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EBM Midterm > Katie EBM Midterm > Flashcards

Katie EBM Midterm Flashcards

1
Q

Descriptive Statistics

A

The presentation, organization and summarization of data

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2
Q

Frequently used graphical displays

A
  • study design flow chart
  • KAplan-Meier estimators
  • Forest Plot (line down the center divides 2 treatment arms)
  • Line graph
  • Histogram
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3
Q

Inferential Statistics

A

allows researchers to generalize from our sample of data to a larger group or population

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4
Q

Key variables of Inferential Statistics

A
  1. Sample size (larger better)

2. Standard deviation (Smaller better)

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5
Q

Dependent Variable

A

The outcome of interest (changes in response to intervention)

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6
Q

Independent Variable

A

The intervention (what is being manipulated by the researcher)

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7
Q

Discrete variable

A
  • Variables that can only take on a finite number of values.
  • All qualitative variables are discrete
  • Some quantitative variables are discrete, such as performance rated as 1,2,3,4, or 5, or temperature rounded to the nearest degree
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8
Q

continuous variable

A

may take any value, within a defined range

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9
Q

Nominal Data

A

used for labeling variables, without any quantitative value. “Nominal” scales could simply be called “labels.”

“nominal” sounds a lot like “name” and nominal scales are kind of like “names” or labels

ex: male vs female or hair color

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10
Q

Ordinal Data

A

The order of the values is what’s important and significant, but the differences between each one is not known. Typically measures of non-numeric concepts like satisfaction, happiness, discomfort

ex: very unsatisfied, mildly unsatisfied, neutral, mildly satisfied, very satisfied

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11
Q

Interval Data

A
  • Numeric scales in which we know the order and also the exact differences between the values.
  • The classic example of an interval scale is Celsius temperature because the difference between each value is the same
  • No “true zero.” For example, there is no such thing as “no temperature.” Without a true zero, it is impossible to compute ratios. With interval data, we can add and subtract, but cannot multiply or divide
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12
Q

Ratio Data

A

Tell us about the order, the exact value between units, AND they also have an absolute zero–which allows for a wide range of both descriptive and inferential statistics to be applied

Example: weight or height

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13
Q

Proportion

A

type of fraction in which the numerator is a subset of the denominator

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14
Q

Rate

A

fraction that contains a time compnent

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15
Q

Percentage

A

a form of proportion where the denominator is artificially set to 100

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16
Q

Central Tendency

A

a central or typical value for a probability distribution

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17
Q

Mean

A

Measure of central tendency for interval and ratio data

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18
Q

Median

A

Value such that half of the data points are above and half are below

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19
Q

Mode

A

most frequently occuring catergory

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20
Q

Steps in Appraising The Evidence About Therapy

A
  1. Validity (can I trust the information)
  2. Important (Will the information, if true, make an important difference?)
  3. Applicability (Can I use this information?)
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21
Q

Validity

A
  • Are the groups balanced?
  • Were the groups randomized?
  • Was randomization concealed?
  • Did experimental and control groups begin with similar prognosis?
  • To what extend was the study blinded?
  • Was follow-up complete?
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22
Q

Importance

A

How large was the treatment effect?

How precise was the estimate of the treatment effect?

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23
Q

Applicability

A

Patients like yours?

Benefits worth the harms and costs?

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24
Q

Confounding variable, Confounder

A

a factor that distorts the true relationship of the study variable of interest by virtue of also being related to the outcome of interest

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25
Q

Selection Bias

A

systemic differences between comparison groups attributable to the manner in which subjects were allocated to experimental and control groups

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26
Q

Contamination

A

subjects in either the experimental or control group receive part or all of the intervention intended for the other arm of the study

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27
Q

Expectation Bias

A

awareness of or information about the intervention influences participatns expectations regarding results and outcomes

28
Q

Key Concepts about appraising the evidence:

A
  1. the size and precision of the treatment effects determines the importance of the results
  2. who was enrolled and what was measured are the most important determinants of applicability
29
Q

Why is normal distribution important?

A
  1. Many statistical tests assume normal distribution
  2. the mean and variance are independent
  3. It’s held that many natural phenomena are normally distributed
  4. Central Line Theorem
30
Q

Central Line Theorem

A

if we draw equally sized samples from a non-normal distribution, the distribution of the means of these samples will still be normal as long as the samples are large enough

31
Q

How large is large enough for central line theorem sample size?

A

30?

32
Q

Standard Score (more commonly referred to as Z-score)

A

Very useful statistic because it:

(a) allows us to calculate the probability of a score occurring within our normal distribution and
(b) enables us to compare two scores that are from different normal distributions.

