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NAPLEX > Biostats and Pharmacoeconomics > Flashcards

Biostats and Pharmacoeconomics Flashcards

1
Q

Continuous data categories

A

Ratio and interval data

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2
Q

Examples of ratio data

A

age, weight, height, time, blood pressure

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3
Q

Examples of interval data

A

temperature scales

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4
Q

Discrete data categories

A

nominal and ordinal

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5
Q

Examples of nominal data

A

gender, ethnicity, marital status, mortality

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6
Q

Examples of ordinal data

A

NYHA Functional Class I-IV, 0-10 pain scale

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7
Q

Comparing difference data (means): when is it statistically significant?

A

When the CI doesn’t cross 0

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8
Q

Comparing ratio data (RR, OR, HR): when is it statistically significant?

A

When the CI doesn’t cross 1

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9
Q

Type I errors are what?

A

False positives

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10
Q

Type 2 errors are what?

A

False negatives

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11
Q

Study power definition

A

Probability that a test will reject the null hypothesis

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12
Q

RR=1 interpretation

A

No difference in risk of the outcome between the groups

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13
Q

RR >1 interpretation

A

greater risk of the outcome in the treatment group

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14
Q

RR <1 interpretation

A

lower risk of the outcome in the treatment group

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15
Q

Wording for RR

A

“AS likely vs. control”

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16
Q

RRR definition

A

Indicates how much risk is REDUCED in treatment group

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17
Q

RRR interpretation wording

A

“LESS likely vs. control”

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18
Q

ARR definition

A

The reduction in risk and the incidence rate of the outcome

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19
Q

ARR interpretation wording

A

“X out of every 100 patients will benefit”

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20
Q

NNT definition

A

How many patients need to be treated before 1 patient benefits

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21
Q

NNT rounding rules

A

Round UP, you don’t want to underestimate how many people need to be treated!

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22
Q

NNT interpretation

A

“For every X patient who receives Y treatment, Z (adverse outcome) is prevented in one patient”

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23
Q

NNH definition

A

How many patients need to be treated before 1 patient gets harmed

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24
Q

NNH rounding rules

A

ROUND DOWN, you don’t want to underestimate the potential harm of an intervention!

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25
NNH interpretation
"One case of Z (adverse outcome) is expected to occur for every X patients who take Y (treatment)"
26
Odds ratio definition
Used to estimate the risk of unfavorable events associated with a treatment/intervention
27
OR formula
AD/BC (just cross multiply and then divide)
28
OR interpretation
X treatment is associated with a Y% increased risk of Z (bad outcome)
29
HR definition
Rate at which an unfavorable event occurs within a short period of time
30
HR formula
HR in treatment group/HR in placebo
31
OR and HR interpretation: OR/HR=1
event rate is the same in the treatment and control arms, no advantage to the treatment
32
OR and HR interpretation: OR/HR >1
event rate in the treatment group is higher than the event rate in the control group example: if the OR/HR is 2 for an outcome of death means that there are twice as many deaths in the treatment group
33
OR and HR interpretation: OR/HR <1
event rate in the treatment group is lower than the event rate in the control group example: HR of 0.5 for an outcome of death indicates there are half as many deaths in the treatment group
34
Sensitivity definition
True positive! The higher the sensitivity, the better
35
Specificity definition
True negative!
36
Sensitivity formula
(A/A+C)*100%
37
Specificity formula
(D/B+D)*100%
38
Case-control study features
retrospective comparisons of cases and controls
39
Cohort study features
retrospective or prospective comparisons of patients with an exposure to those without it
40
RCT features
prospective comparison of patients who were randomly assigned to groups
41
Meta-analyses features
analyzes the results of multiple studies
42
Parametric test for continuous data with 1 group
one-sample t-test
43
Parametric test for continuous data with 1 group that has before and after measures
dependent/paired t-test
44
Parametric test for continuous data with 2 groups
Independent/unpaired student t-test
45
Parametric test for continuous data with ≥3 groups
ANOVA
46
Non-parametric test for continuous data with 1 group
Sign test
47
Non-parametric test for continuous data with 1 group that has before and after measures
Wilcoxon Signed Rank test
48
Non-parametric test for continuous data with 2 groups
Mann-Whitney test
49
Non-parametric test for continuous data with ≥3 groups
Kruskall-Wallis test
50
Discrete/Categorical test for 1 group
Chi-square test
51
Discrete/Categorical test for 1 group with before and after measuress
Wilcoxon Signed Rank test
52
Discrete/Categorical test for 2 groups
Chi-square or Fisher's Exact test
53
Discrete/Categorical test for ≥3 groups
Kruskal-Wallis test
54
When is CMA used?
Used when 2 or more interventions have demonstrated equivalence in outcomes
55
What does CMA compare?
Costs of each intervention
56
When is CBA used?
Used to calculate and comparing benefits and costs of an intervention in terms of monetary units
57
What does CBA compare/determine?
If the benefits of the intervention exceed the costs of implementation; can also be used to compare multiple programs for similar or unrelated outcomes as long as the outcome measures can be converted to dollars
58
What does CEA compare?
Clinical effects of two or more interventions to the respective costs
59
What can CUA measure?
The quality of life but not the quality or utility of the years
60
Outcome unit of CMA
Demonstrated or assumed to be equivalent in comparative groups
61
Outcome unit of CBA
Dollars
62
Outcome unit of CEA
Natural units (life-years gained, blood pressure, % at treatment goal)
63
Outcome unit of CUA
QALY

NAPLEX (18 decks)

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