Adaptive enrichment designs for confirmatory trials.

After an overview of the Food and Drugs Administration's 2012 draft guidance on enrichment strategies for clinical trials to support drug/biologic approval, we describe subsequent advances in adaptive enrichment designs in this direction. We also provide a concrete application in the enrichment design of the Diffusion and Perfusion Imaging Evaluation for Understanding Stroke Evolution 3 trial comparing a new endovascular treatment with standard of care for ischemic stroke patients.

A new approach to regression analysis of censored competing-risks data.

An approximate likelihood approach is developed for regression analysis of censored competing-risks data. This approach models directly the cumulative incidence function, instead of the cause-specific hazard function, in terms of explanatory covariates under a proportional subdistribution hazards assumption. It uses a self-consistent iterative procedure to maximize an approximate semiparametric likelihood function, leading to an asymptotically normal and efficient estimator of the vector of regression parameters. Simulation studies demonstrate its advantages over previous methods.

Adaptive trial designs.

We review adaptive designs for clinical trials, giving special attention to the control of the Type I error in late-phase confirmatory trials, when the trial planner wishes to adjust the final sample size of the study in response to an unblinded analysis of interim estimates of treatment effects. We point out that there is considerable inefficiency in using the adaptive designs that employ conditional power calculations to reestimate the sample size and that maintain the Type I error by using certain weighted test statistics. Although these adaptive designs have little advantage over familiar group-sequential designs, our review also describes recent developments in adaptive designs that are both flexible and efficient. We also discuss the use of Bayesian designs, when the context of use demands control over operating characteristics (Type I and II errors) and correction of the bias of estimated treatment effects.

A new approach to designing phase I-II cancer trials for cytotoxic chemotherapies.

Recently, there has been much work on early phase cancer designs that incorporate both toxicity and efficacy data, called phase I-II designs because they combine elements of both phases. However, they do not explicitly address the phase II hypothesis test of H0 : p ≤ p0 , where p is the probability of efficacy at the estimated maximum tolerated dose η from phase I and p0 is the baseline efficacy rate. Standard practice for phase II remains to treat p as a fixed, unknown parameter and to use Simon's two-stage design with all patients dosed at η. We propose a phase I-II design that addresses the uncertainty in the estimate p=p(η) in H0 by using sequential generalized likelihood theory. Combining this with a phase I design that incorporates efficacy data, the phase I-II design provides a common framework that can be used all the way from the first dose of phase I through the final accept/reject decision about H0 at the end of phase II, utilizing both toxicity and efficacy data throughout. Efficient group sequential testing is used in phase II that allows for early stopping to show treatment effect or futility. The proposed phase I-II design thus removes the artificial barrier between phase I and phase II and fulfills the objectives of searching for the maximum tolerated dose and testing if the treatment has an acceptable response rate to enter into a phase III trial.

Sequential design of phase II-III cancer trials.

Although traditional phase II cancer trials are usually single arm, with tumor response as endpoint, and phase III trials are randomized and incorporate interim analyses with progression-free survival or other failure time as endpoint, this paper proposes a new approach that seamlessly expands a randomized phase II study of response rate into a randomized phase III study of time to failure. This approach is based on advances in group sequential designs and joint modeling of the response rate and time to event. The joint modeling is reflected in the primary and secondary objectives of the trial, and the sequential design allows the trial to adapt to increase in information on response and survival patterns during the course of the trial and to stop early either for conclusive evidence on efficacy of the experimental treatment or for the futility in continuing the trial to demonstrate it, on the basis of the data collected so far.

Clinical trial designs for testing biomarker-based personalized therapies.

BACKGROUND: Advances in molecular therapeutics in the past decade have opened up new possibilities for treating cancer patients with personalized therapies, using biomarkers to determine which treatments are most likely to benefit them, but there are difficulties and unresolved issues in the development and validation of biomarker-based personalized therapies. We develop a new clinical trial design to address some of these issues. The goal is to capture the strengths of the frequentist and Bayesian approaches to address this problem in the recent literature and to circumvent their limitations. METHODS: We use generalized likelihood ratio tests of the intersection null and enriched strategy null hypotheses to derive a novel clinical trial design for the problem of advancing promising biomarker-guided strategies toward eventual validation. We also investigate the usefulness of adaptive randomization (AR) and futility stopping proposed in the recent literature. RESULTS: Simulation studies demonstrate the advantages of testing both the narrowly focused enriched strategy null hypothesis related to validating a proposed strategy and the intersection null hypothesis that can accommodate to a potentially successful strategy. AR and early termination of ineffective treatments offer increased probability of receiving the preferred treatment and better response rates for patients in the trial, at the expense of more complicated inference under small-to-moderate total sample sizes and some reduction in power. LIMITATIONS: The binary response used in the development phase may not be a reliable indicator of treatment benefit on long-term clinical outcomes. In the proposed design, the biomarker-guided strategy (BGS) is not compared to 'standard of care', such as physician's choice that may be informed by patient characteristics. Therefore, a positive result does not imply superiority of the BGS to 'standard of care'. The proposed design and tests are valid asymptotically. Simulations are used to examine small-to-moderate sample properties. CONCLUSION: Innovative clinical trial designs are needed to address the difficulties and issues in the development and validation of biomarker-based personalized therapies. The article shows the advantages of using likelihood inference and interim analysis to meet the challenges in the sample size needed and in the constantly evolving biomarker landscape and genomic and proteomic technologies.

