POPH progress test - study designs

Question 1

Prevalence

Answer 1

The proportion of a population who have the disease at a point in time

Able to look at burden of diseases and leads to better resource allocation

Equation for finding prevalence = number of people with the disease at a given point in time/ total number of people in the population at that point in time

When reporting prevalence must state …. 
Measure of occurrence e.g. The prevalence 
Exposure or outcome e.g. of asthma 
Population e.g. in the POPH192 class 	
Time point e.g. on August 10th 2020 	
Value e.g. was 10% 

Limitations of prevalence
Difficult to assess the development of disease
Is influenced by the duration of the disease - diseases with a longer duration have a larger prevalence by default

Question 2

Incidence proportion

Answer 2

The occurrence of new cases of an outcome in a population during a specific period of follow-up // The proportion of an outcome-free population that develops the outcome of interest in a specified time period

Incidence proportion (IP) and incidence rate (IR) - key difference is what you use as the denominator

Equation for finding incidence proportion = number of people who develop the disease in a specified period/ number of people at risk of developing the disease at the start of the period

Why might people not be considered ‘at risk’ at the start of a study?
They already have the condition
The condition is something that they cannot develop such as women developing prostate cancer

Reporting incidence proportion …
Measure of occurrence e.g. The incidence proportion 
Outcome e.g. of low back pain
Population e.g. in nurses	
Time period e.g. in 12 months  	
Value e.g. was 35% 

Take the incidence proportion because it gives risk (average)

Limitations of incidence proportion
Assumes a ‘closed’ population - Does not account for people coming and going and it assumes that everyone is exposed to the risk over the same time period
Highly dependent on the time period - Longer time period = higher incidence proportion

Question 3

Incidence rate

Answer 3

The rate at which the new cases of the outcome of interest occur in a population

Equation for finding incidence rate = number of people who develop the disease in a specified period / number of person-years at risk of developing the disease

Person-years at risk = sum of everyone in the population’s time at risk of becoming a case
48 person-months = 4 person-years (divide by 12)
cases/ person-years = _____ cases per person-year (times by 100 to get rid of decimals)

Why might someone stop being ‘at risk’?
They become a case
They are lost to follow-up e.g. die, move away, no longer take part
Follow-up time ends

Incidence rate reporting 
Measure of occurrence e.g. The incident rate  
Outcome e.g. of glandular fever 
Population e.g. in the class	
Value e.g. was 50 per 100 person-years 

Limitations of incident rate
Person-time not available
Complex to calculate

Question 4

Epidemiology

Answer 4

the study of the distribution (descriptive epidemiology) and determinants (analytical epidemiology) of health related states or events in specified populations

Question 5

Cross sectional study

Answer 5

Measures exposures and/or outcomes at one point in time (snapshot in time)
Point in time describes a particular date (12th August 2020), a specific event (retirement), a specific period of time (last 12 months)

Descriptive and observational study

Often survey/census

Cross-sectional studies measure prevalence - the proportion of a defined population who have a disease at a given point in time
Prevalence = number of people with disease at a given point in time/total number of people in the population at that point in time
Remember that prevalence is affected by incidence (onset of disease) AND duration (how long the disease lasts)
We can use cross-sectional studies to describe and compare prevalence/s, and generate hypotheses about potential risk factors and to plan (e.g. Health service delivery)

Question 6

Limitations of cross-sectional studies

Answer 6

Temporal sequencing - since exposure and outcome are measured at the same time you are unable to distinguish which came first between the exposure and the outcome

Measures prevalence not incidence, it cannot tell us about the onset of disease

Not good for studying rare outcomes or exposures

Not good for assessing variable and transient (short-lasting) exposures or outcomes

Question 7

Strengths of cross sectional studies

Answer 7

Can assess multiple exposures and outcomes

Can be used to calculate prevalence, distribution of prevalence in the population and hypothesis generation

Can be less expensive than some other study designs and is relatively quick (because they only collect data at one point so pretty quick and inexpensive)

Question 8

Ecological studies

Answer 8

Compare exposures and outcomes across GROUPS (e.g. states, countries, regions) not individuals - Cannot link back to the individual

