Measure of Disease/Risk

Question 1

How is ratio measured? Example?

Answer 1

a:b
number of individuals diseased (cases) : number of not disease (non-cases/control)
4:1 - female:male in vet school

Question 2

How is proportion measured? Example?

Answer 2

a / (a+b)
A fraction in which numerator is also included in denominator (# of case/ # of population)

Question 3

How is probability measured? Example?

Answer 3

Proportion x 100 = %
Probability that you get heads when flip a coin is # of case / # of population = 1/2 x 100 = 50%

Question 4

How is rate measured? Example?

Answer 4

Rate = a/x = disease / defined population at risk evaluated over a specific time period (a is included in x).

Question 5

How to calculate crude mortality?

Answer 5

Death from all causes / population at risk for death

Question 6

How to calculate cause-specific mortality?

Answer 6

Death from disease “X” / population at risk for death

Question 7

How to calculate case fatality for disease “X”?

Answer 7

Death from disease “X” / Cases from disease “X”

Question 8

Proportionate mortality for disease “X”

Answer 8

Death from disease “X” / death from all causes

Question 9

Difference between prevalent cases and incident cases

Answer 9

Prevalent cases are all existing cases at a given point in time. Incident cases are new cases occurred during a given time interval.

Question 10

How to calculate prevalent cases?

Answer 10

Number of cases observed at time t / total number of individuals at risk at time t

Question 11

What measures the risk of being a case rather than becoming a case?

Answer 11

Prevalence

Question 12

How does risk ratio measure?

Answer 12

Risk of disease in exposed group (either with or without outcome) / risk of disease in unexposed group (either with or without outcome)
Need to calculate 2 separate RISKs, then do a ratio

Question 13

How does risk difference measure?

Answer 13

AKA attribute risk due to exposure factor.
Risk in exposed group - risk in unexposed group
Need to calculate 2 separate RISKs, then do subtraction

Question 14

True but unknown values in populations called _______. We don’t know them unless we study 100% of a population.

Answer 14

parameters

Question 15

We make interferences about the population _______ by measuring statistics on a _______ of the population.

Answer 15

parameter - sample

Question 16

What is null hypothesis and how is it related to statistics?

Answer 16

Null hypothesis = assumes no difference, no association, equality of groups.
- Null hypothesis assume to be true, then we see how compatible our data are with null hypothesis.
- My logic: null hypothesis is true until proven wrong. Null hypothesis is another condition probability.

Question 17

What is the question to ask when null hypothesis is assumed to be true at the beginning of the study?

Answer 17

What is the probability of me getting the data?
(You begin with a hypothesis and you ask about the probability of getting a data)

Question 18

What is a p-value?

Answer 18

Probability of doing a study and obtaining the data actually collected (or data more extreme) when the null hypothesis is assumed to be true.

Question 19

When it comes to probability of a scientific study, what probability are we talking about?

Answer 19

Probability of obtaining the data.

Question 20

What does high and low p-value mean in terms of null hypothesis?

Answer 20

• High p-value tells us the data is compatible with null hypothesis.
• Low p-value tells us that we are UNLIKELY to observe the data when the null hypothesis is true. So in this case, null hypothesis can be rejected.

Question 21

What is type 1 error?

Answer 21

Incorrect rejection of the null hypothesis.

Question 22

What does “statistically significant” finding mean?

Answer 22

Finding that is real, reliable, and not due to chance.

Question 23

Any difference can be found to be statistically _______ if the sample size is large enough.

Answer 23

significant.
(The bigger size the sample size, you’re more likely to get statistically significant result).

Question 24

What is type 2 error?

Answer 24

Investigator fails to reject a null hypothesis that is actually false in the population (false negative).

Question 25

What does confidence intervals tell you?

Answer 25

How confident that the confidence interval contains the true population parameter. “We’re 95% confident that the confidence interval contains the true population parameter.”

Question 26

What is your sample profile for case-control study?

Answer 26

Select how many of cases and controls you want first (disease/undiseased), then measure exposure.

Question 27

What can you NOT calculate in case-control studies?

Answer 27

You cannot calculate the incidence (aka risk) of disease in neither the exposed group nor the unexposed group in a case control study (because the number of cases and controls is determined by the investigator).

Question 28

How to calculate odd of exposure among cases in case-control study?

Answer 28

Proportion of cases that are exposed divided by proportion of cases that are NOT exposed.
(A/(A+B)) / (B/(A+B)) = A/B

Question 29

How to calculate odd of exposure among controls in case-control study?

Answer 29

Proportion of controls that are exposed divided by the proportion of controls that are NOT exposed
(C/(C+D)) / (D/(C+D)) = C/D

Question 30

Cohort vs. case-control study

Answer 30

• If you assess the exposure first = cohort study
• If you assess the outcome first = case-control study

Question 31

Define sensitivity

Answer 31

Ability of a test to correctly classify an individual as diseased when in fact that individual is disease.

A / (A+C)

Condition on HAVING the disease.
P (test pos | dz pos)

Question 32

Define specificity

Answer 32

Ability of a test to correctly classify an individual as not diseased when in fact that individual is disease-free.

