Evidence Based Medicine Burack Wsu-Som Year2

Front 1

4 step method to decide if evidence reported in the literature is applicable to a patient's condition

Back 1

1. ask - the right question
2. access - the right information
3. assess - validity and utility of the information
4. apply - skillfully to patient care

Front 2

what does the pico model apply to?

Back 2

a format to asking questions very specific to a patients situations.
P = patient specific
I - intervention and
C = comparison
O = outcome

Front 3

decision support tools including "guidelines" and care support systems

Back 3

systems

Front 4

address broader care, integrating multiple sources

Back 4

synopses

Front 5

integrat multiple studeies addressing single question.

uses explicit criteria to select studies, quantitatively summarize results

Back 5

systematic reviews

Front 6

a quantitiive summary of results from multiple carefully selected studies examining the same hypothesis

Back 6

meta-analysis

Front 7

a way to take multiple studies the impercisions to make them collectively more precise

Back 7

meta-analysis

Front 8

what ways can be applied to Pubmed to look for "pre-reviewed" literature

Back 8

limits
-> setting limits to search

clinical queries
->clinical study category = randomized trials
->systemic reviews = meta-analysis

Front 9

1. not all imp clin question have been addressed
2. results may not be generaliizable to pt like yours
3. comparable txmt may not be acheived in clincial settings
4. rct even if appropraite may not be feasible for reasons of sample size, interest,cost and time required

Back 9

real world limits of rct

Front 10

what are three blindedness in a rct?

Back 10

1. assigner of txmt - allocation concealment
->biased assignment to txmt group
2. subject
->compliance, placebo, and hawthorne effect
3. investigator -> differencial intensity

Front 11

when looking at
-blinded delivery
-compliance
-contamination of controls
-conintervention within either group

what question is being asked?

Back 11

was the txmt fairly applied?

Front 12

what 3 concepts are looked at when studying the validity of a study?

Back 12

1. architecture of the study, is the study a randomized study
2. delivery - was the txmt fairly applied
3. outcome - was the outcome fairly assessed

Front 13

what question is being asked when looking at the following:
-blinded assessment
-follow up complete
-analysis by intention to tx

Back 13

Was the outcome fairly assessed?

Front 14

how to solve for relative risk reduction (rrr)?

Back 14

ARD / risk control group = RRR

ARD = difference btwn placebo and exp group

Front 15

what is absolute realative risk (ARR)?

Back 15

difference between the the placebo and treated group (% difference)

Front 16

how is number needed to tx (NNT) calculated?

Back 16

NNT = 100/ARR(as %)

or

NNT = 1/ARR (as a proportion)

ARR = absolute rick reduction or risk difference

ARR = pt w/o txmnt - pt w/txmt

each divided by total # of pt in the study.

Front 17

what does the number need to treat depend on?

Back 17

1. what proportion of the population is at risk

2. how effect the txmt is

Front 18

falsely rejecting the null hypothesis (inferring txmt has a real effect when it does not)

Back 18

type 1 error (alpha)

Front 19

level of tolerance for type 1 error (often 5%)

Back 19

alpha

Front 20

the probability that a difference as large (or larager) than the one observed could have occured by chance alone

Back 20

p - value

Front 21

when is the null hypothesis rejeted?

Back 21

when the p-value is smaller than alpha

Front 22

falsely failing to reject the null hypothesis (inferring there is no txmt effect when there is one) missing a difference

Back 22

type 2 error (beta)

Front 23

= 1 - B

depends upon sample size, our tolerance for type 1 error (alpha), and magnitude of the effect size we want to be able to detect

Back 23

power

Front 24

if infer no difference

Back 24

power

Front 25

if infer difference

Back 25

p-value

Front 26

txmt will be "acceptablly close" in effectiveness to standard

Back 26

non-inferior txmt

-must offer other benifit (cost, harm, other factor)

Front 27

comparing the new and standard txmt ( with a difference of 5% being acceptable)

Back 27

non-inferior txmt

Front 28

txmt with all results above zero line -> mean difference of 95% CI

Back 28

superior txmt

Front 29

txmt with all results below zero line -> mean difference of 95% CI

-and below acceptable margin of inferiority

Back 29

inferior txmt

Front 30

when a study applies information to a sub-groups after a randomized study has been performed, not randomized study

Back 30

post hoc

Front 31

3 key concepts to address when questioning therapy?

Back 31

architecture - randomized trial or not

delivery - was txmt fairly applied

outcome - was the outcome fairly assessed

Front 32

number of people to tx to save one person

Back 32

NNT = 100/ARR

absolute relative risk = placebo group - txmt group

Front 33

does p-value prove txmt works?

