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YR3 RESS > RESS 3 > Flashcards

RESS 3 Flashcards

1
Q

Why do we study healthcare practice?

A
  1. Provide better quality evidence rather than guesswork

2. Primary + secondary studies allow results to generate guidelines, standards and targets

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2
Q

Study types used in healthcare development and assessment

A
  1. Research
  2. Audit
  3. Service Evaluation
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3
Q

Wha does research allow us to do?

A

Generate new knowledge where there is no or limited research evidence available and which has the potential to be generalisable of transferable

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4
Q

What is an audit?

A

A quality improvement process that seeks to improve patient care and outcomes through systematic review of care against explicit criteria and the implementation of change

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5
Q

What is a service evaluation?

A

Evaluates a proposed service or current practice with the intention of generating information to inform local decision making

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6
Q

What method can be used to define the search terms used in research lit. searches?

A

PECOS

Patient
Exposure
Comparison
Outcome
Study design
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7
Q

What are the competing priorities which clinical studies require us to balance?

A
  1. design and conduct studies that provide the correct answer
  2. maximise the efficient use of resources
  3. comply with ethical and legal regulations
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8
Q

What are the 4 potential sources of bias?

A

Sampling/selection bias
Measurement bias
Analytical bias
Dissemination bias

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9
Q

How do each of the following study designs help address the issue of bias?

  • cross sectional
  • case control
  • cohort
  • trial
  • meta analysis
A
  • cross sectional: provides evidence of association within a sample
  • case control: provides evidence of association between samples
  • cohort: provides evidence of directionality of associations
  • trial: provides evidence of causality
  • meta analysis: provides evidence of reproducibility/generalisability
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10
Q

What 3 ethical concepts came from the Belmont Report’s

A

Respect for persons
Justice
Beneficence

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11
Q

What types of projects do not require formal ethical approval?

A
  1. Secondary research
  2. Non human research
  3. Audit/Service evaluation
  4. Service evaluation (collects info on existing service)
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12
Q

What types of projects do require formal ethical approval?

A
  1. Non human subjects/animals
  2. Vulnerable groups
  3. Experimental research
  4. Non experimental research where information on more than only existing service delivery is required
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13
Q

What is a sample?

A

A collection of data drawn from a population

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14
Q

What is a target population?

A

The total finite population we wish to know about from which your sample is drawn

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15
Q

What is the study sample?

A

The units/participants drawn from the target population that constitute our data set

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16
Q

What are the types of samples?

A

Complete: all

Unstratified Random: every member of the target population has the same chance of being sampled

Stratified Random: randomly sample from target pop.

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17
Q

Pros and cons of complete sampling

A

Pro: no bias introduced by design
Con: potentially expensive

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18
Q

Pros and cons of unstratified sampling

A

Pro: easy to design and conduct
Cons: Smaller groups may be under represented by chance

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19
Q

Pros and cons of stratified sampling

A

Pro: representative of population
Pro: unequal sampling of strata improves power for rare strata
Con: population may not be easily divisible
Con: strata may now be known until after sampling

20
Q

Why do we statistically analyse data?

A

Estimation

Hypothesis testing

21
Q

What happens to the confidence interval if the sample size is increased?

A

It decreases and becomes more reliable

22
Q

What does hypothesis testing tell you?

A

How unlikely you are to see an effect this big by chance is there is no genuine effect

23
Q

When is the association significant?

A

When the CI does not include the null at p=0.05.

24
Q

What is power?

A

The probability of rejecting the null hypothesis when the null hypothesis is false

25
Q

When is the power greater?

A
  1. the mean effect is bigger
  2. variation in effect is smaller
  3. sample is bigger (the only one that can be influenced
26
Q

When should power be calculated?

A

Before a study to ensure it is well designed

After a study when no association is found - to demonstrate if the study was sufficiently powered

27
Q

What is the ‘odds of an event’

A

= the probability an event occurs / probability that it doesn’t occur

28
Q

What is the odds ratio?

A

The odds of an event for exposed / odds of an event for unexposed

29
Q

Which of confounders, competing exposures and mediators, are not adjusted for?

A

Mediators

30
Q

What are confounders

A

They cause both the outcome and exposure

31
Q

Why must confounders be adjusted for?

A

They will generate a statistical relationship between the outcome and exposure even when none exist

32
Q

What are mediators?

A

They cause the outcome and are caused by the exposure

33
Q

Whey are mediators NOT adjusted for?

A

They are part of the causal path between O and E so should not be adjusted

34
Q

What are competing exposures?

A

They cause the outcome but have no relationship with the exposure

35
Q

When would a competing exposure be adjusted for?

A

If they cause a substantial amount of the variation in the exposure

36
Q

A causal relationship can be:

A
  1. functional
  2. emperical
  3. theoretical
  4. speculative
37
Q

When is a causal relationship possible

A

Only when the cause variable precedes the effect

38
Q

What do prospective studies do?

A

Record variables over the study period, with the outcome measured subsequently

39
Q

What do retrospective studies do?

A

Measure the outcome and then look backwards to measure the exposure and other variables

40
Q

Pros and cons of prospective data collection

A

Pros:

  • fewer sources of bias
  • less chance of confounding
  • some data can only be measured prospectively

Cons:

  • time and resource intensive
  • tendency to collect data on more variables than you need or can use in your analyses
  • subjects can drop out
  • usually unfeasible for rare outcomes
41
Q

Pros and cons of retrospective data collection

A

Pros:

  • data collection is less time and resource intensive
  • allows oversampling of rare outcomes

Cons:

  • more susceptible to bias
  • some variables cannot be measured directly
  • if data are from records, little control over these
42
Q

What are some sources of measurement error?

A

Instability in the variable

Imperfect measurement

43
Q

What allows for a better line of best fit

A

Less residual variation

44
Q

What does linear regression allow?

A

Comparison of two variables

45
Q

What is R squared?

A

Proportion of variation explained by the linear model. Values closer to 1 = more of the variation is explained by the model

46
Q

What can R squared not calculate for?

A

Logistic regression

47
Q

What do the results of the odds ratio mean?

A 
>1 = control is better than intervention
<1 = intervention is better than control

YR3 RESS (1 decks)

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