Lectures 1-4 Flashcards
1
Q
Definition of EBM:
A
- medicine combining the best current research evidence with clinical expertise and patient values.
2
Q
The five outcomes of disease:
A
- death
- disease
- discomfort
- disability
- dissatisfaction
3
Q
Prevalence =
A
(total #sick)/(total population)
- how many people are sick
- a single time point
- Prevalence can be used to estimate the likelihood of a diagnosis before any “tests”
4
Q
Cumulative incidence:
A
(new cases)/(total population at risk at study start)
- how many people are getting sick (NEW CASES)
- incidence is a rate, it happens over a time interval
5
Q
Incidence density =
A
- the incidence rate in a dynamic, changing population in which people are under study and at risk for varying periods of time.
- “person-years” or “person-days”
6
Q
Incidence equation in steady state:
A
prevalence / duration
7
Q
Duration equation in steady state:
A
prevalence / incidence
8
Q
Prevalence equation in steady state:
A
(incidence) X (average duration)
9
Q
Apparent incidence depends on:
A
- intensity of effort to identify cases, not just the true underlying incidence.
10
Q
Epidemic:
A
- an increase in incidence of a disease in a community or region.
- Outbreak is sometimes used to mean a small epidemic in a limited location.
- An epidemic curve is a plot of the distribution of cases over time.
11
Q
Pandemic:
A
- an epidemic that crosses many international boundaries.
12
Q
Endemic:
A
- the constant presence of a disease or infectious agent within a given geographic area or population group.
13
Q
Random samples:
A
- Every person has an equal probability of being sampled
14
Q
Probability sample:
A
- Every person has a known (but not necessarily equal) probability of being selected.
- Can weight the sample towards some low-frequency groups of interest.
15
Q
“grab” samples:
A
- convenient
- susceptible to bias
16
Q
alpha =
A
- the probability you’ll make a false hit.
- Arbitrarily, p = 0.05
- false hit = type 1 error
- 5% of the time, we’ll make an error
17
Q
beta =
A
- probability you won’t find a difference when one actually exists (false miss)
- false miss = type 2 error
- probability of missing a reality
18
Q
power =
A
1 - beta
- power of study to pick up a study when it actually does exist
- Low power is a common reason for type II errors.
19
Q
Low power is a common reason for what types of errors?
A
type 2
20
Q
Power can be reduced by:
A
BLAMSE
- beta too lax (~0.2)
- low prevalence of outcome in population
- alpha too stringent (~ 0.01)
- multiple comparison adjustments
- sample size (small)
- effect size (small)
21
Q
A confidence interval is:
A
- the range of values you get for repeated/multiple samples
- 95% CI means that 95% of values fall within 2SD
22
Q
2 X 2 tables for hypothesis testing:
(alpha, beta, and power)
A
23
Q
2 X 2 tables for hypothesis testing:
(type 1 and 2 errors)
A
24
Q
2 X 2 tables for hypothesis testing:
(false hits and false misses)
A
25
95% CI equation:
**95% CI = mean +/- 1.96(SEM)**
* SEM = SD/ √n
26
Standard error of the mean (SEM) eqaution:
**SEM = SD/ √n**
* SD = standard deviation
* n = sample size
27
In order to be signficant, confidence intervals cannot:
1. cannot overlap / touch
2. cannot include 0 when doing correlations
3. cannot include 1 when relative risk, hazards ratio, odds ratio
28
When to use T-test:
* use when comparing means of **_2_** groups
* PARAMETRIC
29
Mann-Whitney test:
* non-parametric test similar to t-test
* does not rely on mean or variation
30
Risk can be offset by:
1. Intervention
2. Treatment
3. Prevention
4. Education
31
A risk factor is:
* anything which increases the likelihood that a disease will occur
* can be genetic or environmental
32
The three types of prevention:
1. Primary: before exposure (PREVENT)
2. Secondary: after exposure (SCREEN)
3. Tertiary: after disease process occurs (TREAT)
33
Primary prevention:
* before exposure
* prevent disease occurrence
* e.g. vaccinations
34
Secondary prevention:
* after exposure
* screening early for disease
* e.g. pap smears
35
Tertiary prevention:
* after disease process
* treat
* e.g. chemotherapy
36
When to use ANOVA:
* when comparing means of **_3 or more_** groups
37
Pathogenic triangle:
* host — environment — agent (all are connected in an exposure)
38
Latency:
* Time between exposure and event
39
Null Hypothesis (Ho):
* states that there is no difference
40
Type I error:
* FALSE POSITIVE
* saying there is a difference when there is not.
* rejecting the null hypothesis when it should be accepted
41
Type II error:
* FALSE NEGATIVE
* saying there is no difference in treatment effects when there is.
* failing to reject (accepting) the null hypothesis when it should be rejected
42
How to increase the power of a study:
* increase the number of subjects
