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1. NAPLEX Prep > Biostats > Flashcards

Biostats Flashcards

1
Q

Types of study data

A

Continuous
Discrete (Categorical)

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2
Q

Continuous Data

A

Has a logical order with values that continuously increase by the same amount.
Includes interval data and ratio data

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3
Q

Interval data

A

Type of continuous data, has no meaningful zero
Example-C and F temperature scales

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4
Q

Ratio data

A

Type of continuous data with a meaningful zero
Example- Age, height, weight, time, BP

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5
Q

Discrete (categorical data)

A

Includes nominal and ordinal data
Has categories

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6
Q

Nominal data

A

Type of discrete (categorical) data
Categories are in arbitrary order- the order does not matter.
Example- gender, ethnicity, marital status, mortality

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7
Q

Ordinal data

A

Type of discrete (categorical) data
Categories are ranked in a logical order, but the difference between the categories is not equal.
Example- NYHA class, 0-10 pain scale

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8
Q

Standard Deviation

A

How spread out the data is, and to what degree it is dispersed away from the mean.
Data that is highly dispersed has a larger SD

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9
Q

Gaussian (normal) distribution

A

Symmetrical curve, half of the values on the left and right
Mean, median, mode are equal

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10
Q

Large sample sets of continuous data tend to form

A

Gaussian or “normal” distribution
“bell-shaped curve”

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11
Q

In Gaussian distribution, __________of the values fall within 1 SD of the mean and ___________of the values fall within 2 SDs from the mean.

A

68%- 1 SD
95%- 2 SD

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12
Q

When does skewed distribution occur?

A

When the number of values (sample size) is small and/or there are outliers in the data

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13
Q

When there are small numbers of values, what measure of central tendency is the best?

A

Median

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14
Q

The distortion of central tendency caused by outliers is decreased by

A

collecting more values

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15
Q

Variable

A

any data point or characteristic that can be measured or counted.

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16
Q

Independent variable

A

Changed by the researcher in order to determine whether it has an effect on the dependent variable (outcome)

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17
Q

The outcome is the

A

dependent variable

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18
Q

HF progression is an example of

A

dependent variable

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19
Q

Comorbid conditions, doses are examples of

A

Independent variables

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20
Q

Null hypothesis

A

There is no statistically significant difference between groups.
The researcher is trying to disprove or reject the null hypothesis.

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21
Q

Alternative hypothesis

A

There is a statistically significant difference between groups. The researcher is trying to prove or accept

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22
Q

Error margin

A

Alpha

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23
Q

The alpha level is commonly set at

A

5% or 0.05

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24
Q

The p value is compared to

A

the alpha

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25
Q

How to compare the p value to the alpha

A

p-value < alpha- reject null hypothesis, alt hypothesis is accepted- statistically significant
p-value >alpha- accept the null hypothesis, alt hypothesis is rejected

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26
Q

Confidence interval

A

Provides the same information about significance as the p value, plus the precision of the result

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27
Q

How to calculate CI

A

CI=1-alpha
An alpha of 0.05 represents a 95% CI

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28
Q

A CI of 95% indicates

A

you are 95% confident that the true value for the population lies somewhere within the range

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29
Q

A narrow CI indicates

A

high precision

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30
Q

A wide CI indicates

A

poor precision

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31
Q

Type I error

A

False positive
The null hypothesis was rejected in error

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32
Q

Probability of a type I error

A

CI=1-alpha (type I error)
When alpha is 0.05 and a study result is reported with p<0.05, it is statistically significant and the probability of making a type 1 error is <5%

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33
Q

Type II error

A

False negative
The null hypothesis is accepted when it should have been rejected.

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34
Q

Power

A

The probability that a test will reject the null hypothesis correctly (the power to avoid a type II error)
Power=1-beta
Determined by the outcome values, difference in outcome rates, and the significance (alpha level)

35
Q

Relative risk

A

Risk in exposed group (treatment) divided by the risk in the control group

36
Q

Risk

A

of subjects with unfavorable event/total number of subjects

37
Q

RR=1

A

No difference in risk

38
Q

RR>1

A

Greater risk of outcome in treatment group

39
Q

RR<1

A

Lower risk of outcome in the treatment group

40
Q

How to interpret a RR of 0.57

A

Patients treated were 57% AS LIKELY to have disease progression/event as placebo patients

