Flashcards in Epidemiology Week 3 Deck (22):

1

## Describe the nominal measurement scale

###
◾data that can be named and put into categories

◾categories are mutually exclusive and unordered

2

## Describe the ordinal measurement scale

###
◾named data

◾categories are mutually exclusive and ordered

3

## Describe the count/discrete measurement scale

###
◾numeric/measured data

◾specific values, integers

4

## Describe the continuous measurement scale

###
◾numeric/measured data

◾data that can take on any value

5

## What is central tendency?

### measures of location (ex. mean, median)

6

## Give four measures of central tendency

###
◦Mean - arithmetic average

◦Median - middle value of sorted data, less influenced by outliers

◦Mode - most frequent value

◦Geometric mean - nth root of the product of the data

7

## What is variability?

### measures of spread (ex. range, std dev)

8

## Give different measures of variability

###
◦Range - max value - min value

◦Interquartile range - 75th percentile - 25th percentile

◾Captures central 50% of the data

◦Variance - average squared deviation from the mean

◦Standard deviation - square root of variance

◦Coefficient of variation - standard deviation/mean

9

## What are the major sources of variation?

###
◦Biologic variation

◾Between-individual

◾Within-individual

◦Measurement variation

◾Between-observer

◾Within-observer

◦Instrument or analytical error

10

## What is reliability?

###
- Precision

- Random error

11

## What is validity?

###
- Accuracy

- Systematic error

12

## How is data displayed graphically?

###
- Histograms

- Box charts

- Bar charts/Pie charts

- Distribution curves

13

## Give the properties of normal distribution curves

###
◾Bell shaped

◾Total area under curve = 1

◾Extends to infinity in both directions

◾Probability corresponds to area under the curve

•68% within +/- 1 standard dev of mean

•95% within +/- 2 standard devs of mean

•99% within +/- 3 standard devs of mean

◾Only need to know mean and standard dev

14

## What is sensitivity:

###
Proportion of those with disease who also have a positive test result

Sensitive test means TP/(TP + FN) close to 100%

few false negatives

most negative results are true

SNOUT (Sensitive test with negative result rules out disease)

Sensitivity and negativity both have Ns in them

15

## Specificity

###
Proportion of those without the disease who give a negative test result

Specific test means TN/(TN+FP) close to 100%

gives few false positives

most positive results are true

Patients with positive results likely have the disease

SPIN = Specific test with Positive results rules in disease

Specificity and positive both have Ps in them

16

##
False positive and false negative

###
If FN is worse than FP use a sensitive test

If FP worse than FN, use a specific test

17

##
Positive predictive value

###
Calculate the probability of the patient having the disease given a positive test result

TP/(TP+FP)

prevalence is positively associated with +PV (prevalence increases, +PV increases)

18

## Negative predictive value

###
Calculate the probability of the patient not having the disease given a negative test result

TN/(FN+TN)

prevalence is negatively associated with -PV (prevalence increases, -PV decreases)

19

## Use properties of the normal (Gaussian) distribution and the standard normal (Z) distribution to estimate probabilities of clinical events:

###
normal (Gassian) distribution: describes frequency distribution of repeated measurements of the same physical object by the same instrument

standard normal distribution:

- curve is symmetrical and bell-shaped

- two-thirds of observations fall within 1 standard deviation of mean

- 95% of observations fall within 1 standard deviation of the mean

20

##
Methods to determine reference intervals: normal distribution method

###
mean +/- 2x standard deviation

68% within +/1 1 standard dev of the mean

95% within +/- 2 standard dev of the mean

99% within +/- 3 standard dev of the mean

21

## Transformation method:

###
transform values to make data more like a normal distribution using logs

use the normal distribution method

22