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EDRA > 9. Statistics > Flashcards

9. Statistics Flashcards

1
Q

Continuous

A

Continuous:

can take any value in a
given range
e.g. height or weight

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2
Q

Discrete:

A

Discrete:

can take an integer value only
e.g. visual analogue scale

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3
Q

Ratio:

A

Ratio:

a data series that has zero as its
baseline, such as heart rate and
temperature (degrees Kelvin)

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4
Q

Interval:

A

Interval:

a data series that has zero as a
point on a larger scale, such as
temperature (degrees Celsius)

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5
Q 
Numerical data (obtained from
measurements)
A

Continuous

Discrete:

Ratio:

Interval:

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6
Q

Categorical data (grouped data

A

Nominal (unordered)

Ordinal (ordered):

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7
Q

Nominal (unordered):

A

Nominal (unordered):

data comes from mutually exclusive
unranked groups, e.g. procedural outcome (success or
failure) and gender (male or female)

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8
Q

Ordinal (ordered):

A

Ordinal (ordered):

ranked groups, e.g. categorical pain
rating scale (mild, moderate and severe), ASA grade (class I,
II, III, IV, V)

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9
Q

Probability

A

Probability is the chance of occurrence of an event.

It has a value
between 0 and 1.

The probability density curves are used to describe the
distribution of data in the given population.

They can be of different types:

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10
Q

Types of distribution of data

A

normal (most important), binomial (value of 0 or 1) or Poisson
distribution.

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11
Q

Characteristics of a normal distribution:

A

also called the Gaussian distribution

describes the distribution of continuous variables

bell-shaped symmetrical curve

mean, median and mode are identical

tails do not touch the baseline

peaks if variance (standard deviation) is low

flattens if variance (standard deviation) is high.

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12
Q

Mean:

A

it is the sum of all the values, divided by the number of values.

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13
Q

Median

A

it is the point that has half the values above and half below.

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14
Q

Mode:

A

it is used when we need a label for the most frequently occurring event.

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15
Q

Standard deviation:

A

it indicates how much a set of values is spread

around the average. It is a measure of dispersion.

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16
Q

Standard error of the mean

A

: it is the standard deviation of the
sample-mean estimate of a population mean. It gives an idea of how
closely the estimated mean value (from the sample) is likely to
represent the true mean value (from the general population).

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17
Q

It is worth remembering that:

what are the % SD for 1 2 3

A

± 1 standard deviations includes 68.2% of the data

± 2 standard deviations includes 95.4% of the data

± 3 standard deviations includes 99.7% of the data.

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18
Q

Reliability

A

Reliability is the dependability of a test

(consistency and
reproducibility).

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19
Q

Precision

A

Precision is the extent to which random variability is absent from the
test.

Reliability of a test is dependent upon its precision.

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20
Q

Validity

A

Validity is the extent to which the test measures what it was designed
to measure.

It has two components: sensitivity and specificity.

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21
Q

Accuracy

A

Accuracy is the ability of the test to produce the true values of the
measurements.

22
Q

Sensitivity

A

The ability of a test to correctly
identify the individuals
who have the condition

A/(A + C)

23
Q

Specificity

A

The ability of a test to correctly identify
the individuals who
do not have the condition

D/(B + D)

24
Q

False-positive rate

A

Proportion of false positives
in the non-diseased population

B/(B + D)
or
(1 – specificity)

25
Q

False-negative rate

A

Proportion of false negatives
in the diseased population

C/(A + C)
Or
(1 – sensitivity)

26
Q

Positive predictive value

A

Proportion of true positives among all positives

A/(A + B)

27
Q

Negative predictive value

A

Proportion of true negatives among all negatives

D/(C + D)

28
Q

Accuracy

A

Proportion of true results

(true positive and true negative)

among all results
(A + B)/(A + B
+ C + D)

29
Q

having a lax criteria
(three criteria – Point A) for
diagnosis leads to:

A

more total positives and less total negatives

high true positive (high sensitivity)

high false positive (low specificity)

low false negative

low true negative

30
Q

stringent criteria (four criteria – Point B) for diagnosis means:

A

less total positives and more total negatives

low true positive (low sensitivity)

low false positive (high specificity)

high false negative

high true negative.

