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Current issues in Design and Analysis of Mental Health Trials: Beyond ITT: Explanatory Analysis of RCTs

2. Contents: Beyond ITT

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Current issues in Design and Analysis of Mental Health Trials: Beyond ITT: Explanatory Analysis of RCTs

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    1. 1 Current issues in Design and Analysis of Mental Health Trials: Beyond ITT: Explanatory Analysis of RCTs MHRN conference, Nottingham, 20 May 2009 Prof Graham Dunn and Dr Richard Emsley Health Methodology Research Group The University of Manchester

    2. 2 Contents: Beyond ITT – Explanatory Analysis of RCTs Part 1 – Departures from random allocation Complier-Average Causal Effect (CACE) estimation Part 2 – Mediation and moderation of treatment effects Problems with traditional approach Instrumental variables methods Principal stratification method

    3. 3 What’s the effect of treatment in the treated? Allowing for non-compliance with randomisation. Basic design: 1000 participants are randomised to (a) Treatment As Usual (TAU) or (b) Mindfulness-Based Cognitive Therapy (MBCT), in addition to TAU The 500 allocated to TAU cannot get access to MBCT, but, of the 500 randomised to receive MBCT, only 250 actually turn up to get the therapy (i.e. only 50% compliance with allocated treatment).

    4. 4 Analysis Strategies Intention-To-Treat (ITT) – analyse as randomised, ignoring treatment actually received. Conservative but will lead to attenuated estimates of treatment effect. This attenuation is a particular problem in an equivalence or non-inferiority trial and, in this situation ITT is the opposite of conservative. Per Protocol – compare the outcomes in the 500 controls with those in the 250 who comply with the protocol (random allocation). If compliance is associated with treatment-free prognosis then this will lead to biased estimates. As Treated – forgot randomisation and compare the 250 who receive treatment with the 750 who don’t. Again, likely to lead to biased estimates. Not comparing like with like. CACE (Complier-Average Causal Effect) estimation – compare the outcomes in the 250 known compliers in the MBCT arm with those of the would-be compliers in the TAU arm (i.e. those who would have complied with allocation if they had been allocated to MBCT).

    5. 5 CACE Estimation Randomisation, on average, ensures that the number (proportion) of (would-be) compliers in the TAU arm is the same as the observed number (proportion) in the MBCT arm. Let this proportion be PC.This is estimated from the number (proportion) of compliers in the MBCT arm. Let’s assume that randomisation has no effect on the outcome in the non- compliers. That is ITTNC = 0. The unknown CACE is the ITT effect in the Compliers (ITTC). But, the overall ITT effect (ITTAll) is a weighted average of ITTC and ITTNC. ITTAll = PC.ITTC + (1-PC) ITTNC. Therefore, CACE = ITTC = ITTAll/PC (estimate by simply plugging in the observed statistics - can be shown to be the same as the instrumental variable estimate).

    6. 6 CACE Estimation – ITT effects within Latent Classes We have postulated the existence of two latent classes of participant: Compliers and Non-compliers. We can distinguish Compliers and Non-compliers in the MBCT arm but not in the TAU arm (i.e. class membership remains hidden or latent). The two classes may have very different treatment-free prognosis. Class membership is independent of treatment allocation. Class membership determines response to allocation in terms of treatment receipt. The compliance classes illustrate a more general concept of Principal Strata. In general, we are concerned with the estimation and comparison of stratum- specific ITT effects (i.e. treatment effect heterogeneity – see later).

    7. 7 A hypothetical example TAU 500 participants BDI mean = 18 MBCT 250 Compliers (PC=0.5) BDI mean = 12 250 Non-compliers BDI mean = 12 Beware of naively comparing outcomes for Compliers and Non-compliers in the MBCT group (analogous to what is frequently done in the psychotherapy literature when examining the role of process variables) Treatment-effect estimates: ITTAll = 12 - 18 = -6 CACE = -6/0.5 = -12 Per Protocol = 12 - 18 = -6 As Treated = 12 - (2*18 + 12)/3 = 12 - 16 = -4

    8. 8 Two types of non-compliance A randomised trial comparing Community Care (CC) and Hospital Admission (HA) for participants with recent-onset psychosis. Allocation to CC Most receive CC but some have to be admitted because of an unanticipated crisis (threat to self or others, for example). The latter will get admitted irrespective of allocation. Allocation to HA Most are admitted but some remain in the community because shortage of beds. The latter group receive community care irrespective of allocation. Here we have three latent classes: Compliers and two types of Non-compliers (Always admitted and Never admitted). PC can be estimated simply from the comparison of the proportions admitted in the two arms. Again, CACE = ITTAll/PC. = ITT effect on outcome/ITT effect on treatment received

    9. 9 A compliance-response model: complete mediation (direct effect assumed to be absent)

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