nih.Gov/about/despr/despr.Htm
A study in Jakarta of 100,000 pregnancies withoffspring followed to age 21?
http://www.nichd.nih.gov/about/despr/despr.htmhttp://www.nichd.nih.gov/about/despr/despr.htmhttp://www.nichd.nih.gov/about/despr/despr.htmhttp://www.nichd.nih.gov/about/despr/despr.htm -
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3. Special exposures
Atomic Bomb Casualty Commission (ABCC):
Hiroshima and Nagasaki survivors (effects ofradiation)
Dutch famine survivors (effects of starvation)
Seveso (effects of dioxin exposure)
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Case-cohort design:purpose
The case-cohort design is used to reduce
the costs of exposure assessment
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Case-cohort design: approach
1. A population at risk is identified and
screened for disease, and prevalentcases are omitted.
2. A case-identification procedure is
developed to detect new cases ofdisease in the cohort.
(So far all is the same as any cohortstudy)
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Case-cohort design: approach
3. The whole cohort is subject to case-identification, but only a random sample (calledthe sub-cohort) receives detailed exposureassessment.
4. The cases are those emerging in thepopulation (both in and out of the sub-cohort);the controls are subjects in the sub-cohort who
are not cases.5. Analysis is like a cohort study. Since the
sampling fraction is known, and the entirepopulation is sampled for caseness, true
incidences and relative risks can be calculated.
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Nested case-control study
1. A case-control study that is nested in thecohort study
2. Purpose: to reduce cost of exposure
assessment3. In a cohort study (for other exposure),
specimen is kept until the cohort study finishes
4. Subjects who developed outcome are chosenas CASE; the CONTROLS are selectedrandomly from the subjects who did notdevelop outcome
5. Assess risk factors for case and controls
6. Anal sis is similar with case-control stud
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Case-Control Studies
(Adapted from slides by
Schenker M)
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Objectives
After this session, you will be familiar with:
The basic design features of a case-control study
Rationale for applying case-control
designs
Limitations of case-control studies
Example applications applying case-
control designs
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Case-control studies (1)
For rare disease
Subjects with disease are selected first
Find subject without disease with similar
characteristics
Determine the exposure in case and incontrols
Compare / analysis: odds ratio
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Case-control studies (2)
Odds = Probability of event / prob ofnon event
Odds ratio (OR) shows how great a
risk factor play role in the occurrenceof a disease
Odds = p/(1-p)
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Case-control study: example
Case
Exp. Yes
Exp. Yes
Exp. No
Control
Exp. No
(Disease Yes)
(Disease No)
50
50
10
40
48
2
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Case-control study: example
20Odds ratio = odds in exposed/odds non-exposed
= (10/12 : 2/12)/(40/88: 48/88) = 6
Exp + 10 2 12
Exp - 40 48 88
Disease + Disease -
50 50 100
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OR = 6
Originally: The probability exposure incases is 6 times than the probability of
exposure in controls
Mathematically similar to: The probability of developing the disease in
exposed group is 6 x the probability of
developing the disease in non-exposed group Exposed individuals are 6 x more likely to
develop the disease compared with non-
exposed
If CI includes 1not si nificant
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Design of Case-Control
Studies
The investigator selects
cases with the disease,and appropriate
controls without the disease
and obtains data regarding pastexposure to possible etiologic factorsin both groups. The investigator thencompares the frequency of exposureof the two rou s.
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Odds Ratio = a/c : b/d = ad / bc
a
b
c d
Cas
eContr
ol
E+E
-
a
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When to use a case-controlapproach
1. Rare disease: Case-control approaches arethe most efficient for rare diseases, e.g.,
idiopathic pulmonary fibrosis, most cancers.
Cohort approaches would require largepopulations and prohibitive expense and
follow-up time. Case-control designs may also
be appropriate for more common diseases,
such as COPD.
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2. Case ascertainment system in place:The conduct of a case-control studymay be facilitated by the availability of acase-ascertainment system.
a) Population-based cancer registryb) Hospital-based surveillance
systems
c) Mandated disease reportingsystems
3. When funding and time constraints arenot compatible with a cohort study.
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Issues in Case-Control Studies
A. Issues in Ascertainment of Cases
1. Diagnostic criteria for case studies
a) Specificitye.g. lung cancer vs wheezing
b) Diagnostic bias
c) Validation2. Sources (hospital, general
population)
3. Incident or prevalent cases
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Issues in Selection of Controls
1. General questions
a) Conceptual
(i) Should the controls becomparable to the cases in all respectsother than having the disease?
(ii) Should the controls berepresentative of all non-diseasedpeople in the population from which thecases are selected?
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Total Population
ReferencePopulation
Cases Controls
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b) Practical Questions
(i) Is the approach selected forcontrol selection feasible?
(ii) Can this approach be used
given the funds available?
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Sources of controls
a) Population of defined areab) Hospital patients
c) Probability sample of total population
d) Neighborsi. walk (door to door)ii. phone (random digit dialing)
iii. letter carrier routes
e) Friends or associates of casesf) Siblings, spouses or other relatives
g) Other
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Methodologic Issues
1. Handling potential confounding factors
a) In the process of selecting controls:
Matching
The process of selecting controls so thatthey are similar to the cases in regard tocertain characteristics such as age, sex andrace.
(i) Group matching (frequency matching,stratification)
(ii) Individual matching (matched pairs)
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Handling potential confounding factors inmatching:
(iii) Problems with matching:
- Matching on many variables may make it
difficult or impossible to find an appropriate
control.
- Cannot explore possible association ofdisease with any variable on which cases
and controls have been matched.
C. Methodologic Issues
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Handling potential confounding factors in
matching:
b) In the process of selecting controls:Restriction
c) In the data analysis:
(i) Stratification(ii) Adjustment
Methodologic Issues
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Methodologic Issues
2. Evaluating Information on Exposure
a) Problems of recall in case-controlstudies
(i) Limitations in human ability torecall
(ii) Recall bias (cases may remember
their exposure with a higher orlower accuracy than controls do)
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b) Avoiding other biases(i) Selection bias
(ii) Information bias
(iii) Non-response bias
(iv) Analysis bias
c) Validity testing (reliability, sensitivity
and specificity)
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Using Multiple Controls in Case-Control
tudies
a) Multiple controls of a similar type (e.g. 2
controls per case)
b) Different types of controls (e.g. hospitaland neighborhood controls)
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Advantages of Nested Case-
Control Studies
1. Possibility of recall bias is eliminated, sincedata on exposure are obtained beforedisease develops.
2. Exposure data are more likely to representthe pre-illness state since they are obtainedyears before clinical illness is diagnosed.
3. Costs are reduced compared to those of a
prospective study, since laboratory testsneed to be done only on specimens fromsubjects who are later chosen as cases oras controls.