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.

Sample size determination for the association between longitudinal and time-to-event outcomes using the joint modeling time-dependent slopes parameterization.

Given their improvements in bias reduction and efficiency, joint models (JMs) for longitudinal and time-to-event data offer great potential to clinical trials. However, for JM to become more widely used, there is a need for additional development of design considerations. To this end, Chen et al previously developed two closed-form sample size formulas in the JM setting. In this current work, we expand upon this framework by utilizing the time-dependent slopes parameterization, where the change in the longitudinal outcome influences the hazard, in addition to the current value of the longitudinal process. Our extended formula for the required number of events can be used when testing significance of the association between the longitudinal and time-to-event outcomes. We find that if the data indeed are generated such that not only the current value, but also the slope of the longitudinal outcome influence the hazard of the time-to-event process, it is advisable to use the current formula developed utilizing the time-dependent slopes parameterization. In this setting, our proposed formula will provide a more accurate estimate of power compared to the method by Chen et al. To illustrate our proposed method, we present power calculations of a biomarker qualification study for Hutchinson-Gilford progeria syndrome, an ultra-rare premature aging disease.

A roadmap to using randomization in clinical trials.

BACKGROUND: Randomization is the foundation of any clinical trial involving treatment comparison. It helps mitigate selection bias, promotes similarity of treatment groups with respect to important known and unknown confounders, and contributes to the validity of statistical tests. Various restricted randomization procedures with different probabilistic structures and different statistical properties are available. The goal of this paper is to present a systematic roadmap for the choice and application of a restricted randomization procedure in a clinical trial. METHODS: We survey available restricted randomization procedures for sequential allocation of subjects in a randomized, comparative, parallel group clinical trial with equal (1:1) allocation. We explore statistical properties of these procedures, including balance/randomness tradeoff, type I error rate and power. We perform head-to-head comparisons of different procedures through simulation under various experimental scenarios, including cases when common model assumptions are violated. We also provide some real-life clinical trial examples to illustrate the thinking process for selecting a randomization procedure for implementation in practice. RESULTS: Restricted randomization procedures targeting 1:1 allocation vary in the degree of balance/randomness they induce, and more importantly, they vary in terms of validity and efficiency of statistical inference when common model assumptions are violated (e.g. when outcomes are affected by a linear time trend; measurement error distribution is misspecified; or selection bias is introduced in the experiment). Some procedures are more robust than others. Covariate-adjusted analysis may be essential to ensure validity of the results. Special considerations are required when selecting a randomization procedure for a clinical trial with very small sample size. CONCLUSIONS: The choice of randomization design, data analytic technique (parametric or nonparametric), and analysis strategy (randomization-based or population model-based) are all very important considerations. Randomization-based tests are robust and valid alternatives to likelihood-based tests and should be considered more frequently by clinical investigators.

Implementing unequal randomization in clinical trials with heterogeneous treatment costs.

Equal randomization has been a popular choice in clinical trial practice. However, in trials with heterogeneous variances and/or variable treatment costs, as well as in settings where maximization of every trial participant's benefit is an important design consideration, optimal allocation proportions may be unequal across study treatment arms. In this paper, we investigate optimal allocation designs minimizing study cost under statistical efficiency constraints for parallel group clinical trials comparing several investigational treatments against the control. We show theoretically that equal allocation designs may be suboptimal, and unequal allocation designs can provide higher statistical power for the same budget or result in a smaller cost for the same level of power. We also show how optimal allocation can be implemented in practice by means of restricted randomization procedures and how to perform statistical inference following these procedures, using invoked population-based or randomization-based approaches. Our results provide further support to some previous findings in the literature that unequal randomization designs can be cost efficient and can be successfully implemented in practice. We conclude that the choice of the target allocation, the randomization procedure, and the statistical methodology for data analysis is an essential component in ensuring valid, powerful, and robust clinical trial results.