Ecological studies are descriptive and observational

We use ecological studies to compare between populations, assess population level factors in disease (such as air pollution that cannot be examined at an individual level) and hypothesis generation

Question 9

Limitations of ecological studies

Answer 9

Ecological fallacy = making assumptions about individuals based on data from the group they belong to

Cannot control for confounding

Cannot show causation

Question 10

Strengths of ecological studies

Answer 10

Assesses population level exposures such as air pollution and legislation

Good to use for considering hypotheses - to see whether ecological data is consistent with your idea

Can be used for hypothesis generation

Inexpensive and easy and data is often routinely collected

Question 11

PECOT

Answer 11

P = Population → The group of people in the study
E = Exposure → what the potential determinant is
C= Comparison → what the potential determinant is being compared to
O = Outcome → the health outcome being assessed
T = Time → How long people are being followed-up
- Source = population the sample is recruited from
- Sample = population included in the study

Question 12

Three measures of association

Answer 12

Relative risk = incidence in exposed/incidence in unexposed
Risk difference = incidence in exposed - incidence in unexposed
Odds ratio = odds of exposure in cases/ odds of exposure in controls

Question 13

Relative risk

Answer 13

Ratios of incidences

Tells us the strength of association - how linked the exposure is to the outcome
Relative risk = incidence of exposed group/incidence of unexposed group(comparison group)
How many times as likely is the exposed group to develop the outcome than the comparison?

When incidence of outcome is the same as the incidence of the exposure and the incidence of the comparison group then you get equal likelihood of outcome in both groups.
Exposure does not change the likelihood of the outcome, so no association between exposure and outcome - this is the null value = 1

Number above 1 means that there is …
Greater incidence of the outcome in the exposed group
Greater likelihood of outcome in exposed group
If the outcome is bad, exposure is potentially a risk factor for the outcome

Number less than 1 but above 0 (because rations are never 0 or below it) means that there is …
Greater incidence of outcome in the comparison group
Greater likelihood of outcome in the comparison group
If outcome is bad, exposure is potentially a protective factor for the outcome

Question 14

Interpreting relative risk

Answer 14

The EXPOSED GROUP were VALUE as likely to develop OUTCOME compared to COMPARISON GROUP

Same interpretation with as likely phrase for both incidence proportion and for incidence rate

Question 15

Risk difference

Answer 15

Differences in the incidences

Tells us the impact of the exposure - how much of the outcome is due to the exposure, and how much disease we would prevent by removing the exposure

Risk difference = incidence in exposed - incidence in unexposed

When RD is greater than 0 …
Meaning there is a greater likelihood of the outcome in the exposed group.
Risk factor if the outcome is bad

When RD = 0 ….
The RD null value
No association between exposure and outcome

When RD is less than 0 …
Meaning there is a greater likelihood of the outcome in the unexposed group
Protective factor is the outcome is bad

Question 16

Interpreting risk difference

Answer 16

There were VALUE extra/fewer cases of OUTCOME in EXPOSED GROUP compared to COMPARISON GROUP

But report differently for incidence proportion and incidence rate - the way you write the value changes…
For incidence proportions e.g. 15 extra cases per 100 people over 10 years
For incidence rate e.g. 15 extra cases per 100 person-years

Question 17

RR vs RD

Answer 17

RR
Clues to causes
Strength of association
Null value = 1

RD
Impact of exposure
Impact of removing exposure
Null value = 0

Question 18

Cohort studies

Answer 18

Analytical and observational
Analytical epidemiology = exposures and outcomes
Observational = observe people’s exposures and what happens to them

Individuals are defined on the basis of presence or absence of exposure to a suspected risk factor

Process of a cohort study …
1- Identify a source population
2- Recruit the sample population who don’t have the outcome of interest
3-Assess their exposure level and categorise participants into appropriate groups i.e. exposed and not exposed
4-Follow up over time and see who develops the outcome
5-Calculate the measures of occurrence and association

So in a cohort study, we know the exposure comes before the outcome

Question 19

What can we measure using cohort studies?

