confounding and validity 2009

8/22/2019 Confounding and Validity 2009

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LULU E. BUDIMAN - 2009

What is the odds of getting pancreaticcancer among coffee drinkers?

Coffee

drinking

Pancreatic

cancer

Smoking


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What is the odds of having a Downsyndrome child in higher parity?

Parity Down syndrome


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0

1

2

3

4

5

OR of Down syndrome


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Parity Down syndrome

Age


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Exposure of Interest

Precursors

Mechanisms

Health Outcome

Confoundersestimateassociation

causalinferenceeasure


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A situation in which effects of two riskfactors are mixed in the occurrence of thehealth problem under study May lead to

overestimation or under-estimationof the true association between exposureand outcome

Can change the direction of the observedeffect



Must be a risk factor for the disease among

unexposed; do not have to be a true cause of

disease.

Must be associated with the exposure under study inthe source population.

The confounder cannot be an intermediate step in

the causal path between the exposure and the

disease.



Exposure Case ControlPresent 30 18

Absent 70 82

Total 100 100

OR= 30 . 82 = 1.9570 . 18

Case and control are not matched

Is this association a causal one or could ithave resulted from differences in agedistribution?



Age (yr) Case Control40 50 20

Total 100 100

Older age is associated with being a case

Is age related to the degree of exposure?



Age (yr) Total Exposed Not exposed % exposed40 70 35 35 50

Age is related to exposureAge is related to being a case

Is the association between exposure and disease causal?Or caused by the age differences?


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Age (yr) Exposed Cases Control OR 40 + 25 10 25 . 10- 25 10 25 . 10

Total 50 20 = 1.0

The OR = 1.94 was because of the differencein age distribution between case and control

Age is a confounder


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In designing ad carrying out a study Matching (individual or group)

In the analysis of data Stratification

Adjustment


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Confounder

Exposure Outcome

Non-causal

Causal

Intermediate factor

Exposure Outcome

Ex : high fat diet obesity coronary heart disease

Ex : high fat diet DM coronary heart disease


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Diet/lifestyle

Vitamin C Cancer

Non-causalCausal

Case-control study to determine whether vitamin C intakeis associated with colon cancer.

People who take vitamin C may eat a healthierdiet and live a healthier lifestyle


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Interpreting data requires assumptions

about causal relations (including what

factors are potential confounders, i.e., whatfactors affect incidence and are not

themselves caused by the exposure).

If exposed people and unexposed people

differ on factors that affect disease

incidence, then those factors may confound

(distort) the observed relation betweenexposure and disease (i.e., actualconfounding).


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We can control confounding by studydesign if we can make the exposed andunexposed groups similar in respect to alldisease determinants, though matching orrandomized assignment of exposure

We can control confounding in the analysis

if we can stratify the data by disease

determinants that are not themselvescaused by the exposure (i.e., not causal

intermediates).


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VALIDITY

ANDRELIABILITY


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What is the prevalence of coronaryarterial disease among post-graduate

students of UNPAD?

How will you conduct the research?

Design?

Measurement?

Result?


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Susceptiblehost

Subclinicaldisease

Clinicaldisease

Recoverycondition,

disability ordeath

Point ofexposure

Onset ofSymptoms


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Population

Test -ve Test +ve

Unaffected

Re-test

Affected

intervene

Screening

Clinical exam


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The degree to which a

measurement or study reaches a

correct conclusion ~accuracy

The observed measurements will

be compared with accepted(gold) standard


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Internal validity

The degree to which the observed results of the studyare true

Inferences are correct regarding the participants in the

study

External Validity

Generalizability of the result

Inferences are correct regarding the population at risk


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POPULATION

SAMPLE SAMPLE

Selection bias

CONCLUSION

Measurement bias &

confounding

Chance

Internal validity

External validity(generalizability)


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The observed results(conclusion) occurredbecause:

Chance

Random error Bias

Systematic error

Confounding Truth

IF: the role of chance is small

bias can be reasonablyexcluded

confounding is addressed

THENthe study is internally valid


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Content validity: Measurement includes all the dimension

Construct validity: Measurement is related in a coherent way

Criterion validity: Measurement predict a directly observable

phenomenon


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Consistency of Measurement Reproducibility over time

Consistency between different

coders/observers Consistency among multiple indicators

Estimates of Reliability

Statistical coefficients that tell use how

consistently we measured something


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1. Stability Consistency across time: repeat measurements

2. Reproducibility Consistency between observer

3. Homogeneity Consistency between different measures of the

same concept: use different items to get aconclusion of the same concept

4. Accuracy Lack of mistakes in measurement: god concept of

definition and procedures Dedicated observers: training, motivation,

concentration


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Reliability is a necessary condition for validity If it is not reliable it cannot be valid

Reliability is NOT a sufficient condition for

validity If it is reliable it may not necessarily be valid


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Reliable BUT NOT Valid

Reliable AND valid

Not reliable, nor valid


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confounding and validity 2009

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