Cluster Randomised Trials

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Cluster Randomised Trials
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Background

• In most RCTs people are randomised as individuals
  to treatment. Whilst this method is appropriate
  for many interventions (e.g. drug trials), in
  some types of intervention individuals cannot be
  randomised.
• An alternative approach is randomise groups of
  individuals or clusters.

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History

• Cluster trials originated from educational
  research. Intact classes or schools were
  randomised to an intervention or no intervention.
• Sadly educational researchers have all but
  abandoned RCTs in favour of qualitative research.

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Rationale For Cluster Randomisation

• Some interventions have to be delivered at a
  group level.
• Guidelines for clinicians
• Interventions to reduce infectious diseases
• Practical considerations
• Potential for treatment contamination

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Clusters

• A cluster can take many forms
• GP practice or patients belonging to an
  individual practitioner
• Hospital ward
• A period of time (week day month)
• Geographical area (village town postal
  district).

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Cluster allocation

• Because the unit of allocation is the cluster and
  the sample size of clusters tends to be small
  care needs to be taken with cluster allocation.
• With typically only 10 or so clusters simple
  randomisation is likely to lead to chance
  imbalance.

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Cluster allocation

• Need to use some form of stratification.
• Pairing is often used match clusters on an
  important co-variate and randomly allocate a
  member of each pair to the intervention.
• Stratification using blocking or the use of
  minimisation is an alternative.

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Problems with Cluster Randomisation

• Possible Selection Bias
• Inadequate uptake of intervention by either group
  reduces study power
• Sample size needs to be increased (typically
  between 50 to 100), which will often increase
  the cost and complexity of a trial.

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Selection Bias - A Reminder

• This is where individuals who are using a
  treatment have some difference, unrelated to the
  treatment, that affects outcome.
• For example, women using HRT take more exercise,
  are slimmer, have higher social class compared
  with those who do not - may explain
  cardiovascular benefit.

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Randomisation

• Randomisation, or similar procedure, will balance
  known and unknown co-variates or confounders
  across the groups and therefore selection bias
  should not occur.
• Thus, in an HRT trial women in treatment and
  placebo groups will have the same weight,
  exercise levels etc.

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Selection Bias in Randomised Trials

• This should not occur in an individually
  randomised trial unless the randomisation has
  been subverted.
• However, in cluster trials it is possible for
  selection bias to occur after successful cluster
  randomisation.
• This defeats the objective of randomisation.

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Selection Bias in Cluster Trials

• Given enough clusters bias should not occur in
  cluster trials as randomisation will deal with
  this.
• HOWEVER, the clusters are balanced at the
  individual level ONLY if all eligible people, or
  a random sample, within the cluster are included
  in the trial.

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Recruitment into cluster trials

• A key issue is individual participant recruitment
  into cluster trials.
• There are a number of ways where biased
  participant recruitment can occur, which can lead
  to baseline imbalances in important prognostic
  factors.

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Participant flow in cluster trial sources of bias
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Identification Problems

• For example, in a cluster trial of backpain equal
  number of patients with same severity of back
  pain will be present in both clusters. The
  problem lies in how to identify such patients to
  include them in the interventions. Unless one is
  very careful different numbers and types of
  patient can be selected.

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UK BEAM Trial

• The UKBEAM pilot study used a cluster design.
  Eligible patients were identified by GPs for
  trial inclusion.
• GP practices were randomised to usual care or
  extra training.
• The primary care team were trained to deliver
  active management of backpain.

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UK BEAM Selection bias

• The pilot showed that practices allocated to
  active management were more adept at
  identifying patients with low back pain and
  including them in the trial.
• Patients had different characteristics in one arm
  than the other.

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UK BEAM participant recruitment
P 0.025 P 0.001 P 0.03
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UKBEAM pilot study.
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UK BEAM

• Because of the selection bias in the cluster
  design that element of the trial was abandoned
  and the trial reverted to completely individual
  allocation.

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Cluster Trials Rule 1

• All eligible patients or a random sample ideally
  MUST be identified BEFORE clusters are
  randomised.
• Alternatively systems must be put into place to
  PREVENT selective recruitment.

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Trial Consent Problems

• Even when it is possible to identify all eligible
  members of a cluster some may not consent to take
  part in the trial. If there is differential
  consent, in particular, this can lead to
  selection bias again.

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Hip Protector Trial
At this point trial is balanced for all
co-variates
Kannus. N Eng J Med 20003431506.
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First Rule

• Kannus trial DID identify all eligible patients
  at baseline, thus, fulfilling first rule of
  cluster randomisation.

