Adaptive enrichment trials: What are the benefits?

Adding experimental treatment arms to multi-arm multi-stage platform trials in progress

Multi-arm multi-stage (MAMS) platform trials efficiently compare several treatments with a common control arm. Crucially MAMS designs allow for adjustment for multiplicity if required. If for example, the active treatment arms in a clinical trial relate to different dose levels or different routes of administration of a drug, the strict control of the family-wise error rate (FWER) is paramount. Suppose a further treatment becomes available, it is desirable to add this to the trial already in progress; to access both the practical and statistical benefits of the MAMS design. In any setting where control of the error rate is required, we must add corresponding hypotheses without compromising the validity of the testing procedure.To strongly control the FWER, MAMS designs use pre-planned decision rules that determine the recruitment of the next stage of the trial based on the available data. The addition of a treatment arm presents an unplanned change to the design that we must account for in the testing procedure. We demonstrate the use of the conditional error approach to add hypotheses to any testing procedure that strongly controls the FWER. We use this framework to add treatments to a MAMS trial in progress. Simulations illustrate the possible characteristics of such procedures.

Recent innovations in adaptive trial designs: A review of design opportunities in translational research

Clinical trials are constantly evolving in the context of increasingly complex research questions and potentially limited resources. In this review article, we discuss the emergence of "adaptive" clinical trials that allow for the preplanned modification of an ongoing clinical trial based on the accumulating evidence with application across translational research. These modifications may include terminating a trial before completion due to futility or efficacy, re-estimating the needed sample size to ensure adequate power, enriching the target population enrolled in the study, selecting across multiple treatment arms, revising allocation ratios used for randomization, or selecting the most appropriate endpoint. Emerging topics related to borrowing information from historic or supplemental data sources, sequential multiple assignment randomized trials (SMART), master protocol and seamless designs, and phase I dose-finding studies are also presented. Each design element includes a brief overview with an accompanying case study to illustrate the design method in practice. We close with brief discussions relating to the statistical considerations for these contemporary designs.

Methylphenidate for children and adolescents with attention deficit hyperactivity disorder (ADHD)

BACKGROUND

Attention deficit hyperactivity disorder (ADHD) is one of the most commonly diagnosed and treated psychiatric disorders in childhood. Typically, children and adolescents with ADHD find it difficult to pay attention and they are hyperactive and impulsive. Methylphenidate is the psychostimulant most often prescribed, but the evidence on benefits and harms is uncertain. This is an update of our comprehensive systematic review on benefits and harms published in 2015.

OBJECTIVES

To assess the beneficial and harmful effects of methylphenidate for children and adolescents with ADHD.

SEARCH METHODS

We searched CENTRAL, MEDLINE, Embase, three other databases and two trials registers up to March 2022. In addition, we checked reference lists and requested published and unpublished data from manufacturers of methylphenidate.

SELECTION CRITERIA

We included all randomised clinical trials (RCTs) comparing methylphenidate versus placebo or no intervention in children and adolescents aged 18 years and younger with a diagnosis of ADHD. The search was not limited by publication year or language, but trial inclusion required that 75% or more of participants had a normal intellectual quotient (IQ > 70). We assessed two primary outcomes, ADHD symptoms and serious adverse events, and three secondary outcomes, adverse events considered non-serious, general behaviour, and quality of life.

DATA COLLECTION AND ANALYSIS

Two review authors independently conducted data extraction and risk of bias assessment for each trial. Six review authors including two review authors from the original publication participated in the update in 2022. We used standard Cochrane methodological procedures. Data from parallel-group trials and first-period data from cross-over trials formed the basis of our primary analyses. We undertook separate analyses using end-of-last period data from cross-over trials. We used Trial Sequential Analyses (TSA) to control for type I (5%) and type II (20%) errors, and we assessed and downgraded evidence according to the GRADE approach.

MAIN RESULTS

We included 212 trials (16,302 participants randomised); 55 parallel-group trials (8104 participants randomised), and 156 cross-over trials (8033 participants randomised) as well as one trial with a parallel phase (114 participants randomised) and a cross-over phase (165 participants randomised). The mean age of participants was 9.8 years ranging from 3 to 18 years (two trials from 3 to 21 years). The male-female ratio was 3:1. Most trials were carried out in high-income countries, and 86/212 included trials (41%) were funded or partly funded by the pharmaceutical industry. Methylphenidate treatment duration ranged from 1 to 425 days, with a mean duration of 28.8 days. Trials compared methylphenidate with placebo (200 trials) and with no intervention (12 trials). Only 165/212 trials included usable data on one or more outcomes from 14,271 participants. Of the 212 trials, we assessed 191 at high risk of bias and 21 at low risk of bias. If, however, deblinding of methylphenidate due to typical adverse events is considered, then all 212 trials were at high risk of bias.

