Epidemiology

Front 1

What are examples of continuous variables?

Back 1

1) blood pressure
2) Blood glucose level
*a test for which there is no +/- result.

Front 2

What is the trade-off btwn sensitivity and specificity?

Back 2

If you increase the sensitivity (by lowering the cutoff level) you decrease the specificity; if you increase the specificity (by raising cutoff level) you decrease the sensitivity

Front 3

Define a bimodal curve

Back 3

The type of dist. in which there are 2 peaks
permits separation btwn idividuals who had no prior exp. w/ dz X and those who had exp. W/ dz X
Ex: ppd test for TB

Front 4

define a unimodal curve:

Back 4

a single peak distribution
In order to separate those w/ dz from those w/out dz a cutoff is necessary

Front 5

Determining cutoff levels are very much related to

Back 5

the level of importance place on FP and FN for the dz in question

Front 6

for a two stage (sequential) testing to find the net sensitivity

Back 6

# of TP (correctly id) in 2nd test
------------------------------
Total # of people w/ dz in the 1st test

Front 7

for a two stage (sequential) testing to find the net specificity

Back 7

The combined TN in test 1 and 2
------------------------------
Total # of people w/out dz in the first test

Front 8

How can you handle confounding in designing and carrying out the study?

Back 8

1.individual matching

2. group matching

Front 9

How do you handle confounding in the analysis of data?

Back 9

1. stratification

2. adjustment

Front 10

What is the PPV(postive predictive Value)?

Back 10

in clinical setting it is the probability that the pop. that tested + has the dz

Front 11

What is the NPV(Negative predictive value)?

Back 11

the probability that the pop. that tested - does not have the dz

Front 12

What is the formula for:
PPV=

Back 12

TP/TP+FP

Front 13

What is the formula for:
NPV=

Back 13

TN/TN+FN

Front 14

What 2 factors affect the PPV?

Back 14

1) the prevalenc of a dz

2) When its a rare dz the specificity of the test being used

Front 15

What is the relationship btwn Predictive value and Prevalence?

Back 15

they are directly related if you have an increase in PV there is an increase in Prev.

Front 16

What is the relationship btwn Predictive value and specificity?why is this so?

Back 16

An increase in Specificity has a much greater effect on Predictive value than an increase in sensitivity because of the infrequency of dz.

Front 17

intrasubject variation

Back 17

variation within individual subjects; taking into consideration under which the test were performed

Front 18

interobserver variation

Back 18

The ability to express the extent of agreement in quantitative terms

Front 19

intraobserver variation

Back 19

when the test and examinations differ in the degree to which subjective factors enter the observers conclusions

Front 20

How do you calculate the overall percent agreement?

Back 20

add the #'s of all the cells(cross tabulation) inwhich reading by both observers agreed
------------------------ x 100
Total # of readings by combined observers

Front 21

Percent agreement when using paired observation btwn obs.1 and obs. 2

Back 21

a+d
--------
a+b+c+d

Front 22

What can you do when the D cell (neg. observation pop.) is large

Back 22

when cell d is large let d=disregard and exclude it from the percent agreement formula:
a
----- x 100
a+b+c

Front 23

What is the validity of a test?

Back 23

Its ability to distinguish btwn who has dz and who doesnt

Front 24

sensitivity

Back 24

The ability to identify correctly who has dz

Front 25

specificity

Back 25

The ability to identify correctly who does not have dz

Front 26

true positives (TP)

Back 26

people w/ the dz who were correctly called postive by the test

Front 27

true negatives(TN)

Back 27

people w/ the dz who were correctly call negative by the test

Front 28

False Positive (FP)

Back 28

people who dont have the dz who are erroneously called postive by the test

Front 29

False Negative (FN)

Back 29

people who do have the dz who are erroneously call negative by the test

Front 30

what are important issues with false postives?

Back 30

-the burden on healthcare
-emotional effect on people
-delabeling is never fully achieved

Front 31

what are important issues with True negatives?

Back 31

-if the dz is serious it could be a death sentence
-depends on the nature and severity of the dz being screended for.

Front 32

What is the definition of a bias?

Back 32

"any systematic error in the design, conduct or analysis of a study that results in a mistaken estimate of an exposure's effect on the risk of disease."

Front 33

selection bias

Back 33

observe an assoc. btwn exposures and outcome that arises from the process of identifying the study pop.

Front 34

exclusion bias

Back 34

when a different set of criteeria are used for the cases and controls

Front 35

to be a confounder in the relationship btwn exposure and a dz the factor must be:

Back 35

A risk factor for the dz

assoc. with the exposure but not caused by the exposure

Front 36

confounding

Back 36

confounding is a valid finding that describes the nature of the relationship among several factors and the risk of disease. However, failure to take confounding into account in interpreting the results of a study is indeed an error in the conduct of the study and can bias the conclusions of the study.*confounding is a function of the complex interrelationships btwn various exposures and dz

Front 37

What is meant by interaction

Back 37

"When the incidence rate of disease in the presence of two or more risk factors differs from the incidence rate expected to result from their individual effects."

Front 38

List 2 types of bias:

Back 38

1 Selection bias (exclusion bias)
2 information bias (x6)

Front 39

What are two things that should be taken into account in regards to confounding when designing a study:

Back 39

1. Ensure that adequate date are collected on possible confounders.

2. impossible to control for confounder when there is not enough data about them

Front 40

How do you collect data on possible confounders?

