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Year 2 LMAP > Designing epidemiological studies > Flashcards

Designing epidemiological studies Flashcards

1
Q

descriptive

A

describes a problem at an aggregated level

can be used to later inform analytic research

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2
Q

analytic

A

deploy and test hypothesis often at person level

association can be measure and causation inferred

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3
Q

describing a problem- what dimensions?

A

person

place

time

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4
Q

parameter

A

– a fixed, often unknown value, which describes an entire population

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5
Q

Statistic

A

a fixed value, derived from a sample that estimates the value in the population

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6
Q

CASE REPORT & CASE SERIES

A

Often these are used to communicate new diseases, new presentations or new findings.
Today they’re often about unusual findings and can be structured as a bulletin or as a learning opportunity - so-called Continuing Medical Education (CME) or in the UK ‘Continuing Professional Development (CPD).
In new diseases – such as MERS, COVID-19 and other conditions, more than one case reported becomes a case series.

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7
Q

cross sectional

A

Typically describes the prevalence of a condition across a population at a single-point in time

eg survey
risk/temporal relationships cannot be determined

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8
Q

longitudinal studies

A

It may be made up of more than one cross-sectional analysis. That is aggregated data

describes prevalence or incidence of an exposre or outcome over time

measurement at more than one point

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9
Q

person-level longitudinal study?

A

same participants looked at over time

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10
Q

ecological study?

A

compares groups not individuals - aggregate data

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11
Q

how do statistic and parameters relate?

A

A statistic relates to the measure of interest in a sample (which is drawn from a population)

A statistic is an estimate of a parameter

A sample is always smaller than a population

A statistic is typically presented as a point estimate with associated confidence intervals

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12
Q

how do statistic and parameters relate?

A

A statistic relates to the measure of interest in a sample (which is drawn from a population)

A statistic is an estimate of a parameter

A sample is always smaller than a population

A statistic is typically presented as a point estimate with associated confidence intervals

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13
Q

primary data

A

collected by the researcher first-hand
pre-specified purpose ; relevant
financial costs and time costs

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14
Q

secondary data

A

downside is that secondary data analyses may have to make a series of assumptions because the data analysed weren’t intended for the new purpose. This may in turn introduce critical limitations on how the findings of such a study are interpreted. But the upside, is that secondary data analyses are usually faster and cheaper to undertake.

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15
Q

routinely collected data

A

Routinely collected data form the mainstay of day-to-day demography and epidemiology in the field. large administrative datasets that allow us to understand populations and their health. eg: census – This is a source of routinely collected data. Always out of date. Improve our understanding of a local area by looking at the number of patients registered in a general practice or on the electoral register. In London; rapidly changing populations, the first language of reception-class school children can often give us a good understanding of changing ethnic diversity in a local area.

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16
Q

examples of routinely collected data?

A

first language of reception-class school children: ethnic diversity

number of patients registered in a general practice or on the electoral register: local area understanding - demographics

examine prescribing data: billing data by thousands of local pharmacies

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17
Q

Hospital Episode Statistics (HES)

A

most comprehensive hospital administrative datasets

examine emergency department attendances, hospital admissions and outpatient attendances from the last ten years

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18
Q

non-routinely collected data

A

primary data : surveys and bespoke data sets

expensive and time-consuming to operate

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19
Q

data linkage

A

Data linkage involves joining two or more datasets together and in doing so, finding out more than was possible by analysis of either original dataset alone

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20
Q

ecological fallacy?

A

assuming associations hold true for individuals - aggregation bias

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21
Q

why undertake an ecological study?

A

Can generate hypotheses about disease etiology

secondary data: cheap and quick to complete

Suitable when the variability of exposure in each group is limited

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22
Q

why cross-sectional studies?

A

cheap and easy to conduct

Temporal relationship between exposure and outcome enables inference if exposure preceded the outcome.

With repeated cross-sectional studies changes in prevalence of risk factors may be observed.

calculates prevalence

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23
Q

why not cross-sectional?

A

temporality cannot be easily assessed

causal inference cannot happen

measurement of true extent of disease is missed ; Those that have died or have been cured of disease not in sample

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24
Q

methods of cross sectional?

A

Blood tests

Diagnostic or laboratory testing

Physical measurements

surveys

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25
Q

case control is an example of __

A

observational study

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26
Q

case control study design

case definition?

A

diseased and non-diseases groups : then you see exposure and not exposed status

clear eligibility criteria must be defined

Cases should be representative of everyone with the disease under investigation

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27
Q

sources of cases?

A

hospitals
clinics
community

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28
Q

source of controls?

A

controls should be from same study population as cases

should be representative of the population at risk

exposures should be measure-able with similar accuracy as cases

29
Q

what type of bias comes into play with case- control studies?

A

recall bias- more likely to remember exposure extent if faced with outcome

30
Q

what increases statistical confidence in case/ control studies?

A

if number of controls can be increased to more than one per case

31
Q

disadvantages of case- control studies

A

no info on temporal relationship between exposure and outcome

limited to assessing just one outcome

diff selecting control group

recall bias

32
Q

most to least robust observational study design

A

cohort studies

case control

cross-sectional

ecological

case report/ series

33
Q

what is a prospective cohort study a type of?

