why to randomize a randomized controlled trial? (and how to do it)
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Why to Randomize a Randomized Controlled Trial? (and how to do it). John Matthews University of Newcastle upon Tyne. Schema of a simple trial. Randomize. Rx group 1. Eligible patients. Rx group 2. Outline of talk. Many aspects to a trial: this talk focuses on just two - PowerPoint PPT PresentationTRANSCRIPT
Why to Randomize a Randomized Controlled Trial?
(and how to do it)John Matthews
University of Newcastle upon Tyne
Outline of talk
• Many aspects to a trial: this talk focuses on just two
• Why you should randomize– benefits of doing so– dangers of failing to do so
• How to randomize– often glossed over & unspecified
Why Randomize?
• Compare groups at the end of the trial
• Difference is because of the Rx
• For this you need comparable groups
• Purpose of randomization is to make the treatment groups comparable
• Ensures that only difference in groups is due to trial treatments
How does it do it?
• Each group is a random sample of eligible patients, so both are representative of that same population
• In this sense they are comparable– same proportions of males, stage IV tumours,
ambulant cases, elderly patients etc.
• Anything which subsequently changes the groups will destroy this balance.
Why Randomize?
• Other benefits are– Randomization is largely unpredictable
• Why this is a good thing and why it might not obtain will emerge in the talk
– Randomization provides a valid basis for statistical inference
• This is important but is not addressed at all in this talk
What is wrong with non-randomized studies?
• Two main types of study, those with and those without concurrent control groups
Non-randomized studies II
• Without concurrent controls– Uncontrolled
• cannot really make much of such studies if there is any variation in outcomes.
– Historical controls • type of patient may change, due to eligibility criteria
• environment changes, due to trial
• data quality often quite different between groups
Non-randomized studies III
• Non-randomized concurrent controls– Alternation
– Odd/Even hospital no. or date of birth
– First letter of surname
• Difficult to argue that one group is different from another but allocation is predictable, so bias can arise from selection of patients: see Keirse (1988)– so randomization must be unpredictable
Features of a RCT
• Provide reliable evidence of Rx efficacy
• Essentially simple
• Much attendant methodology– ensure reliability of evidence– give credibility to results
• CONSORT statements www.consort-statement.org
indicate good practice in trial reporting
How to Randomize
• Toss a coin
• Essentially the right thing to do
• Try not to do it in front of the patient
• More sophisticated implementations possible
Is coin tossing OK?
• OK for big trials
• For small trials, such ‘simple randomization’ can lead to imbalance in group sizes
Example: trial with 30 patients
• If 30 patients are in a trial randomized using coin tossing there is a 14% chance of 15:15 split
• For 16:14 chance is 27%
• ‘Worse’ than 20:10 is 10%
• Why ‘worse’?
• Because imbalance leads to loss of power
Alternatives
• Could use a restricted randomization scheme– legitimate, intended to protect power– but often not mentioned in trial report: see Altman
& Doré, 1990; Schulz et al., 1994
• Needs to be done properly
• Only ensures similar numbers in groups
• Combine with stratification to ensure comparability for prognostic factors
Random Permuted Blocks
• An allocation sequence is, e.g.,A,B,A,A,A,A,B,B,B,Ai.e. 6 As, 4 Bs
• This sequence built up by using a computer to ‘toss a coin’
• Random Permuted Blocks (RPBs) is an alternative method which ensures imbalance can never be substantial
RPBs II
• All sequences of length 4 comprising 2 As and 2 Bs are1. AABB 2. ABAB 3. ABBA4. BBAA 5. BABA 6. BAAB
• Generate random sequence of numbers 1 to 6, say 6,5,2,6,… and substitute from above to give allocation sequence ofBAAB BABA ABAB BAAB
RPBs III
• Such sequences cannot be more than two out of balance
• Must be in exact balance after 4, 8, 12, etc. patients have been recruited
• So RPBs are, to some extent, predictable
• To avoid this, vary block length at random: use blocks of length six (3t) as well as 4 (2t)
Is it enough to equalise numbers?
• No, can still have imbalance in important prognostic factors– E.g. two groups of size 15: one comprises 14
young children and the other comprises 14 adolescents in a trial for diabetes
• Stratify recruitment with respect to age– i.e. use separate allocation sequence within
each stratum
Stratification
• RPBs can be used without stratification
• Stratification without using RPB (or an
equivalent device) is nonsensical
• Separate allocation sequence in each stratum can become cumbersome with many prognostic factors
• e.g. ambulant/not, over/under 55, M/F gives 8 allocation sequences
Minimisation
• More complicated, in principle• ensures balance on each factor separately, not for all
combinations
• keeps track of patients already in trial, computes an imbalance score and allocates to minimise this
• can include a random element
• Less cumbersome, in practice• largely because you need a computer
• Good if there are many prognostic factors
How to serve it all up
• Methods for delivering randomisation sequences to the clinic are important.
• They hold the key to ensuring adequate concealment of the allocation until the patient has been randomized.
Implementation methods
• Need to separate the person who generates allocation from those who assess eligibility
• Third party schemes• Telephone randomization service
• Pharmacy randomization
• Web-based service?
• Envelopes• Serially numbered, sealed and opaque
Then what?
• You will have two groups that are comparable and free from bias
• Well, sort of
• You have the best start, certainly
• Drop-outs, protocol violations etc. etc. disturb the comparability
• Might not have been comparable to start with!
• Need to allow for baseline imbalance and stratifying variables
Conclusion
• Randomization is needed in all clinical trials
• As with most aspects of trial design, the details of how you randomize are important
• The analysis needs to respect the design (esp. stratification) and make sensible adjustment for baselines
• All looking more awkward if there isn’t a statistician involved.
• Some details given at
www.mas.ncl.ac.uk/~njnsm/talks/titles.htm