A comparative study of restricted randomization procedures for multiarm trials with equal or unequal treatment allocation ratios

Understanding an impact of patient enrollment pattern on predictability of central (unstratified) randomization in a multi-center clinical trial

In a multi-center randomized controlled trial (RCT) with competitive recruitment, eligible patients are enrolled sequentially by different study centers and are randomized to treatment groups using the chosen randomization method. Given the stochastic nature of the recruitment process, some centers may enroll more patients than others, and in some instances, a center may enroll multiple patients in a row, for example, on a given day. If the study is open-label, the investigators might be able to make intelligent guesses on upcoming treatment assignments in the randomization sequence, even if the trial is centrally randomized and not stratified by center. In this paper, we use enrollment data inspired by a real multi-center RCT to quantify the susceptibility of two restricted randomization procedures, the permuted block design and the big stick design, to selection bias under the convergence strategy of Blackwell and Hodges (1957) applied at the center level. We provide simulation evidence that the expected proportion of correct guesses may be greater than 50% (i.e., an increased risk of selection bias) and depends on the chosen randomization method and the number of study patients recruited by a given center that takes consecutive positions on the central allocation schedule. We propose some strategies for ensuring stronger encryption of the randomization sequence to mitigate the risk of selection bias.

Steady-state statistical properties and implementation of randomization designs with maximum tolerated imbalance restriction for two-arm equal allocation clinical trials

In recent decades, several randomization designs have been proposed in the literature as better alternatives to the traditional permuted block design (PBD), providing higher allocation randomness under the same restriction of the maximum tolerated imbalance (MTI). However, PBD remains the most frequently used method for randomizing subjects in clinical trials. This status quo may reflect an inadequate awareness and appreciation of the statistical properties of these randomization designs, and a lack of simple methods for their implementation. This manuscript presents the analytic results of statistical properties for five randomization designs with MTI restriction based on their steady-state probabilities of the treatment imbalance Markov chain and compares them to those of the PBD. A unified framework for randomization sequence generation and real-time on-demand treatment assignment is proposed for the straightforward implementation of randomization algorithms with explicit formulas of conditional allocation probabilities. Topics associated with the evaluation, selection, and implementation of randomization designs are discussed. It is concluded that for two-arm equal allocation trials, several randomization designs offer stronger protection against selection bias than the PBD does, and their implementation is not necessarily more difficult than the implementation of the PBD.

Selecting a randomization method for a multi-center clinical trial with stochastic recruitment considerations

BACKGROUND

The design of a multi-center randomized controlled trial (RCT) involves multiple considerations, such as the choice of the sample size, the number of centers and their geographic location, the strategy for recruitment of study participants, amongst others. There are plenty of methods to sequentially randomize patients in a multi-center RCT, with or without considering stratification factors. The goal of this paper is to perform a systematic assessment of such randomization methods for a multi-center 1:1 RCT assuming a competitive policy for the patient recruitment process.

METHODS

We considered a Poisson-gamma model for the patient recruitment process with a uniform distribution of center activation times. We investigated 16 randomization methods (4 unstratified, 4 region-stratified, 4 center-stratified, 3 dynamic balancing randomization (DBR), and a complete randomization design) to sequentially randomize n = 500 patients. Statistical properties of the recruitment process and the randomization procedures were assessed using Monte Carlo simulations. The operating characteristics included time to complete recruitment, number of centers that recruited a given number of patients, several measures of treatment imbalance and estimation efficiency under a linear model for the response, the expected proportions of correct guesses under two different guessing strategies, and the expected proportion of deterministic assignments in the allocation sequence.

RESULTS

Maximum tolerated imbalance (MTI) randomization methods such as big stick design, Ehrenfest urn design, and block urn design result in a better balance-randomness tradeoff than the conventional permuted block design (PBD) with or without stratification. Unstratified randomization, region-stratified randomization, and center-stratified randomization provide control of imbalance at a chosen level (trial, region, or center) but may fail to achieve balance at the other two levels. By contrast, DBR does a very good job controlling imbalance at all 3 levels while maintaining the randomized nature of treatment allocation. Adding more centers into the study helps accelerate the recruitment process but at the expense of increasing the number of centers that recruit very few (or no) patients-which may increase center-level imbalances for center-stratified and DBR procedures. Increasing the block size or the MTI threshold(s) may help obtain designs with improved randomness-balance tradeoff.

CONCLUSIONS

The choice of a randomization method is an important component of planning a multi-center RCT. Dynamic balancing randomization with carefully chosen MTI thresholds could be a very good strategy for trials with the competitive policy for patient recruitment.

