Drug Lit Exam 1- Statistics

Front 1

Population- definition

Back 1

all persons with a characteristic in the
universe

Front 2

sample definition

Back 2

a portion of that population (e.g. representative sample of type 2 diabetics)

Front 3

parameter definition

Back 3

measurement that describes the population
(average age, average blood glucose)

Front 4

statistics definition

Back 4

measurement that describes a sample

Front 5

If you could measure the whole population you would not need.....because...

Back 5

“statistics” you have parameters already

Front 6

Why are stats needed (and what part of stats helps you to achieve whatever, for 2 of them) (4)

Back 6

 Describe study population- descriptive statistics
 Tell likelihood experimental results occurred due to chance- test of statistical significance
or analytic statistics
 Draw conclusions
 Increase experimental efficiency

Front 7

what determines the proper
descriptive statistics and statistical tests
that can be used?

Back 7

Type of data

Front 8

4 levels of measurement and whether they are discrete or continuous

Back 8

Nominal (discrete)
– Ordinal (discrete)
– Interval (continuous)
– Ratio (continuous)

Front 9

nominal data properties (4)
what's it mean?

Back 9

basically means...are you this or that?

 Weakest level of measurement
 Assigns numbers or symbols for identity purposes
 Mutually exclusive and internally equivalent subgroups
 Only relationship between numbers subgroup is equivalence; can't do any math- no avgs or anything, no order

Front 10

ordinal data properties (5)
property of ordinal data vs. nominal
strength of measurement
data measured how?
distance b/t subgroups
subgroups property
example

what DOESN'T matter for ordinal variables

Back 10

 Stronger level of measurement
 Same characteristics as nominal, however, there is a definite order
 Data measured qualitatively, not quantitatively
 Distance between subgroups does not need to be equal
 Subgroups have transitivity
ex) pain scale
– (if a>b & b>c, then a>c)


A ordinal variable, is one where the order matters but not the difference between values. For example, you might ask patients to express the amount of pain they are feeling on a scale of 1 to 10. A score of 7 means more pain that a score of 5, and that is more than a score of 3. But the difference between the 7 and the 5 may not be the same as that between 5 and 3. The values simply express an order. Another example would be movie ratings, from * to *****

Front 11

interval vs. ratio measurements

Back 11

interval and ratio data are treated the same for all intents and purposes

Front 12

interval mesaurements- level of strength for measurement

same characteristics as what type of data except for...(3)

Back 12

 Stronger level of measurement
 Same characteristics as ordinal, however...

– true quantitative characteristics
– constant distance between numbers
– numbers have a defined unit of measurement and represent real
numbers

Front 13

example of interval data

Back 13

ex) blood pressure or age- continuous scale
-can do averages now

Front 14

ratio measurement- level of measurement
what is it?

3 properties

Back 14

 Highest level of measurement
 interval scale absolute zero- like zero actually has meaning (kelvin vs. F/C)

 Absolute zero allows the use of all arithmetic operations
 Numbers represent actual quantities
 Normally treated like interval data statistically in medical articles

Front 15

2 types of interval scales

Back 15

– numerically discrete scale
– numerically continuous scale

Front 16

descriptive statistics (5 kinds)

Back 16

mean, median, mode
frequency distribution
st dev
st derr
95% confidence interval

Front 17

measures of the central tendency of the data (3)

Back 17

mean median mode

Front 18

Frequency distribution is useful for what type of data

Back 18

discrete (nominal or ordinal) data

Front 19

st dev definition

Back 19

a measure of the variability in
response from one subject to another in the sample

Front 20

st error what is it? equation

Back 20

SD/ square root of sample size- a
measure of how close the sample mean is to the true population mean

Front 21

what is a 95% confidence level?

Back 21

the estimated value from the
study + twice the SE (standard error)

Front 22

mean- useful in describing interval/ratio data?

is it used to describe ordinal data? nominal data?
affected by outliers?

Back 22

yes

no
no

yes

Front 23

median- useful in describing interval/ratio data?

is it used to describe ordinal data? nominal data?
affected by outliers?

Back 23

yes
yes
no
no

Front 24

mode- useful in describing interval/ratio data?

is it used to describe ordinal data? nominal data?
affected by outliers?

Back 24

yes
yes
yes
no

Front 25

interquartile range definition

Back 25

measure of statistical dispersion, being equal to the difference between the third and first quartiles. IQR = Q3 − Q1

Front 26

RANGE:
useful to describe interval or ratio data?

used to describe ordinal data?

descriptive of sample variability?

assists in statistical inference?

used to calc confidence intervals?

