Lectures 1-4 Flashcards

1
Q

Definition of EBM:

A
  • medicine combining the best current research evidence with clinical expertise and patient values.
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2
Q

The five outcomes of disease:

A
  1. death
  2. disease
  3. discomfort
  4. disability
  5. dissatisfaction
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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”
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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
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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”
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6
Q

Incidence equation in steady state:

A

prevalence / duration

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7
Q

Duration equation in steady state:

A

prevalence / incidence

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8
Q

Prevalence equation in steady state:

A

(incidence) X (average duration)

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9
Q

Apparent incidence depends on:

A
  • intensity of effort to identify cases, not just the true underlying incidence.
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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.
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11
Q

Pandemic:

A
  • an epidemic that crosses many international boundaries.
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12
Q

Endemic:

A
  • the constant presence of a disease or infectious agent within a given geographic area or population group.
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13
Q

Random samples:

A
  • Every person has an equal probability of being sampled
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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.
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15
Q

“grab” samples:

A
  • convenient
  • susceptible to bias
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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
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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?

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)
  4. multiple comparison adjustments
  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)

23
Q

2 X 2 tables for hypothesis testing:

(type 1 and 2 errors)

24
Q

2 X 2 tables for hypothesis testing:

(false hits and false misses)

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