Trials and developmental research
Substantial trial funding is a major research investment and should maximise
its scientific output. The first priority is naturally to test the
effectiveness of interventions but, when appropriately designed, we argue that
trials in developmental psychiatry can and should also be used to illuminate
basic science. Whereas academic and funding traditions can sometimes act to
pull apart basic science and intervention research, this use of trials
potentially provides a more integrated clinical research approach giving added
value to expensive trials.
The classic view of treatment – as an episode of care of discrete disorder
leading to reversal or removal of pathology – rarely applies in developmental disorder.
Treatments here are rarely definitive or short term. They often need
phasing over a much longer period and aim to target the developmental course of
a disorder to alter its primary progression (insofar as this is tractable), or
its secondary sequelae. Research into the multiple varying influences on the
course of disorders has led to the tendency for such interventions to become
more complex and multimodal. Such intervention can be conceptualised as a kind
of ‘developmental perturbation’ in longitudinal course of a complex
New trial designs
Testing such interventions raises significant challenges to trial design,
but also opportunities. For instance, so-called ‘hybrid’ clinical trial designs
judiciously add elements from longitudinal association studies to the
classic randomised controlled trial. Experimental studies generate methods and
hypotheses regarding proximal mediators or moderators of treatment effect; the
longitudinal design adds repeated measures analysis of proposed risk and
protective factors–so that the two arms of the trial become in effect parallel
longitudinal cohort studies. In principle, such hybrid trials can be used to
study questions as diverse as causal effects in complex disorders,
gene–environment interactions and the timing of the effect of risk or
protective factors in development.
The idea of combining the best elements of randomised intervention trials with
the use of statistical and econometric methods characteristic of observation
studies has also been advocated in the social sciences. Bloom
argues that by combining the two approaches investigators ‘can
capitalise on the strengths of each approach to mitigate the weaknesses of the
other’. He builds on ideas first proposed by Boruch to advocate methods to
evaluate the effects of treatment received from the results of randomised
trials in which not everyone receives the treatment they are offered.
Causal inference in analysis
Since the late 1980s there have been exciting developments in both statistics
(particularly medical statistics) and econometrics for the use of so-called
‘causal inference’ in the modelling of the influences of post-randomisation
covariates (levels of treatment adherence, surrogate endpoints and other
potential mediators) on final outcome.
In considering the possible causal influence of an intervention on
outcome from data in an observational study there is always the possibility of
an unmeasured variable (U1, say–Figs 1
and 2) which is associated with receipt
of the intervention and also has a causal effect on the outcome. The variable
U1 is known as a hidden or unmeasured confounder in the epidemiological
literature and as a hidden selection effect in econometrics. In the presence of
U1, straightforward methods of estimating the effects of intervention on
outcome (through some form of regression model, for instance) will lead to
biased results. When there is a potential mediator involved the situation is
considerably more complex. Here there might be hidden confounding between
intervention and mediator (U2) and also between mediator and outcome (U3). The
great strength of randomisation is that it breaks the link between intervention
and outcome (giving the possibility of valid intention-to-treat estimates) and
between intervention and mediator. Hence, both U1 and U2 are no longer a
problem. The effects of U3 (what Howe et al
call mediated confounding), however, remain. It is the possible (or, in
fact, very likely) existence of U3 that is the major challenge to valid
inference from trials (including inferences regarding developmental causality).
Typically it is implicitly assumed to be absent
and the vast majority of the investigators using methods such as those
first introduced by Baron & Kenny
seem to be blissfully unaware of this threat to the validity of their
Fig. 1 Without randomisation (observational study). The confounders (U1, U2
and U3) may be correlated.
Fig. 2 With randomisation (randomised trial). U1 and U2 are no longer
The key to the solution to this validity threat comes from recent statistical
developments that enable us to evaluate both direct and indirect (mediated)
effects of a randomised intervention on outcome in the presence of mediated
confounding. The solution involves finding baseline variables (called
instrumental variables or instruments) which have a strong influence on the
mediator (and hence on the outcome) but a priori can be
assumed to only influence outcome via the mediator (i.e. complete mediation).
Further technical details can be found elesewhere.
Linking methodological developments with clinical questions
The challenge for designers of clinical trials here is to identify real-world
clinical analogues of these instrumental variables within a trial design, which
can then be used simultaneously to test relevant aspects of treatment process
and of developmental theory. For instance, in developmental psychiatry,
parent-mediated treatments of child disorder are common. The final aim of such
interventions is to improve child functioning; but the immediate focus is on
working with the parent to improve parent–child interaction. It is this
parent–child interaction (an aspect of the non-shared environment for the
child) that will be the hypothesised mediator of change in the primary target
child outcome (say behaviour disorder). However, this interaction is also
likely to be influenced by pre-treatment parental variables such as personality
or social functioning. Such parental variables may have a direct effect on
child outcome through shared genetic effects in some disorders, but in the
majority of cases will have an impact on the result of treatment (child
functioning) largely or solely through their effect on the parent–child
interaction. The mediation effect of the parent–child interaction is then said
to be moderated by the pretreatment parental trait.
Measurement of this parental variable can therefore have two simultaneous and
related uses: first, as a real-world factor in the child outcome of treatment;
and second, fulfilling statistical conditions to be used as an instrumental
variable as described above in the context of U3. Including such variables
allows the trial analysis to define more precisely the causal roots of a
treatment effect: the instrumental variable (in this case parental functioning)
is not just used as a covariate against which to undertake the rest of the
analysis but is entered into a more complex causal analysis modelling. Although
few trials of parent-mediated treatments report additional measurement of
relevant parental variables, even though they are theoretically relevant to
causal effects, a recent trial that did measure them
found that they contributed to the explanation of treatment variance.
Developmental psychopathology research has been challenged by difficulties in
untangling the causal relationships between parental functioning, parent–child
interaction and child functioning. Such designs could help address these causal
questions in a powerful and novel way.
A second area in which this approach has been applied productively is in the
investigation of the effect of process variables such as therapeutic alliance.
A worked example of the analysis of therapeutic alliance in trial in
this way is set out in Dunn & Bentall.
Implications for clinical trials
Key criteria for the kind of trial in which developmental questions can be
tested were identified by Howe et al:
(a) the intervention must be theory-based and clearly constructed;
(b) the proximal target of the intervention should be a variable known
from developmental theory to be a likely candidate for an important
developmental process worth testing; this implies that the
developmental theory behind a particular research question must be
(c) the intervention must have been shown in pilot studies to be able to
change this intermediate mediating variable as well as the
(d) sampling for the trial needs to be consistent with the theories to be
tested in the developmental aspect of the trial.
It is self-evident that funding for such a design must be adequate and that
there needs to be active collaboration between designers of clinical trials,
statisticians, and developmental scientists in the design phase.
There is potential synergy between methodological developments in causal
analysis, the need to have trials better modelling the process and outcome of
and basic science research in developmental psychiatry. Clinical
decision-making as well as scientific studies rely on implicit procedures for
establishing causal relationships. New causal analytic methods in trials may
lead to better understanding of how interventions have their effect, while
simultaneously allowing testing of basic science hypotheses. These
considerations could inform future studies of developmental interventions and
suggest that funding for clinical trials should not necessarily be considered
separately from basic science research.
G.D. is a member of the UK Mental Health Research Network Methodology Research
Group. Methodological research funding for both G.D. and J.G. is provided by
the UK Medical Research Council (grant numbers G0600555/G0401546).
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