# 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
1. death
2. disease
3. discomfort
4. disability
5. 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

A

prevalence / duration

7
Q

A

prevalence / incidence

8
Q

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

1. beta too lax (~0.2)
2. low prevalence of outcome in population
3. alpha too stringent (~ 0.01)
5. sample size (small)
6. 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
Q

95% CI equation:

A

95% CI = mean +/- 1.96(SEM)

• SEM = SD/ √n
26
Q

Standard error of the mean (SEM) eqaution:

A

SEM = SD/ √n

• SD = standard deviation
• n = sample size
27
Q

In order to be signficant, confidence intervals cannot:

A
1. cannot overlap / touch
2. cannot include 0 when doing correlations
3. cannot include 1 when relative risk, hazards ratio, odds ratio
28
Q

When to use T-test:

A
• use when comparing means of 2 groups
• PARAMETRIC
29
Q

Mann-Whitney test:

A
• non-parametric test similar to t-test
• does not rely on mean or variation
30
Q

Risk can be offset by:

A
1. Intervention
2. Treatment
3. Prevention
4. Education
31
Q

A risk factor is:

A
• anything which increases the likelihood that a disease will occur
• can be genetic or environmental
32
Q

The three types of prevention:

A
1. Primary: before exposure (PREVENT)
2. Secondary: after exposure (SCREEN)
3. Tertiary: after disease process occurs (TREAT)
33
Q

Primary prevention:

A
• before exposure
• prevent disease occurrence
• e.g. vaccinations
34
Q

Secondary prevention:

A
• after exposure
• screening early for disease
• e.g. pap smears
35
Q

Tertiary prevention:

A
• after disease process
• treat
• e.g. chemotherapy
36
Q

When to use ANOVA:

A
• when comparing means of 3 or more groups
37
Q

Pathogenic triangle:

A
• host — environment — agent (all are connected in an exposure)
38
Q

Latency:

A
• Time between exposure and event
39
Q

Null Hypothesis (Ho):

A
• states that there is no difference
40
Q

Type I error:

A
• FALSE POSITIVE
• saying there is a difference when there is not.
• rejecting the null hypothesis when it should be accepted
41
Q

Type II error:

A
• 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
Q

How to increase the power of a study:

A
• increase the number of subjects