Adaptive seamless designs with interim treatment selection: a case study in oncology

The planning of an oncology clinical trial with a seamless phase II/III adaptive design is discussed. Two regimens of an experimental treatment are compared to a control at an interim analysis, and the most‐promising regimen is selected to continue, together with control, until the end of the study. Because the primary endpoint is expected to be immature at the interim regimen selection analysis, designs that incorporate primary as well as surrogate endpoints in the regimen selection process are considered. The final testing of efficacy at the end of the study comparing the selected regimen to the control with respect to the primary endpoint uses all relevant data collected both before and after the regimen selection analysis. Several approaches for testing the primary hypothesis are assessed with regard to power and type I error rate. Because the operating characteristics of these designs depend on the specific regimen selection rules considered, benchmark scenarios are proposed in which a perfect surrogate and no surrogate is used at the regimen selection analysis. The operating characteristics of these benchmark scenarios provide a range where those of the actual study design are expected to lie. A discussion on family‐wise error rate control for testing primary and key secondary endpoints as well as an assessment of bias in the final treatment effect estimate for the selected regimen are also presented. Copyright © 2015 John Wiley & Sons, Ltd.     

Interim treatment selection with a flexible selection margin in clinical trials

When several treatment arms are administered along with a control arm in a trial, dropping the non‐promising treatments at an early stage helps to save the resources and expedite the trial. In such adaptive designs with treatment selection, a common selection rule is to pick the most promising treatment, for example, the treatment with the numerically highest mean response, at the interim stage. However, with only a single treatment selected for final evaluation, this selection rule is often too inflexible. We modified this interim selection rule by introducing a flexible selection margin to judge the acceptable treatment difference. Another treatment could be selected at the interim stage in addition to the empirically best one if the differences of the observed treatment effect between them do not exceed this margin. We considered the study starting with two treatment arms and a control arm. We developed hypothesis testing procedures to assess the selected treatment(s) by taking into account the interim selection process. Compared with the one‐winner selection designs, the modified selection rule makes the design more flexible and practical. Copyright © 2012 John Wiley & Sons, Ltd.     

Optimizing interim analysis timing for Bayesian adaptive commensurate designs

In developing products for rare diseases, statistical challenges arise due to the limited number of patients available for participation in drug trials and other clinical research. Bayesian adaptive clinical trial designs offer the possibility of increased statistical efficiency, reduced development cost and ethical hazard prevention via their incorporation of evidence from external sources (historical data, expert opinions, and real-world evidence), and flexibility in the specification of interim looks. In this paper, we propose a novel Bayesian adaptive commensurate design that borrows adaptively from historical information and also uses a particular payoff function to optimize the timing of the study's interim analysis. The trial payoff is a function of how many samples can be saved via early stopping and the probability of making correct early decisions for either futility or efficacy. We calibrate our Bayesian algorithm to have acceptable long-run frequentist properties (Type I error and power) via simulation at the design stage. We illustrate our approach using a pediatric trial design setting testing the effect of a new drug for a rare genetic disease. The optimIA R package available at https://github.com/wxwx1993/Bayesian_IA_Timing provides an easy-to-use implementation of our approach.     

A group-sequential design for clinical trials with treatment selection

A group-sequential design for clinical trials that involve treatment selection was proposed by Stallard and Todd (Statist. Med. 2003; 22:689-703). In this design, the best among a number of experimental treatments is selected on the basis of data observed at the first of a series of interim analyses. This experimental treatment then continues together with the control treatment to be assessed in one or more further analyses. The method was extended by Kelly et al. (J. Biopharm. Statist. 2005; 15:641-658) to allow more than one experimental treatment to continue beyond the first interim analysis. This design controls the familywise type I error rate under the global null hypothesis, that is in the weak sense, but may not strongly control the error rate, particularly if the treatments selected are not the best-performing ones. In some cases, for example when additional safety data are available, the restriction that the best-performing treatments continue may be unreasonable. This paper describes an extension of the approach of Stallard and Todd that enables construction of a group-sequential design for comparison of several experimental treatments with a control treatment. The new method controls the type I error rate in the strong sense if the number of treatments included at each stage is specified in advance, and is indicated by simulation studies to be conservative when the number of treatments is chosen based on the observed data in a practically relevant way.     

