Epidemiology

Front 1

Incidence & prevalence

Back 1

Incidence: new cases in a population during the time period of a study. Incidence rate is the # of new diagnoses over the TOTAL population

Prevalence: # of people with a disease at a given point in time

Front 2

Rate

Back 2

# of events that occur during a specific time period / average # of people at risk for the event during the time period of study

Front 3

Three types of rates

Back 3

Crude: # of deaths in entire population
Specific: # of deaths in specific group
Adjusted: crude rates adjusted to control for other variables

Front 4

Type I vs. type II error

Back 4

Type 1: alpha error, false positive
Reject the null when it is true

Type 2: beta error, false negative
Accept the null when it is indeed true

Front 5

What is the alpha value?

Back 5

Associated to confidence level of the test, indicates the probability that a type I error will occur

Compare the alpha value with the p-value.
p-value < alpha value --> reject the null
p-value > alpha value --> accept the null (fail to reject it)

Front 6

What does a p-value represent?

Back 6

Probability that the result is based on chance alone

Front 7

What are sensitivity and specificity?

Back 7

Sensitivity: ability of test to detect disease when disease is present. The higher sensitivity, less false negative.

Specificity: ability of test to detect no disease when disease is not present. The higher the specificity, the less false positive.

Front 8

What are the types of sampling?

Back 8

Random: each member in population has same chance of being chosen

Stratified: break up population into groups, then select from those groups

Systemic: picked via system, ss#, etc

Convenience: use volunteers or readily available subjects

Front 9

What is bias?

Back 9

Differential error that weakens true associations, produces false associations, or distorts direction of associations

Occurs when a nonrandom allocation is used for study

Front 10

What are validity and reliability?

Back 10

Validity: accuracy of scale to measure what it's supposed to

Reliability: repeatability

Front 11

The 5 studies classified by data type are:
and their data includes:

Back 11

Cross-sectional: all data comes from 1 point in time
Good to see associations, but not causality

Retrospective: data is collected from the past

Observational study: no changes by experimenter

Experiemental: randomly divide sampe into experimental and control groups

Front 12

Summarize a case study

Back 12

Reports on 1 person with novel or rare disease

Observational data

Front 13

Summarize a cross-sectional survey

Back 13

Survey of a sample at a given point in time via interviewers

Quick, easy, but data is only from 1 time

Cross-sectional data

Front 14

Summarize a cohort study

Back 14

Cohort is a clearly defined group of persons, usually have a common factor

Retrospective cohort: recruit study group, get baseline data, continue to follow

Prospective cohort: use historical data to get group, follow members up through present to see what outcomes occur

Front 15

Case-control studies

Back 15

Subjects placed in groups whether or not they have a disease. Used to study rare diseases with many risk factors at once.

Compare cases vs. non-cases or controls

Nested case-control: a case-control study nested in a cohort study. Take a cohort, and choose cases vs. non-cases within the group

Front 16

Clinical trial

Back 16

Gold standard

Tests treatment vs. placebo

Double blind: participants and administrators don't know any info on the study to prevent bias

Randomized

Controlled: intervention group gest tx, control group gets placebo

Front 17

Positive and negative predictive values

Back 17

Positive: proportion of people with a positive test result that have the disease

Negative: proportion of people with negative test results that do not have disease

Front 18

Negative or positive confounder

Back 18

Negative: weakens or masks a true association

Positive: makes an association that doesn't really exist

Front 19

What is effect modification or interaction

Back 19

Direction OR strength of 2 variables differs according to a third variable

Females an coffee

Front 20

What are the two main types of data?

Back 20

Discrete/categorical

Continuous

You can take continuous data and convert it to discrete or categorical data

Front 21

What are the scales of measurement? NOIR

Back 21

Nominal - categories in no order, pie chart
Ordinal - ordered categories with unequal intervals btw, bar graph
Interval - equal intervals btw data points with no true zero, histogram or frequency polygon
Ratio - equal intervals btw data points with true zero, histogram or frequency polygon

Front 22

Descriptive statistics do what?

Back 22

Describe a sample

Front 23

What are measures of central tendency?

Back 23

Mean - average
Median - middle #
Mode - most commonly occurring number

Front 24

What are measures of dispersion?

Back 24

Distribution - bell-shaped curve that can be skewed

Skewed right - tail falls right, mound falls left. Large outliers pull the mean to the right

Skewed left - tail falls left, mound falls right. Large outliers pull the mean to the left

Front 25

What is the equation for variance?

Back 25

Look at it

Front 26

What is variance?

Back 26

How far the data is spread out

Front 27

What is SD?

Back 27

Average deviation from the mean, regardless of direction

Front 28

T-test compare what?

Back 28

Two groups or two sets of data. Compares the variances and SD's with p-value to determine if it's significant

Paired t-test will compare 2 groups with same characteristic, or 1 group with 2 scores or data points

Independent t-test will compare 2 independent groups on differences of the man value of a variable

Front 29

Assumptions of t-test include

Back 29

Continuous data on interval or ratio scale
Normal distribution of data
Variances are equal unless sample size is large

Front 30

What is ANOVA/ANCOVA?

Back 30

ANOVA: compares mean values for MORE than 2 groups at the same time. Tests variance in groups to determine if the null hypothesis or alternate hypothesis is true.

Tells that there's a diff, but NOT where the diff lies via f-statistic and p-value

ANCOVA = ANOVA minus cofounders

Front 31

What is regression?

Back 31

Prediction of scores on Y from scores on X

Linear - variables have linear association

Front 32

How do odds ratios relate to risk?

Back 32

OR = 1, no difference
OR < 1, less risk
OR > 1, increased risk

If confidence interval is above 1 for both range, it's good

Front 33

What is logistic regression?

Back 33

Tests 2 variables with an outcome that is dichotomous

Front 34

What is chi-square test?

Back 34

Tests 2 groups frequency or rate observed to see if there's statistically significant diff

Front 35

What does OR show

Back 35

Risk or odds of a particular outcome

Front 36

Parametric vs. non-parametric statistics

Back 36

Non-parametric are used when the data is nominal or ordinal, categorical outcome