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

Randomization designs for multiarm clinical trials are increasingly used in practice, especially in phase II dose-ranging studies. Many new methods have been proposed in the literature; however, there is lack of systematic, head-to-head comparison of the competing designs. In this paper, we systematically investigate statistical properties of various restricted randomization procedures for multiarm trials with fixed and possibly unequal allocation ratios. The design operating characteristics include measures of allocation balance, randomness of treatment assignments, variations in the allocation ratio, and statistical characteristics such as type I error rate and power. The results from the current paper should help clinical investigators select an appropriate randomization procedure for their clinical trial. We also provide a web-based R shiny application that can be used to reproduce all results in this paper and run simulations under additional user-defined experimental scenarios.

RARtool: A MATLAB Software Package for Designing Response-Adaptive Randomized Clinical Trials with Time-to-Event Outcomes.

Response-adaptive randomization designs are becoming increasingly popular in clinical trial practice. In this paper, we present RARtool, a user interface software developed in MATLAB for designing response-adaptive randomized comparative clinical trials with censored time-to-event outcomes. The RARtool software can compute different types of optimal treatment allocation designs, and it can simulate response-adaptive randomization procedures targeting selected optimal allocations. Through simulations, an investigator can assess design characteristics under a variety of experimental scenarios and select the best procedure for practical implementation. We illustrate the utility of our RARtool software by redesigning a survival trial from the literature.

Efficient and ethical response-adaptive randomization designs for multi-arm clinical trials with Weibull time-to-event outcomes.

We consider a design problem for a clinical trial with multiple treatment arms and time-to-event primary outcomes that are modeled using the Weibull family of distributions. The D-optimal design for the most precise estimation of model parameters is derived, along with compound optimal allocation designs that provide targeted efficiencies for various estimation problems and ethical considerations. The proposed optimal allocation designs are studied theoretically and are implemented using response-adaptive randomization for a clinical trial with censored Weibull outcomes. We compare the merits of our multiple-objective response-adaptive designs with traditional randomization designs and show that our designs are more flexible, realistic, generally more ethical, and frequently provide higher efficiencies for estimating different sets of parameters.

Decentralized Clinical Trials: Scientific Considerations Through the Lens of the Estimand Framework.

While industry and regulators' interest in decentralized clinical trials (DCTs) is long-standing, the Covid-19 pandemic accelerated and broadened the adoption and experience with these trials. The key idea in decentralization is bringing the clinical trial design, typically on-site, closer to the patient's experience (on-site or off-site). Thus, potential benefits of DCTs include reducing the burden of participation in trials, broadening access to a more diverse population, or using innovative endpoints collected off-site. This paper helps researchers to carefully evaluate the added value and the implications of DCTs beyond the operational aspects of their implementation. The proposed approach is to use the ICH E9(R1) estimand framework to guide the strategic decisions around each decentralization component. Furthermore, the framework can guide the process for clinical trialists to systematically consider the implications of decentralization, in turn, for each attribute of the estimand. We illustrate the use of this approach with a fully DCT case study and show that the proposed systematic process can uncover the scientific opportunities, assumptions, and potential risks associated with a possible use of decentralization components in the design of a trial. This process can also highlight the benefits of specifying estimand attributes in a granular way. Thus, we demonstrate that bringing a decentralization component into the design will not only impact estimators and estimation but can also correspond to addressing more granular questions, thereby uncovering new target estimands.

Pharmacodynamics of selective inhibition of γ-secretase by avagacestat.

A hallmark of Alzheimer's disease (AD) pathology is the accumulation of brain amyloid ß-peptide (Aß), generated by γ-secretase-mediated cleavage of the amyloid precursor protein (APP). Therefore, γ-secretase inhibitors (GSIs) may lower brain Aß and offer a potential new approach to treat AD. As γ-secretase also cleaves Notch proteins, GSIs can have undesirable effects due to interference with Notch signaling. Avagacestat (BMS-708163) is a GSI developed for selective inhibition of APP over Notch cleavage. Avagacestat inhibition of APP and Notch cleavage was evaluated in cell culture by measuring levels of Aß and human Notch proteins. In rats, dogs, and humans, selectivity was evaluated by measuring plasma blood concentrations in relation to effects on cerebrospinal fluid (CSF) Aß levels and Notch-related toxicities. Measurements of Notch-related toxicity included goblet cell metaplasia in the gut, marginal-zone depletion in the spleen, reductions in B cells, and changes in expression of the Notch-regulated hairy and enhancer of split homolog-1 from blood cells. In rats and dogs, acute administration of avagacestat robustly reduced CSF Aß40 and Aß42 levels similarly. Chronic administration in rats and dogs, and 28-day, single- and multiple-ascending-dose administration in healthy human subjects caused similar exposure-dependent reductions in CSF Aß40. Consistent with the 137-fold selectivity measured in cell culture, we identified doses of avagacestat that reduce CSF Aß levels without causing Notch-related toxicities. Our results demonstrate the selectivity of avagacestat for APP over Notch cleavage, supporting further evaluation of avagacestat for AD therapy.