31
Q

Hence adding another criterion as a requirement for diagnosis will lead to

A

lower true-positive rate
(low sensitivity),

but a lower false-negative rate as well
(higher specificity).

The former situation is desirable
in a screening test
(high sensitivity),

while the latter is desirable in a
confirmatory test (high
specificity).

32
Q

Negatively skewed data

A

Most of the values are
positive,
tail points negatively (left)

Mode is least changed, while mean the most

Mean < Median < Mode

Example: a very easy test will be high-scoring, so
it will be negatively skewed

33
Q

Positively skewed data

A

Most of the values are negative, tail points positive
(right)

Mode is least changed, while mean the most

Mean > Median > Mode

Example: a very difficult test will be poor-scoring,
so it will be positively skewed

34
Q

Comparison of case-control and cohort study

Case-control study

A

Example: study of a group of chronic obstructive
pulmonary disease patients (case) and without
chronic obstructive pulmonary disease (control) to
identify risk factors (smoking)

Retrospective study

Outcome is measured before exposure

Inexpensive, easier, hospital-based

Needs small sample size

Allows estimation of odds risk only

Used to study relatively rare conditions

Selection bias more likely

35
Q

Cohort study

A

Example: follow-up of smokers (cohort) and
non-smokers (another cohort) and the
development of chronic obstructive
pulmonary disease in each cohort

Prospective study

Outcome is measured after exposure

Expensive, harder, community-based

Needs large sample size

Allows determination of incidence and
relative risk

Used to study common conditions

Selection bias less likely

36
Q

Cross-sectional study

A

(prevalence study):

studies present cases,

allowing estimation of prevalence
and
risk factors at the same time.

It is easy and
inexpensive.

37
Q

Randomised clinical trial:

A

is a prospective study
with randomised study groups.

It may be blinded to reduce selection bias.

38
Q

Randomised clinical trial:

A

is a prospective study
with randomised study groups.

It may be blinded to reduce selection bias.

39
Q

Relative risk is

A

a ratio of the probability
of the event (disease)

occurring in the exposed group
versus a non-exposed group

40
Q

Attributable risk

A

is the difference in rate
of a condition between an

exposed population
and an unexposed population

41
Q

The odds ratio

A

is the ratio of the odds of an event
occurring in one group to the odds
of it occurring in another group

42
Q

Absolute risk reduction

A

is the reduction in risk associated
with treatment
(or removal of risk factor)
as compared with placebo

43
Q

Number needed to treat

A

is the average number of patients
who need to be
treated to prevent one
additional bad outcome.

44
Q

NNT

A

It is the inverse of absolute risk reduction.

Example: number of children we need to vaccinate to prevent one case of disease.

The lower the number needed to treat, the more effective the intervention is.

45
Q

Number needed to harm

A

is the number of patients that

need to be exposed to a risk factor

over a specific period to cause harm

in one additional patient.

46
Q

NNH

A

It is the inverse of the attributable risk.

Example: number of adults that need
to be exposed to smoking to have
one more case of lung cancer.

The lower the number needed to harm,
the worse the risk factor

47
Q

Null hypothesis

A

States that there is no difference between the two groups being studied

48
Q

Null hypothesis

define

A

It provides a starting point for the study.

Then, if no difference is found,
it is accepted and
there is no statistical difference noted.

However, if a difference is noted, it is rejected and the result is ascribed a
statistical significance.

49
Q

The level of statistical significance is called

A

the P value (usually 0.05)

and is the

probability of occurrence
of a type I error
(α).

50
Q

Type I error

A

Cause: rejecting null hypothesis when it is true

Inference: finding a false difference between groups when none exists

Probability of type I error is α

51
Q

Type II error

A

Cause: accepting null hypothesis when it is
false

Inference: missing a true difference between groups

Probability of type II error is β

52
Q

Power of a study is described as

A

its ability to detect a
difference between groups.

It may be described as the probability
of not obtaining a type II error
(β).

Hence, power = (1 – β).

For a good study,
Power should be 0.8 or more.

EDRA (26 decks)

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