Randomisation and Treatment Allocation Flashcards

(46 cards)

1
Q

Double blind vs single blind

A

Double - no one knows, single - one or other knows

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2
Q

Why blind?

A

get rid of placebo effect and observer bias

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3
Q

Who is prone to bias?5

A

those recruiting, treating, patients, assessing outcomes, statisticians

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4
Q

When is blinding less important

A

When hard outcomes like death

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5
Q

Why randomise (3)

A

Eliminate bias, treatment groups don’t differ systematically. balances both known and unknown prognostic factors

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6
Q

Why simple randomisation isn’t good?

A

Groups can be unbalanced

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7
Q

What to use instead of simple randomisation?

A

Random permuted blocks

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8
Q

What do you need to do to stop prediction when using blocks? 2

A

Don’t reveal block length and vary it

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9
Q

If block size is too big

A

imbalance

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10
Q

Why use stratification?

A

In trials we want treatment groups to be balanced with respect to patient characteristics

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11
Q

When is it important to use stratification?

A

When there is factors of particular importance and groups need to be balanced

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12
Q

common stratification factors

A

age, disease stage, sex, country

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13
Q

What do you use to stratify? explain

A

Stratification lists - create sep random lists within each stratum- so london m and f - next patient assigned to treatment for sex/center

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14
Q

Other way of stratification?

A

minimisation

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15
Q

when to use minimisation?

A

a lot of stratification factors

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16
Q

What is Zelen’s design?

A

randomised to treatment or control before consent, if refuse treatment, move to standard of care group

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17
Q

playing the winner’s rule

A

weigh probability of allocation in favor of treatment with best results - bias

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18
Q

When to use unequal randomisation?

A

New drug vs standard, already know alot about standard

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19
Q

Problem with unequal randomisation

A

Stat efficiency

20
Q

What is an example of cluster randomisation?

A

Randomise a whole school

21
Q

What is allocation concealment?

A

Ensure person randomising doesn’t know what treatment

22
Q

What happens if there is too few patients? 2

A

Important treatment effects may be missed, or may show X works when it doesn’t

23
Q

What are the problems when there is too many patients 4

A

Unethical risk, extra time and money, delay imp results, delay more trials

24
Q

What is null hypothesis?

A

Statement we want to reject to prove effect of our treatment

25
What is alternative hypothesis?
statement that we will accept if enough evidence to reject null hypothesis
26
what is a type II error?
False -ve fail to reject H0 but treatment actually better
27
What is type I error?
false positive - rejected H0 but new treatment no dif or worse
28
What is significance?
probability that we reject H0 given that it is correct so probability of type I error
29
What is significance linked to?
Prevalence
30
What is often the value of significance?
5%
31
What is power?
Probability that we reject H0 given that H1 is true- so getting the sig difference correct
32
Significance's sign
alpha
33
Power's formula?
1-prob of type II (1-beta)
34
What is usually the value of the power?
80-90%
35
What happens if we decrease significance?
Sacrifice power...
36
What are sample size calculations based on? how?
primary endpoint - formula depends on outcome measure
37
What does sample size depend on? 4 + signs
1. significance level alpha 2. Power 1-B 3. Effect of size delta 4. variability - sigma squared
38
As significance increases, what happens to sample size?
decreases
39
As power increases, what happens to sample size?
increases
40
As effect size increases, what happens to sample size?
decreases
41
As variability increases, what happens to sample size?
increases
42
Formula for sample size for continuous outcome measures:
(2(variance)/(diff)^2 )* f(alpha, beta)
43
Variance =
SD^2
44
What is the treatment dif in the formula?
difference between two means
45
for binary outcome the formula is:
check notes
46
What do we do when expecting loss to follow up?
Adjust estimate using: group/ 1-rate of expected loss