An objective Bayesian analysis of a crossover design via model selection and model averaging

Inference about the treatment effect in a crossover design has received much attention over time owing to the uncertainty in the existence of the carryover effect and its impact on the estimation of the treatment effect. Adding to this uncertainty is that the existence of the carryover effect and its size may depend on the presence of the treatment effect and its size. We consider estimation and testing hypothesis about the treatment effect in a two‐period crossover design, assuming normally distributed response variable, and use an objective Bayesian approach to test the hypothesis about the treatment effect and to estimate its size when it exists while accounting for the uncertainty about the presence of the carryover effect as well as the treatment and period effects. We evaluate and compare the performance of the proposed approach with a standard frequentist approach using simulated data, and real data. Copyright © 2016 John Wiley & Sons, Ltd.     

On optimal treatment regimes selection for mean survival time

In clinical studies with time‐to‐event as a primary endpoint, one main interest is to find the best treatment strategy to maximize patients' mean survival time. Due to patient's heterogeneity in response to treatments, great efforts have been devoted to developing optimal treatment regimes by integrating individuals' clinical and genetic information. A main challenge arises in the selection of important variables that can help to build reliable and interpretable optimal treatment regimes as the dimension of predictors may be high. In this paper, we propose a robust loss‐based estimation framework that can be easily coupled with shrinkage penalties for both estimation of optimal treatment regimes and variable selection. The asymptotic properties of the proposed estimators are studied. Moreover, a model‐free estimator of restricted mean survival time under the derived optimal treatment regime is developed, and its asymptotic property is studied. Simulations are conducted to assess the empirical performance of the proposed method for parameter estimation, variable selection, and optimal treatment decision. An application to an AIDS clinical trial data set is given to illustrate the method. Copyright © 2014 John Wiley & Sons, Ltd.     

Performance of adaptive sample size adjustment with respect to stopping criteria and time of interim analysis

The benefit of adjusting the sample size in clinical trials on the basis of treatment effects observed in interim analysis has been the subject of several recent papers. Different conclusions were drawn about the usefulness of this approach for gaining power or saving sample size, because of differences in trial design and setting. We examined the benefit of sample size adjustment in relation to trial design parameters such as 'time of interim analysis' and 'choice of stopping criteria'. We compared the adaptive weighted inverse normal method with classical group sequential methods for the most common and for optimal stopping criteria in early, half-time and late interim analyses. We found that reacting to interim data might significantly reduce average sample size in some situations, while classical approaches can out-perform the adaptive designs under other circumstances. We characterized these situations with respect to time of interim analysis and choice of stopping criteria.     

Bayesian and frequentist two-stage treatment strategies based on sequential failure times subject to interval censoring

For many diseases, therapy involves multiple stages, with the treatment in each stage chosen adaptively based on the patient's current disease status and history of previous treatments and clinical outcomes. Physicians routinely use such multi-stage treatment strategies, also called dynamic treatment regimes or treatment policies. We present a Bayesian framework for a clinical trial comparing two-stage strategies based on the time to overall failure, defined as either second disease worsening or discontinuation of therapy. Each patient is randomized among a set of treatments at enrollment, and if disease worsening occurs the patient is then re-randomized among a set of treatments excluding the treatment received initially. The goal is to select the two-stage strategy having the largest average overall failure time. A parametric model is formulated to account for non-constant failure time hazards, regression of the second failure time on the patient's first worsening time, and the complications that the failure time in either stage may be interval censored and there may be a delay between first worsening and the start of the second stage of therapy. Four different criteria, two Bayesian and two frequentist, for selecting a best strategy are considered. The methods are applied to a trial comparing two-stage strategies for treating metastatic renal cancer, and a simulation study in the context of this trial is presented. Advantages and disadvantages of this design compared to standard methods are discussed.     

Estimation in multi‐arm two‐stage trials with treatment selection and time‐to‐event endpoint

We consider estimation of treatment effects in two‐stage adaptive multi‐arm trials with a common control. The best treatment is selected at interim, and the primary endpoint is modeled via a Cox proportional hazards model. The maximum partial‐likelihood estimator of the log hazard ratio of the selected treatment will overestimate the true treatment effect in this case. Several methods for reducing the selection bias have been proposed for normal endpoints, including an iterative method based on the estimated conditional selection biases and a shrinkage approach based on empirical Bayes theory. We adapt these methods to time‐to‐event data and compare the bias and mean squared error of all methods in an extensive simulation study and apply the proposed methods to reconstructed data from the FOCUS trial. We find that all methods tend to overcorrect the bias, and only the shrinkage methods can reduce the mean squared error. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.     

