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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
Q

NNH interpretation

A

“One case of Z (adverse outcome) is expected to occur for every X patients who take Y (treatment)”

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26
Q

Odds ratio definition

A

Used to estimate the risk of unfavorable events associated with a treatment/intervention

27
Q

OR formula

A

AD/BC (just cross multiply and then divide)

28
Q

OR interpretation

A

X treatment is associated with a Y% increased risk of Z (bad outcome)

29
Q

HR definition

A

Rate at which an unfavorable event occurs within a short period of time

30
Q

HR formula

A

HR in treatment group/HR in placebo

31
Q

OR and HR interpretation: OR/HR=1

A

event rate is the same in the treatment and control arms, no advantage to the treatment

32
Q

OR and HR interpretation: OR/HR >1

A

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
Q

OR and HR interpretation: OR/HR <1

A

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
Q

Sensitivity definition

A

True positive! The higher the sensitivity, the better

35
Q

Specificity definition

A

True negative!

36
Q

Sensitivity formula

A

(A/A+C)*100%

37
Q

Specificity formula

A

(D/B+D)*100%

38
Q

Case-control study features

A

retrospective comparisons of cases and controls

39
Q

Cohort study features

A

retrospective or prospective comparisons of patients with an exposure to those without it

40
Q

RCT features

A

prospective comparison of patients who were randomly assigned to groups

41
Q

Meta-analyses features

A

analyzes the results of multiple studies

42
Q

Parametric test for continuous data with 1 group

A

one-sample t-test

43
Q

Parametric test for continuous data with 1 group that has before and after measures

A

dependent/paired t-test

44
Q

Parametric test for continuous data with 2 groups

A

Independent/unpaired student t-test

45
Q

Parametric test for continuous data with ≥3 groups

A

ANOVA

46
Q

Non-parametric test for continuous data with 1 group

A

Sign test

47
Q

Non-parametric test for continuous data with 1 group that has before and after measures

A

Wilcoxon Signed Rank test

48
Q

Non-parametric test for continuous data with 2 groups

A

Mann-Whitney test

49
Q

Non-parametric test for continuous data with ≥3 groups

A

Kruskall-Wallis test

50
Q

Discrete/Categorical test for 1 group

A

Chi-square test

51
Q

Discrete/Categorical test for 1 group with before and after measuress

A

Wilcoxon Signed Rank test

52
Q

Discrete/Categorical test for 2 groups

A

Chi-square or Fisher’s Exact test

53
Q

Discrete/Categorical test for ≥3 groups

A

Kruskal-Wallis test

54
Q

When is CMA used?

A

Used when 2 or more interventions have demonstrated equivalence in outcomes

55
Q

What does CMA compare?

A

Costs of each intervention

56
Q

When is CBA used?

A

Used to calculate and comparing benefits and costs of an intervention in terms of monetary units

57
Q

What does CBA compare/determine?

A

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
Q

What does CEA compare?

A

Clinical effects of two or more interventions to the respective costs

59
Q

What can CUA measure?

A

The quality of life but not the quality or utility of the years

60
Q

Outcome unit of CMA

A

Demonstrated or assumed to be equivalent in comparative groups

61
Q

Outcome unit of CBA

A

Dollars

62
Q

Outcome unit of CEA

A

Natural units (life-years gained, blood pressure, % at treatment goal)

63
Q

Outcome unit of CUA

A

QALY

NAPLEX (18 decks)

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