Back 33

p-value has absolutely nothing to do with magnitude of effect, only the probablity of drawing fals inference about its existence

Front 34

what is compliance?

Back 34

was txmt received as intended?

Front 35

what group is effected by contamination?

Back 35

control group

Front 36

what group is evidence of co-intervention a concern?

Back 36

experimental group

Front 37

by how many percentage points does probability of improvement inc in exp compared to control

Back 37

absolute benefit improvement

Front 38

how many fold higher is probability of improvement in the experimental as compared to control group

Back 38

relative "risk" for improvement

risk (exp) / risk (control)

Front 39

reduce risk of adverse outcome

Back 39

therapy

Front 40

reduce risk of developing disease

Back 40

prevention

Front 41

type of prevention?

-prevent asymptomatic illness

Back 41

primary prevention

Front 42

type of prevention?

-prevent symptomatic illness

Back 42

secondary prevention

Front 43

type of prevention?

-prevent complication or death

Back 43

tertiary prevention

Front 44

knowing about a disease longer bc dx earlier due to screening but dont change likelihood of death

Back 44

lead time bias

Front 45

2 paralell phenomenon that appear related but that may not be causally linked

Back 45

ecologic fallacy

Front 46

what does compliance, contamination and co-intervention assess in a study?

Back 46

was txmt delivered as intended

Front 47

what are 3 aspects of validity of a study?

Back 47

1. is this a randomized, controlled study?

2. was txmt fairly applied?

3. was the outcome fairly assessed

Front 48

what are 3 components of the actually observed (summary of results)?

Back 48

1. how large was teh txmnt effect

2. what is a plausable range of the effective size

3. how likely is it that my conclustion is in error

Front 49

can change explain results

Back 49

yes

Front 50

does a highly statistically significant number indicate to you the magnitude of benifine is high, low or you can't tell

Back 50

can't tell , bc has nothing to do with how good the p-value is

Front 51

what are 3 key questions about utility (will the results help me in caring for my pt?

Back 51

1. do the results apply to my pt.

2. is the effect large enough to matter

3. were all clincially imp our come considered

4. are the likely benefits worth the potential harms and costs

Front 52

appropriate ranges for probabilities and values can be substituted for the baseline estimate and alternatives compared

Back 52

sensitivity analysis

Front 53

predicted outcome per alternative (outcome may be assigned a value but costs of acheivement are not included)

Back 53

decision analysis

Front 54

cost per health outcome
-ex, cost per year oflife saved

Back 54

cost effectiveness

Front 55

cost per quality adjusted health outcome
-ex, cost per quality adjused life year

Back 55

cost utility

Front 56

cost ($) to acheive outcome compared to value to outcome ($)

Back 56

cost benefit

Front 57

primary
secondary
and tertiary prevention

Back 57

focus of prevention policy

Front 58

-burden
-tests for early detection
-effectiveness of early txmt
-"wisdom" which may include cost-effectiveness

Back 58

criterias of prevention policy

Front 59

what do all 4 of these represent:

1. patient description
2. intervention
3. comparison
4. outcome

Back 59

the summary of methods in a study PICO

Front 60

not pre-specified hypotheses that can be tested and are subject to multiple sources of bias (sub-groups were not randomly assigned to treatment status)

Back 60

post hoc

Front 61

the capacity of , on average, being correct

Back 61

accuracy

Front 62

if disease is present, test is correct - positive

Back 62

sensitivity

Front 63

if disease is absent, test is correct - negative

Back 63

specificity

Front 64

the capacity to consistently provide the same answer

Back 64

reliability

Front 65

test-retest results are the same

Back 65

precision

Front 66

a/(a + c)

Back 66

sensitivity

Front 67

d/(b + d)

Back 67

specificity

Front 68

the ability of the test to correctly id those with disease

-erroded by false negatives

Back 68

sensitivity

Front 69

the ability to correctly id those without (free) of disease

-erroded by false positives

Back 69

specificity

Front 70

1 - specificity =

Back 70

false positive "rate"

Front 71

a very sensitive test when neg rules out disease

why?

Back 71

bc no fals negatives - did not miss anyone

-reduce specifity

Front 72

graph that plots trade off btwn sensitivity and specificity

-a method to quantify the amount of info captured by a test

Back 72

ROC - receiver operating characteristic curve

Front 73

where would the best point be on the receiver operating characteristic (ROC)curve and why?