33
Q

How to calculate Z-score

A

(raw score- mean)/standard deviation

34
Q

Properties of Normal Curve

A
  1. The mean, median and mode all have the same value
  2. The curve is symmetric around the mean
  3. The tails of the curve approach but never cross x-axis
  4. theoretic, not realistic
35
Q

Confidence Intervals

A

The range of numerical values in which we can be confident (to a computed probability, such as 90 or 95%) that the population value being estimated will be found. Confidence intervals indicate the strength of evidence; where confidence intervals are wide, they indicate less precise estimates of effect

36
Q

Precision

A

The range in which the best estimates of a true value approximate the true value

37
Q

Larger group effects on CI and precision

A

larger groups= smaller CI, higher precision

38
Q

Smaller group effects on CI and precision

A

smaller groups= larger CI, less precision

39
Q

Probability

A

deals with the relative LIKELIHOOD that a certain event will or will not occur, relative to some other events

40
Q

Empirical Probability

A
  • based on past performance, holds true now and in future only under similar circumstances
  • if the circumstances have changed, than the probabilities no longer exist
  • accounts for most probabilities in medicine
41
Q

Mutually Exclusive Events

A

the occurrence of one event is not influenced or caused by another event. It is impossible for mutually exclusive events to occur at the same time

ex: coin toss heads/tails

42
Q

Conditionally Probable Events

A

the probability of an event ( A ), given that another ( B ) has already occurred

-calculated using multiplicative law

43
Q

Independent Events

A

The probability that one event occurs in no way affects the probability of the other event occurring.

example: roll a die and flip a coin

44
Q

Intention to Treat

A

A method for data analysis in a randomized clinical trial in which individual outcomes are analyzed according to the group to which they have been randomized, even if they never received the treatment they were assigned.

By simulating practical experience it provides a better measure of effectiveness

45
Q

ITT: what does a high rate of noncompliance lead to?

A

underestimate of effectiveness

46
Q

ITT Pros

A
  1. preserves sample size
  2. reflects the practical clinical scenario
  3. gives an unbiased estimate of TX effect
  4. Limits analysis based on arbitrary subgroups
  5. Minimizes risk of Type 1 error (false positive)
47
Q

ITT cons

A
  1. estimate tx effect generally conservative because of dilution of noncompliance
  2. heterogenity may be introduced into the RCT if compliant, noncompliant and dropouts analyzed together
  3. susceptible to Type II error (false negative)
48
Q

Null Hypothesis

A

States that there is no difference

Researched tries to disprove null hypothesis

49
Q

Type 1 Error

A

Incorrect rejection of a true null hypothesis

“False Positive”

-this is less likely using ITT analysis (because ITT is a conservative approach)

50
Q

Type 2 Error

A

Failure to reject a false null hupothesis

“False negative”

-occurs when analysis is too cautious

51
Q

ITT methodology

A
  • “once randomized, always randomized”

- ignores noncompliance, withdrawal, anything that happens AFTER randomization

52
Q

Alpha Level

A

The probability of a type I error

53
Q

Beta Level

A

Probability of a Type 2 error

54
Q

P-Value

A
  • helps you determine the significance of your results
  • small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, so you reject the null hypothesis
  • A large p-value (> 0.05) indicates weak evidence against the null hypothesis, so you fail to reject the null hypothesis
55
Q

Absolute Risk (AR)

A

-The observed or calculated probability of an event in the population under study

How to calculate: the number of events in treated or control groups, divided by the number of people in that group (a/b, c/d)

56
Q

Absolute Risk Reduction (ARR)

A

the difference in the rates of adverse events between study and control populations

How to calculate:
(AR of control group) - (AR of treatment group)

57
Q

Relative Risk

A

The ratio of risk in the treated group to the risk in the control group

How to calculate:
(AR of control group)/(AR of treatment group)

58
Q

Relative Risk Reduction

A

the percent reduction in events in treated compared to controls

How to calculate:
((AR control group) - (AR treatment group)) / (AR control group)

59
Q

Number Needed to Treat

A

1/absolute risk reduction

60
Q

Specificity

A

if the test result for a highly specific test is positive you can be nearly certain that they actually have the disease (Ex: gallop murmur= CHF)

100% specific=
positive= has disease!

61
Q

Sensitivity

A

among patients with disease, the probability of a positive test

Sensitive test when Negative rules Out disease (ex: neg D-dimer= No PE)

100% sensitive=
Negative= doesn’t have disease!

62
Q

Positive Predictive Value

A

the probability that subjects with a positive screening test truly have the disease

63
Q

Negative Predictive Value

A

the probability that subjects with a negative screening test truly don’t have the disease

64
Q

The Law Of At Least One

A

used to determine the probability of at least one event occurring

65
Q

Lessons to Remember in regards to “law of at least one”

A

1) There are no perfect tests
2) the more tests you run, the higher the rate of at least one erroneous result
3) limit the number of tests that you order

66
Q

Binomial Distribution Definition

A

the probabilities for dichotomous variables
(ex: coin toss, mortality)

Unlike normal distribution because normal distribution is based on a continuous variable (such as blood pressure)

67
Q

Size of Binomial Distribution

A

The larger the sample size (“n) the more the binomial distribution shifts to the right. The more it shifts to the right, the more closely is resembles the normal distribution.

-larger sample sizes, even though dichotamous, can use the Z-score for calculations and normal curve for probabilities

EBM Midterm (1 decks)

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