41
Q

RRR

A

Indicates how much the risk is reduced
1-RR (must use decimal form)

42
Q

RRR interpretation

A

LESS likely (vs control)

43
Q

RR+RRR=

A

100

44
Q

Absolute risk reduction

A

Indicates the reduction in risk AND the incidence rate of the outcome
ARR= (%risk control)-(%risk tx)

45
Q

ARR of 12% indicates

A

12 out of every 100 patients benefit from the tx

46
Q

NNT

A

Number of patients needed to be treated for a certain period of time in order for 1 patient to benefit.
1/ARR (in decimal)

47
Q

NNH

A

Number of patients who need to be treated for a certain period of time in order for 1 patient to experience harm
1/ARR (in decimal)

48
Q

Odds ratio

A

Used to estimate the risk of unfavorable events in case control stubdies

49
Q

Odds ratio calculation

A

OR=AD/BC
A=Outcome present, treatment group
C=Outcome present, control group
B=Outcome absent, treatment
D= Outcome absent, control

50
Q

Hazard ratio

A

The rate in which an unfavorable event occurs within a short period of time.
HR=Hazard rate tx/Hazard rate control

51
Q

OR or HR=1

A

Event rate is the same

52
Q

OR or HR >1

A

event rate in tx is higher

53
Q

OR or HR <1

A

Event rate in tx is lower

54
Q

Normally distributed continuous data

A

Use parametric tests

55
Q

Not normally distributed continuous data

A

Use nonparametric tests

56
Q

T-tests are used when

A

Continuous data is normally distributed

57
Q

ANOVA is used for

A

continuous data with 3 or more samples

58
Q

Chi-square test is used for

A

nominal or ordinal data

59
Q

Selecting a test to analyze data:
Parametric tests with 1 group

A

One-sample T test
If you have before and after measurements, use dependent/paired T test

60
Q

Selecting a test to analyze data:
Parametric tests with 2 groups (tx, control)

A

Independent, unpaired student t test

61
Q

Selecting a test to analyze data:
>/=3 groups, parametric tests

A

ANOVA

62
Q

Selecting a test to analyze data:
Discrete/categorical data with 1 group

A

Chi-square

63
Q

Selecting a test to analyze data:
Discrete/categorical data with 2 groups (tx, control)

A

Chi-square or Fishers exact

64
Q

Correlation

A

Statistical technique to determine if 1 variable changes or is related to another variable.
Does not mean causation

65
Q

Regression

A

Used to describe the relationship between a dependent variable and one or more independent variables.
Common in observational studies where researchers need to assess multiple independent variables or need to control for confounding factors.

66
Q

Sensitivity

A

True positive
100% sensitivity=will be positive in all pts with condition

67
Q

Specificity

A

True negative
100% specificity=will be negative in all patients without the condition

68
Q

Intention to treat analysis

A

Includes data for all patients originally allocated to each treatment group even if the patient did not complete the trial according to the study protocol

69
Q

Per protocol analysis

A

Includes the subset of patients who completed the study according to the protocol

70
Q

Equivalence trials

A

Want to show that the treatments have the same effect

71
Q

Non-inferiority trials

A

Want to show that the new treatment is no worse than the current standard of care

72
Q

Forest plots provide CI for

A

difference data or ratio data

73
Q

The boxes on a forest plot show

A

effect estimate

74
Q

The diamonds on a forest plot show

A

pooled results from multiple studies

75
Q

Horizontal lines on forest plots show

A

CI

76
Q

Vertical solid line on forest plot

A

The line of no effect
0 for difference data
1 for ratio data

77
Q

Case control studies

A

Retrospective comparisons of cases (pts with disease) and controls (pts without disease)

78
Q

Cohort studies

A

Retrospective or prospective comparisons of pts with an exposure compared to those without the exposure

79
Q

RCTs

A

Prospective comparison of patients who were randomly assigned to groups

80
Q

ECHO model

A

Shows economic, clinical, and humanistic outcomes

81
Q

Cost minimization analysis

A

Interventions have equal outcomes and just the costs are being compaared

82
Q

Cost benefit analysis

A

Compares benefits and costs of an intervention in terms of monetary units.
Converts benefits of tx into dollars

83
Q

Cost effectiveness analysis

A

Compares the clinical effects of interventions ot teh costs

84
Q

Cost utility analysis

A

Uses QALYs and DALYs

1. NAPLEX Prep (15 decks)

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