A

longitudinal

track people forward in time from exposure > outcome

so you’re looking at exposed and unexposed groups and seeing what happens to their outcome [disease status]

34
Q

selection of target population in cohort studies

A

The exposure of interest may be rare so may need to target a specific population

Should initially attempt to identify as many subjects as possible without invoking any restrictions

If you want to study a chronic disease associated with ageing, you may want to restrict your target population

35
Q

cohort assembled how?

A 
geographic region 
occupation 
based on database 
risk factor 
birth cohort
36
Q

assement of exposure in cohort?

A

Self-report
Taking physical measurements
Using existing records

37
Q

outcome in cohort study

A

Routine surveillance
Death certificates
Medical records
Directly form the participant

38
Q

historical cohort studies

A

Historical cohort studies are typically lower quality than prospective cohort studies because there is a greater risk of both selection and information biases.

looking back at data

39
Q

what assumptions can be made about loss to follow up?

A

that they suffered outcome / didnt suffer outcome

be transparent ; report loss to follow up - does it represent a bias?

be conservative ; if loss pushes to rejecting null hypothesis ignore it and look at output statistic which biases towards null

40
Q

intention to treat analytic

A

consider participants as the original group allocation - even if side effects happen and treatment is switched

analyse data with original group and explain- so results are an accurate representation of truth

41
Q

randomisation process involes?

A

the generation / randomisation

implementation of allocation- allocation concealment

42
Q

allocation concealment

A

Sequentially numbered opaque envelopes

Central randomisation

43
Q

what does multiple study arms mean?

A

Many RCTs will involve three or possibly more arms. While a control (either placebo or the standard care) is required, it is possible to have more than one intervention group. For example, intervention group 1 might be 30mg of the drug, and intervention group 2 may be 60mg of the drug.

44
Q

sources of bias in RCT?

A

clinical staff administering treatment; want to give better treatment to those who are more likely to show positive findings

patient- if they know it’s placebo not as vigilant about taking it

team interpreting results

physician assessing treatment

45
Q

blinding helps against what 2 bias?

A

performance bias: refers to systematic differences between groups in care that is provided, or in exposure to factors other than the exposure of interest

detection bias: refers to systematic differences between groups in how outcomes are determined

withdrawal from studies

46
Q

why might blinding not be possible?

A

certain techniques: surgical techniques

medication that requires titration

ethical reasons

increased £ implication

47
Q

what does blinding help ascertain?

A

internal validity

48
Q

what is study’s power?

increasing power does what?

A

ability to detect difference if a difference exists

reduces risk of type 2 error - so risk of a false negative

49
Q

what increases sample size?

A

small difference

increasing power - 80% is aim

decrease in study’s alpha-

50
Q

what is a study’s alpha?

what does decreasing alpha do?

A

95% confident that your findings are true

decrease alpha to decrease the risk that chance is creating the positive finding

decreasing alpha increases statistical significance - decreases risk of false positive

51
Q

what is a type 1 error?

A

positive finding due to chance

p-value beneath 0.05 eliminates this

52
Q

does statistical significance mean clinical significance?

A

no

having multiple analyses can create a statistical significance

53
Q

systematic reviews

what steps?

A
  1. Research questions
  2. Structured search
  3. Indices
  4. Screening/ inclusion
  5. Reporting
  6. Writing
  7. Submitting, revising, publishing
54
Q
  1. Indices
A

Platforms publishing articles based on research that has been conducted

55
Q

Why are registries important?

A

To avoid duplication or omission

56
Q

grey literature

A

Evidence that has not been peer-reviewed and is not in the published literature eg. government reports

57
Q
  • What is meta-analysis?
A

A quantitative, formal, epidemiological study design used to systematically assess previous research studies to derive conclusions about that body of research

58
Q

What does meta-analysis require?

A

That the pooled studies are sufficiently similar or else the results are meaningless

59
Q

what is heterogeneity and what are sources of it in meta-analysis

A

The differences between the studies

  • Clinical- patients, selection criteria
  • Methodological- study design, blinding, intervention approach
  • Statistical- reporting differences
60
Q

publication bias?

A

The bias that studies with positive findings are more likely to be submitted for publication and are more likely to be published by journal editors

61
Q

publication funnel plot?

A
  • plot can show the balance of evidence between studies assuming an overall effect size: more publications on one side of the line may suggest that some studies may not have been reported
62
Q

An outcome that usually describes a clinically meaningful outcome

A

trial end-point

63
Q

main outcomes of clinical trials

A
  • Efficacy- how well a therapy works in achieving a desired outcome
  • Safety- how well a therapy works in not causing adverse events
64
Q

types of efficacy endpoints

A

primary - for which study has been powered

secondary - softer measure eg recurrence of disease being secondary to a trial powered to outcome of survival

65
Q

composite endpoints

A

Composite means that multiple potential endpoints have been added together- common when the outcome is uncommon

Eg. combining MI with ischaemic stroke to give a new composite endpoint of ‘cardiovascular event’

66
Q

safety end points

A

Measuring observed adverse effects and grading them into a hierarchy of significance

67
Q

survival analysis

A

combats losing statistical precision by generating arbitary timepoints

gives you a range of survival times

68
Q

“Meta-analysis

A

is a quantitative, formal, epidemiological study design used to systematically assess previous research studies to derive conclusions about that body of research.”

69
Q

pooled estimate

A

Meta-analysis combines the quantitative findings from separate studies into a pooled estimate

diamond at bottom of plot

Year 2 LMAP (3 decks)

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