Comparison of Pocock and Simon's covariate-adaptive randomization procedures in clinical trials

When multiple influential covariates need to be balanced during a clinical trial, stratified blocked randomization and covariate-adaptive randomization procedures are frequently used in trials to prevent bias and enhance the validity of data analysis results. The latter approach is increasingly used in practice for a study with multiple covariates and limited sample sizes. Among a group of these approaches, the covariate-adaptive procedures proposed by Pocock and Simon are straightforward to be utilized in practice. We aim to investigate the optimal design parameters for the patient treatment assignment probability of their developed three methods. In addition, we seek to answer the question related to the randomization performance when additional covariates are added to the existing randomization procedure. We conducted extensive simulation studies to address these practically important questions.

Randomizing a clinical trial in neuro-degenerative disease

The paper studies randomization rules for a sequential two-treatment, two-site clinical trial in Parkinson's disease. An important feature is that we have values of responses and five potential prognostic factors from a sample of 144 patients similar to those to be enrolled in the trial. Analysis of this sample provides a model for trial analysis. The comparison of allocation rules is made by simulation yielding measures of loss due to imbalance and of potential bias. A major novelty of the paper is the use of this sample, via a two-stage algorithm, to provide an empirical distribution of covariates for the simulation; sampling of a correlated multivariate normal distribution is followed by transformation to variables following the empirical marginal distributions. Six allocation rules are evaluated. The paper concludes with some comments on general aspects of the evaluation of such rules and provides a recommendation for two allocation rules, one for each site, depending on the target number of patients to be enrolled.

The effectiveness of training community mental health therapists in an evidence-based intervention for ASD: Findings from a hybrid effectiveness-implementation trial in outpatient and school-based mental health services

LAY ABSTRACT

Publicly funded mental health services play an important role in caring for school-age children with autism spectrum disorder (ASD); however, therapists report a lack of specialized ASD training, which families identity as a barrier in obtaining mental health services for their children. An Individualized Mental Health Intervention for ASD (AIM HI) was developed in collaboration with community stakeholders to respond to identified needs of children and community therapists. The current study examined the effects of therapist training in AIM HI on the changes in therapist practice, including therapists' use of evidence-based intervention strategies in session. Data were collected from a study conducted in community outpatient and school based mental health programs randomly assigned to receive AIM HI therapist training or observation of routine care. Therapist and child clients were enrolled from participating programs. Therapists in AIM HI training received training and consultation for 6 months while delivering the AIM HI intervention to a participating client; therapists in usual care delivered routine care. Both groups of therapists video recorded psychotherapy sessions which were scored by trained raters. Differences between training groups were examined using multilevel modeling. Therapists trained in AIM HI were observed to use more extensive active teaching strategies with caregivers, engagement strategies with children, strategies promoting continuity of care, and had more structured sessions with more effective pursuit of caregiver and children skill teaching. Therapist licensure moderated some training outcomes.

Alternative models and randomization techniques for Bayesian response-adaptive randomization with binary outcomes

BACKGROUND

Bayesian response-adaptive designs, which data adaptively alter the allocation ratio in favor of the better performing treatment, are often criticized for engendering a non-trivial probability of a subject imbalance in favor of the inferior treatment, inflating type I error rate, and increasing sample size requirements. The implementation of these designs using the Thompson sampling methods has generally assumed a simple beta-binomial probability model in the literature; however, the effect of these choices on the resulting design operating characteristics relative to other reasonable alternatives has not been fully examined. Motivated by the Advanced R² Eperfusion STrategies for Refractory Cardiac Arrest trial, we posit that a logistic probability model coupled with an urn or permuted block randomization method will alleviate some of the practical limitations engendered by the conventional implementation of a two-arm Bayesian response-adaptive design with binary outcomes. In this article, we discuss up to what extent this solution works and when it does not.

METHODS

A computer simulation study was performed to evaluate the relative merits of a Bayesian response-adaptive design for the Advanced R² Eperfusion STrategies for Refractory Cardiac Arrest trial using the Thompson sampling methods based on a logistic regression probability model coupled with either an urn or permuted block randomization method that limits deviations from the evolving target allocation ratio. The different implementations of the response-adaptive design were evaluated for type I error rate control across various null response rates and power, among other performance metrics.

RESULTS

The logistic regression probability model engenders smaller average sample sizes with similar power, better control over type I error rate, and more favorable treatment arm sample size distributions than the conventional beta-binomial probability model, and designs using the alternative randomization methods have a negligible chance of a sample size imbalance in the wrong direction.