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Hip Protector Trial
Selection Bias
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Fracture risk Important Co-variates

• Most important risk factors for hip fracture are
  (in order of importance)
• Being Female
• Age
• Body Weight

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Important Co-variates Balanced at baseline?
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Results of Trial.

• Hip fractures were reduced by 60 (95 CI 0.2 to
  0.8)
• HOWEVER, arm fractures were also reduced by 30
  (0.3 to 1.5).
• Suggesting that some or all of the hip fracture
  effect could have been due to selection bias.

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Hip Protector Trial
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Cluster Trials Rule 2

• As in individually randomised trials imperative
  to use intention to treat analysis.

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Inadequate uptake of intervention

• Because a robust cluster trial consent to
  randomisation is not given only consent to
  treatment this results in a proportion of
  eligible participants declining the intervention
  BUT have to stay in the trial for intention to
  treat analysis and this reduces study power.
• This also leads to DILUTION BIAS.

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Accident prevention

• In a cluster trial of accident prevention among
  young children 25 of parents in the experimental
  arm did not receive the intervention. Clearly
  this will reduce the power of that trial AND
  dilute any likely treatment effect.

Kendrick et al. BMJ 1999318980.
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Cluster Trials Rule 3

• Increase sample size to compensate for less than
  100 uptake of intervention.
• Or alternatively and in conjunction identify and
  consent before randomisation and then only use
  those participants who have expressed a
  willingness to take part in the trial.

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Review of Cluster Trials

• Because of the BEAM problem we decided to
  undertake a methodological review of cluster
  trials.
• We identified all cluster trials published in the
  BMJ, Lancet, NEJM since 1997.

Puffer et al. BMJ 2003327785.
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Results

• We identified 36 relevant trials. ONLY 13 had
  identified participants prior to randomisation.
• Of the 23 not identifying participants a priori 7
  showed evidence of differential recruitment or
  consent.
• Other biases included differential of inclusion
  criteria or attrition.
• In total 14 (39) showed evidence of bias.

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Underestimate of problem

• Only in 5 papers did authors alert reader to
  possible problem.
• Subsequently one of the trials that looked OK
  was published elsewhere where recruitment bias
  was admitted to have occurred.
• Cluster trials are DIFFICULT to undertake
  robustly.
• Is there an ALTERNATIVE?

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Cluster Sample Size

• Usual sample size estimaes assume independence of
  observations. When people are members of the
  same cluster (e.g., classroom, GP surgery) they
  are more related than we would expect to be at
  random.
• This is the intra-cluster correlation
  co-efficient.

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ICC

• The ICC needs to incorporated into the sample
  size calculations. The formula is as follows
  Design effect 1 (m 1) X ICC. Design effect
  is the size the sample needs to be inflated by.
  M is the number of people in the cluster.

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Sample size example.

• Lets assume for an individually randomised trial
  we need 128 people to detect 0.5 of an effect
  size with 80 power (2p 0.05). Now assume we
  have 24 groups with 7 members. The ICC is 0.05,
  which is quite high.
• 1 (7 1) x 0.05 1.3, we need to increase the
  sample size by 30. Therefore, we will need 166
  participants.

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What happens if cluster gets bigger?

• If our cluster size is twice as big (14), things
  begin to get really interesting.
• 1(14-1)x0.05 1.65.
• What about 30? (1(30-1)x 0.05 2.45 (I.e, 314
  participants).

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What makes the ICC large?

• If the treatment is applied to health care
  provider (e.g., guidelines will increase ICCs for
  patients).
• If cluster relates to outcome variable (e.g.,
  smoking cessation and schools)
• If members of cluster are expected to influence
  each other (e.g., households).

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Reviews of Cluster Trials
Authors Source Years Clustering allowed for in sample size Clustering allowed for in analysis
Donner et al. (1990) 16 non-therapeutic intervention trials 1979 1989 lt20 lt50
Simpson et al. (1995) 21 trials from American Journal of Public Health and Preventive Medicine 1990 1993 19 57
Isaakidis and Ioannidis (2003) 51 trials in Sub-Saharan Africa 1973 2001 (half post 1995) 20 37
Puffer et al. (2003) 36 trials in British Medical Journal, Lancet, and New England Journal of Medicine 1997 2002 56 92
Eldridge et al. (Clinical Trials 2004) 152 trials in primary health care 1997 - 2000 20 59
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Sample Size Problems
Cluster Trials Demand Larger Sample Sizes
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Summary of sample size

• The KEY thing is the size of the cluster. It is
  nearly always best to get lots of small clusters
  than a few large ones (e.g, a trial with small
  hospital wards, GP practices, classrooms will,
  ceteris paribus, be better than large clusters).
• BUT if the ICC is tiny may not affect the sample
  too much.