Bayesian adaptive model selection for optimizing group sequential clinical trials

This article presents a new approach to the problem of deriving an optimal design for a randomized group sequential clinical trial based on right-censored event times. We are motivated by the fact that, if the proportional hazards assumption is not met, then a conventional design's actual power can differ substantially from its nominal value. We combine Bayesian decision theory, Bayesian model selection and forward simulation (FS) to obtain a group sequential procedure that maintains targeted false-positive rate and power, under a wide range of true event time distributions. At each interim analysis, the method adaptively chooses the most likely model and then applies the decision bounds that are optimal under the chosen model. A simulation study comparing this design with three conventional designs shows that, over a wide range of distributions, our proposed method performs at least as well as each conventional design, and in many cases it provides a much smaller trial.     

A Bayesian adaptive dose selection procedure with an overdispersed count endpoint

Adaptive trial designs can considerably improve upon traditional designs, by modifying design aspects of the ongoing trial, like early stopping, adding, or dropping doses, or changing the sample size. In the present work, we propose a two‐stage Bayesian adaptive design for a Phase IIb study aimed at selecting the lowest effective dose for Phase III. In this setting, efficacy has been proved for a high dose in a Phase IIa proof‐of‐concept study, but the existence of a lower but still effective dose is investigated before the scheduled Phase III starts. In the first stage, we randomize patients to placebo, maximal tolerated dose, and one or more additional doses within the dose range. Based on an interim analysis, we either stop the study for futility or success or continue the study to the second stage, where newly recruited patients are allocated to placebo, some fairly high dose, and one additional dose chosen based on interim data. At the interim analysis, we use the criteria based on the predictive probability of success to decide on whether to stop or to continue the trial and, in the latter case, which dose to select for the second stage. Finally, we will select a dose as lowest effective dose for Phase III either at the end of the first stage or at the end of the second stage. We evaluate the operating characteristics of the procedure via simulations and present the results for several scenarios, comparing the performance of the proposed procedure to those of the non‐adaptive design. Copyright © 2013 John Wiley & Sons, Ltd.     

Interim treatment selection using the normal approximation approach in clinical trials

We consider a study starting with two treatment groups and a control group with a planned interim analysis. The inferior treatment group will be dropped after the interim analysis, and only the winning treatment and the control will continue to the end of the study. This 'Two-Stage Winner Design' is based on the concepts of multiple comparison, adaptive design, and winner selection. In a study with such a design, there is less multiplicity, but more adaptability if the interim selection is performed at an early stage. If the interim selection is performed close to the end of the study, the situation becomes the conventional multiple comparison where Dunnett's method may be applied.The unconditional distribution of the final test statistic from the 'winner' treatment is no longer normal, the exact distribution of which is provided in this paper, but numerical integration is needed for its calculation. To avoid complex computations, we propose a normal approximation approach to calculate the type I error, the power, the point estimate, and the confidence intervals. Due to the well understood and attractive properties of the normal distribution, the 'Winner Design' can be easily planned and adequately executed, which is demonstrated by an example. We also provide detailed discussion on how the proposed design should be practically implemented by optimizing the timing of the interim look and the probability of winner selection.     

Unbiased estimation in seamless phase II/III trials with unequal treatment effect variances and hypothesis‐driven selection rules

Seamless phase II/III clinical trials offer an efficient way to select an experimental treatment and perform confirmatory analysis within a single trial. However, combining the data from both stages in the final analysis can induce bias into the estimates of treatment effects. Methods for bias adjustment developed thus far have made restrictive assumptions about the design and selection rules followed. In order to address these shortcomings, we apply recent methodological advances to derive the uniformly minimum variance conditionally unbiased estimator for two‐stage seamless phase II/III trials. Our framework allows for the precision of the treatment arm estimates to take arbitrary values, can be utilised for all treatments that are taken forward to phase III and is applicable when the decision to select or drop treatment arms is driven by a multiplicity‐adjusted hypothesis testing procedure. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.     

Continuous Bayesian adaptive randomization based on event times with covariates

In comparative clinical trials, the randomization probabilities may be unbalanced adaptively by utilizing the interim data available at each patient's entry time to favour the treatment or treatments having comparatively superior outcomes. This is ethically appealing because, on average, more patients are assigned to the more successful treatments. Consequently, physicians are more likely to enroll patients onto trials where the randomization is outcome-adaptive rather than balanced in the conventional manner. Outcome-adaptive methods based on a binary variable may be applied by reducing an event time to the indicator of the event's occurrence within a predetermined time interval. This results in a loss of information, however, since it ignores the censoring times of patients who have not experienced the event but whose evaluation interval is not complete. This paper proposes and compares exact and approximate Bayesian outcome-adaptive randomization procedures based on time-to-event outcomes. The procedures account for baseline prognostic covariates, and they may be applied continuously over the course of the trial. We illustrate these methods by application to a phase II selection trial in acute leukaemia. A simulation study in the context of this trial is presented.     