Back 40

-impossible to collect info on every varible
-choose based on: knowledge of dz,previous evaluations of the same or related questions,best judgement of investigator at the time the study was initiated
-collect data on known risk factors

Front 41

How to determine if a factor is confounder?

Back 41

analyze available data
- obtain a crude overall estimate of the assoc.,control for the factor,observe whether the estimate of the assoc. btwn the expos. and the dz is changed.
-always keep track of which is the exposure and which is the dz and which factor you are evaluating as a possible confounder.

Front 42

What are some methods for controls for confounding in the design of a study?

Back 42

1) randomization (intervention studies)
2) restriction
3)matching (must maintain in the analysis)

Front 43

when controlling for confounding using restriction list 5 important points:

Back 43

1) confounding cannot occur if the potential confounding factor do not vary across either the exposure or the dz
2)if concerned about confounding by sex and race limit study to ex: black women,asian men etc)
3)eliminate any possible effects of confounding
4)straight forward and inexpensive
5)limitations

Front 44

multivariable analysis:

Back 44

1.allows for efficient estimation of measures of assoc. while controlling for a number of confounders simultaneously
2.involves the construct. of a mathmatical model
3.regression and cox proportional hazards model

Front 45

what is the general regression model?

Back 45

y=mx+b

x=ind. variable
y=the dependent variable
m describes the relationship between x and Y

Front 46

Which of the following is an approach to handling confounding?

Back 46

a. individual matching
b. stratification
c. group matching
d. adjustment
e. all of the above

Front 47

It has been suggested that physicians may examine women who wse OC more often or more thoroughly that women who do not, if so, and if a assoc. is observed between phlebitis and oral contraceptive use the association may be due to:

Back 47

a. selection bias
b. interviewer bias
c. surveillance bias
d. nonresponse bias
e. recall bias

Front 48

examples of interaction:

Back 48

RR of ca smokers to nonsmoker=3.0
RR of ca abestos exp to nonsmoker=5.0
RR of ca in smoker with asb exp=50.0
-effect is differ. from an addictive model
-effect is different from a multiplicative model
-suggests something goes on at a biological level

Front 49

Precision

Back 49

-the degree to which there is variation in a measurement
-reliability refers to the precision of screening and diagnostic tests

Front 50

accuracy

Back 50

-Refers to the degree to which the measurement is , on average, correct.
-Validity refers to the
accuracy of screening and diagnostic tests
ex:If each time a phenomenon is measured, the value is the same, but all measurements are far away from the true value, there is high precision but low accuracy
This was the case with the class prediction of average age
The 95% CI was small, but the true value of the average age was not included -- inaccurate

Front 51

If chance, bias, and confounding are unlikely explanations of the findings, have est. assoc. BUT NOT.....

Back 51

causality

Front 52

Temporal Relationship

Back 52

exposure must precede an outcome for the relationship to be causal
-Not always easily demonstrable b/c sometimes symptoms of dz lead to changes in behavior
People who start having resp. problems may stop smoking
W/in the year that they stop smoking, they may be diag. with lung ca. May appear as if stopping smoking brings about lung ca.

Front 53

“confounding by indication”

Back 53

An association based on behavior taken because of the outcome
ex: of temporal as well as confounding;
People with kidney insufficiency often progress to total kidney failure
People with kidney insufficiency to use acetaminophen (Tylenol) for pain and to avoid aspirin and ibuprofen
May look like acetaminophen causes total kidney failure

Front 54

Strength of Association(p213)

Back 54

-The strength of the association is measured by the relative risk or the odds ratio
-The stronger the assoc., the more likely it is that the relationship is causal
-Makes sense – to account for a great increase in risk, there would have to be something really important among the exposed to create such a large increase in risk
-Does not imply that an association of small magnitude cannot be judged to be one of cause and effect – only that it is more difficult to exclude alternative explanations

Front 55

Dose-Response Relationship(p213)

Back 55

-Dose-response rel. that as the dose of the exposure increases, the risk of disease also increases
-Strong evidence of a causal relationship
-Absence of dose-response does not rule out a causal relationship
-Certain exp. may have a threshold: no dz may develop until a certain threshold is reached

Front 56

Replication of Findings(p213)

Back 56

If the relationship is causal, we would expect to find it consistently in different studies and in different populations

Front 57

Biologic Plausibilityp213

Back 57

-Causality is enhanced if there is a known or postulated biologic mechanism by which the exp. might reasonably alter the risk of developing dz
-What is considered biologically plausible at any time depends on the current state of knowledge
Lack of a known or postulated mech.doesn't necessarily mean that a relationship is not causal
E.g. scurvy among sailors in the British Navy

Front 58

Cessation of Exposure p213

Back 58

If a factor is a cause of a disease, expect the risk of dz to decline when the exp. is reduced or eliminated
Eg. Recall the spinach, decrease the rate of E. coli 0157:H7
In some cases, the dz-causing process has already been initiated and dz occurrence may have been det.by the time the exp.is removed

Front 59

Specificity of the Association p215

Back 59

An assoc. is specific when a certain exp.is assoc.w/ only 1 dz
Vinyl chloride and bladder ca
-This is a weak guideline
-Absence of specificity in no way negates a causal relationship

Front 60

Incidence attributable to exposure

Back 60

EXPOSED GROUP
incidence in the exp. group - incidence in the non-exp.
TOTAL POP
(Incidence in total population) - (Incidence in nonexposed group)

Front 61

Proportion of incidence attributable to exposure

Back 61

(Iexp - I nonexp)
---------------------
I exp