1 - Variation, Diag. Testing, Decision Analysis

Question 1

Define continuous scale. Give an example.

Answer 1

A scale used to measure a numerical characteristic in which fractional values can occur
–Ex. body temperature

Question 2

Define dataset.

Answer 2

A collection of data organized into observations and variables

Question 3

Define discrete scale. Give an example.

Answer 3

A numerical scale using only whole numbers

–Ex. number of pregnancies

Question 4

Define distribution.

Answer 4

The values of a characteristic or variable along with the frequency of their occurrence
May be based on empirical observations or may be theoretical probability distributions (normal, binomial, chi-square)

Question 5

Define histogram.

Answer 5

A graphical display of a distribution, illustrating how frequently each value occurs

Question 6

Define mean.

Answer 6

A measure of central tendency

The sum of the values divided by number n in the sample

Question 7

Define measures of central location/tendency. Give an example.

Answer 7

Index or summary numbers that describe the middle of a distribution
–Ex. mean, median, mode

Question 8

Define measures of spread. Give an example.

Answer 8

Index or summary numbers that describe the spread of observations about the mean
–Ex. range, standard deviation

Question 9

Define median.

Answer 9

A measure of central tendency
The middle observation (the one that divides the distribution of values into two halves)
Equal to the 50th percentile

Question 10

Define mode.

Answer 10

A measure of central tendency

The most commonly observed value of a distribution

Question 11

Define nominal scale. Give an example.

Answer 11

The simplest scale of measurement
Used for characteristics that have no numerical values
Aka categorical or qualitative scale
–Ex. race, gender

Question 12

Define normal distribution. What percent fall within 1 SD of mean? 2 SD?

Answer 12

A symmetric, bell-shaped probability distribution with mean u and standard deviation sigma
If observations follow a normal distribution, 68% fall within 1 SD of the mean and 95% of observations fall within 2 SD of the mean
AKA Gaussian distribution

Question 13

Define ordinal scale. Give an example.

Answer 13

Used for characteristics that have an underlying order to their values
Numbers used are arbitrary
–Ex. Apgar scores

Question 14

Define population.

Answer 14

The entire collection of observations or subjects that have something in common and to which conclusions are inferred

Question 15

Define range.

Answer 15

The difference between the largest and the smallest observation

Question 16

Define skewed distribution.

Answer 16

A distribution in which there are a relatively small number of outlying observations in one direction only

• -If outlying distributions are small, skewed left/negatively skewed
• -If outlying observations are large, skewed right/positively skewed

Question 17

Define standard deviation.

Answer 17

The most common measure of dispersion or spread
Can be used with mean to describe distribution of observations
=Square root of variance

Question 18

Define variance.

Answer 18

The square of the standard deviation
A measure of dispersion in a distribution of observations in a population or sample
The sum of squared deviations of the observations from their mean, divided by n-1

Question 19

Define baseline analysis.

Answer 19

In a decision analysis, the expected value of each strategy calculated using best estimates of each probability and utility

Question 20

Define chance node.

Answer 20

Intermediate branch in decision tree from which chance events occur

Question 21

Define decision analysis.

Answer 21

A formal, quantitative approach to examining trade-offs when making patient-care decisions

Question 22

Define decision node.

Answer 22

Proximal branch in decision tree that specifies clinical strategies under consideration

Question 23

Define decision tree.

Answer 23

A diagram used in decision analysis to illustrate the possible clinical options and outcomes

Question 24

Define expected value.

Answer 24

The relative value of clinical strategy, often expressed in quality-adjusted life-years

Question 25

Define false-negative rate.

Answer 25

Probability of a negative test result in a patient who has the disease being tested for
Equal to 1 - sensitivity

Question 26

Define false-positive rate.

Answer 26

Probability of a positive test result in a patient who is free of the disease being tested for
Equal to 1 - specificity

Question 27

Define fold back.

Answer 27

In a decision analysis, the process of calculating expected values by summing outcome utilities, each weighted by its probability of occurrence

Question 28

Define gold standard test.

