Module 10

Question 1

What is the Epidemiologic Triad

Answer 1

Host (e.g. genetics, age)
Agent (e.g. bacteria, chemical)
Environment (e.g. climate)
All diseases are multifactorial

Question 2

What are the types of causes

Answer 2

Cause
Necessary cause
Sufficient cause

Question 3

What is cause

Answer 3

condition/event/characteristic that contributes to a disease

Question 4

What is Necessary cause

Answer 4

condition/event/characteristic that MUST be present for disease to occur

Question 5

What is Sufficient cause

Answer 5

set of MINIMAL conditions/ events to produce disease

Question 6

Modern criteria for causality

Answer 6

• Coherence with known facts • Consistency with repetition • Time sequence • Specificity • Strength of association • Dose-response relationship

Question 7

What is Deductive reasoning

Answer 7

Assuming something with a known fact
– All men are mortal
– Socrates is a man
– Therefore Socrates is mortal

Question 8

Inductive reasoning

Answer 8

– requires qualitative and quantitative evidence to suggest that rejecting the supported conclusion is irrational

“All crows we have seen so far are black. Therefore, all crows are black.”

Question 9

What is the relationship between hypothesis and data

Answer 9

Deduction and induction reasoning are always done

Question 10

What is causality

Answer 10

The real reason of the cause

Question 11

What is the strongest way to prove causality

Answer 11

Casual judgement, stronger as there are more evidence

Question 12

What is syndrome diagnoses

Answer 12

– fixed combination of characteristics e.g. idiopathic cardiomyopathy in dogs, sudden death in ruminants

Question 13

What are the types of evidence

Answer 13

• Personal observation and anecdote
– “I tried this in 3 animals and they all got better”
– POOR source of information

• Scientific studies
– best source of information
– properly conducted
– different types provide different information

Question 14

What are the clinical research, what are the 3 types and their quality

Answer 14

• Goal - infer both causation and treatment effects 
• Descriptive studies – poor quality 
• Experimental studies 
– highest quality 
• Analytical epidemiological studies 
– provide good quality

Question 15

What are descriptive studies

Answer 15

essentially describes what happened, without the analytical advantages of cases vs controls or other factors that can be compared and tested statistically.

• Case reports
– rare condition
– unusual presentation of a disease
– cause/treatment effects?

• Case series
– number of animals

• Survey
– prevalence within a population
NO CONTROL GROUP OF ANIMALS

Question 16

What are experimental studies

Answer 16

specifically designed for the purpose, with controls, animals allocated (randomly) to treatments, often blinded so that there is no observer bias.
• Compare two groups
 – disease cause 
– treatment effect 
• Laboratory experiments 
• Clinical trials 
• Planned, controlled allocation 
• Expensive 
• Ethics

Question 17

What are analytical eqpidemiological studies

Answer 17

Analytical epidemiological study – takes advantage of naturally-occurring circumstance (or circumstances not directly designed by the researchers). Three main types exist:

• Deriving information from natural cases 
• Planned comparison between two groups 
– not allocated by experimenter 
• 3 types 
– cohort studies 
– case-control studies 
– cross-sectional studies 
• sample population for disease/exposure at same time

Question 18

What is cohort study

Answer 18

Cohort study – animals exposed or not exposed to the hypothesised cause (or protectant) of interest are followed forward through time to see if there are differential rates of disease in the two (or more) groups
– select puppies and follow over time
– disadvantages?
– cf. experimental study

Question 19

What are case-control studies

Answer 19

Case-control study – animals with or without the disease are tracked backwards in time (such as through their medical records) to see if they had differential exposure to the hypothesised cause of interest

– identify animals with and without mammary tumours
– investigate if obese as pups

Question 20

What are hypothesis

Answer 20

assertion or conjecture concerning one or more populations

Question 21

What about hypothesis

Answer 21

• Cannot be proven, but can be rejected
• Knowledge accumulates by falsification
– accepting a hypothesis means we do not have enough evidence to reject it (NOT that it is true)

Question 22

What is null hypothesis

Answer 22

• No difference exists between groups
– hope to reject
• Alternate hypothesis
– a difference exists

• Underlies scientific method
– not just in clinical research

Question 23

When can we reject the null hypothesis?

