Study design and summarising data Flashcards

1
Q

What are RTCs?

A

Randomised Controlled Trials
* Intervention study
* Choice of treatment/intervention allocated randomly
* Typically randomised to new vs current or placebo treatment
* ‘Gold standard’ in research studies

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2
Q

Why are Randomised Controlled Trials random ?

A
  • Ensures patients’ characteristics don’t affect which treatmentthey receive
  • Unbiased
  • Treatment groups balanced
  • Any differences in outcome can be attributed to treatment received
  • Fair test of efficacy
  • Usually use computer-generated random list
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3
Q

How does Blinding in Random Controlled Trials take place?
Why?

A
  • Treatment is concealed from either (single) or both (double) patient and assessor
  • Reduce bias e.g. conscious or unconscious bias
  • Double-blind: need placebo drug or sham treatment, not always possible
  • Randomisation makes blindness possible
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4
Q

What is the analysis in randomisation controlled trials?

A

Intention-to-treat: analyse in randomised groups, even if don’t comply or if switch treatments
* Ensures balance remains - VERY IMPORTANT
* Fair test of offer of treatment

Per protocol analysis sometimes used as well , particularly if a large number of patients are not managed as planned.

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5
Q

Limitiations of Randomized controlled trials

A
  • Can be expensive and difficult to organise
  • Some interventions are impossible or unethical to randomise to
    e.g. impact of lead on children’s intelligence
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6
Q

What are Cohort studies?

A
  • Observational study- no intervention
  • Subjects observed in natural state (real world)
  • Investigates causes or factors associated with disease (or condition)
  • Selects group of healthy individuals
  • Follow-up to monitor disease state and possible risk factors over time
  • Disease state (e.g. yes/no) according to risk factor status (e.g.smoking yes/no)
  • Usually prospective (but retrospective designs may be used)
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7
Q

What are Advantages of cohort studies

A
  • Data is collected prospectively, so estimates are less likely to be biased than those from a case-control
  • RCTs can only investigate potential benefits for ethical reasons but cohort studies can investigate potential harm
  • Imbalance between exposed and unexposed can be corrected in the analysis
  • Cohort studies can look at exposure to treatment and harm
  • eg impact of lead on children’s intelligence
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8
Q

What is the design of a weighted sample study?

A
  • Subjects selected from large population on basis of outcome of interest
  • All cases and a random sample of controls are included
  • Full data collected on selected sample
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9
Q

What is advantages of weighted sample study?

A
  • Can be carried out prospectively
  • More cases from a smaller sample
  • Greater power & accuracy without excessive cost
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10
Q

Disadvantages of a weighted sample study

A
  • Sampling frame needed to identify weighted sample
  • Analysis must correct for sample weighting
  • Rarely used ( need to get a statition on board)
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11
Q

What is case-control studies

A
  • Observational study - no intervention
  • Subjects observed in natural state (real world)
  • Investigates causes or factors associated with disease (or condition)
  • Selects group with disease: ‘cases’
  • Choose comparator group without disease: ‘controls’
  • Compare cases and controls with respect to possible risk factors
  • Usually retrospective
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12
Q

What are the limitations of case-control studies?

A
  • Choice of control group affects comparison
  • Data reported by subjects or from records - usually
    retrospective so may be incomplete, inaccurate or biased

✓ BUT often quick to do and inexpensive!
✓ Evidence from case-control studies may be used in
planning further research

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13
Q

What are cross-sectional studies?

A

Observational study
* Subjects observed in natural state (real world)
* Collect data for each subject at one point in time only
* Similar to a cohort study, but without follow-up

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14
Q

What are cross-sectional studies useful?

A

Useful for:
* measuring prevalence of a disease/condition - e.g. % in population with asthma
* surveys of attitudes/views/behaviours - e.g. patient satisfaction, alcohol drinking
* Not useful for looking at cause & effect

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15
Q

Why summarise data?

A
  • To monitor data quality
  • To check for invalid or missing entries
  • To describe characteristics of participants in a study
  • e.g. 1st table in many research articles
  • Before doing a complex analysis
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16
Q

What are the different types of data?

A
  1. Quantitative - Continuous or Discrete
  2. Categorical
17
Q

What are the different types of quantative data?

A
  • Continuous: lies on continuum – any value valid between range e.g. weight, height
  • Discrete: data can only take certain values – usually integers, often counts e.g. number of children in a family
18
Q
A

Individuals fall into one of a number of separate categories
* Can be 2, 3 or more categories:
* 2 categories: dichotomous or binary data
* >2 categories: can be ordered or unordered
Gender: male/female – dichotomous
* Disease status: alive/dead – dichotomous
* Stage of cancer: I, II, III, IV – ordered (‘ordinal’)
* Marital status: single, married, divorced, widowed, legally
separated – unordered (‘nominal’)

19
Q

How are continuous data summarised?

A

Centre of data
* Mean (arithmetic average)
* Median (middle value when data ranked)
Spread of data
* Range (min, max; useful as descriptor)
* Standard deviation (‘SD’; shows spread of data; same units as data)
* Variance = SD2
* Interquartile range (IQR; middle 50% of ordered data when data split into
4 equal-sized groups or quartiles)

20
Q

How do you calculate standard deviation?

A

Need average difference squared (d2)
* Divide sum of d2 by (n-1)
(Reasons for using (n-1) instead of n are complex. We only use n if
have whole population which almost never happens)
* This is called ‘variance’
* Square root of variance is standard deviation (SD) *Here:sumofd2 =52,n=9
* Variance = 52/(9-1) = 52/8 = 6.5
* SD=6.5 = 2.55

21
Q

When to use which summary?

A

Quantitative data: spread of distribution
* Continuous data → use standard deviation (SD)
* Continuous data with skew – consider using IQR as well
* Continuous data → consider giving range (min to max) - useful in addition to SD if space allows

22
Q

What is Unordered categories?

A

Unordered categories (‘nominal data’)
* Frequencies in each category
* Proportion or percentage
* Don’t clutter with too many decimal places

23
Q

What is Ordered categories (‘ordinal data’)?

A

Frequencies in each category
Proportion or percentage
Cumulative proportions/percentages

24
Q

How are histograms presented

A

Histogram: distribution of continuous data
* Shows shape of distribution, range, middle
* Areas in rectangles proportional to number in category

25
Q

How are box plot presented and interpreted?

A
  • median = horizontal line in box
  • upper quartile = top edge of the box
  • lower quartile = lower edge of box
  • Whisker length up to 1.5 times width of box
  • Outliers shown as dots
26
Q

What are the different shapes of distribuition?

A

“Bell-shaped” distribution
* Roughly symmetrical
* Central peak with tails on both sides

Reported alcohol is positively skewed
* Positive skew - long tail on right hand side
* Common distributional shape in
medicine/biology

Gestational age of her baby is negatively skewed
* Negative skew- long tail on left hand side
* Less common in medicine/biology

27
Q

What are the charectiristics of a normal distribution?

A

The Normal distribution has useful features relating to its mean and standard deviation (SD):
* About 95% datalies within mean ± 2SD
* About 68% datalies within mean ± 1SD
* Used for ‘Normal ranges’, t-tests, 51 regression etc.

28
Q

What do bar charts show?
Why are bar charts used?

A
  • Shows frequency or percentage in each category
  • May be quicker to absorb than a table
29
Q

Why are pie charts show and used?

A
  • Size of slice, defined by angle, proportional to frequency in category
  • 2 pie charts show health status in Bristol is similar to whole of England
  • Popular but rarely used
    in academic presentations – bar charts or tables preferred