A contrast in safety, pharmacokinetics and pharmacodynamics across age groups after a single 50 mg oral dose of the γ-secretase inhibitor avagacestat.

AIM: To evaluate the single dose pharmacokinetics, pharmacodynamics, and preliminary tolerability of the γ-secretase inhibitor BMS-708163 (avagacestat) in young and elderly men and women. METHODS: All subjects received double-blinded administration of a single 50 mg dose of avagacestat in capsule form or matching placebo. Main evaluations included pharmacokinetics, safety, plasma amyloid-ß (Aß)(1-40) concentratios and exploration of Notch biomarkers. RESULTS: Avagacestat 50 mg capsule was well tolerated and rapidly absorbed among young and elderly subjects, with a median t(max) between 1 and 2 h post dose and an average half-life between 41 and 71 h. In general, subjects aged 75 years or more had higher AUC(0,∞) values than those aged less than 75 years. An exploratory analysis of Aß(1-40) serum concentrations showed a pattern of decreasing concentrations over the first 4-6 h followed by a rise above baseline that was maintained until the end of the assessment period. Adverse events were generally mild, occurring more frequently in elderly subjects, with no observed difference between subjects receiving avagacestat and placebo. No dose limiting gastrointestinal effects of avagacestat were observed and exploratory biomarkers of Notch inhibition did not change significantly. CONCLUSIONS: The favourable safety profile and pharmacokinetic effects of avagacestat in this study support its continued development, especially in the target population of elderly subjects with mild cognitive impairment or Alzheimer's disease.

Investigating the value of glucodensity analysis of continuous glucose monitoring data in type 1 diabetes: an exploratory analysis.

Introduction: Continuous glucose monitoring (CGM) devices capture longitudinal data on interstitial glucose levels and are increasingly used to show the dynamics of diabetes metabolism. Given the complexity of CGM data, it is crucial to extract important patterns hidden in these data through efficient visualization and statistical analysis techniques. Methods: In this paper, we adopted the concept of glucodensity, and using a subset of data from an ongoing clinical trial in pediatric individuals and young adults with new-onset type 1 diabetes, we performed a cluster analysis of glucodensities. We assessed the differences among the identified clusters using analysis of variance (ANOVA) with respect to residual pancreatic beta-cell function and some standard CGM-derived parameters such as time in range, time above range, and time below range. Results: Distinct CGM data patterns were identified using cluster analysis based on glucodensities. Statistically significant differences were shown among the clusters with respect to baseline levels of pancreatic beta-cell function surrogate (C-peptide) and with respect to time in range and time above range. Discussion: Our findings provide supportive evidence for the value of glucodensity in the analysis of CGM data. Some challenges in the modeling of CGM data include unbalanced data structure, missing observations, and many known and unknown confounders, which speaks to the importance of--and provides opportunities for--taking an approach integrating clinical, statistical, and data science expertise in the analysis of these data.

MIJ821 (onfasprodil) in healthy volunteers: First-in-human, randomized, placebo-controlled study (single ascending dose and repeated intravenous dose).