Answer 23

• Almost always a difference between groups
– chance (random error)

• sample of population
– bias (systematic error)
• poor study design
– real effect

• Require sufficient evidence to rule out bias and chance to reject the null hypothesis

Question 24

What is bias

Answer 24

Systematic error in design, conduct or analysis
–a valid study is (relatively) free from bias
– implies accuracy

Question 25

What are the three types that cause bias

Answer 25

– selection – study groups not representative of target population
• randomisation

– information
• blinding

– confounding
• only variation should be the factor you are trying to test the effect of

Question 26

How do we rule out bias?

Answer 26

Examine materials and methods section – was it a “fair” comparison
– allocation of treatment?
– comparable treatment groups?
– procedures well defined?
– outcome well-defined and assessed without considering treatment?
– follow-up identical?
– masking of treatments?

Question 27

What is P-value

Answer 27

• Is the observed difference between two groups likely to have occurred due to chance?
– probability of having obtained the data if the null hypothesis is true

• small P values (<0.05)
– reject chance as a likely explanation for observed difference

Question 28

How to increase precision in a study

Answer 28

• Precise study relatively free from random error
• In general, increasing study size will increase precision

• Precise study
– favours rejection of null hypothesis (smaller P-value)

• Imprecise study
– favours acceptance of null hypothesis (larger P-value)

Question 29

How to assess precision

Answer 29

• 95% confidence interval
– 95% chance that the real population parameter lies within this range

• Smaller the 95% CI(confidence interval), the more precise the estimate
– i.e. the higher the level of variance, the wider the 95% CI

Question 30

What does precision affect

Answer 30

affects P value and rejection/ acceptance of null hypothesis

Question 31

Power of study

Answer 31

• Accept null hypothesis does not necessarily mean treatments are equivalent
– biological significance
– size of CI
– actual P value (0.08 vs. 0.8)

• Power of the study
– probability the null hypothesis will be rejected, when there is a REAL difference of a given magnitude between treatments

Question 32

How should you estimating the power before the study begins

Answer 32

– specific alternate hypothesis
– decide what magnitude of difference is important e.g. 60% for treatment Avs 80% for treatment B

• Power of 80% considered reasonable

Question 33

How do you determine biologically significant

Answer 33

Consider the point estimate – e.g. 60% vs. 75%

Question 34

How to you determine the precision of the study

Answer 34

Look at confidence interval

Question 35

How do you rule out the chance for the observed treatment?

Answer 35

Look at P-value

affected by size of study as well as magnitude of difference between groups

Question 36

What is cross-sectional studies

Answer 36

Cross-sectional study – animals with or without the disease at this point in time are also checked to see if they have the hypothesised cause of interest or not.

Question 37

Example of cross-sectional studies

Answer 37

n a cross-sectional study, the animals already have the disease (or not) and you are testing to see whether they also have the hypothesised causative factor (or not). An example would be where you are interested in the relationship between skin pigmentation and skin cancer in older cats. You divide cases at your clinic into cats with or without skin cancer and also categorise them into white cats and non-white cats. You analyse to see if there is a greater prevalence of skin cancer in white cats.

Question 38

Example of cohort study

Answer 38

the animals are divided into exposed and non-exposed (to the hypothesised causative factor), but they do not have the disease at the start. You then track them over time, to see if they develop the disease at different rates. In this example, you would identify young cats that are white in colour, plus (otherwise matched) young cats that are not white. You then follow them over time (assuming that most keep visiting your vet clinic) to analyse whether they differ in their rates of developing skin cancer.