Back 26

yes
yes
yes
no
no

Front 27

INTERQUARTILE RANGE
useful to describe interval or ratio data?

used to describe ordinal data?

descriptive of sample variability?

assists in statistical inference?

used to calc confidence intervals?

Back 27

yes
yes
yes
no
no

Front 28

STANDARD DEV

useful to describe interval or ratio data?

used to describe ordinal data?

descriptive of sample variability?

assists in statistical inference?

used to calc confidence intervals?

Back 28

YES
NO
yes
yes
no (but involved indirectly through calc of SE...)

Front 29

standard error (SEM...)
useful to describe interval or ratio data?

used to describe ordinal data?

descriptive of sample variability?

assists in statistical inference?

used to calc confidence intervals?

Back 29

yes
no
no
yes
yes

Front 30

mode

Back 30

most frequent response

Front 31

st dev vs st error DIFFERENCE
tell what each does

Back 31

-st dev is representation within the sample (variability)

-st error is statistical value to estimate how well value represents larger population

Front 32

Application of measure of variability (2)

Back 32

interquartile range- middle half of sample (25-75th percentile)??
i have no idea why i wrote this in my notes

and st dev...

Front 33

how dichotomous (branching into 2 categories) are commonly presented (8)

Back 33

-incidence- how often it happens in a pop
-prevalence- how often, right now (not over time)
-add hazard ratio to slide- similar to RR (interpreted same way)
 Relative risk
 Odds ratio
 Relative risk reduction
 Absolute risk reduction
 Number needed to treat/ Number needed to harm

Front 34

Hazard ratio, when compared to relative risk

Back 34

takes into consideration that incidence of events are occurring all at end of study (incidence is all at the end of study

Front 35

incidence definition

conveys what info?

Back 35

the number of new cases (or events)
that occur per unit of time (often calculated per
year but be sure you notice the specific interval)

conveys information about the risk of contracting the disease

Front 36

calculation of incidence

Back 36

Number of individuals who develop the
disease (or event) over a period of time
divided by:

Total number of individuals at risk during that time

Front 37

prevalence definition

Back 37

the probability of having a disease (or
characteristic) at one point in time

Front 38

prevalence calculation (how)

Back 38

Number of individuals who have the
disease (or characteristic) at one point in time

divided by: total number of individuals in the
population at that time

Front 39

risk definition

Back 39

Synonymous with cumulative
incidence- total number of events over a
period of time

Front 40

risk calculation

Back 40

The number of individuals who
develop the disease during the time period
divided by:
The number of disease -free persons at the
beginning of the time period

Front 41

definition of odds

Back 41

a proportion that compares the number
of times that an event occurs to the number of times that it does not occur

Front 42

odds calculation

Back 42

Number of times the event occurs divided by Number of times the event does not occur

Front 43

risk vs. odds- when can odds be a good approximation for risk? when is it not (and why)?

Back 43

Odds are slightly different than risk but when the event is rare the odds are a good approximation of risk

frequent occurrence means that odds will not approximate risk as well (denominator difference)

example: Risk of drawing an Ace from a deck of cards is 4/52 = 1/13

Odds of drawing an Ace from a deck of cards is 4/48 = 1/12

Front 44

what type of study do you usually see odds calculation approximating risk? why?

Back 44

case control study- retrospective so things have already occurred

can't calculate an incidence- so use odds to approximate risk

Front 45

relative risk definition

Back 45

A ratio of the probability of developing the outcome over a specified period of time if a risk factor is present

divided by the probability of developing the outcome in the same time period if the risk factor is absent

Front 46

calculating RR

Back 46

see slide

rate of disease in people with factor/rate of disease in people without factor

Front 47

relative risk- what do the numbers mean if > 1

what if < 1?

talk about what it means in percentages too

Back 47

If the number is > 1 the risk of that event (or disease) is
greater if the intervention (or risk factor) is present than
if it is absent. e.g. if the relative risk of stroke is 3 if you
take PPA, you have three times the risk of a stroke if
you take it compared to the risk if you do not
 If the number is < 1 the risk of that event is less if the
intervention is present. e.g. if the relative risk of death is 0.8 if you take lisinopril compared to no treatment you have reduced your risk by 20% if you take lisinopril

Front 48

what if RR is = 1?

Back 48

means that risk of dev disease for exposed group is same as unexposed...so no association.

Front 49

confidence intervals- combines info about what 2 things?

Back 49

Combines information about the strength of an association (effect size) and the statistical significance (effects of chance) of the result

Front 50

so what is a 95% confidence interval saying? (say in layman's terms)

Back 50

95% of all samples would give a result within the ranged specific by the CI

Front 51

For RR or OR- value of 1 represents what?

if CI includes 1 then what does this mean?

