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 OUTLINE OF PRESENTATION Systematic(bias)error in epidemiological studies  OUTLINE OF PRESENTATION Systematic(bias)error in epidemiological studies

OUTLINE OF PRESENTATION Systematic(bias)error in epidemiological studies - PowerPoint Presentation

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OUTLINE OF PRESENTATION Systematic(bias)error in epidemiological studies - PPT Presentation

Validity and testing for validity Reliability and testing for reliability Group task for today Bias is a systematic error in the design conduct or analysis of a study that results in errors when calculating measures of association between risk factors and outcomes ID: 776689

test disease positive negative test disease positive negative results people validity sensitivity bias 100 false 500 total population proportion

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Slide1

OUTLINE OF PRESENTATION

Systematic(bias)error in epidemiological studies

Validity and testing for validity

Reliability and testing for reliability

Group task for today

Slide2

Bias is a

systematic

error in the design, conduct, or analysis of a study that results in errors when calculating measures of association between risk factors and outcomes

It is thus always one-sided, either underestimating or over-estimating risk

It is not usually a subjective error, and is often unavoidable, but must be recognised as a limitation of the study to allow for correct interpretation

Slide3

Types

of bias in epidemiological studies

Selection

bias: In

selection bias study participants are selected in one locality of convenience and are therefore not representative of the target population

e.g. Selection

of a sample for high prevalence of alcoholic foetal syndrome in western cape is not representative of prevalence of alcoholic foetal syndrome in

SA.

Information

bias /Recall

bias:

Recall

bias result because those affected by a disease often recall their exposures better than those who are unaffected

e.g. Those

who were exposed to polio virus will remember better than those who were

unexposed

Non-response

bias:

leads

to a lack of information on non-responders. Thus if the non-response rate is high, the study may be severely biased. Sometimes called volunteer bias.

Misclassification bias:

Slide4

Validity of a test

The

validity of a test is defined as its ability to distinguish between those who have the disease(Sensitivity )and those who do not have the disease(Specificity).

Factors

affecting validity

:

Incorrect calibration and or uncalibration of the measuring device e.g. Sphygmomanometer(internal validity)

Unstructured interviews or questionnaires in a study (internal validity

Unrepresentativeness of the sample from the population(external validity.

Components of validity

Validity of test has therefore two components namely sensitivity and specificity.

Sensitivity

Sensitivity of a test is defined as the ability of a test to identify

correctly

those who have the disease

S

pecificity

.

Specificity of a test is defined as the ability of a test to identify

correctly

those who do not have the disease.

Slide5

True characteristic in the population

Test Have the Do not have Result disease the disease Positive True positive(TP) False Positive(FP) Have the disease Do not have the disease and test positive but test positive False negative(FN) True Negative(TN) Negative Have the but Do not have the Test negative and test negative Sensitivity = TP Specificity = TN TP +FN TN+FP

Slide6

Types of validity testsGold standard testScreening testGold standard test refers to a diagnostic test or benchmark that is the best available under reasonable conditions. It is referred to as the most accurate test possible without restrictionsA hypothetical ideal of gold standard test has a sensitivity of 100% with respect to the presence of the disease (it identifies all individuals who have the disease; it does not have any false-negative results) Sensitivity = a OR TP a +c TP +FN

Slide7

Also

a hypothetical ideal of gold standard test has a specificity of 100% with respect to the absence of disease (it identifies all individuals who do not have the disease.it does not have any false-positive results). Specificity = d OR TN d +b TP +FP

Slide8

Example:

Suppose we have a hypothetical population of 1000 people, the gold standard will yield a sensitivity of 500 with respect to the presence of disease with no false negative test results and 500 with respect to the absence of disease with no false positive test results.

See below as shown.

Slide9

True characteristics in the population

Test Have the Do not Results disease have the disease Positive 500 0 Negative 0 500 Total 500 500 Sensitivity= 500 Specificity= 500 500+0 500+0 = 100% = 100% In practice, there are sometimes no true "gold standard" tests.

Slide10

SCREENING VALIDITY TEST

Screenings tests are tests that look for diseases before you have symptoms. Screening tests can detect diseases early and thus start treatment Detectable Preclinical Clinical phase phase ClinicalNo disease outcome Symptoms disease Therapy Biologic first diagnostic given onset of appear test disease Disease detectable by screening

Slide11

Hypothetical example:

Suppose we have a hypothetical population of 1000 people of whom 100 have a certain disease and 900 do not. In this instance we use a screening test to distinguish persons with the disease from those who do not.

The results obtain by applying the test to this population of 1000 people are shown below

Slide12

True

characteristics in the population Test Have the Do not Results disease have the disease Positive 80 100 Negative 20 800 Total 100 900 Sensitivity= 80 Specificity= 800 80 +20 800 +100 = 80% = 89%

Slide13

The question we now ask is how good is the screening test compared to the gold standard.

First, the test indicates that of the 100 people with the disease,80 were correctly identified as positive and a positive identification was in 20 and thus the sensitivity of the test which is defined as the proportion of diseased people were correctly identified as positive by the test is 80/100 or 80%

Secondly, the test indicates that of the 900 people who did not have the ,the test correctly identified 800 people as negative and thus the specificity f the test, which is defined as the proportion of non diseased people who are correctly identified as negative by the test is therefore 800/900 or 89%

Slide14

Why is the issue of false positive important?

