A trial like ALIC4E: why design a platform, response-adaptive, open, randomised controlled trial of antivirals for influenza-like illness?

Abstract

ALIC⁴E is the first publicly funded, multicountry, pragmatic study determining whether antivirals should be routinely prescribed for influenza-like illness in primary care. The trial aims to go beyond determining the average treatment effect in a population to determining effects in patients with combinations of participant characteristics (age, symptom duration, illness severity, and comorbidities). It is one of the first platform, response-adaptive, open trial designs implemented in primary care, and this article aims to provide an accessible description of key aspects of the study design. 1) The platform design allows the study to remain relevant to evolving circumstances, with the ability to add treatment arms. 2) Response adaptation allows the proportion of participants with key characteristics allocated to study arms to be altered during the course of the trial according to emerging outcome data, so that participants' information will be most useful, and increasing their chances of receiving the trial intervention that will be most effective for them. 3) Because the possibility of taking placebos influences participant expectations about their treatment, and determining effects of the interventions on patient help seeking and adherence behaviour in real-world care is critical to estimates of cost-effectiveness, ALIC⁴E is an open-label trial.

Figure 1

A conventional clinical trial typically enrols a homogenous set of patients as shown in the top circle. It then randomises patients equally between two groups (shown by circles “intervention 1” and “intervention 2”) for the entire duration of the trial, and then measures outcomes (shown here as a proportion of responders to each treatment). The primary inference is then an average effect over the whole population.

Figure 2

A response-adaptive platform trial may start by enrolling a broad patient population and randomise patients equally across a range of treatments, shown here as interventions 1, 2 and 3. Interim analyses produce estimates of which patients are responding better to which treatments. The figure shows an interim analysis as “outcome review 1”. Intervention 3 has the highest proportion of treatment success; therefore, according to pre-specified rules, more patients are randomised to that therapy in the next stage and fewer to intervention 1. At outcome review 2 the efficacy of intervention 1 is still small and that treatment arm is terminated. Intervention 3 is so effective it may be changed to be the control. Intervention 4 represents a new treatment that the trial committee has decided to add to the platform trial. Outcome review 3 shows the results when just two treatments, intervention 2 and 4, remain with treatment 4 offering higher efficacy. Additional treatments may be considered at this review. Analyses are performed at a priori described intervals, randomisation rates are constantly updated and decision rules for arm termination tested. Interventions may be terminated within a particular patient characteristic group or overall according to predetermined rules. Finally, unlike conventional trials, additional interventions (shown here as 4 and 5) may be added during the course of the trial.