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Flashcards in FA - Behavioral science - statistics Deck (85):
1

cross-sectional study

what's happening? (observational)

assess prevalence

2

case control study

what happened?

assess risk factors (prior exposures)

assess Odds Ratio

3

cohort study

What will happen? Does exposure increase likelihood of disease

assess Relative Risk

4

case series

observational - smaller # of ppl w/ disease (ø controls) to generate profile of disease (characterize a new type of disease)

5

sensitivity

proportion of ppl w/ disease who test (+)

6

specificity

proportion of ppl w/o disease who test (-)

7

high sensitivity =

when negative - rule out dz!

SN-N-OUT

8

high specificity =

when positive - rule out in!

SP-P-IN

9

PPV

proportion of + tests that are true +

varies with dz prevalence and pre-test probability

(PP = + +)

10

NPV

proportion of - tests that are true -

varies inversely with dz prevalence and pre-test probability

(NP = - +)

11

when is OR used?

case control studies

12

when is RR used?

cohort studies

13

RRR

relative risk reduction - proportion of risk reduction attributable to an intervention compared to a control

= 1 - (% dz w. intervention / %dz w.o intervention)

14

ARR

absolute risk reduction - difference in risk attributable to an intervention compared to a control

= % dz w.o intervention - %dz w. intervention)

15

AR

attributable risk - ∆ risk between exposed and unexposed (proportion of occurrences attributable to exposure

= % exposed w. disease - % unexposed w. disease

16

# needed to treat for one person to benefit

= 1/ ARR

17

# needed to harm (# of patients who need to be exposed to risk factor to be harmed)

= 1/ AR

18

cohort study where 3 different grps are studied over time (ie smokers vs non-smokers vs former smokers)

type of bias?

selection bias

19

study looking only at in patients

type of bias?

selection bias - berkson bias

20

study looking at a disease with an early mortality or loss to follow-up (esp of a particular type of population)

type of bias?

attrition bias (type of selection bias)

note that this type of bias does not occur when the losses happen equally and randomly between the exposed and unexposed groups.

21

studying populations that are generally healthier than the general population

type of bias?

selection bias - healthy workers + volunteer bias

22

patients w. disease remember exposure after learning of similar cases

type of bias?

recall bias

23

groups who know they're being studied behave differently than they would otherwise

type of bias?

measurement bias - hawthorne effect

(think of a hawk watching a bird on a thorne - the bird is likely to act differently if it knew it was being watched by a predator)

24

patients in treatment group spends more time in highly specialized hospital units

type of bias?

procedure bias (subjects in different groups are not treated the same)

25

observer expects treatment group to show signs of recovery, and is likely to document more + outcomes

type of bias?

how to prevent?

observer-expectancy bias

prevent by performing a double blind study in which neither subjects nor investigators are aware of treatment assignments

26

pulmonary dz is more common in coal workers than the general population - however, people who work in coal mines are also smoke more frequently than the general population

type of bias?

confounding bias (factor related to both the exposure + outcome and distorts the effect of the exposure on outcome)

27

early detection = increased survival

type of bias?

lead time bias - early detection shows increased survival, even though the natural history of the disease has not changed

28

SD

how much variability exists from the mean in a set of values "spread"

29

SEM

measure of how accurate the means is relative to the real mean (measure of CONFIDENCE in the sample mean)

= SD/√n

30

1 SD =

contains 68% of values

31

2 SD =

contains 95% of values

32

3 SD =

contains 99.7% of values

33

Type I error (α)

what is it?
what is it also known as?

stating that there IS a difference/effect when none exists
(null hypothesis IS incorrectly rejected)

aka fαlse + error

α = you sαw a difference that did not exist

34

Type II error (ß)

what is it?
what is it also known as?

stating that there is NOT an difference/effect when one exists (null hypothesis is NOT rejected when it is in fact false)

aka false - error

ß = ßlind to difference that exist (setting a guilty man free)

35

meta analysis

pools data together from several similar studies to reach an overall conclusion

36

confidence interval

range of values in which a specified probability of the means of repeated samples would be expected to fall

CI = range from (mean - Z*SEM) to( mean + Z*SEM)

95% CI = Z = 1.96
99% CI = Z = 2.58

if 95% CI for a mean difference btwn 2 variables includes 0, then there is no sig. difference = null is NOT rejected

if 95% CI for OR or RR includes 1, then there is no sig. difference = null is NOT rejected

if CI btwn 2 groups do not overlap = significant difference exists

If CI btwn 2 groups overlap, usually no significant difference exists

37

if 95% CI for a MEAN difference btwn 2 variables include 0, then

there is no sig. difference = null is NOT rejected

38

if 95% CI for OR or RR includes 1, then

there is no sig. difference = null is NOT rejected

39

if CI btwn 2 groups do not overlap

significant difference exists

40

If CI btwn 2 groups overlap

usually no significant difference exists

41

# of groups tested in t-test

means of 2 grps

T is meant for 2

42

# grps tested in ANOVA

3 (or more) groups

anova = analysis of variance = 3 words

43

chi square

checks difference between 2 or more %s or proportions of categorical outcomes

Chi-tegorical outcomes (ex: % of members of 3 different ethnic groups who have essential HTN)

44

disease prevention



1˚ = Primary = Prevent
2˚ = Secondary = Screen
3˚ = Tertiary = Treatment to reduce
4˚ = quaternary = risk/harm of unnecessary treatment