Answer 19

Incidence proportion (IP) and incidence rate (IR) - These are measures of occurence 
Incidence proportion = Number of people who develop the disease in a specific period/  number of people at risk of developing the disease at the start of the period

Incidence rate = Number of people who develop the disease in a specific period/ number of person-years at risk of developing the disease

Relative risk (RR) and Relative Difference (RD) 
Relative Risk (ratio) 
Rate ratio = IR exposed/ IR control
Risk ratio = IP exposed/ IR control 
People in the exposed group were \_\_\_\_ times as likely to have \_\_\_\_\_\_\_\_ compared to those \_\_\_\_\_\_\_\_

Risk Difference (subtraction) 
Rate difference = IR exposed - IR control 
Proportion difference = IP exposed - IP control

Question 20

What other considerations do we need to make in a cohort study?

Answer 20

The health worker effect → when studies are derived only from a population of adult works and this cannot be generalised to the population at large. This is because those who are working are overall healthier than those who are not. This could only apply to our study because hospital workers are likely healthier than the general population

We need to be sure that the sample population does not have the outcome
For some conditions there might be a preclinical stage where the disease outcome has started but it is at a stage where it cannot be detected and in this case the exposure might not actually proceed the outcome as you might think

Ensure that participants have been correctly classified into exposure groups (and haven’t changed exposure groups during the study period)

Loss to follow up - did any participants leave the study

Has the outcome been classified correctly?

Question 21

Strengths of cohort studies

Answer 21

Temporal sequence (the exposure comes before the outcome)

Can examine multiple outcomes from an exposure

Can calculate incidence (and therefore relative risk and risk difference)

Good for studying rare exposures (because researchers are defining people based on their exposure so this can ensure that there is enough people with and without the exposure in the study)

Question 22

Limitations of cohort studies

Answer 22

Loss to follow up can lead to bias if related to the exposure and outcome

Potential for exposure/outcome missclassification

Time consuming

Expensive due to their nature

Generally not good for studying rare outcomes

Question 23

Two types of cohort studies

Answer 23

Prospective cohort studies
Prospective cohort study starts with the exposure…researcher classifies the exposure and follows up over time and observes the outcome
Everyone is outcome free and you are following them up

Historical cohort studies (sometimes called retrospective cohort studies)
Historical cohort study starts after the outcome ….exposure and outcome have already occurred so the researcher is starting here
Use existing data
Reconstruct follow-up period in the past

Strengths
Less expensive
Less time consuming in comparison with prospective cohort studies
Good for outcomes that take a long time to develop or are rare

Limitations
Use existing data (collected for other reasons) - quality?
Researchers don’t have control over the quality of the data and the data may not include exactly what the researchers want to know
May not know about all the relevant factors
Selection bias - One of the strengths of a prospective cohort study design is that because the outcome hasn’t happened yet, the classification of people to whether they are exposed or not isn’t going to be affected by the outcome as it hasn’t happened yet but a potential limitation of a historical cohort study is that because the outcome has already actually occurred it is possible the outcome could in some way influence the exposure categorisation and introduce selection bias

Question 24

Case-control studies

Answer 24

Addresses some of the issues with doing a cohort study
Analytical, observational
They work backwards - we start with the participants with a known outcome status
Cohort you start with exposure status and then find outcomes
Case-control you start with outcome status and then find exposures
Designed for rare/slow to develop outcomes
Can efficiently examine acute or transient exposures

Process
1- Identify a source population
2- Identify the cases (people with the outcome) and controls (people without the outcome)
3- Assess their prior exposure level in cases and controls
4- Calculate your odds ratio (measure of association)

Question 25

Limitations of cohort studies

Answer 25

Can be inefficient with rare/slow to develop outcome Can be inefficient with transient/acute exposures Alternatives to combat these limitations Historical cohort studies Case-control studies

Question 26

Logic of case-control studies

Answer 26

If cases and controls are a good representation of the source population Ratio of odds of exposure quantifies association between exposure and outcome Odds of exposure (cases)/Odd of exposure (controls) = odds ratio Odds of exposure in cases is greater than exposure is more likely in cases so the exposure is a potential risk factor Odds of exposure in controls is greater than exposure is more likely in controls so the exposure is a potential protective factor

