1) The document discusses non-compliance in randomized controlled trials comparing vascular and endovascular interventions, where patients may not follow their randomized treatment. 2) It presents the IMPROVE trial comparing endovascular aneurysm repair (EVAR) to open repair for ruptured abdominal aortic aneurysms, which had high levels of non-compliance as patients switched between treatments. 3) Instrumental variable methods are proposed to estimate the causal effect of treatments by using randomization as an instrument, as these methods can provide less biased estimates than intention-to-treat when non-compliance is high. The results of applying these methods to the IMPROVE trial data are also presented.

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Non-compliance in randomised controlled
trials comparing vascular and endovascular
interventions for cardiovascular care
2nd CUTEHeart Workshop
Manuel Gomes
April 23, 2016

2. • Non-compliance in RCTs – cardiovascular care
• Defining the question of interest
• Specific challenges in HTA
• Methods for handling non-compliance
• Results from the IMPROVE trial
• Discussion
Overview
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3. Non-compliance in RCTs
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• Patients often do not comply with their randomised treatment:
– Switch to other randomised arm (treatment switching)
– Change to non-trial treatment
– Stop receiving treatment altogether
• Reasons
– Intervention not suitable (randomisation happens before assessment)
– Patient is responding poorly to their allocated treatment (unethical)
– Patient’s disease progresses and requires alternative treatment
– Clinical expert is more familiar with particular intervention
• Problem
– Non-compliance is usually related to individual characteristics and
prognosis, and can lead to misleading inferences.

4. High levels of non-compliance
in cardiovascular trials
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• Drug therapies for treating patients with coronary heart disease
– Uncertainty about the benefits of statins; perceived adverse effects
• Medication/diet for treating patients with chronic heart failure
– Complicated drug regimens; difficulties in changing lifestyle
• Cardiac rehabilitation programmes
– Low levels of physical activity; pain/depression/anxiety
• Surgical interventions (emergency setting)
– Surgeon’s expertise; other clinical indications

5. Motivating example
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IMPROVE trial
• Aim: to compare a preferential endovascular strategy (EVAR)
with emergency Open Repair for the management of suspected
ruptured abdominal aortic aneurysm (AAA)
• Pragmatic RCT (EVAR: n=316; Open repair: n=297)
– Randomisation happens before CT scan
– EVAR to Open Repair switch (42%); e.g. surgeon expertise
– Open Repair to EVAR (12%); e.g. not fit for general anaesthesia
• Cost-effectiveness outcomes (1-year)
– Overall mortality (life – years)
– Quality of life (EQ-5D) at 3 and 12 months
– costs

6. Defining the question
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Clinically relevant:
1. De jure questions (efficacy):
• What are the relative benefits of actually receiving the interventions?
– How do EVAR and Open Repair compare, if patients had been operated as per
clinical indication (e.g. after CT scan)
2. De facto questions (effectiveness):
• What is the relative effectiveness of a policy/strategy to provide EVAR?
– Under the conditions of the trial
– In other circumstances (if practice differs from trial setting)
Policy-relevant:
• What’s the decision problem?
– Which hospitals should patients with suspected ruptured AAA be sent to?
– Is a strategy to provide EVAR a good use of resources?

7. HTA-specific challenges
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• Decision makers require an assessment of the relative
effectiveness and cost-effectiveness over a long-period of time
– Clinically-relevant outcomes such as progression-free survival raise less
concerns about non-compliance, but are insufficient for decision making
• Levels of non-compliance may not be representative of those
seen in practice
• Differences in costs often depend on intervention receipt, not on
the intention to receive treatment
– Drug trial: expensive drug is prescribed but may not always be taken
• Added complexity for analysis
– E.g. correlations between the multiple outcomes

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Simple approaches
• Per-protocol analysis
– Non-compliers are excluded (or censored at the time of switching)
– Breaks the randomisation balance and likely to introduce selection bias
• Intention-to-treat (ITT) analysis
– Provides an unbiased treatment effect (to de facto questions); preserves
the randomisation design
– But tends to underestimates the ‘true’ treatment effect (e.g. ‘control’
patients may benefit from treatment)
• When is ITT insufficient?
– The causal effect is of interest (e.g. efficacy)
– The level of compliance is not typical of that seen in practice
– Sensitivity analysis (recent NICE decisions differed according to method)
Handling non-compliance

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Randomisation-based methods: Instrumental variable (IV) estimation
Randomised
treatment (Z)
Outcome (Y)
Treatment
received (D)
Unobserved
factors (U)
ITT estimates
- Patient underlying
health status
- Surgeon ability
- Centre context
- …
Causal
effect
Criteria for instrument
- Strongly predicts D
- Independent of U
- Only affects Y via D
(Complier-Average) Causal Effect = ITT / 𝜶 (Wald estimate)
𝜶
IV methods

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Continuous outcomes (e.g. quality of life, costs)
- 2-stage methods (2SLS)
- Stage 1: Regress D on Z
- Stage 2: Regress Y on the predicted D (Variance needs to be corrected)
- Likelihood-based methods (LIML, full-Bayesian analysis)
- Joint estimation of the outcome and treatment models
- Usually assumes multivariate Normality
- Semi-parametric approaches (GMM, G-estimation)
- Relaxes assumptions about error distribution and model specification
IV methods

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Limitations
- Survival outcomes (e.g. overall survival)
- Cox regression could be used (stage 2), but resulting estimates may differ from
population-average hazard difference (marginal ≠ conditional effect)
- Rank-preserving structural failure time models: use randomisation to estimate
counterfactual survival times, but assumes common treatment effect
- Multiple switches
– Marginal Structural models: Compliers are re-weighted by the inverse of the
probability of being censored (switch treatment) at each time point
– Rely on the ‘no unobserved confounding’ assumption; i.e. non-compliance is
independent of unobserved factors
- Clustered designs (e.g. cluster trials)
- In principle, hierarchical approaches (ML) can be used, but properties unknown
IV methods

12. IMPROVE trial results
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1-year effectiveness and cost-effectiveness of EVAR vs Open Repair
according to method
ITT estimate Complier-average
causal effect
Mortality (OR) 0.86 [0.62, 1.21] 0.65 [0.28, 1.51]
EQ-5D at 3 months 0.073 [0.007, 0.138] 0.193 [0.033, 0.352]
EQ-5D at 12 months 0.043 [-0.024, 0.110] 0.184 [0.009, 0.358]
Incremental QALY 0.053 [-0.008, 0.113] 0.138 [-0.004, 0.280]
Incremental cost (£) - 2329 [-5489, 922] - 4731 [-12967, 3504]
Incremental net-benefit (£) 3877 [253, 7408] 8871 [-393, 18 135]

13. • Non-compliance is a major issue in clinical trials of
cardiovascular interventions
• Defining the question beforehand is crucial (does ITT answer it?)
• HTA raises additional challenges for design/methods of analysis
• IV methods promising - randomisation is a valid instrument
• Consider sensitivity analysis to departures from identifying
assumptions
Discussion
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