The issue of false positive is important because:

All people who screened positive are brought back for sophisticated and more expensive test which becomes a burden on the health care system.

Another is the anxiety and worry induced in persons who have been told that they tested positive and this labelling is not completely erased even if the results of subsequent evaluation are negative.

Slide15

Why is the issue of false

negative important

?

The issue of false positive is important because

:

If for example, the disease is a type of cancer that is curable only in its early stages, a false negative result could represent a virtual death sentence.

Thus the result depends on the nature of the severity of the disease being screened for effectiveness of available intervention measure and whether the effeteness is greater if the intervention is administered early in the natural history of the disease

Slide16

Predictive value of a test

We have so far asked the question "

How good is the

test(gold

std

)

at identifying people with the disease and people without the disease”.

Put in another way "If we screen a population, what proportion of people who have the disease will be correctly identified?”

These question is clearly for

public health consideration.

Positive predictive value of a test

In the clinical setting, a different question for the clinician would be "

If the test results are positive in this patient, what is the probability that this patient has the disease

"This is called the positive predictive value(PPV) In other words the question is “what proportion of patients who test positive actually have the disease in question?

Slide17

Negative predictive

value of a

test

Also a parallel question can be asked about negative test results as follows

If the test results is negative ,what is the probability that this patient does not have the disease "This is called negative predictive

value(NPV) of the test.

Slide18

Calculation of both PPV and NPV

PPV of a test is calculated by dividing the number of true positive(TP) by the total number of people who tested positive, i.e. TP divide by TP plus FP

NPV of a test is calculated by dividing the number of true negatives(TN) by the total number of people who tested negative i.e. TN divide by TN plus FN

Slide19

True characteristics in the population Test Have the Do not Totals Results disease have the disease Positive 80 100 180 PPV=80/180=44% Negative 20 800 820 NPV=800/820=90% A Positive predictive value of 44% means that of the 180 tested only 80 have the disease A Negative predictive of 90% means that of the 820 tested, only 800 do not have the disease

Slide20

Reliability(Repeatability) of a diagnostic or screening tests

Reliability is the ability of a measure(both the subject and the observer to produce a repeatable result .

This

does not mean that the result is accurate, it just means that if the test is repeated, a similar result will be

obtained.

Clearly ,regardless of the sensitivity and specificity of test, if the test results cannot be reproduced, the value of and usefulness of the test are minimal

The factors contributing to the variability or non reliability of a test are listed.

Intra-subject variation( variation within individual subject0

Intra-observer

variation

(variation in reading of test result by same person

Inter-subject

variation(variation of test results between or among individual

Intra-observer variation(variation of test results between observers

Slide21

Number of patients

Observer 1Observer 21002013104005106007118009101011

n=10 0=Negative 1=Positive

Slide22

Positive for test 1 and

2Patients

7 and 10= 2 positive’s

Negative for 1 and

2Patient

1, 4, 6, 8 =4 negative’s

Positive for 1 and negative for

2Patient

3, 5, and 9=3

positives

Negative

for 1 and positive for

2

Patient

2, =1 negative

Observer 1

Positive Negative Total

Observer 2 Positive 2 1 3

Negative 3 4 7

Total 5 5 10

Calculate the expected the total expected proportion of agreement (

P

e

)

Calculate the expected the total expected proportion of

agreement(P

o

)

The (kappa) statistic

Slide23

Use the 2x2 table on the next slide for calculating the kappa statistic (strength of agreement):

The total

expected

proportion of agreement, P

e

,

is given by :P

e

=

[

(

a+c)

*

(a+b)

]

+ [

(

b+d

)

*

(

c+d

)

]

N N N N

The total observed proportion of agreement, P

o

is

given by: P

o

= (

a+d)

N

The (kappa) statistic is given by:

P

o

- P

e

1-

P

e

where

Po-Pe

represents the proportion of the observed

agreement in excess of chance, and 1-

P

e

represent the

maximum proportion of agreement in excess of chance.

Slide24

Value of

kappa

Strength

of agreement

<0.2

Poor

Poor

0.21-0.4

Fair

0.41-0.6

Moderate

0.61-0.8

Good

0.81-1.0

Very

good

Slide25

Read chapters

………………………….

Elect 2 students to act as Doctor X and Doctor Y

The rest of the students will be patients, half of which will go to Dr X, the other half to Dr Y

The doctors will interview the patients, and decide whether they are eating a healthy diet or should be referred to a nutritionist

The recommendation for each patient will be recorded on the data collection sheet by each doctor.

Then the doctors will swop patients and repeat the interviews.

No contamination must take place, i.e. the doctors and patients should not share with each other anything that was said by their counterpart in

the previous interview.

The 2 doctors will make 10 copies of their data collection sheets and

distribute these to the 10 class groups.

The groups will summarise the data using the data summary sheet.

Each group will calculate the kappa statistic, and the results will be presented to the class.