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Analysis

• Many cluster randomised health care trials have
  been INCOMPETENTLY analysed. Most analyses use
  t-tests, chi-squared tests, which assumes
  independence of observations, which are violated
  in a cluster trial.
• This leads to spurious p values and narrow CIs.
• Various methods exist, e.g., multilevel models,
  comparing means of clusters, which will produce
  correct estimates.

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Cluster Trials Should I do one?

• If possible avoid like the plague. BUT although
  they are difficult to do, properly, they WILL
  give more robust answers than other methods,
  (e.g., observational data), when done properly.
• Is it possible to avoid doing them and do an
  individually randomised trial?

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Contamination

• An important justification for their use is
  SUPPOSED contamination between participants
  allocated to the intervention with people
  allocated to the control.

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Spurious Contamination?

• Trial proposal to cluster randomise practices for
  a breast feeding study new mothers might talk
  to each other!
• Trial for reducing cardiac risk factors patients
  again might talk to each other.
• Trial for removing allergens from homes of
  asthmatic children.

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Contamination

• Contamination occurs when some of the control
  patients receive the novel intervention.
• It is a problem because it reduces the effect
  size, which increases the risk of a Type II error
  (concluding there is no effect when there
  actually is).

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Patient level contamination

• In a trial of counselling adults to reduce their
  risk of cardiovascular disease general practices
  were randomised to avoid contamination of control
  participants by intervention patients.

Steptoe. BMJ 1999319943.
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Accepting Contamination

• We should accept some contamination and deal with
  it through individual randomisation and by
  boosting the sample size rather than going for
  cluster randomisation

Torgerson BMJ 2001322355.
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Counselling Trial

• Steptoe et al, wanted to detect a 9 reduction in
  smoking prevalence with a health promotion
  intervention. They needed 2000 participants
  (rather than 1282) because of clustering.
• If they had randomised 2000 individuals this
  would have been able to detect a 7 reduction
  allowing for a 20 CONTAMINATION.

Steptoe. BMJ 1999319943.
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Comparison of Sample Sizes
NB Assuming an ICC of 0.02.
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Misplaced contamination

• The ONLY study, Im aware of to date, to directly
  compare an individually randomised study with a
  cluster design, showed no evidence of
  contamination.
• In an RCT of nurse led cardiovascular risk factor
  screening some intervention clusters had
  participants allocated to no treatment. NO
  contamination was observed.

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Cluster Trials

• Can cluster trials give different results?
• All things being equal this shouldnt happen
  (except for a more imprecise estimate). BUT
  because of the greater potential for selection
  bias cluster trials MAY give the wrong answer.

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An example.

• There are 14 RCTs of hip protectors for the
  prevention of hip fracture.
• Nine RCTs are individually randomised trials,
  whilst 5 are cluster trials (e.g., hospital ward,
  nursing home).
• Cluster trials, without exception show a benefit
  of hip protectors.

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Hip Protector Trials
Individual RCTS Cluster RCTs
1.19 (0.8 to 1.7) 0.34
0.94 (0.5 to 1.7) 0.53
0.93 (0.5 to 1.7) 0.44
1.17 (0.4 to 3.0) 0.34
0.39 (0.1 to 1.4) 0.11
0.20 (0.0 to 1.6) All Cluster trials, bar , significant, No individual trial was significant
1.49 (0.3 to 7.1) All Cluster trials, bar , significant, No individual trial was significant
3.03 (0.6 to 14.8) All Cluster trials, bar , significant, No individual trial was significant
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Hip Protector Trials Cluster vs Individually
Randomised.
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Age differences between good cluster and poor
cluster trials.
Data from Puffer et al.
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Guidelines

• Royal College of Physician guidelines for
  fracture prevention grade hip protectors as grade
  A evidence based on flawed, cluster trial,
  evidence.

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Cluster Trials- What Should We Do?

• Identify ALL eligible people if possible BEFORE
  randomisation
• ALWAYS use Intention To Treat analysis
• INCREASE sample size not only for cluster effects
  but also because of treatment refusal

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Cluster designs
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Cluster designs
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Cluster designs
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Summary

• Cluster Trials are currently very trendy
• Whilst in principle they are a robust. design in
  practice fraught with difficulty.
• If possible avoid and opt for individual
  randomisation
• If cluster trial is necessary follow rules to
  avoid bias.