45

effect modification

occurs when the effect of a main exposure of an outcome is modified by another variable
Note: this is NOT a bias (it is NOT due to flaws in the design or analysis phases of the study)

Ex1 - smokers taking the Rx have an increased risk of developing DVT while non-smokers taking the Rx do not

Ex2 - likelihood of asbestos exposure will result in lung cancer (a phenomenon significantly impacted by smoking)

46

latent period

time elapsed from:

initial exposure to clinically apparent disease
(ie infectious dz)

or

exposure to risk modifiers (ie antioxidants, better diet) to when the effects of the exposure is clinically evident

47

berkson's bias

a type of selection bias created by selecting hospitalized patients as the control group

48

pygmalion effect

researcher's beliefs in the efficacy of treatment that can affect the outcome (ie the greater the expectation placed on people, the better they will perform; form of self-fulfilling prophecy)

49

lead time bias

apparent prolongation of survival after applying a screening test w/o any real effect on prognosis

50

recall bias effect

inaccurate recall of past exposures by patient (esp if they're asked to recall something from long ago)

51

hawthorne effect

tendency of a study population to affect an outcome due to knowledge of being studied

52

case fatality rate

# of fatal cases / (# fatal + # non-fatal)

53

if RR is given and the 95% CI does not include 1, what p value is expected?

p

54

if RR is given and the 95% CI does include 1, what p value is expected?

p>0.5 (not significant)

55

if 95% CI for a mean difference btwn 2 variables includes 0, what p value is expected?

p>0.5 (not significant)

56

if 95% CI for a mean difference btwn 2 variables does not include 0, what p value is expected?

p

57

referral (admission rate) bias

when the case and control population differ due to admission or referral practices

(ie study involving cancer risk factors performed at cancer center may enroll patients from all over the country, whereas hospitalized control subjects may come from only the local area

58

detection bias

risk factor itself may lead to extensive diagnostic investigation and increase the probability that a disease is identified.

59

allocation bias

may result from the way that treatment and control groups are assembled (if it's not assigned in a non-random fashion)

60

sampling bias

non-random sampling of a population. can lead to a study population that has characteristics that differ from the target population.

61

severely ill patients are the most likely to enroll in cancer trials, leading to results that are not applicable to patients w/ less advanced cancers. type of bias?

sampling bias

62

patients who smoke may undergo increased surveillance due to their smoking status, which would detect more cases of cancer in general. type of bias?

detection bias

63

in a study comparing oral NSAIDs and intraarticular steroid injections for treatment of OA, obese patients may preferentially be assigned to the steroid group. type of bias?

allocation bias

64

attributable risk percent (ARP)

measure of the impact of a risk factor (or excess risk in a population that can be explained by exposure to a particular risk factor)

ARP = (RR - 1)/ RR

65

probability/chance of getting one (+) test result of x number of tests using a serologic test that has 95% specificity

probability (all negative) = 0.95^x

probability (at least 1 positive) = 1 - 0.95^x

66

Power of a study - what it is and how do you calculate it?

indicates probability of seeing a difference when one truly exists; reciprocally related to a type II error (ß) ie stating that there is no difference between groups when one truly exists.

Power = 1-ß

67

to measure validity of a new screening test, what must you do?

results must be compared to those obtained with the "gold standard" test on the same individual.

68

validity (accuracy)

how close is the test to the true value?

69

reliability

how reproducible is a test? does it give similar or very close results on repeat measures or are they far apart?

70

draw a 2x2 and calculate OR

when do you use OR?

OR = (a/c) / (b/d)

used for case control

71

draw a 2x2 and calculate RR

when do you use RR?

RR = ( a / (a+b) ) / ( c / (c+d) )

used in cohort studies

72

Relative risk reduction (RRR) equation

= 1-RR
= (unexposed - exposed )/ unexposed

73

Attributable risk (AR) equation

Attributable risk percentage (ARP) equation

AR = exposed - unexposed
= ( a / (a+b) ) - ( c / (c+d) )

ARP = (RR - 1 )/ RR

74

# needed to harm 1

1/ AR

75

Absolute Risk Reduction (ARR) equation

= unexposed - exposed
= ( c / (c+d) ) - ( a / (a+b) )

76

# needed for one person to benefit equation

1 / ARR

77

incidence calculation

# new cases per year / total population at risk (does not include those already affected)

78

late look bias

problem with gathering information about severe diseases, since the most severe cases will be dead or inaccessible before their information can be gathered.

79

calculating SEM?

SD/√n

80

of these, which one will change BOTH incidence and prevalence?

new treatment
new vaccine
increased death from disease
decreased risk factors
increased recovery
increased survival

new vaccine
decreased risk factors (1˚ prevention programs)

81

What is a (+) skew distribution and how will it affect:

mean, median, mode with respect to one another?
where the bulk of the population lies?
where the longer tail is?

mean > median > mode
bulk of population lies towards the L
longer tail: R

82

What is a (-) skew distribution and how will it affect:

mean, median, mode with respect to one another?
where the bulk of the population lies?
where the longer tail is?

mean

83

If there is a guassian distribution (100% bell shaped), how are these values affected?

mean, median, mode with respect to one another?
where the bulk of the population lies?
where the longer tail is?

mean = median = mode
bulk of population lies in the center
longer tail: equal on both ends

84

How do you determine the median if there are even numbers?

median is then the average of the middle two numbers

duhhhh

85

what can be done to control potential confounding data (ie age, race)

MATCHING