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Evidence Based Medicine > Lecture 1 > Flashcards

Lecture 1 Flashcards

1
Q

Evidence-based medicine is

A

Use of mathematical estimates of risk of benefit & harm, derived from high-quality research on population samples, to inform clinical decision-making in the diagnosis, investigation, or management of individual patients

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2
Q

EBM practice emphasizes

A

best evidence, clinical expertise, patient values & circumstances

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3
Q

Basic steps

A
  1. Ask answerable questions
  2. Search for best evidence
  3. Critical appraisal for validity & relevance
  4. Integrate evidence, clinical expertise, & patient values/preferences & apply
  5. Evaluate results
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4
Q

Answerable questions consist of:

A 
PICO
Patient or problem
Intervention
Comparison intervention
Outcomes
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5
Q

Best medicine should be

A

patient-centered

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6
Q

What is the best source for evidence

A

Electronic online sources

Current, recently updated, & evidence-based

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7
Q

Most reliable studies are

A 
Systematic Reviews (or meta-analyses) of RCTs
(followed by Randomized, controlled trials)
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8
Q

Least reliable studies are

A

Case reports

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9
Q

Hierarchy of study reliability:

A
  1. Systematic Reviews (or meta-analyses) of RCTs
  2. Randomized, controlled trials
  3. Prospective studies
  4. Retrospective studies
  5. Cross-sectional surveys
  6. Case series
  7. Case reports
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10
Q

Are prospective or retrospective studies more reliable?

A

Prospective studies

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11
Q

Systematic reviews present

A

evaluations of groups of studies relating to diagnosis & screening, therapy, prognosis, & Harm/risk/etiology

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12
Q

Descriptive statistics

A

Involves collecting data, presenting data, & characterizing data in order to describe data

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13
Q

Inferential statistics

A

Involves estimation & hypothesis testing in order to make decisions about population characteristics

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14
Q

Experimental units (elements)

A

Object upon which we collect data

Sometimes called units of analyses

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15
Q

Population

A

All items of interest

Statistic coming from whole population is called a parameter

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16
Q

Sample

A

Subset of units of a population

Statistics come from samples

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17
Q

Variable

A

Characteristic of an individual experimental unit

Symbol of event, act, trait, or attribute that can be measured & we assign some values

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18
Q

Categorical Variable

A

Some numeric or character codes that represent either the presence or absence of something that is of interest OR
the relative weight or rank of the thing that is of interest

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19
Q

Quantitative variable

A

Variable that holds the numerical result of some measurement usually taken using some standard unit

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20
Q

Process

A

Series of actions or operations that transforms inputs to outputs; produces output over time

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21
Q

Nominal Scale

A

Simplest level of measurement - categories without order

i.e. hair color

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22
Q

Ordinal Scale

A

Nominal variables with an inherent order among categories

i.e. disease stages (no set difference, equidistant between values)

23
Q

Interval Scale

A

Measurable difference or interval or distance between observations
I.e. height, age

24
Q

Ratio

A

Same as interval but with an absolute reference point (such as 0)

25
Q

Qualitative Data is described using a

A

Bar Graph or Pie Chart (derived from a summary table or frequency table)

26
Q

Quantitative Data is described using

A

Dot Plot, Stem & Leaf display, Frequency distribution (histogram)

27
Q

Bar Graph

A

Has vertical bars for qualitative variables

Equal bar widths, height shows frequency

28
Q

Class

A

One of the categories into which qualitative data can be classified

29
Q

Class relative frequency

A

Class frequency (number of observations) divided by total numbers of observations in data set

30
Q

Histogram

A

Used for quantitative variables (numbers not categories) arranged into intervals (same width)
Bars touch

31
Q

Central tendency

A

tendency of data to cluster, or center about certain numerical values - location
Measured by mean, median, & mode

32
Q

Variability

A

Dispersion, spread of data

measured by range

33
Q

Variance & Standard deviation

A

Measure dispersion & show variation about mean

Consider how data are distributed

34
Q

Standard deviation

A

s

Dispersion about sample or population mean

35
Q

Variance

A

s^2

squared dispersion about sample mean

36
Q

Left-Skewed

A

Means that the majority of the data points are more than the mean
(mean is skewed to left; median is greater than mean)

37
Q

Right-Skewed

A

Majority of data points are less than the mean

mean is skewed to the right; median is less than mean

38
Q

Symmetric

A

Mean = median

Normal distribution

39
Q

68% of measurements lie in interval

A

sample mean - s to sample mean + s
One standard deviation
z-score -1 to 1

40
Q

95% of measurements lie in interval

A

sample mean - 2s to sample mean + 2s
2 standard deviations
z-score -2 to 2

41
Q

99.7% of measurements lie in interval

A

sample mean - 3s to sample mean + 3s
3 standard deviations
z-score -3 to 3

42
Q

Calculate probability

A

How often an outcome of interest occurs/ sample space (number of total possible outcomes)

43
Q

Event

A

Occurrence: event that 30 year old lives to be 70
OR
Collection of one or more outcomes of an experiments

44
Q

Intersection

A

Both event A & event B occur

45
Q

Union

A

Either event A or event B or both event A & B occur

46
Q

Complement

A

Everything except event A

47
Q

Special rule of addition

A

Probability that 2 mutually exclusive events will occur is equal to the sum of the probabilities of individual events
P(A or B) = P(A) + P(B)

48
Q

2 events that can not occur simultaneously are

A

Mutually exclusive or disjoint

49
Q

General rule of addition

A

If 2 events can occur at same time, probability of both occurring is equal to the sum of the probabilities of the individual events minus the probability of the intersection (of the 2 events)
P(A or B) = P(A) + P(B) - P(A and B)

50
Q

Probability of event B occurring given that event A has already occurred (B given A) if events are not independent is

A

P(A given B) = P(A and B)/P(B)
OR
P(A and B) = P(A given B) x P (B)

51
Q

Special rule of multiplication

A

If 2 events are unrelated, or independent
P(A given B) = P(A) and P(B given A) = P(B), then
P (A and B) = P(A) x P(B)

52
Q

Unbiased estimate of sampling distribution

A

If sample statistic has a mean equal to the population parameter

53
Q

Biased estimate of sampling distribution

A

If mean of sample statistic is not equal to the population parameter

Evidence Based Medicine (1 decks)

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