This single-center study administered MIJ821 (onfasprodil) as an intravenous infusion to healthy volunteers and included two parts: a single ascending dose study (Part 1) and a repeated intravenous dose study (Part 2). Primary objective was to evaluate the safety and tolerability of single ascending intravenous doses infused over a 40-min period and of two repeated doses (1 week apart) of MIJ821 in healthy volunteers. Secondary objectives were to assess the pharmacokinetics of MIJ821 after intravenous infusion in Part 1 and Part 2 of the study. Overall, 43 subjects in Part 1 and 12 subjects in Part 2 were randomized in the study. Median age in Part 1 and Part 2 was 45.0 and 43.5 years, respectively, with the majority being Caucasian (Part 1: 84%; Part 2: 92%). 19 subjects (44.2%) in Part 1 and 8 subjects (66.7%) in Part 2 experienced at least one adverse event (AE). Following single dose in Part 1 and Part 2, the AUCinf values of MIJ821 increased in a dose-proportional manner across the dose range 0.016-0.48 mg/kg and the Cmax values in a slight overproportional manner across the dose range 0.048-0.48 mg/kg. At the highest dose of 0.48 mg/kg, the geometric mean AUCinf was 708 h ng/mL and the geometric mean Cmax was 462 ng/mL. Inspection of 1-h post-dose resting electroencephalography activity across cohorts showed a relationship to administered dose, providing exploratory evidence of distal target engagement. In conclusion, MIJ821 showed a good safety and tolerability profile in healthy volunteers. Dissociative AEs were mild, transient, and dose-dependent.

Adaptive randomization for clinical trials.

In February 2010, the U.S. Food and Drug Administration (FDA, 2010 ) drafted guidance that discusses the statistical, clinical, and regulatory aspects of various adaptive designs for clinical trials. An important class of adaptive designs is adaptive randomization, which is considered very briefly in subsection VI.B of the guidance. The objective of this paper is to review several important new classes of adaptive randomization procedures and convey information on the recent developments in the literature on this topic. Much of this literature has been focused on the development of methodology to address past criticisms and concerns that have hindered the broader use of adaptive randomization. We conclude that adaptive randomization is a very broad area of experimental design that has important application in modern clinical trials.

A Smartphone-Based Intervention as an Adjunct to Standard-of-Care Treatment for Schizophrenia: Randomized Controlled Trial.

BACKGROUND: Antipsychotic medications have limited benefits in schizophrenia, and cognitive behavioral therapy may be beneficial as an adjunct. There may be potential for implementing mobile cognitive behavioral therapy-based treatment for schizophrenia in addition to standard antipsychotic medications. OBJECTIVE: This study aims to determine whether PEAR-004, a smartphone-based investigational digital therapeutic, improves the symptoms of an acute psychotic exacerbation of schizophrenia when it is added to standard treatments. METHODS: This was a 12-week, multicenter, randomized, sham-controlled, rater-blinded, parallel group proof­of­concept study of 112 participants with moderate acute psychotic exacerbation in schizophrenia. This study was conducted in 6 clinical trial research sites in the United States from December 2018 to September 2019. The primary outcome, change in Positive and Negative Syndrome Scale (PANSS) from baseline to week 12 or the last available visit, was analyzed using the mixed-effects regression model for repeated measures, applied to an intent-to-treat sample. RESULTS: The total PANSS scores slightly decreased from baseline over the study period in both groups; the treatment difference at day 85 between PEAR-004 and sham was 2.7 points, in favor of the sham (2-sided P=.09). The secondary scales found no benefit, except for transient improvement in depressive symptoms with PEAR-004. Application engagement was good, and patient and clinical investigator satisfaction was high. No safety concerns were observed. There was some evidence of study site heterogeneity for the onboarding processes and directions on PEAR-004 product use at baseline and throughout the study. However, these differences did not affect the efficacy results. CONCLUSIONS: In the largest-to-date randomized, sham-controlled study of a digital therapeutic in schizophrenia, PEAR-004 did not demonstrate an effect on the primary outcome-total PANSS scores-when compared with a nonspecific digital sham control. The secondary and exploratory results also did not demonstrate any notable benefits, except for possible temporary improvement in depressive symptoms. This study provided many useful scientific and operational insights that can be used in the further clinical development of PEAR-004 and other investigational digital therapeutics. TRIAL REGISTRATION: ClinicalTrials.gov NCT03751280; https://clinicaltrials.gov/ct2/show/NCT03751280.