Condition on individual NOT having a disease. P (test neg | dz neg)

D / (B+D)

Question 33

Define true prevalence

Answer 33

How common is a disease in a population at some point in time. A sense of inventory at that time.

(A+C) / (A+B+C+D)
Dz pos / total population

Question 34

Define apparent prevalence

Answer 34

How common is a positive test result in a population at some point in time.

Test pos / total population
(A+B) / (A+B+C+D)

Question 35

Define positive predictive value

Answer 35

Average conditional probability that a patient whose test is positive actually has the disease.

P (dz pos | test pos)
A / (A+B)

Conditioned on Positive test result

Question 36

Define negative predictive value

Answer 36

Average conditional probability that a patient whose test is negative is actually disease-free.

P (dz neg | test neg)
D / (C+D)

Conditioned on Negative test result

Question 37

What is unconditional probability?

Answer 37

The probability of an event/outcome occurring that does NOT depend on any other results, findings, or states.
P(A)

Question 38

Why average risk?

Answer 38

Because risk is the unconditional probability of developing disease in SOME specified period of time.
- Average risk = sum (new case/population at risk)

Question 39

Review your 2x2 table

Answer 39

Dz in row, Test in column
A (true pos) - B (false pos)
C (false neg) - D (true neg)

Question 40

MATH question: 99% sensitivity, 99% specificity, 25% prevalence. What is the P (dz pos | test pos)?

Answer 40

Assume total is 100K. Calculate prevalence yields 25K for Dz (+) and 75K for Dz (-). Then use 99% sensitivity to calculate A/(A+C) and 99% specificity to calculate D/(B+D).

Answer: A / (A+B) = 97.1%

Question 41

MATH question: 99% sensitivity, 99% specificity, 0.1% prevalence (rare disease). What is the P (dz pos | test pos)?

Answer 41

Assume total is 100K. Calculate prevalence yields 100 for Dz (+) and 99,900 for Dz (-). Use 99% sensitivity to calculate A / (A+C) and 99% specificity to calculate D / (B+D).

Answer: A / (A+B) = 0.090 = 9%

Question 42

What does high and low prevalence mean?

Answer 42

• High prevalence: positive test is more likely true positive case.
• Low prevalence: positive test is more likely to be a false positive non-case (dz negative).

Question 43

Accuracy

Answer 43

= the total number of true positives and true negatives DIVIDED by the total number of tests

Question 44

Explain valid vs. precise?

Answer 44

• Precise: How close the measurements are to each other.
• Valid: The extent to which the study measures what it is intended to measure. Are the values describing what was supposed to be measured?

Question 45

Distinguish between experimental and non-experimental (also called observational) study design

Answer 45

• Experimental studies: investigator intervenes and assigns exposure (tx) to subjects.
• Non-experimental or observational studies: investigator observes and records exposure and outcome.

Question 46

Understand the role of a control group in experimental study designs (example clinical trial) compared to in an observational study (such as case-control)

Answer 46

???

Question 47

Understand the concept of randomization of a treatment or exposure to patients in a clinical trial

Answer 47

Randomization = assign participants to treatment and control groups, assuming each participant has equal chance of being assigned.

Randomization avoids confounding. Larger sample size achieves comparability between groups.

Question 48

List the major types of observational studies

Answer 48

• Cohort
• Case-control
• Cross-sectional

Question 49

Distinguish between two important observational study design, namely the cohort study Vs case-control study

Answer 49

• Cohort: start as a single cohort of disease-free subject. Assess their exposure. Followed over time. Disease (outcome) determined in exposed vs. unexposed subjects.
• Case-control: Assess their disease (outcome) first. Followed over time to determine exposure status. Controls provide frequency of exposed and unexposed in source population.

Question 50

Define and identify confounders

Answer 50

Cofounding is bias that creeps into the study because of the study design - it’s not true in nature. It is attributing an effect to a factor when it is due to another correlated factor.

Question 51

Understand ecological bias associated with ecological studies

Answer 51

Ecological fallacies (biases) occur when we try to draw conclusions about individuals based on data collected at the group level.

Question 52

How to calculate odd ratio?

Answer 52

Odds that a case was exposed / odds that a control was exposed

AC / BD

Question 53

How to calculate odd of exposure in cases?

Answer 53

number of cases with exposure / number of cases without exposure

E.g.: number of azotemia with methadone exposure / number of azotemia without methadone exposure

Question 54

How to calculate odd of exposure in controls?

Answer 54

number of controls with exposure / number of controls without exposure

E.g.: number of healthy with methadone exposure / number of healthy without methadone exposure

Question 55

95% confidence interval for the odd ratio include 1 in the odd ratio. What does it mean?

Answer 55

Because the lower bound of the 95% confidence interval includes 1.0, we cannot conclude with 95% confidence that the true (population parameter) odds ratio does not equal 1.0. This also means that a statistical test of the null hypothesis that the odds ratio = 1 would not be significant at the 5% level of significance.

Question 56

95% confidence interval for the odd ratio EXclude 1 in the odd ratio. What does it mean?

Answer 56

Because the lower bound of the 95% confidence interval excludes 1.0, we can conclude with 95% confidence that the true (population parameter) odds ratio does not equal 1.0. This also means that a statistical test of the null hypothesis that the odds ratio = 1 would be significant at the 5% level of significance.