Question 27

Odds in what study type and explain why

Answer 27

Case-control studies Can’t calculate prevalence or incidence of outcome - have selected number of people in study with and without outcome Odds of exposure in cases = a/c Odds of exposure in controls = b/d Odds are not measure of occurrence Ration of odds = how many times as likely cases are to have the exposure compared to the controls Interpreting odds ratio Different interpretation to the RR RR = people with exposure are X times as likely to develop the outcome as people with comparison OR = people with outcome are X times as likely to have had the exposure than people without the outcome Fortunately when disease is rare, OR approximates the RR (rare disease assumption) and so you can interpret OR just like RR Use RR interpretation in this course

Question 28

Obvious problem with case-controls

Answer 28

Controls do not have the outcome - so when do you measure exposure for controls Index date = pretend control had event on same date as case (or close to it) Can be used for transient exposures in controls who didn’t have an associated event - the exposure is measured on the same date as the case E.g. texting while driving (exposure) and having a car accident - see whether controls used their phone on the date the case had the car accident (index date)

Question 29

Case selection for case control

Answer 29

Defined by outcome so only one Clear outcome definition and identification The purpose of having a control group is to estimate the prevalence of exposure in the population from which the cases come from Comprehensive case finding Prevalence cases Usually try to identify incident cases - sometimes just recruit people with the outcome (prevalent cases) As people come along and develop the outcome they are recruited into the study Controls must also be capable of becoming a case, and often we select multiple controls per one case - for better statistical power Using an inappropriate group of controls we fail to find the association that actually exists and this is why it is important to have correct controls

Question 30

Exposure measurement for case control

Answer 30

Need to measure exposure period before outcome Differential recall Cases trying to work out what made them sick Outcome may affect recall ability Exposure measurement must be comparable Dead cases vs alive controls Interviewers may act differently for cases and controls Using hospital based controls can cause problems as they are not necessarily representative of the populations which the cases came from (they may have diseases which are also related to the exposure being studied) When measuring exposure level in a case-control study, there is the potential for information bias (recall and interviewer bias)

Question 31

Strengths for case-control studies

Answer 31

Good for rare outcomes (specifically choose the cases with the rare outcome) and transient exposures (can ask study participants about this exposure and their history) Can assess multiple exposures Temporal sequencing - we know that exposures will have come before the outcome in a good case control study Quick and inexpensive in comparison with other studies such as cohort studies

Question 32

Limitations for case-control studies

Answer 32

Can only study one outcome Difficult to select an appropriate control group Prone to selection and recall bias

Question 33

Randomised control trials

Answer 33

Randomised - Participants randomly allocated to groups Controlled - Always have a comparison (control) group Trial - Testing effect of treatments/intervention Trial involves assigning participants to exposed or comparison group Analytic, interventional They are similar to a cohort study however instead of measuring an exposure, we randomise an intervention such as a medication Process 1- Identify a source population 2- Randomly select the sample population who don’t have the outcome of interest 3- Randomise the sample to either the intervention or control group 4- Follow up over time and see who develops the outcome 5- Calculate the measures of association and occurrence

Question 34

Randomisation

Answer 34

Random allocation This is when we randomly allocate the participants into either the control group or the intervention group It is not the same as random selection Random selection is about how you recruit people into the study in the firs place whereas randomisation/random allocation is about how the people who are recruited into the study are randomly allocated to receive the treatment or be a control The purpose of randomisation is to control for confounding (when another variable like age or sex distorts the relationship between exposure and outcome) Randomisation does this as if done correctly, there should be the same proportion of known and unknown confounders in each group meaning that the groups are comparable Successful randomisation means that confounding is an unlikely reason for difference in outcomes between groups Randomisation is more likely to be successful if applied to large numbers