Back 51

For RR or OR the value of 1 represents no difference between groups (no effect of treatment)

i.e. If the 95% CI of the OR or RR includes 1, the result is not statistically significant

Front 52

confidence interval size relation to sample size

Back 52

Bigger sample size = smaller confidence interval

Front 53

how to calculate NNT

Back 53

1/ARR

Front 54

if RRR is the same in 2 instances, does this mean NNT will be the same?

Back 54

NO. depends on ARR

Front 55

CER

Back 55

CER = control event rate (comes from study? event rate in control group?)

Front 56

PEER- stands for?
what is it?

Back 56

PEER = patient expected event rate

refers to the rate of events we'd expect in a patient who received no treatment or conventional treatment.

Front 57

Adjusting Estimate of Benefit (NNT) for an
Individual Patient- why?

Back 57

I guess...if there are factors that increase risk for an event...your NNT will vary depending on risk factors in specific pt groups

Front 58

how to adjust estimate of benefit (NNT) for individual patients

2 equations

Back 58

use equations like
CER/PEER (i guess this...controls...for it) x NNT

or 1/(PEER x RRR)

Front 59

steps for constructing a statistical significance test (6)

Back 59

state hypothesis: there is a difference between groups
formulate null hypothesis (the one where no difference exists)

decide significance level (usually 5%)

collect data
apply significance test. this determines probability of obtaining the observed data if null is true
reject or fail to reject null (reject = effect)

Front 60

type I vs. II error

Back 60

Type I: Reject the null when it is true (made a conclusion there was a difference but there wasn't)
Type II: Accept the null when it is false (say there is no difference when there actually is)

Front 61

st dev vs st error DIFFERENCE
tell what each does

Back 61

-st dev is representation within the sample (variability)

-st error is statistical value to estimate how well value represents larger population

Front 62

Application of measure of variability (2)

Back 62

interquartile range- middle half of sample (25-75th percentile)??
i have no idea why i wrote this in my notes

and st dev...

Front 63

how dichotomous (branching into 2 categories) are commonly (8)

Back 63

-incidence- how often it happens in a pop
-prevalence- how often, right now (not over time)
-add hazard ratio to slide- similar to RR (interpreted same way)
 Relative risk
 Odds ratio
 Relative risk reduction
 Absolute risk reduction
 Number needed to treat/ Number needed to harm

Front 64

Hazard ratio, when compared to relative risk

Back 64

takes into consideration that incidence of events are occurring all at end of study (incidence is all at the end of study

Front 65

incidence definition

Back 65

the number of new cases (or events)
that occur per unit of time (often calculated per
year but be sure you notice the specific interval)

Front 66

calculation of incidence

Back 66

Number of individuals who develop the
disease (or event) over a period of time
divided by:

Total number of individuals at risk during that time

Front 67

prevalence definition

Back 67

the probability of having a disease (or
characteristic) at one point in time

Front 68

prevalence calculation (how)

Back 68

Number of individuals who have the
disease (or characteristic) at one point in time

divided by: total number of individuals in the
population at that time

Front 69

risk definition

Back 69

Synonymous with cumulative
incidence- total number of events over a
period of time

Front 70

risk calculation

Back 70

The number of individuals who
develop the disease during the time period
divided by:
The number of disease -free persons at the
beginning of the time period

Front 71

For RR or OR- value of 1 represents what?

if CI includes 1 then what does this mean?

Back 71

For RR or OR the value of 1 represents no difference between groups (no effect of treatment)

i.e. If the 95% CI of the OR or RR includes 1, the result is not statistically significant

Front 72

confidence interval size relation to sample size

Back 72

Bigger sample size = smaller confidence interval

Front 73

how to calculate NNT

Back 73

1/ARR

Front 74

if RRR is the same in 2 instances, does this mean NNT will be the same?

Back 74

NO. depends on ARR

Front 75

CER

Back 75

CER = control event rate (comes from study? event rate in control group?)

Front 76

PEER- stands for?
what is it?

Back 76

PEER = patient expected event rate

refers to the rate of events we'd expect in a patient who received no treatment or conventional treatment.

Front 77

Adjusting Estimate of Benefit (NNT) for an
Individual Patient- why?

