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EVIDENCE-BASED MEDICINE > 7 – Clinical Trails III > Flashcards

7 – Clinical Trails III Flashcards

1
Q

Types of outcome measures

A
  • Categorical (qualitative)
  • Continuous (quantitative, numerical)
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2
Q

Examples of categorical (qualitative) measures

A
  • Nominal
  • Ordinal
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3
Q

Nominal measures

A
  • Named categories
  • Ex. dead/alive, male/female, blood types
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4
Q

Ordinal measures

A
  • Ordered categories
  • Ex. cancer stages, clinical scoring systems
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5
Q

Examples of continuous (quantitative, numerical) measures

A
  • Discrete
  • Continuous
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6
Q

Discrete measures

A
  • Only integers
  • Ex. piglets/litter
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7
Q

Continuous measures

A
  • Any numerical value
  • Ex. rectal T, blood glucose
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8
Q

‘clinical effect size’ for continuous variable outcomes

A
  • Get a mean or median for the various treatment groups and compare (SIMPLE)
    o Also want 95% confidence interval to determine uncertainty
  • *difference between the means or medians
    o Need to determine if it is important (‘clinically significant’)
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9
Q

Trial that tested vaccine vs. placebo for pink eye

A
  • First showed that randomization was done
  • Weight between groups: NOT significantly different
  • Weight between those who got pink eye and those that didn’t=weight less when infected (less money when you sell)
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10
Q

2 issues that need to be explored with categorical variables

A
  • How to measure frequency of health outcome event in trial (INCIDIENCE)
  • How to measure the magnitude of the effect (impact) of treatment or interventions
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11
Q

How to compare groups with a categorical variable?

A
  • Calculate incidence or risk in both groups and compare risks
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12
Q

Relative risk equation

A
  • Risk in controls DIVIDED by risk in treated
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13
Q

Relative risk (RR)

A
  • Index of STRENGTH OF THE ASSOCIATION b/w exposure and the disease
  • Index of MAGNITUDE OF THE CLINICAL EFFECT of treatment (clinical trial)
  • Try set up ratio so that RR>1 (easier to interpret)
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14
Q

RR=1

A
  • NO treatment effect
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15
Q

RR>1

A
  • Controls are at a greater RISK of disease
  • Treatment group is protected by treatment
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16
Q

RR<1

A
  • Controls are at a reduced risk of disease
  • Treatment may actually make disease worse
17
Q

Attributable fraction (AF)

A
  • Useful measure of clinical significance (ex. vaccine efficacy)
  • What proportion of disease in the untreated animals could have been prevented by the vaccine/treatment?
18
Q

AF equation

A
  • AF = (RR-1) / RR
  • Ex. if 50%=50% of mortality in control group could have been PREVENTED by vaccination
19
Q

Absolute risk reduction (ARR)

A
  • Amount by which therapy reduces risk of a bad outcome
20
Q

ARR equation

A
  • Percent that got disease with placebo - percent that go disease with vaccine
21
Q

RR vs APR example

A
  • RR= 20% that got placebo developed strangles / 12% that got vaccine developed strangles = 1.67
  • ARR= 20%-12%=8%
    o If 100 were vaccinated, 8 would be prevented from developing strangles
22
Q

Number needed to treat (NNT)

A
  • Different way of expressive absolute risk reduction
  • How many patients do you need to treat to PREVENT ONE additional bad outcome?
23
Q

NNT equation

A
  • NNT = 1/ARR
  • Ex. 1/0.08=13 (need to treat 13 to prevent 1 additional bad outcome)
24
Q

Odds ratios

A
  • Another way of estimating RR
  • Tends to overestimate RR
  • Not as common in clinical trials
  • More commonly used in observational studies or field investigations (case-control studies)
  • *odds of exposure in disease group / odds of exposure in a control group
25
Q

Chi-squared statistic

A
  • Extent to which observed values differ from EXPECTED values
    o Expected=same proportions of mortality (or risk) in both groups
  • Need to square everything
26
Q

Chi-squared statistic interpretation

A
  • Need to look up p-value that corresponds to the chi-squared value
  • *if bigger than 3.84 to be statistically significant at p<0.05
27
Q

If a large Chi-square value, what does it mean?

A
  • That likelihood of the RR (relative risk) occurring by chance alone would be less than 5%
28
Q

Difference between two means (control and treatment group)

A
  • Clinical effect difference
  • *need to decide if that difference is CLINICALLY significance
29
Q

Confidence intervals

A
  • View results of study and what they might be if study was repeated over and over again
  • How precise the estimate of the study is (indicates level of uncertainty around measure of clinical effect)
  • Large=not confident in RR
  • 95%
30
Q

Small studies (lower power) and confidence interval

A
  • Wide confidence interval
31
Q

Large studies (higher power) and confidence interval

A
  • Narrower confidence interval
  • More precise estimate of RR
32
Q

What if a confidence interval crosses 1

A
  • Implies NO statistical difference (p>0.05) for the estimate of RR
33
Q

95% confidence interval

A
  • Useful in negative results
  • If p value is greater than 0.05: does NOT tell us anything about the POWER of the trial
    o Maybe trail was too small for vaccine to have a ‘fighting chance’
  • *width of CI gives idea of precision of their estimate of RR
  • **19/20 times the true value will lie in this range
34
Q

Upper end of CI

A
  • Place treatment in most favourable light
  • If that level of RR is NOT CLINICALLY significant then trial is TRULY NEGATIVE
35
Q

Forest plot (if vaccinated group on the top)

A
  • Size of dot is related to size of trial (larger=more power)
  • Only show significance if they do not CROSS the RR=1 line
  • Lowest power=smallest square
  • **RR should have a normal distribution between papers but often don’t see the ones where the vaccine ‘didn’t work’

EVIDENCE-BASED MEDICINE (8 decks)

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