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Y3/ EBM > Observational Studies > Flashcards

Observational Studies Flashcards

1
Q

Descriptive studies ……. hypotheses

Analytic studies ……… hypotheses

A

generate

test

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2
Q

Define bias

A

Systematic deviation of results from the truth, or the processes leading to such deviation

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3
Q

Give examples of the following:

  • selection bas
  • information (observation) bias
A 
Selection bias
- sampling bias 
- responder bias 
- follow-up bias 
Information (observation) bias 
- recall bias/social acceptability bias 
- recording bias/interviewer bias
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4
Q

Define confounding

A

effects of additional variable that might be responsibly for an observed association
Confounder can:
- cause spurious association
- exaggerate association
- or mask association between exposure and disease

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5
Q

Define incidence

A
  • the total number of new cases commending during a specified period in a defined population
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6
Q

Define prevelance

A
  • the total number of individuals who have the diseases at a particular time
    Prevalence = incidence x duration of disease
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7
Q

Explain what incidence rate is and how to calculate it

A

number of new cases of a specific disease arising within a population over a specified time period divided by the person-time accumulated

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8
Q

Explain what incidence risk is and how to calculate it

A

number of new cases of a specific diseases arising within a population over a specified time period divided by the number of persons at res at the beginning of the time period

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9
Q

Explain was point prevalence is and how to calculate it

A

number of persons with disease at some time point
divided by
total population at risk of disease at the same time point

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10
Q

Explain what period prevalence is and how to calculate it

A

number of persons with disease at any time during a specified period
divided by
total population sen over the period of time

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11
Q

What are the 8 criteria for assessing causality?

A
  1. biological plausibility
  2. time
  3. strength of association
  4. biological gradient or dose-response relationship
  5. consistency
  6. specificity
  7. coherence
  8. experiments
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12
Q

Criteria for Assessing Causality

1. biological plausibilty

A

does it make biological sense

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13
Q

Criteria for Assessing Causality

2. time

A

logically a cause must precede its potential effect

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14
Q

Criteria for Assessing Causality

3. strength of association

A

the stronger the association of an exposure with disease occurence then the harder to conceive of likely confounders which might explain the association

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15
Q

Criteria for Assessing Causality

4. biological gradient or dose-response relationship

A

causality as a plausible interpretation is strengthened if there is a strong dose-response

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16
Q

Criteria for Assessing Causality

5. Consistency

A
  • consistent with other studies in different populations, places and times add weight to a cause-effect interpretation
17
Q

Criteria for Assessing Causality

6. Specificity

A

if the supposed cause is associated with one disease only ,or the dissed is associated with one cause only, can add weight to causal interpretation

18
Q

Criteria for Assessing Causality

7. coherence

A

should not contradict what is already known about the natural history and biology of the disease

19
Q

Criteria for Assessing Causality

8. experiments

A

occasionally natural experiments offer themselves, such as tap-water fluoride levels and specific disease outcomes

20
Q

How would you go about determining if the association is causal?

A

First consider confounding, bias and chance

Secondary use 8 criteria for causality as aid to inferring causation

21
Q

  • -
A
  • to alert the medical community to what types of persons were most/least affect by disease
  • to assist in the evidence-based planning of health and medical care facilities
  • to provide suggestions concerning dissed aetiology for further investigation using analytic studies
22
Q

What are case reports/series

A
  • careful detailed report of individual patients or a series of patients
  • suggest hypotheses
  • no control groups
  • can be useful even though no control e.g. thalidomide and congenital malformations
23
Q

When would a cross-sectional study be undertaken?

Give 3 examples

A
  • if in a particular geographical area we were to determine the point prevalence of a particular disease
    Examples
  • determining need for specific health or social services
  • development of hypotheses concerning risk factors for a disease
  • determining trends in prevalence
24
Q

Give three examples of random sampling methods

A
  • simple random sampling
  • cluster sampling
  • stratified sampling
25
Q

Give three examples of non-random sampling methods

A
  • systematic sampling e.g. every 2nd patient
  • snowball sampling
  • street survey
26
Q

Can a casual relationship be establish with a cross-sectional study

A

unclear of direction of relationship as only have a snapshot in time

27
Q

What are the advantages of cross sectional studies

A

quick

inexpensive

28
Q

What are the disadvantages of cross sectional studies?

A
  • time sequence and causation
  • cohort effects when interpreting relationships with age
  • problems with interpretation of prevalence which is a mixture of incidence and survival relating to a disease
29
Q

What are the four ways of analysing cross sectional studies?

A
  • compare means (T test)
  • compare proportions (Chi squared)
  • correlation (Pearson)
  • strength of association: prevalence ratio, odds ratio
30
Q

Describe ecological studies

A

measure rates of death or disease in populations and the population rate of a risk factor
Groups of individuals as units of study

31
Q

What are scatter plots and Pearson’s correlation coefficient?

A

Scatter plots = visually inspect the relationship between exposure and disease
Correlation =r
- numerically decreases the association between exposure and disease
- values from +1 to -1

32
Q

What are the advantages of ecological studies?

A

quick
inexpensive
hypothesis generation

33
Q

What are the disadvantages of ecological studies?

A
  • subject to confounding
  • prone to sampling bias
  • prone to information bias
  • the group of people described by disease data are often not the same people as those described by the exposure data
  • the time relationship between the measurements for exposure and disease is often unclear
    So we cannot assume from a relationship found amongst groups of individuals that the same relationship also holds at the individual level

Y3/ EBM (7 decks)

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