Answer 28

A diagnostic test used to ascertain the true disease status when estimating the sensitivity and specificity of another diagnostic test

Question 29

Define negative predictive value.

Answer 29

Probability of patient does not have the disease being tested for following a negative diagnostic test

Question 30

Define operating characteristics.

Answer 30

The accuracy parameters of the diagnostic test: sensitivity, specificity, positive predictive value and negative predictive value

Question 31

Define positive predictive value.

Answer 31

Probability that a patient has the disease being tested for following a positive diagnostic test

Question 32

Define post-test probability.

Answer 32

An estimate of the probability a patient has a given disease after the results of a diagnostic test are known
AKA posterior probability

Question 33

Define pre-test probability.

Answer 33

Estimate of the probability a patient has a given disease prior to the use of the diagnostic test
AKA prior probability

Question 34

Define probability revision.

Answer 34

The process of computing positive predictive value and negative predictive value from the pre-test probability

Question 35

Define quality-adjusted life-years (QALYs).

Answer 35

Common measure of utility based on multiplying life expectancy by a quality-adjustment factor

Question 36

Define sensitivity.

Answer 36

Probability of a positive test result in a patient who has the disease being tested for
AKA true-positive rate

Question 37

Define sensitivity analysis.

Answer 37

Process of testing stability of a decision analysis by allowing input probabilities and utilities to vary

Question 38

Define specificity.

Answer 38

Probability of a negative test result in a patient was free of the disease being tested for
AKA true-negative rate

Question 39

Define terminal node.

Answer 39

Most distal node in decision tree

Represents a final clinical outcome or condition

Question 40

Define threshold value.

Answer 40

In a decision analysis, the value of any input probability or utility that makes the expected values of two clinical strategies equivalent

Question 41

Define utility.

Answer 41

The relative value of a clinical outcome in a decision analysis

Question 42

Describe the three basic types of clinical data and the concept of a distribution.

Answer 42

NOMINAL DATA: categorical scale with no particular ordering (positive or negative, ABO blood typing)

ORDINAL SCALE: a categorical scale with an order (not necessarily equal stages) among the categories (stage I-IV cancer)

NUMERICAL DATA: discrete = counting of objects (number of pregnancies); continuous = includes fractions (temperature, height)

DISTRIBUTION: the pattern of the values of a variable; captures how frequently each value occurs

Question 43

What things should you consider when deciding to use mean or median?

Answer 43

Skewed: median is closer to center (mean affected by extreme values
Mean easier to work with
Median tougher to compare

Question 44

Understand the concept of sampling and estimation of population parameters.

Answer 44

a

Question 45

Define and contrast qualitative versus quantitative assessments of clinical uncertainty.

Answer 45

QUALITATIVE:

• -Likely, probable, suspicious
• -Unlikely, possible, can’t rule out
• -Lack precision, open to interpretation

QUANTITATIVE: [probabilities]
–Assessed on continuous scale of 0 (impossible) to 1 (certain)

Question 46

What questions should be asked about the decision model in clinical decision analysis (structuring the clinical problem as a decision tree)? (2)

Answer 46

Were all relevant strategies included?

Did the authors include all important outcomes

Question 47

What type of test would you want to use to rule-in a disease? Rule-out?

Answer 47

RULE-IN = high PPV = highly SPECIFIC

RULE-OUT = high NPV = highly SENSITIVE

SPIN = specific test to rule in
SNOUT = sensitive test to rule out

Question 48

What questions should be asked when assigning PROBABILITIES to all potential clinical outcomes included in a clinical decision model? (2)

Answer 48

Are probabilities based on reliable source studies?

Can results found in the literature be generalized to the patient population considered in the decision analysis?

Question 49

What questions should be asked when assigning UTILITIES to all potential clinical outcomes included in a clinical decision model? (3)

Answer 49

How reliable are the source studies?

Were appropriate methods used to estimate life expectancy?

Are quality of life estimates consistent with reader’s clinical experience?

Question 50

What questions should be asked about the analysis and interpretation of a clinical decision model? (2)

Answer 50

Is the expected benefit from the favored strategy clinically important?

How stable are the results when probability and utility estimates are allowed to vary