A Bayesian adaptive design for clinical trials of rare efficacy outcomes with multiple definitions

INTRODUCTION

Bayesian adaptive designs for clinical trials have gained popularity in the recent years due to the flexibility and efficiency that they offer. We consider the scenario where the outcome of interest comprises events with relatively low risk of occurrence and different case definitions resulting in varying control group risk assumptions. This is a scenario that occurs frequently for infectious diseases in global health research.

METHODS

We propose a Bayesian adaptive design that incorporates different case definitions of the outcome of interest that vary in stringency. A set of stopping rules are proposed where superiority and futility may be concluded with respect to different outcome definitions and therefore maintain a realistic probability of stopping in trials with low event rates. Through a simulation study, a variety of stopping rules and design configurations are compared.

RESULTS

The simulation results are provided in an interactive web application that allows the user to explore and compare the design operating characteristics for a variety of assumptions and design parameters with respect to different outcome definitions. The results for select simulation scenarios are provided in the article.

DISCUSSION

Bayesian adaptive designs offer the potential for maximizing the information learned from the data collected through clinical trials. The proposed design enables monitoring and utilizing multiple composite outcomes based on rare events to optimize the trial design operating characteristics.

Prospective individual patient data meta-analysis: Evaluating convalescent plasma for COVID-19

As the world faced the devastation of the COVID‐19 pandemic in late 2019 and early 2020, numerous clinical trials were initiated in many locations in an effort to establish the efficacy (or lack thereof) of potential treatments. As the pandemic has been shifting locations rapidly, individual studies have been at risk of failing to meet recruitment targets because of declining numbers of eligible patients with COVID‐19 encountered at participating sites. It has become clear that it might take several more COVID‐19 surges at the same location to achieve full enrollment and to find answers about what treatments are effective for this disease. This paper proposes an innovative approach for pooling patient‐level data from multiple ongoing randomized clinical trials (RCTs) that have not been configured as a network of sites. We present the statistical analysis plan of a prospective individual patient data (IPD) meta‐analysis (MA) from ongoing RCTs of convalescent plasma (CP). We employ an adaptive Bayesian approach for continuously monitoring the accumulating pooled data via posterior probabilities for safety, efficacy, and harm. Although we focus on RCTs for CP and address specific challenges related to CP treatment for COVID‐19, the proposed framework is generally applicable to pooling data from RCTs for other therapies and disease settings in order to find answers in weeks or months, rather than years.

Variable-Length Stopping Rules for Multidimensional Computerized Adaptive Testing

In computerized adaptive testing (CAT), a variable-length stopping rule refers to ending item administration after a pre-specified measurement precision standard has been satisfied. The goal is to provide equal measurement precision for all examinees regardless of their true latent trait level. Several stopping rules have been proposed in unidimensional CAT, such as the minimum information rule or the maximum standard error rule. These rules have also been extended to multidimensional CAT and cognitive diagnostic CAT, and they all share the same idea of monitoring measurement error. Recently, Babcock and Weiss (J Comput Adapt Test 2012. https://doi.org/10.7333/1212-0101001) proposed an "absolute change in theta" (CT) rule, which is useful when an item bank is exhaustive of good items for one or more ranges of the trait continuum. Choi, Grady and Dodd (Educ Psychol Meas 70:1-17, 2010) also argued that a CAT should stop when the standard error does not change, implying that the item bank is likely exhausted. Although these stopping rules have been evaluated and compared in different simulation studies, the relationships among the various rules remain unclear, and therefore there lacks a clear guideline regarding when to use which rule. This paper presents analytic results to show the connections among various stopping rules within both unidimensional and multidimensional CAT. In particular, it is argued that the CT-rule alone can be unstable and it can end the test prematurely. However, the CT-rule can be a useful secondary rule to monitor the point of diminished returns. To further provide empirical evidence, three simulation studies are reported using both the 2PL model and the multidimensional graded response model.

Bayesian adaptive dose-escalation designs for simultaneously estimating the optimal and maximum safe dose based on safety and efficacy

The main purpose of dose-escalation trials is to identify the dose(s) that is/are safe and efficacious for further investigations in later studies. In this paper, we introduce dose-escalation designs that incorporate both the dose-limiting events and dose-limiting toxicities (DLTs) and indicative responses of efficacy into the procedure. A flexible nonparametric model is used for modelling the continuous efficacy responses while a logistic model is used for the binary DLTs. Escalation decisions are based on the combination of the probabilities of DLTs and expected efficacy through a gain function. On the basis of this setup, we then introduce 2 types of Bayesian adaptive dose-escalation strategies. The first type of procedures, called "single objective," aims to identify and recommend a single dose, either the maximum tolerated dose, the highest dose that is considered as safe, or the optimal dose, a safe dose that gives optimum benefit risk. The second type, called "dual objective," aims to jointly estimate both the maximum tolerated dose and the optimal dose accurately. The recommended doses obtained under these dose-escalation procedures provide information about the safety and efficacy profile of the novel drug to facilitate later studies. We evaluate different strategies via simulations based on an example constructed from a real trial on patients with type 2 diabetes, and the use of stopping rules is assessed. We find that the nonparametric model estimates the efficacy responses well for different underlying true shapes. The dual-objective designs give better results in terms of identifying the 2 real target doses compared to the single-objective designs.

Quantitative Framework for Retrospective Assessment of Interim Decisions in Clinical Trials

This article presents a quantitative way of modeling the interim decisions of clinical trials. While statistical approaches tend to focus on the epistemic aspects of statistical monitoring rules, often overlooking ethical considerations, ethical approaches tend to neglect the key epistemic dimension. The proposal is a second-order decision-analytic framework. The framework provides means for retrospective assessment of interim decisions based on a clear and consistent set of criteria that combines both ethical and epistemic considerations. The framework is broadly Bayesian and addresses a fundamental question behind many concerns about clinical trials: What does it take for an interim decision (e.g., whether to stop the trial or continue) to be a good decision? Simulations illustrating the modeling of interim decisions counterfactually are provided.