CONCLUSION

Pairing the logistic regression probability model with either of the alternative randomization methods results in a much improved response-adaptive design in regard to important operating characteristics, including type I error rate control and the risk of a sample size imbalance in favor of the inferior treatment.

Statistical consideration when adding new arms to ongoing clinical trials: the potentials and the caveats

BACKGROUND

Platform trials improve the efficiency of the drug development process through flexible features such as adding and dropping arms as evidence emerges. The benefits and practical challenges of implementing novel trial designs have been discussed widely in the literature, yet less consideration has been given to the statistical implications of adding arms. MAIN: We explain different statistical considerations that arise from allowing new research interventions to be added in for ongoing studies. We present recent methodology development on addressing these issues and illustrate design and analysis approaches that might be enhanced to provide robust inference from platform trials. We also discuss the implication of changing the control arm, how patient eligibility for different arms may complicate the trial design and analysis, and how operational bias may arise when revealing some results of the trials. Lastly, we comment on the appropriateness and the application of platform trials in phase II and phase III settings, as well as publicly versus industry-funded trials.

CONCLUSION

Platform trials provide great opportunities for improving the efficiency of evaluating interventions. Although several statistical issues are present, there are a range of methods available that allow robust and efficient design and analysis of these trials.

Training Community Therapists to Deliver an Individualized Mental Health Intervention for Autism Spectrum Disorder: Changes in Caregiver Outcomes and Mediating Role on Child Outcomes

OBJECTIVE

This study examines the impact of training therapists to deliver "An Individualized Mental Health Intervention for Autism Spectrum Disorder (ASD)" (AIM HI) for children with autism spectrum disorder on caregiver outcomes and the mediating role of changes in caregiver outcomes on child outcomes.

METHOD

Data were drawn from a cluster randomized trial conducted in 29 publicly funded mental health programs randomized to receive AIM HI training or usual care. Therapists were recruited from enrolled programs and child/caregiver participants enrolled from therapists' caseloads. Participants included 202 caregivers of children 5 to 13 years of age with autism spectrum disorder. Caregiver strain and sense of competence were assessed at baseline and 6 month postbaseline. Child behaviors were assessed at baseline and 6, 12, and 18 months postbaseline. Therapist delivery of evidence-based intervention strategies were assessed between baseline and 6 months.

RESULTS

A significant training effect was observed for caregiver sense of competence, with AIM HI caregivers reporting significantly greater improvement relative to usual care. There was no significant training effect for caregiver strain. Observer-rated therapist delivery of evidence-based interventions strategies over 6 months mediated training effects for sense of competence at 6 months. Changes in sense of competence from baseline to 6 months was associated with reduced child challenging behaviors at 6 months and mediated child outcomes at 12 and 18 months.

CONCLUSION

Combined with research demonstrating effectiveness of therapist AIM HI training on child outcomes, this study provides further evidence of the positive impact of training community therapists in the AIM HI intervention.

CLINICAL TRIAL REGISTRATION INFORMATION

Effectiveness and Implementation of a Mental Health Intervention for ASD (AIM HI); https://clinicaltrials.gov/; NCT02416323.

Inference under covariate-adaptive randomization: A simulation study

In clinical trials, several covariate-adaptive designs have been proposed to balance treatment arms with respect to key covariates. Although some argue that conventional asymptotic tests are still appropriate when covariate-adaptive randomization is used, others think that re-randomization tests should be used. In this manuscript, we compare by simulation the performance of asymptotic and re-randomization tests under covariate-adaptive randomization. Our simulation study confirms results expected by the existing theory (e.g. asymptotic tests do not control type I error when the model is miss-specified). Furthermore, it shows that (i) re-randomization tests are as powerful as the asymptotic tests if the model is correct; (ii) re-randomization tests are more powerful when adjusting for covariates; (iii) minimization and permuted blocks provide similar results.

Including non-concurrent control patients in the analysis of platform trials: is it worth it?

Background

Platform trials allow adding new experimental treatments to an on-going trial. This feature is attractive to practitioners due to improved efficiency. Nevertheless, the operating characteristics of a trial that adds arms have not been well-studied. One controversy is whether just the concurrent control data (i.e. of patients who are recruited after a new arm is added) should be used in the analysis of the newly added treatment(s), or all control data (i.e. non-concurrent and concurrent).

Methods

We investigate the benefits and drawbacks of using non-concurrent control data within a two-stage setting. We perform simulation studies to explore the impact of a linear and a step trend on the inference of the trial. We compare several analysis approaches when one includes all the control data or only concurrent control data in the analysis of the newly added treatment.