Back 77

I guess...if there are factors that increase risk for an event...your NNT will vary depending on risk factors in specific pt groups

Front 78

how to adjust estimate of benefit (NNT) for individual patients

2 equations

Back 78

use equations like
CER/PEER (i guess this...controls...for it) x NNT

or 1/(PEER x RRR)

Front 79

steps for constructing a statistical significance test (6)

Back 79

state hypothesis: there is a difference between groups
formulate null hypothesis (the one where no difference exists)

decide significance level (usually 5%)

collect data
apply significance test. this determines probability of obtaining the observed data if null is true
reject or fail to reject null (reject = effect)

Front 80

type I vs. II error

Back 80

Type I: Reject the null when it is true (made a conclusion there was a difference but there wasn't)
Type II: Accept the null when it is false (say there is no difference when there actually is)

Front 81

Type I error parameter and accepted value

Back 81

accepted value of this (alpha) is 0.05

Front 82

Type II error parameter and accepted value

Back 82

accepted value (beta) not as standard, typical is between 0.10 and 0.20 but many studies fail

Front 83

what is power?

Back 83

1 - beta

With a beta of 0.10 you have a 90% chance to detect a difference (of a specified size) if it exists, or 90 % power

Front 84

consequences of Repeated Tests of Significance such as interim analyses (at 2, 5, 10, 20 tests what happens)

look this up more i don't get why

Back 84

if alpha = 0.05
– 2 tests: 8% probability of type I error
– 5 tests: 14% probability of type I error
– 10 tests: 20% probability of type I error
– 20 tests: 35% probability of type I error

Front 85

If the null hypothesis is tested once, at
significance level of 5% (alpha), this means what?

Back 85

the probability of a type I error is 5% by definition

Front 86

Sample Size and Power relationship

Back 86

greater sample size = greater power

Front 87

Sample size is usually calculated based on...(5)

Back 87

a, b, d , sigma

– Accepted alpha (usually 0.05)
– Accepted beta (0.1 to 0.2)
– Size of treatment difference you want to detect (referred to as delta)
– Measure of variability (SD) for continuous data or Rate of events in control patients for dichotomous data
(- if continuous fourth factor is SD, if dichotomous it's rate of events

Front 88

positive vs negative study

Back 88

positive study finds that there is a difference (definition); negative study fails to find that difference

Front 89

regression to the mean: this usually shows up like...(2)

Back 89

Extreme measures move closer to the mean when they are repeated
 Patients selected for “abnormalcy” will tend to become more normal when reassessed

Front 90

for a negative study, how can we tell if it's legit

Back 90

for negative study- look for power to see if conclusion is legit

Front 91

_____ drivers power
why?

Back 91

-events drives power- if nobody dies/gets hospitalized/gets sick you can't prove that your treatment worked)

Front 92

Regression to the mean: things that affect the size of regression (2)

Back 92

The less reliable the form of measurement for the outcome... the greater the expected improvement when remeasured

The more abnormal the initial result of the measure the
greater the expected improvement when remeasured

Front 93

how to control for regression to the mean? (4)

Back 93

use most reliable measures, use
averages of more than one measurement, take measures at more than one time, use a run-in period before trial

Front 94

Subgroup analysis- how to approach

what 2 questions should you ask yourself and why?

subgroup analysis power

Back 94

be cautious...in interpreting.

was the subgroup and specific analysis planned before the trial began?
Was the sample size adequate to find a difference in the subgroup if one exists (power)

study is rarely powered to find treatment difference in subgroups- typically only powered for overall sample; so if fail to find diff, doesn't mean it doesn't exist

Front 95

subgroup analysis- what must be adjusted for? why?

Back 95

f they don't adjust for multiple testing of subgroups (multiple subgroups?), run risk of type I error (increased)

Front 96

subgroup analysis- many times more useful for what?

Back 96

Many times this is more useful for hypothesis generation rather than proof of a hypothesis

Front 97

what is an interim analysis?

Back 97

Predefined time points to examine the results during the trial to determine if proof of benefit or of harm has been established

Front 98

who typically analyzes interim studies?

Back 98

There is a separate monitoring board designated to do this analysis independent of the primary
study investigators

Front 99

interim analysis- what must be adjusted?

Back 99

p values must be adjusted for multiple testing

Front 100

per protocol analysis

Back 100

only those who actually
complete the protocol are included in the analysis
i.e. drop outs are basically ignored

Front 101

intent to treat analysis

Back 101

each subject will be included
in the analysis for the group to which they were randomized; i.e. their results are counted (usually as a failure) even if they did not complete the full
treatment

Front 102

intent to treat vs. per protocol- which is more rigorous and what does this mean

Back 102

more rigorous analysis (more "real world") and if the results are still positive by this method the conclusions are more reliable

Front 103

what must we keep in mind about p-values (2)

Back 103

 Statistical significance by itself means little
 Weigh against practical concerns

Front 104

for p-values- what practical concerns must we weigh "statistical significance" against? (5)

Back 104

– what is the size of benefit
– how does it compare to alternatives
– what is the CI or other measure of
precision
– what are the relative toxicities
– cost