Continuously updated network meta-analysis and statistical monitoring for timely decision-making

Pairwise and network meta-analysis (NMA) are traditionally used retrospectively to assess existing evidence. However, the current evidence often undergoes several updates as new studies become available. In each update recommendations about the conclusiveness of the evidence and the need of future studies need to be made. In the context of prospective meta-analysis future studies are planned as part of the accumulation of the evidence. In this setting, multiple testing issues need to be taken into account when the meta-analysis results are interpreted. We extend ideas of sequential monitoring of meta-analysis to provide a methodological framework for updating NMAs. Based on the z-score for each network estimate (the ratio of effect size to its standard error) and the respective information gained after each study enters NMA we construct efficacy and futility stopping boundaries. A NMA treatment effect is considered conclusive when it crosses an appended stopping boundary. The methods are illustrated using a recently published NMA where we show that evidence about a particular comparison can become conclusive via indirect evidence even if no further trials address this comparison.

A curve-free Bayesian decision-theoretic design for two-agent Phase I trials

Although Bayesian statistical methods are gaining attention in the medical community, as they provide a natural framework for incorporating prior information, the complexity of these methods limited their adoptions in clinical trials. This article proposes a Bayesian design for two-agent Phase I trials that is relatively easy for clinicians to understand and implement, yet performs comparably to more complex designs, so that it is more likely to be adopted in actual trials. In order to reduce model complexity and computational burden, we choose a working model with conjugate priors so that the posterior distributions have analytical expressions. Furthermore, we provide a simple strategy to facilitate the specification of priors based on the toxicity information accrued from single-agent Phase I trials. The proposed method should be useful in terms of the ease of implementation and the savings in sample size without sacrificing performance. Moreover, the conservativeness of the dose-finding algorithm renders it a relatively safe method.

How to optimize current therapy of HCV genotype 1 infection with boceprevir

Treatment with first generation protease inhibitors (PIs) is a milestone in the history of HCV therapy. Triple therapy with boceprevir (BOC) improves sustained virological response (SVR) by 30% in treatment naïve genotype 1 patients and by 50-60% in relapsers, 40-45% in partial responders and 25% in null responders compared with the Pegylated Interferon (PEG-IFN) and ribavirin regimen. To optimize BOC treatment, screening and access to treatment must be improved in genotype 1 patients. To select the ideal candidate for immediate treatment with triple therapy, an individual risk/benefit ratio must be assessed. Recent data have shown that patients with compensated cirrhosis and more advanced disease may also benefit from this regimen. Moreover, in HCV patients with extrahepatic manifestations, patients with HCV recurrence after liver transplantation and HIV-HCV co-infected patients, immediate treatment with triple therapy should be discussed. There is growing evidence that triple therapy with BOC is cost-effective in genotype 1 patients. Finally, the treatment design of BOC must be optimized in relation to baseline characteristics, so that optimal stopping rules can be followed, Drug-drug interactions (DDIs) can be prevented and AEs can be accurately prevented and managed.

Statistical conclusion validity: some common threats and simple remedies

The ultimate goal of research is to produce dependable knowledge or to provide the evidence that may guide practical decisions. Statistical conclusion validity (SCV) holds when the conclusions of a research study are founded on an adequate analysis of the data, generally meaning that adequate statistical methods are used whose small-sample behavior is accurate, besides being logically capable of providing an answer to the research question. Compared to the three other traditional aspects of research validity (external validity, internal validity, and construct validity), interest in SCV has recently grown on evidence that inadequate data analyses are sometimes carried out which yield conclusions that a proper analysis of the data would not have supported. This paper discusses evidence of three common threats to SCV that arise from widespread recommendations or practices in data analysis, namely, the use of repeated testing and optional stopping without control of Type-I error rates, the recommendation to check the assumptions of statistical tests, and the use of regression whenever a bivariate relation or the equivalence between two variables is studied. For each of these threats, examples are presented and alternative practices that safeguard SCV are discussed. Educational and editorial changes that may improve the SCV of published research are also discussed.

A safety grading scale to support dose escalation and define stopping rules for healthy subject first-entry-into-man studies: some points to consider from the French Club Phase I working group

AIM

To propose a relevant grading scale for clinical adverse events or laboratory results, electrocardiogram (ECG) and vital sign findings supporting both dose escalation and stopping decisions in first-entry-into-man (FIM) studies conducted in young healthy subjects.

METHODS

A three-level scale was used for the proposed grading system. The grading is directly derived from the observed severity of discontinuous variables, as are most of clinical adverse events. A 'combined method' based on normal ranges and spontaneous variation is suggested for grading the findings which are continuous variables mainly numerical in nature. One grade, at the subject level, and one algorithm, at the cohort level, support the proposed decision rules. This work was managed by a Club Phase I working group.

RESULTS

Examples of grade 1, 2 and 3 limits are given for the most frequent clinical adverse events and laboratory tests, ECG and vital sign findings. When available, the proposed NIH and FDA limits are also provided. The safety recommendation is to use the grade 2 at least as an alert for caution and the grade 3 as a maximum for stopping, applying the algorithm at the cohort level.

CONCLUSIONS

This paper proposes a safety grading system based on relevant criteria which might be used by investigators and sponsors to support and rationalize dose escalation decisions in healthy young subject FIM studies. These proposals are designed not to be a guideline but some 'points to consider' helping the dose escalation process. This paper supports the recent reinforcement of the safety requirements for FIM studies by European authorities.