Randomisation and Treatment Allocation Flashcards

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
Q

What is alternative hypothesis?

A

statement that we will accept if enough evidence to reject null hypothesis

26
Q

what is a type II error?

A

False -ve fail to reject H0 but treatment actually better

27
Q

What is type I error?

A

false positive - rejected H0 but new treatment no dif or worse

28
Q

What is significance?

A

probability that we reject H0 given that it is correct so probability of type I error

29
Q

What is significance linked to?

A

Prevalence

30
Q

What is often the value of significance?

A

5%

31
Q

What is power?

A

Probability that we reject H0 given that H1 is true- so getting the sig difference correct

32
Q

Significance’s sign

A

alpha

33
Q

Power’s formula?

A

1-prob of type II (1-beta)

34
Q

What is usually the value of the power?

A

80-90%

35
Q

What happens if we decrease significance?

A

Sacrifice power…

36
Q

What are sample size calculations based on? how?

A

primary endpoint - formula depends on outcome measure

37
Q

What does sample size depend on? 4 + signs

A
  1. significance level alpha 2. Power 1-B 3. Effect of size delta 4. variability - sigma squared
38
Q

As significance increases, what happens to sample size?

A

decreases

39
Q

As power increases, what happens to sample size?

A

increases

40
Q

As effect size increases, what happens to sample size?

A

decreases

41
Q

As variability increases, what happens to sample size?

A

increases

42
Q

Formula for sample size for continuous outcome measures:

A

(2(variance)/(diff)^2 )* f(alpha, beta)

43
Q

Variance =

A

SD^2

44
Q

What is the treatment dif in the formula?

A

difference between two means

45
Q

for binary outcome the formula is:

A

check notes

46
Q

What do we do when expecting loss to follow up?

A

Adjust estimate using: group/ 1-rate of expected loss