Question 35

Protection of randomisation and its benefits

Answer 35

Large numbers Concealment of allocation It is a different concept to blinding It means that the person randomising the participants does not know what the next treatment allocation will be - it is concealed and unpredictable Concealment of allocation could be achieved by a computer generated randomisation code As well as preserving the benefits of randomisation, concealment of allocation prevents selection bias - from participants or their doctors from selecting the treatment they want Intention-to-treat analysis The is when we analyse as we randomise - it means that we are not swapping people between groups (and therefore maintaining the randomisation) Intention to Treat analysis also gives us a real world effect - people do not take interventions perfectly in real life The other type of analysis for an RCT is a ‘Per Protocol’ analysis - where we analyse participants who fully complied to the study protocol Per protocol analysis can show the efficacy of the treatment, but we lost the benefits of randomisation

Question 36

Potential sources of bias for randomised control trial

Answer 36

Lack of Blinding If people involved in the study (i.e. the patient and the doctor) know whether they were in the intervention or control group, this may influence them … E.g. if someone in the control group knew they were taking sugar pills they may be less likely to report a benefit or side effects Studies can be single blinded (participants) or double blinded (participants and researchers) however it is better just to be specific who is blinded in the study We can use certain methods like matching placebo pills to blind but sometimes it is tricky e.g. if things like physiotherapy or surgery are an intervention Blinding is an important way to protect against bias Challenging to achieve in practice … Safety concerns for participants e.g. if someone has to go to hospital and don’t know whether they are receiving treatment or not Can be obvious which group a participant is in Loss to follow up If people leave the study or are loss to follow up this can cause confounding and bias - the people who left may be different to those that stayed Similar problem as in cohort studies Non-adherence Participants do not do what they are supposed to do Can include doing what the other group is doing

Question 37

Strengths of RCTs

Answer 37

Best way to evaluate an intervention Can calculate incidence so can directly calculate relative risk and risk difference Strongest design for demonstrating a causal association Eliminate confounding and bias if done well

Question 38

Limitations of RCTs

Answer 38

Many exposures cannot be randomised, and we need to have clinical equipoise (genuine uncertainty about benefit/harm of intervention) - It is unethical to give known harmful interventions to people - It is unethical to give interventions known to be less effective than current treatments - It is unethical to waste resources and risk people’s well-being if already know the answer Expensive and resource intensive Sometimes the participants aren’t representative of the general population, therefore are not generalisable (highly selective) Not good for rare outcomes Exposure needs to be modifiable - if not modifiable like vaccinations then you need to do an observational study

Question 39

External validity

Answer 39

The extent to which the findings of the study be applied to the broader population Also known as generalisability Judgement call is made depending on what is being studied and who it is being applied to

Question 40

Internal validity

Answer 40

The extent to which the findings of the study are free of chance, bias and confounding

Question 41

Sampling

Answer 41

When we do a study, we take a sample ( a subset of the source population) The study sample gives us an ‘estimate’ value of the population parameter (e.g. population relative risk) - this is the unknown, true value of the measure that the study is trying to estimate However we don't always get our sampling right… Parameter = The true value of the measure in the population that the study is trying to discover Estimate = The measure found in the study sample. Sometimes referred to as the point estimate

Question 42

Sampling error

Answer 42

Sampling is unlikely to be perfect due to chance If we took lots of samples from the same source population, each sample would be different simply because of chance This is called sampling error and is a form of random error which is commonly just called chance Because of chance our study might not be an accurate representation of the population Chance is a random sampling error - the most common reason for this is through a small sample sice To reduce the likelihood of chance, we can increase the sample size, which: - Reduces the sample variability - Increases the likelihood of having a representative sample - Increases the precision of the parameter estimate Cannot eliminate sampling error but can reduce with large sample sizes

Question 43

95% confidence intervals

Answer 43

If you repeated a study 100 times with a different random sample each time, you would get 100 different estimates and 100 different confidence intervals….In 95 of the 100 studies (95% of the time) the population parameter (e.g. RR) would lie within that study’s 95% confidence interval. 5% of the time the population parameter would lie outside of the confidence interval In other words - we are 95% confident that our TRUE population parameter lies between the bounds of the confidence interval STATEMENT = We are 95% confident that the parameter lies between (lower bound) and (upper bound) // We are 95% confident that the true population value lies between the limits of the confidence level Width of the confidence interval indicates precision Narrow confidence interval is more precise (smaller range of values that the parameter could be) Wide confidence interval is less precise (greater range of values of what the parameter could be) Precision is important because it helps us decide how useful a finding is