Description of the Method for Evaluating Digital Endpoints in Alzheimer Disease Study: Protocol for an Exploratory, Cross-sectional Study.

BACKGROUND: More sensitive and less burdensome efficacy end points are urgently needed to improve the effectiveness of clinical drug development for Alzheimer disease (AD). Although conventional end points lack sensitivity, digital technologies hold promise for amplifying the detection of treatment signals and capturing cognitive anomalies at earlier disease stages. Using digital technologies and combining several test modalities allow for the collection of richer information about cognitive and functional status, which is not ascertainable via conventional paper-and-pencil tests. OBJECTIVE: This study aimed to assess the psychometric properties, operational feasibility, and patient acceptance of 10 promising technologies that are to be used as efficacy end points to measure cognition in future clinical drug trials. METHODS: The Method for Evaluating Digital Endpoints in Alzheimer Disease study is an exploratory, cross-sectional, noninterventional study that will evaluate 10 digital technologies' ability to accurately classify participants into 4 cohorts according to the severity of cognitive impairment and dementia. Moreover, this study will assess the psychometric properties of each of the tested digital technologies, including the acceptable range to assess ceiling and floor effects, concurrent validity to correlate digital outcome measures to traditional paper-and-pencil tests in AD, reliability to compare test and retest, and responsiveness to evaluate the sensitivity to change in a mild cognitive challenge model. This study included 50 eligible male and female participants (aged between 60 and 80 years), of whom 13 (26%) were amyloid-negative, cognitively healthy participants (controls); 12 (24%) were amyloid-positive, cognitively healthy participants (presymptomatic); 13 (26%) had mild cognitive impairment (predementia); and 12 (24%) had mild AD (mild dementia). This study involved 4 in-clinic visits. During the initial visit, all participants completed all conventional paper-and-pencil assessments. During the following 3 visits, the participants underwent a series of novel digital assessments. RESULTS: Participant recruitment and data collection began in June 2020 and continued until June 2021. Hence, the data collection occurred during the COVID-19 pandemic (SARS-CoV-2 virus pandemic). Data were successfully collected from all digital technologies to evaluate statistical and operational performance and patient acceptance. This paper reports the baseline demographics and characteristics of the population studied as well as the study's progress during the pandemic. CONCLUSIONS: This study was designed to generate feasibility insights and validation data to help advance novel digital technologies in clinical drug development. The learnings from this study will help guide future methods for assessing novel digital technologies and inform clinical drug trials in early AD, aiming to enhance clinical end point strategies with digital technologies. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/35442.

Optimal response-adaptive randomized designs for multi-armed survival trials.

We considered design issues for multiple treatment arms in survival intervention trials and used optimal design theory to allocate patients adaptively in such trials. We proposed three types of optimal designs: one ensures that we have the most precise estimates of the treatment effects, another guarantees that we have the minimal sample size subject to user-specified allocation ratio assignments among treatment arms, and the third ensures that the design has minimal total hazard for the cohort. The latter two types of optimal designs are also subject to user-specified power constraints for testing contrasts among treatment effects. The operating characteristics of these optimal designs along with balanced designs are compared theoretically and by simulation, including their robustness properties with respect to model misspecifications. Our results show that the proposed optimal designs are frequently unbalanced and that they are generally more efficient and more ethical than the popular balanced designs. We also apply our response-adaptive allocation strategy to redesign a three-arm head and neck cancer trial and make comparisons.

Opportunity for efficiency in clinical development: An overview of adaptive clinical trial designs and innovative machine learning tools, with examples from the cardiovascular field.

Modern data analysis tools and statistical modeling techniques are increasingly used in clinical research to improve diagnosis, estimate disease progression and predict treatment outcomes. What seems less emphasized is the importance of the study design, which can have a serious impact on the study cost, time and statistical efficiency. This paper provides an overview of different types of adaptive designs in clinical trials and their applications to cardiovascular trials. We highlight recent proliferation of work on adaptive designs over the past two decades, including some recent regulatory guidelines on complex trial designs and master protocols. We also describe the increasing role of machine learning and use of metaheuristics to construct increasingly complex adaptive designs or to identify interesting features for improved predictions and classifications.