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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
How to compare the p value to the alpha
p-value < alpha- reject null hypothesis, alt hypothesis is accepted- statistically significant p-value >alpha- accept the null hypothesis, alt hypothesis is rejected
26
Confidence interval
Provides the same information about significance as the p value, plus the precision of the result
27
How to calculate CI
CI=1-alpha An alpha of 0.05 represents a 95% CI
28
A CI of 95% indicates
you are 95% confident that the true value for the population lies somewhere within the range
29
A narrow CI indicates
high precision
30
A wide CI indicates
poor precision
31
Type I error
False positive The null hypothesis was rejected in error
32
Probability of a type I error
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%
33
Type II error
False negative The null hypothesis is accepted when it should have been rejected.
34
Power
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
Relative risk
Risk in exposed group (treatment) divided by the risk in the control group
36
Risk
of subjects with unfavorable event/total number of subjects
37
RR=1
No difference in risk
38
RR>1
Greater risk of outcome in treatment group
39
RR<1
Lower risk of outcome in the treatment group
40
How to interpret a RR of 0.57
Patients treated were 57% AS LIKELY to have disease progression/event as placebo patients
41
RRR
Indicates how much the risk is reduced 1-RR (must use decimal form)
42
RRR interpretation
LESS likely (vs control)
43
RR+RRR=
100
44
Absolute risk reduction
Indicates the reduction in risk AND the incidence rate of the outcome ARR= (%risk control)-(%risk tx)
45
ARR of 12% indicates
12 out of every 100 patients benefit from the tx
46
NNT
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
NNH
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
Odds ratio
Used to estimate the risk of unfavorable events in case control stubdies
49
Odds ratio calculation
OR=AD/BC A=Outcome present, treatment group C=Outcome present, control group B=Outcome absent, treatment D= Outcome absent, control
50
Hazard ratio
The rate in which an unfavorable event occurs within a short period of time. HR=Hazard rate tx/Hazard rate control
51
OR or HR=1
Event rate is the same
52
OR or HR >1
event rate in tx is higher
53
OR or HR <1
Event rate in tx is lower
54
Normally distributed continuous data
Use parametric tests
55
Not normally distributed continuous data
Use nonparametric tests
56
T-tests are used when
Continuous data is normally distributed
57
ANOVA is used for
continuous data with 3 or more samples
58
Chi-square test is used for
nominal or ordinal data
59
Selecting a test to analyze data: Parametric tests with 1 group
One-sample T test If you have before and after measurements, use dependent/paired T test
60
Selecting a test to analyze data: Parametric tests with 2 groups (tx, control)
Independent, unpaired student t test
61
Selecting a test to analyze data: >/=3 groups, parametric tests
ANOVA
62
Selecting a test to analyze data: Discrete/categorical data with 1 group
Chi-square
63
Selecting a test to analyze data: Discrete/categorical data with 2 groups (tx, control)
Chi-square or Fishers exact
64
Correlation
Statistical technique to determine if 1 variable changes or is related to another variable. Does not mean causation
65
Regression
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
Sensitivity
True positive 100% sensitivity=will be positive in all pts with condition
67
Specificity
True negative 100% specificity=will be negative in all patients without the condition
68
Intention to treat analysis
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
Per protocol analysis
Includes the subset of patients who completed the study according to the protocol
70
Equivalence trials
Want to show that the treatments have the same effect
71
Non-inferiority trials
Want to show that the new treatment is no worse than the current standard of care
72
Forest plots provide CI for
difference data or ratio data
73
The boxes on a forest plot show
effect estimate
74
The diamonds on a forest plot show
pooled results from multiple studies
75
Horizontal lines on forest plots show
CI
76
Vertical solid line on forest plot
The line of no effect 0 for difference data 1 for ratio data
77
Case control studies
Retrospective comparisons of cases (pts with disease) and controls (pts without disease)
78
Cohort studies
Retrospective or prospective comparisons of pts with an exposure compared to those without the exposure
79
RCTs
Prospective comparison of patients who were randomly assigned to groups
80
ECHO model
Shows economic, clinical, and humanistic outcomes
81
Cost minimization analysis
Interventions have equal outcomes and just the costs are being compaared
82
Cost benefit analysis
Compares benefits and costs of an intervention in terms of monetary units. Converts benefits of tx into dollars
83
Cost effectiveness analysis
Compares the clinical effects of interventions ot teh costs
84
Cost utility analysis
Uses QALYs and DALYs

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