Question 44

Clinical importance

Answer 44

RCTs often state what they consider to be a clinically important result - it is set to decide if the intervention has a real, genuine, and noticeable effect on daily life Confidence intervals can help us decide whether the study findings are clinically important For something to be considered clinically important, the entire confidence interval has to be below the level of clinical importance and if it is not clinically important then the confidence interval is entirely above

Question 45

P-values

Answer 45

Probability of getting study estimate (or a study estimate further from the null), when there is really no association, because of sampling error (chance) If probability really low, then it is unlikely that the estimate is due to sampling error P value = the probability that our study result is WRONG (and is due to chance/sampling error) The higher the p value, the more likely the association is due to chance (e.g. a p value of 1 - certainly due to chance) A small P value (<0.05) - means a SMALL chance the association we have found is incorrect (due to chance) and there is no true association A large P value (>0.05) - a LARGER chance the association we have found is incorrect due to chance and there is actually no association If the 95% confidence interval includes the null value, then p>0.05 If the 95% confidence interval does not include the null value, then p<0.05 Statistical significance P values tell us whether a result is statistically significant or not P > 0.05 - not statistically significant P< 0.05 - statistically significant Statistical significance …. P>0.05 it is highly likely that our results are due to chance ….. P<0.05 there is a less than 5% chance that our results are due to chance

Question 46

Null hypothesis

Answer 46

There is not true association between exposure and outcome (and that our population parameter equals the null value) Null value for RR and OR is 1 and the null value for RD is 0 Small studies usually have a higher p value than larger studies which have a smaller p value The smaller the p-value, the stronger the evidence that you should reject the null hypothesis. A p-value less than 0.05 (typically ≤ 0.05) is statistically significant. It indicates strong evidence against the null hypothesis, as there is less than a 5% probability the null is correct (and the results are random).

Question 47

Alternative hypothesis

Answer 47

There truly is an association between exposure and outcome (population parameter does not equal the null value) Parameter does not equal to 1 for RR and OR and for RD the parameter does not equal to 0

Question 48

Type I errors

Answer 48

When the study shows a statistically significant result when there truly isn’t one (null hypothesis is true) Reduced risk of type I error when smaller P values are used

Question 49

Type II errors

Answer 49

Don’t find a statistically significant result when there is truly one (alternative hypothesis is true and there is a statistically significant association) More likely if our sample size is small Incorrectly fail to reflect H0 when should have (p value should have been <0.05 but got >0.05) Typically due to having too few people in the study Bigger sample size = more likely to get small p Smaller sample size = less likely to get small p Statisticians can calculate power to find out how many participants are needed to minimise chance of Type-II error The power of a study is the probability that it will detect an association of a particular size if it truly exists in the general population

Question 50

Why are P-values problematic?

Answer 50

Arbitrary threshold Is p = 0.06 that different to p=0.04? Always useful to report P values rather than just saying if it is statistically significant or not Only about Ho Just give evidence about consistency with the null hypothesis Don’t say anything about precision Best presented with confidence intervals Nothing about importance Statistical significance is not clinical significance Statistical significance depends on whether the CI crosses the null value and clinical importance depends on where the CI lies based on the clinical importance threshold Don’t say anything about whether the results are valid, useful or correct Absence of a statistically significant association is not evidence of absence of a real association

Question 51

Statistical significance

Answer 51

Statistical significance P values tell us whether a result is statistically significant or not P > 0.05 - not statistically significant P< 0.05 - statistically significant Statistical significance …. P>0.05 it is highly likely that our results are due to chance ….. P<0.05 there is a less than 5% chance that our results are due to chance

Question 52

Statistical significance and clinical importance

Answer 52

Statistical significance depends on whether the CI crosses the null value and clinical importance depends on where the CI lies based on the clinical importance threshold