Results

When there is a positive trend and all the control data are used, the marginal power of rejecting the corresponding hypothesis and the type one error rate can be higher than the nominal value. A model-based approach adjusting for a stage effect is equivalent to using concurrent control data; an adjustment with a linear term may not guarantee valid inference when there is a non-linear trend.

Conclusions

If strict error rate control is required then non-concurrent control data should not be used; otherwise it may be beneficial if the trend is sufficiently small. On the other hand, the root mean squared error of the estimated treatment effect can be improved through using non-concurrent control data.

Effectiveness of Training Therapists to Deliver An Individualized Mental Health Intervention for Children With ASD in Publicly Funded Mental Health Services: A Cluster Randomized Clinical Trial

IMPORTANCE

Publicly funded mental health services play an important role in addressing co-occurring mental health problems in children with autism spectrum disorder (ASD); however, therapists report lacking training to effectively serve this complex population.

OBJECTIVE

To test the effectiveness of training community therapists in An Individualized Mental Health Intervention for ASD (AIM HI) on challenging behaviors across 18 months among children with ASD and identify moderators and mediators of any intervention effects.

DESIGN, SETTING, AND PARTICIPANTS

Cluster randomized trial conducted in 29 publicly funded outpatient and school-based mental health programs in southern California from 2012 to 2017. Programs were randomized to receive immediate AIM HI training or provide usual care followed by receipt of AIM HI training. Therapist participants were recruited from enrolled programs, and child participants were recruited from participant therapists' caseloads. Data were analyzed from 202 children with ASD who were aged 5 to 13 years.

INTERVENTIONS

The AIM HI protocol is a package of parent-mediated and child-focused strategies aimed to reduce challenging behaviors in children with ASD who are 5 to 13 years old. It was designed for delivery in publicly funded mental health services based on a systematic assessment of therapist training needs and child clinical needs. The therapist training and consultation process takes approximately 6 months and includes an introductory workshop, 11 structured consultation meetings as the therapist delivers AIM HI with a current client, and case-specific performance feedback from trainers.

MAIN OUTCOMES AND MEASURES

Child participants were assessed for challenging behaviors using the Eyberg Child Behavior Inventory (ECBI) and Social Skills Improvement System (SSIS) Competing Problem Behaviors scales based on parent report at baseline and at 6-month intervals for 18 months. Outcomes were analyzed using intent-to-treat models.

RESULTS

In total, 202 children with ASD (mean [SD] age, 9.1 [2.4] years; 170 [84.2%] male; 121 [59.9%] Latinx) were eligible, enrolled, and included in the analyses. Statistically significant group by time interactions for the ECBI Intensity (B = -0.38; P = .02) and ECBI Problem (B = -1.00; P = .005) scales were observed, with significantly larger decreases in ECBI Intensity scores in the AIM HI group (B = -1.36; P < .001) relative to the usual care group (B = -0.98; P < .001) and a significantly larger decrease in ECBI Problem scores in the AIM HI group (B = -1.22; P < .001) relative to the usual care group (B = -0.20; P = .29). Therapist fidelity moderated these intervention effects.

CONCLUSIONS AND RELEVANCE

The present findings support the effectiveness of training therapists to deliver the AIM HI model to children with ASD receiving publicly funded mental health services.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT02416323.

Implementing Optimal Designs for Dose-Response Studies Through Adaptive Randomization for a Small Population Group

In dose-response studies with censored time-to-event outcomes, D-optimal designs depend on the true model and the amount of censored data. In practice, such designs can be implemented adaptively, by performing dose assignments according to updated knowledge of the dose-response curve at interim analysis. It is also essential that treatment allocation involves randomization-to mitigate various experimental biases and enable valid statistical inference at the end of the trial. In this work, we perform a comparison of several adaptive randomization procedures that can be used for implementing D-optimal designs for dose-response studies with time-to-event outcomes with small to moderate sample sizes. We consider single-stage, two-stage, and multi-stage adaptive designs. We also explore robustness of the designs to experimental (chronological and selection) biases. Simulation studies provide evidence that both the choice of an allocation design and a randomization procedure to implement the target allocation impact the quality of dose-response estimation, especially for small samples. For best performance, a multi-stage adaptive design with small cohort sizes should be implemented using a randomization procedure that closely attains the targeted D-optimal design at each stage. The results of the current work should help clinical investigators select an appropriate randomization procedure for their dose-response study.