Observational study designs

Question 1

Define prevalence

Answer 1

Measure the frequency of “cases” in a given population at a designated time

Requires a suitable denominator (e.g. GP registered patients)

Expressed as a percentage

Question 2

What are cross sectional studies

Answer 2

• Used to measure prevalence by testing individuals in a population individually
• Can also measure exposures
• Numerator (number of people with diagnosis)/ denominator (total population)

Question 3

What is the different between point and period prevalence

Answer 3

Point prevalence- prevalence a moment in time

Period prevalence- for things that fluctuate e.g. hayfever

Question 4

Give three strengths of cross sectional studies

Answer 4

• Measure prevalence and thus disease burden in whole populations and subpopulations
• Can compare prevalence in exposed and non exposed to risk factors
• Quick and inexpensive
• Can be used to initially explore a hypothesis, prior to another study type

Question 5

Give three weaknesses of cross sectional studies

Answer 5

• Not suitable for rare diseases
• Not suitable for diseases of short duration
• Cannot separate cause and effect as they are measured at the same time
• Cannot measure rate of new cases arising and any changes therein

Question 6

What are cohort studies?

Answer 6

• They measure incidence by following a group of people over time and the onset of a disease/ health event measured
• Incidence of disease is compared among those exposed and unexposed to a risk factor

Question 7

What is incidence?

Answer 7

The number of instances of illness/disease onset, in a given period in a defined population
- the numerator is the number of new events in a population; the denominator is the average number of persons exposed to risk during this period

Question 8

Consider the direction of association in relative risk

What does a relative risk of the following mean:

a) <1
b) RR=1
c) >1

Answer 8

a) Risk in exposed group less than the risk in non-exposed group. Therefore the exposure may be protective against the disease.= Negative association
b) Risk in exposed group equal to risk in non-exposed group. No association
c) Risk in exposed group greater than the risk in non-exposed group. The exposure may be a risk factor for disease (positive association)

Question 9

What do the following relative risk scores mean about the strength of association?

a) RR=1.5
b) RR= 3.0
c) RR= 0.8

Answer 9

a) risk of outcome 50% greater in exposed than unexposed group
b) risk in exposed is 3x unexposed
c) risk of outcome 20% lower in exposed than unexposed.

Question 10

State 3 strengths of cohort studies

Answer 10

• Able to calculate incidence and relative risk
• Can offer some evidence of cause- effect relationship
• Can identify more than one disease related to single exposure
• Good when exposure is rare
• Minimises selection and information bias

Question 11

State 3 weaknesses of cohort studies

Answer 11

• Potential for losses to follow up
• Often requires large sample, can take time to complete
• Less suitable for rare diseases
• Expensive
• If retrospective, data availability and quality may be poor

Question 12

What are case-control studies?

Answer 12

• two groups of participants are selected with conditon an without
• controls selected to be as similar as possible to the cases (e.g. age, gender, occupation, stage of illness)
• variables not of interest are matched at selection (potential confounders)
• exposures of interest are not matched
• past exposures in both groups are measured

Question 13

State 3 weaknesses of case control studies

Answer 13

• cannot calculate prevalence or incidence
• less suitable for rare exposures
• can be hard to ensure exposure occurred before onset
• retrospective data availability and quality may be poor
• suitable control group may be difficult

Question 14

State 3 strengths of case control studies

Answer 14

• can offer some evidence of cause-effect relationship
• can identify multiple exposures (both positive and negative associations, interactions)
• good when disease is rare
• minimises selection and information bias
• retrospective: cheaper and typically shorter in duration

Question 15

What is the different between risk and odds?

What is an odds ratio?

Answer 15

Risk= outcome of interest/total number of all possible outcomes

Odds= outcome of interest/ outcomes not of interest

Odds ratio= odds in exposed/ odds in non-exposed

Question 16

Define relative risk?

Answer 16

= incidence of disease in exposed divided by incidence of disease in unexposed

the risk is

Question 17

How does relative risk compare to odds ratio?

Answer 17

Risk is calculated using the total population at risk of developing the disease. Noone starts with the disease at outset

In a CCS, participants are selected on basis of having a disease (or not)

• therefore we don’t know the size of population at risk/the absolute risk of developing disease
• OR always overestimates RR. Don’t use interchangeably, can be converted.

Question 18

How do you calculate relative risk?

Answer 18

Incidence of adverse outcome in sample with one intervention/Incidence of adverse outcome in sample with another intervention

Question 19

What is a p value?

When will you see it?

Answer 19

The probability that the difference observed could have occurred by chance if the groups compared were really alike.

e.g P 0.05= 1/20

Results of a comparative statistical test (t test/chi squared) have p values

Question 20

What are confidence intervals?

Answer 20

The confidence interval describes the range of values with a given probability (e.g. 95%) that the true value of a variable is contained within that range.

Question 21

When do we need confidence intervals?

Answer 21

• Measures of effect (group comparisons)

- Population estimates (single population parameters)

Question 22

Define sensitivity

Answer 22

Proportion of people with disease correctly test positive for disease

A/(A+C) on 2x2 table for diagnostic test

Question 23

Define specificity

Answer 23

Proportion of people without disease who test negative

D/(D+B) on 2x2 table for diagnostic tests

Question 24

Typically, a hypothesis is that we will see a difference between two groups because of different interventions.

What is a type 1 error?

Answer 24

We observed a difference when there wasn’t really one e.g. our intervention was significantly better in our study, but this effect does not actually exist

The null hypothesis will be wrongly rejected

Question 25

Typically, a hypothesis is that we will see a difference between two groups because of different interventions.

What is a type 2 error?

Answer 25

We didn’t observe a difference when there actually was one e.g. our interventions looked equivalent but actually the new intervention is better

Null hypothesis is wrongly accepted

Question 26

What is the different between the significance level and the power of the study?

Answer 26

The significance level is the rate at which we say we are comfortable in making a type 1 error – type 1 error rate. Usually 5%

Power is the opposite of the type II error rate – i.e. it is the probability that a test will not miss an effect when an effect truly exists, therefore power tends to be set at 1 minus 0.2 – so 0.8 or 80%

Question 27

What is a positive predictive value?

What is a negative predictive value?

What doe these factors depend on?

Answer 27

Positive Predictive Value (PPV) = likelihood patient with positive test result actually has the disease

Negative Predictive Value (NPV) = likelihood patient with negative test result does not have the disease

Predictive values depend on the sensitivity and specificity of the test – and prevalence of the disease

Question 28

What is the relationship between prevalence, PPV and NPV?

Why is this?

Answer 28

As prevalence increases, PPV increases and NPV decreases

Because underlying frequency of disease has increased in the given population

*sensitivity and specificity are independent of prevalence

Question 29

What do you tell the woman with a positive mammogram?

Answer 29

Positive predictive value
- The likelihood of having the disease given that you have tested positive

“The probability that you really have the disease is 8.3%”

Question 30

What is the difference between PPV/NPV and sensitivity/specificity?

Answer 30

PPV and NPV use the prevalence of a condition to determine the likelihood of a test diagnosing that specific disease. Whereas sensitivity and specificity are independent of prevalence.

Question 31

What do you tell the woman with a negative mammogram?

Answer 31

“The probability that following your negative result you do not have the disease is 99.9”

Question 32

What happens to PPV/NPV as prevalence

a) increases
b) decreases

Answer 32

a) PPV increases, NPV decreases

b) PPV decreases, NPV increases