ComPAS: A Bayesian drug combination platform trial design with adaptive shrinkage

Combining different treatment regimens provides an effective approach to induce a synergistic treatment effect and overcome resistance to monotherapy. The challenge is that, given the large number of existing monotherapies, the number of possible combinations is huge and new potentially more efficacious compounds may become available any time during drug development. To address this challenge, we propose a flexible Bayesian drug combination platform design with adaptive shrinkage (ComPAS), which allows for dropping futile combinations, graduating effective combinations, and adding new combinations during the course of the trial. A new adaptive shrinkage method is developed to adaptively borrow information across combinations and efficiently identify the efficacious combinations based on Bayesian model selection and hierarchical models. Simulation studies show that ComPAS identifies the effective combinations with higher probability than some existing designs. ComPAS provides an efficient and flexible platform to accelerate drug development in a seamless and timely fashion.     

Designing multi‐arm multi‐stage clinical trials using a risk–benefit criterion for treatment selection

Multi‐arm clinical trials that compare several active treatments to a common control have been proposed as an efficient means of making an informed decision about which of several treatments should be evaluated further in a confirmatory study. Additional efficiency is gained by incorporating interim analyses and, in particular, seamless Phase II/III designs have been the focus of recent research. Common to much of this work is the constraint that selection and formal testing should be based on a single efficacy endpoint, despite the fact that in practice, safety considerations will often play a central role in determining selection decisions. Here, we develop a multi‐arm multi‐stage design for a trial with an efficacy and safety endpoint. The safety endpoint is explicitly considered in the formulation of the problem, selection of experimental arm and hypothesis testing. The design extends group‐sequential ideas and considers the scenario where a minimal safety requirement is to be fulfilled and the treatment yielding the best combined safety and efficacy trade‐off satisfying this constraint is selected for further testing. The treatment with the best trade‐off is selected at the first interim analysis, while the whole trial is allowed to compose of J analyses. We show that the design controls the familywise error rate in the strong sense and illustrate the method through an example and simulation. We find that the design is robust to misspecification of the correlation between the endpoints and requires similar numbers of subjects to a trial based on efficacy alone for moderately correlated endpoints. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.     

Time-varying feature selection for longitudinal analysis

We propose time-varying coefficient model selection and estimation based on the spline approach, which is capable of capturing time-dependent covariate effects. The new penalty function utilizes local-region information for varying-coefficient estimation, in contrast to the traditional model selection approach focusing on the entire region. The proposed method is extremely useful when the signals associated with relevant predictors are time-dependent, and detecting relevant covariate effects in the local region is more scientifically relevant than those of the entire region. Our simulation studies indicate that the proposed model selection incorporating local features outperforms the global feature model selection approaches. The proposed method is also illustrated through a longitudinal growth and health study from National Heart, Lung, and Blood Institute.     

基于医患偏好差异的治疗方案决策模型

文章针对医生、患者等对治疗方案的疗效和风险等存在不同的偏好,难以达成一致的治疗决策,提出了一种基于离散选择实验（DCE）和逼近理想解法（TOPSIS）的治疗方案选择方法。在应用离散选择实验调查患者及医生的偏好基础上,引入逼近理想解法对各种治疗方案进行定量评价。最后,以转移性肾细胞癌的靶向治疗为例说明该方法的具体应用,并比较了医生决策、患者决策、医患共同参与决策等不同决策方式下各治疗方案的评价排序,为临床决策制定提供参考意见。     

Covariate-adjusted adaptive randomization in a sarcoma trial with multi-stage treatments

We present a Bayesian design for a multi-centre, randomized clinical trial of two chemotherapy regimens for advanced or metastatic unresectable soft tissue sarcoma. After randomization, each patient receives up to four stages of chemotherapy, with the patient's disease evaluated after each stage and categorized on a trinary scale of severity. Therapy is continued to the next stage if the patient's disease is stable, and is discontinued if either tumour response or treatment failure is observed. We assume a probability model that accounts for baseline covariates and the multi-stage treatment and disease evaluation structure. The design uses covariate-adjusted adaptive randomization based on a score that combines the patient's probabilities of overall treatment success or failure. The adaptive randomization procedure generalizes the method proposed by Thompson (1933) for two binomial distributions with beta priors. A simulation study of the design in the context of the sarcoma trial is presented.