Lectures 1-4 Flashcards
Definition of EBM:
- medicine combining the best current research evidence with clinical expertise and patient values.
The five outcomes of disease:
- death
- disease
- discomfort
- disability
- dissatisfaction
Prevalence =
(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”
Cumulative incidence:
(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
Incidence density =
- 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”
Incidence equation in steady state:
prevalence / duration
Duration equation in steady state:
prevalence / incidence
Prevalence equation in steady state:
(incidence) X (average duration)
Apparent incidence depends on:
- intensity of effort to identify cases, not just the true underlying incidence.
Epidemic:
- 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.
Pandemic:
- an epidemic that crosses many international boundaries.
Endemic:
- the constant presence of a disease or infectious agent within a given geographic area or population group.
Random samples:
- Every person has an equal probability of being sampled
Probability sample:
- Every person has a known (but not necessarily equal) probability of being selected.
- Can weight the sample towards some low-frequency groups of interest.
“grab” samples:
- convenient
- susceptible to bias
alpha =
- 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
beta =
- probability you won’t find a difference when one actually exists (false miss)
- false miss = type 2 error
- probability of missing a reality
power =
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.
Low power is a common reason for what types of errors?
type 2
Power can be reduced by:
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)
A confidence interval is:
- the range of values you get for repeated/multiple samples
- 95% CI means that 95% of values fall within 2SD
2 X 2 tables for hypothesis testing:
(alpha, beta, and power)
2 X 2 tables for hypothesis testing:
(type 1 and 2 errors)
2 X 2 tables for hypothesis testing:
(false hits and false misses)
95% CI equation:
95% CI = mean +/- 1.96(SEM)
- SEM = SD/ √n
Standard error of the mean (SEM) eqaution:
SEM = SD/ √n
- SD = standard deviation
- n = sample size
In order to be signficant, confidence intervals cannot:
- cannot overlap / touch
- cannot include 0 when doing correlations
- cannot include 1 when relative risk, hazards ratio, odds ratio
When to use T-test:
- use when comparing means of 2 groups
- PARAMETRIC
Mann-Whitney test:
- non-parametric test similar to t-test
- does not rely on mean or variation
Risk can be offset by:
- Intervention
- Treatment
- Prevention
- Education
A risk factor is:
- anything which increases the likelihood that a disease will occur
- can be genetic or environmental
The three types of prevention:
- Primary: before exposure (PREVENT)
- Secondary: after exposure (SCREEN)
- Tertiary: after disease process occurs (TREAT)
Primary prevention:
- before exposure
- prevent disease occurrence
- e.g. vaccinations
Secondary prevention:
- after exposure
- screening early for disease
- e.g. pap smears
Tertiary prevention:
- after disease process
- treat
- e.g. chemotherapy
When to use ANOVA:
- when comparing means of 3 or more groups
Pathogenic triangle:
- host — environment — agent (all are connected in an exposure)
Latency:
- Time between exposure and event
Null Hypothesis (Ho):
- states that there is no difference
Type I error:
- FALSE POSITIVE
- saying there is a difference when there is not.
- rejecting the null hypothesis when it should be accepted
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
How to increase the power of a study:
- increase the number of subjects