Back 73

upper left corner bc both sensitivity and specificity would be 100%

Front 74

provides an estimate of teh area under the ROC curve

Back 74

c-statistic or concordance statistic

Front 75

implication of a diagnoistic test

Back 75

predictive value

Front 76

characteristics of a diagnoistic test

Back 76

specificity and sensitivity

Front 77

a/(a + b)

Back 77

postive predictive value = ppv

-proportion of people who test postitive for the diseae

Front 78

d/(d + c)

Back 78

normal predictive value

-of those who are negative how many are actually free of disease

Front 79

c/(c + d)

Back 79

missed - possiblity the remaing people who tested negative are actually positive for the disease

Front 80

the estimated prevalance of disease in a similar population of individuals

Back 80

best "guess" of pre-test population for a pt

Front 81

correct amoung those WITH disease

Back 81

sensitivity

Front 82

correct amoung those WITHOUT disease

Back 82

specificity

Front 83

describe the performance (accuracy) of a test

Back 83

sensitivity and specificty

Front 84

best guess likelihood of disease in our patient before testing

Back 84

pre-test probablilty

Front 85

estimate of likelihood of disease following testing

Back 85

post test probability (PPV or NPV)

Front 86

depends on both test charactieristics AND the patient tested

Back 86

post test probablility

Front 87

-classic epidemiology and clinical research
-test score
-substatial data

Back 87

harder data

Front 88

clinical judgement - particularly applying epidemiolgy to individual pt

-expert opinion

Back 88

soft data

Front 89

the probability that a given test result came from an individual with rather than without the disease

Back 89

likelihood ratio (LR)

Front 90

the test result provides no new info, disease is no more likely or less likely than it appeared before the test

Back 90

Likelihood ratio of 1

Front 91

sensitivity / (1-specificity) =

Back 91

likelihood ration (LR)

Front 92

what equations applies to the following statement?

What we know about something depends on what we know before and what we've learned from the test

Back 92

post-test odds = pre-test odds x LR

Front 93

- proportion "correct" for a diagnostic test (true positivies pluse true negatives among all tested)

-calibration of a predictive model (comparing numbers observed verses expected for levels of predicted risk)

Back 93

Accuracy (average) can be assessed for diagnostic tests and predictive models

Front 94

comparing numbers observed verses expected for levels of predicted risk

Back 94

calibration of a predictive model

Front 95

true positivies pluse true negatives among all tested

Back 95

proportion "correct" for a diagnostic test

Front 96

the ability to distinguish amoung individuals as affected or not

Back 96

discrimination

-for both dianostic tests and predictive models this is assessed as area under the ROC curve (c-statistic)

Front 97

with this question what is being measured and what is the focus?

How close (accurate) is the average level of risk predicted for a group to the level actually observed?

Back 97

measure: calibration

focus: group

Front 98

with this question what is being measured and what is the focus?

How often are we correct (accurate) in distinguishing individuals at high risk vs low risk?

Back 98

measure: discrimination

focus: individual

Front 99

with this question what is being measured and what is the overall measure is?

"correctness on average" in the population

Back 99

measure: calibraion

measure: observed-expected across sub-groups

Front 100

with this question what is being measured and what is the overall measure is?

"correctness" in ranking for pairs of individuals (one with and one without outcome)

Back 100

discrimination

concordance statistic (c-statistic) and roc curve

Front 101

if a patient has ascites but no ankle swelling what are the chances he/she has ascities

Back 101

absence of ankle swelling is going to substantially reduce the likelihood of ascities (5 to 10%)

Front 102

if the patient does have ascites and ankle swelling does that automatically mean that he/she has ascites?

Back 102

it supports but not confirm the suspicion of ascities

Front 103

how likely is a pt have ascities if the pt does have a fluid wave?

Back 103

presence of fluid wave ismost specific and is particularly informative when prsent, thus its present inc the probablility of ascities by about 30 - 40 %

Front 104

what lab measure absolutely rules out portal htn

Back 104

low serum - ascities albumin gradient (SAAG) - the odds of portal HTN dec by 15 fold when the SAAG < 1.1

Front 105

what virtually confirms the dx of CHF?

Back 105

S3

Front 106

What lab test if elevated is associated with CHF?

Back 106

BNP - a polypeptide secreted by cardiac ventricules in response to "stretching" of myocyte

->inc levels associated with CHF

Front 107

how does presence or absence of BNP effect dx of CHF?

Back 107

if elevated BNP cant be used to confirm dx of CHF bc with a positive LR of 4.1, only modest inc the odds of chf (inc absolute probability by perhaps 25%)

a negative BNP is quite helpful in excluding CHF bc negative LR of < 0.1 the odds are reduced 10 fold and the absolute probability by 50%