Metlit-10 Studi Kohort - Prof. Dr. Sudigdo S, SpA(K)

8/12/2019 Metlit-10 Studi Kohort - Prof. Dr. Sudigdo S, SpA(K)

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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.

Metlit-10 Studi Kohort - Prof. Dr. Sudigdo S, SpA(K)

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