PRIMARY OUTCOMES

methylphenidate versus placebo or no intervention may improve teacher-rated ADHD symptoms (standardised mean difference (SMD) -0.74, 95% confidence interval (CI) -0.88 to -0.61; I² = 38%; 21 trials; 1728 participants; very low-certainty evidence). This corresponds to a mean difference (MD) of -10.58 (95% CI -12.58 to -8.72) on the ADHD Rating Scale (ADHD-RS; range 0 to 72 points). The minimal clinically relevant difference is considered to be a change of 6.6 points on the ADHD-RS. Methylphenidate may not affect serious adverse events (risk ratio (RR) 0.80, 95% CI 0.39 to 1.67; I² = 0%; 26 trials, 3673 participants; very low-certainty evidence). The TSA-adjusted intervention effect was RR 0.91 (CI 0.31 to 2.68).

SECONDARY OUTCOMES

methylphenidate may cause more adverse events considered non-serious versus placebo or no intervention (RR 1.23, 95% CI 1.11 to 1.37; I² = 72%; 35 trials 5342 participants; very low-certainty evidence). The TSA-adjusted intervention effect was RR 1.22 (CI 1.08 to 1.43). Methylphenidate may improve teacher-rated general behaviour versus placebo (SMD -0.62, 95% CI -0.91 to -0.33; I² = 68%; 7 trials 792 participants; very low-certainty evidence), but may not affect quality of life (SMD 0.40, 95% CI -0.03 to 0.83; I² = 81%; 4 trials, 608 participants; very low-certainty evidence).

AUTHORS' CONCLUSIONS

The majority of our conclusions from the 2015 version of this review still apply. Our updated meta-analyses suggest that methylphenidate versus placebo or no-intervention may improve teacher-rated ADHD symptoms and general behaviour in children and adolescents with ADHD. There may be no effects on serious adverse events and quality of life. Methylphenidate may be associated with an increased risk of adverse events considered non-serious, such as sleep problems and decreased appetite. However, the certainty of the evidence for all outcomes is very low and therefore the true magnitude of effects remain unclear. Due to the frequency of non-serious adverse events associated with methylphenidate, the blinding of participants and outcome assessors is particularly challenging. To accommodate this challenge, an active placebo should be sought and utilised. It may be difficult to find such a drug, but identifying a substance that could mimic the easily recognised adverse effects of methylphenidate would avert the unblinding that detrimentally affects current randomised trials. Future systematic reviews should investigate the subgroups of patients with ADHD that may benefit most and least from methylphenidate. This could be done with individual participant data to investigate predictors and modifiers like age, comorbidity, and ADHD subtypes.

Improving adaptive seamless designs through Bayesian optimization

We propose to use Bayesian optimization (BO) to improve the efficiency of the design selection process in clinical trials. BO is a method to optimize expensive black-box functions, by using a regression as a surrogate to guide the search. In clinical trials, planning test procedures and sample sizes is a crucial task. A common goal is to maximize the test power, given a set of treatments, corresponding effect sizes, and a total number of samples. From a wide range of possible designs, we aim to select the best one in a short time to allow quick decisions. The standard approach to simulate the power for each single design can become too time consuming. When the number of possible designs becomes very large, either large computational resources are required or an exhaustive exploration of all possible designs takes too long. Here, we propose to use BO to quickly find a clinical trial design with high power from a large number of candidate designs. We demonstrate the effectiveness of our approach by optimizing the power of adaptive seamless designs for different sets of treatment effect sizes. Comparing BO with an exhaustive evaluation of all candidate designs shows that BO finds competitive designs in a fraction of the time.

Optimizing subgroup selection in two-stage adaptive enrichment and umbrella designs

We design two‐stage confirmatory clinical trials that use adaptation to find the subgroup of patients who will benefit from a new treatment, testing for a treatment effect in each of two disjoint subgroups. Our proposal allows aspects of the trial, such as recruitment probabilities of each group, to be altered at an interim analysis. We use the conditional error rate approach to implement these adaptations with protection of overall error rates. Applying a Bayesian decision‐theoretic framework, we optimize design parameters by maximizing a utility function that takes the population prevalence of the subgroups into account. We show results for traditional trials with familywise error rate control (using a closed testing procedure) as well as for umbrella trials in which only the per‐comparison type 1 error rate is controlled. We present numerical examples to illustrate the optimization process and the effectiveness of the proposed designs.

Adaptive enrichment trials: What are the benefits?

When planning a Phase III clinical trial, suppose a certain subset of patients is expected to respond particularly well to the new treatment. Adaptive enrichment designs make use of interim data in selecting the target population for the remainder of the trial, either continuing with the full population or restricting recruitment to the subset of patients. We define a multiple testing procedure that maintains strong control of the familywise error rate, while allowing for the adaptive sampling procedure. We derive the Bayes optimal rule for deciding whether or not to restrict recruitment to the subset after the interim analysis and present an efficient algorithm to facilitate simulation-based optimisation, enabling the construction of Bayes optimal rules in a wide variety of problem formulations. We compare adaptive enrichment designs with traditional nonadaptive designs in a broad range of examples and draw clear conclusions about the potential benefits of adaptive enrichment.