Sequential generalized likelihood ratio tests for vaccine safety evaluation.

The evaluation of vaccine safety involves pre-clinical animal studies, pre-licensure randomized clinical trials, and post-licensure safety studies. Sequential design and analysis are of particular interest because they allow early termination of the trial or quick detection that the vaccine exceeds a prescribed bound on the adverse event rate. After a review of the recent developments in this area, we propose a new class of sequential generalized likelihood ratio tests for evaluating adverse event rates in two-armed pre-licensure clinical trials and single-armed post-licensure studies. The proposed approach is illustrated using data from the Rotavirus Efficacy and Safety Trial. Simulation studies of the performance of the proposed approach and other methods are also given.

Design of clinical trials with failure-time endpoints and interim analyses: An update after fifteen years.

Time to event is the clinically definitive endpoint in Phase III trials of new treatments of cancer, cardiovascular and many other diseases. Because these trials involve relatively long follow-up, their protocols usually incorporate periodic interim analyses of the data by a Data and Safety Monitoring Board/Committee. This paper gives a review of the major developments in the design of these trials in the 21st century, spurred by the need for better clinical trial designs to cope with the remarkable advances in cancer biology, genomics and imaging that can help predict patients' sensitivity or resistance to certain treatments. In addition to this overview and discussion of related issues and challenges, we also introduce a new approach to address some of these issues.

Innovative designs of point-of-care comparative effectiveness trials.

One of the provisions of the health care reform legislation in 2010 was for funding pragmatic clinical trials or large observational studies for comparing the effectiveness of different approved medical treatments, involving broadly representative patient populations. After reviewing pragmatic clinical trials and the issues and challenges that have made them just a small fraction of comparative effectiveness research (CER), we focus on a recent development that uses point-of-care (POC) clinical trials to address the issue of "knowledge-action gap" in pragmatic CER trials. We give illustrative examples of POC-CER trials and describe a trial that we are currently planning to compare the effectiveness of newly approved oral anticoagulants. We also develop novel stage-wise designs of information-rich POC-CER trials under competitive budget constraints, by using recent advances in adaptive designs and other statistical methodologies.

Adaptive design of confirmatory trials: Advances and challenges.

The past decade witnessed major developments in innovative designs of confirmatory clinical trials, and adaptive designs represent the most active area of these developments. We give an overview of the developments and associated statistical methods in several classes of adaptive designs of confirmatory trials. We also discuss their statistical difficulties and implementation challenges, and show how these problems are connected to other branches of mainstream Statistics, which we then apply to resolve the difficulties and bypass the bottlenecks in the development of adaptive designs for the next decade.

Adaptive choice of patient subgroup for comparing two treatments.

This paper is motivated by a randomized controlled trial to compare an endovascular procedure with conventional medical treatment for stroke patients, in which the endovascular procedure may be effective only in a subgroup of patients. Since the subgroup is not known at the design stage but can be learned statistically from the data collected during the course of the trial, we develop a novel group sequential design that incorporates adaptive choice of the patient subgroup among several possibilities which include the entire patient population as a choice. We define the type I and type II errors of a test in this design and show how a prescribed type I error can be maintained by using the closed testing principle in multiple testing. We also show how asymptotically optimal tests can be constructed by using generalized likelihood ratio statistics for parametric problems and analogous standardized or Studentized statistics for nonparametric tests such as Wilcoxon's rank sum test commonly used for treatment comparison in stroke patients.

Group sequential designs for developing and testing biomarker-guided personalized therapies in comparative effectiveness research.

Biomarker-guided personalized therapies offer great promise to improve drug development and improve patient care, but also pose difficult challenges in designing clinical trials for the development and validation of these therapies. We first give a review of the existing approaches, briefly for clinical trials in new drug development and in more detail for comparative effectiveness trials involving approved treatments. We then introduce new group sequential designs to develop and test personalized treatment strategies involving approved treatments.

Efficient Adaptive Randomization and Stopping Rules in Multi-arm Clinical Trials for Testing a New Treatment.

Motivated by applications to confirmatory clinical trials for testing a new treatment against a placebo or active control when the new treatment has k possible treatment strategies (arms)-for example, k possible doses for a new drug-we develop an asymptotic theory for efficient outcome-adaptive randomization schemes and optimal stopping rules. Our approach consists of developing asymptotic lower bounds for the expected sample sizes from the k treatment arms and the control arm and using generalized sequential likelihood ratio procedures to achieve these bounds. Implementation details of our design and analysis and comparative simulation studies are also provided.

Efficient adaptive designs with mid-course sample size adjustment in clinical trials.

Adaptive designs have been proposed for clinical trials in which the nuisance parameters or alternative of interest are unknown or likely to be misspecified before the trial. Although most previous works on adaptive designs and mid-course sample size re-estimation have focused on two-stage or group-sequential designs in the normal case, we consider here a new approach that involves at most three stages and is developed in the general framework of multiparameter exponential families. This approach not only maintains the prescribed type I error probability but also provides a simple but asymptotically efficient sequential test whose finite-sample performance, measured in terms of the expected sample size and power functions, is shown to be comparable to the optimal sequential design, determined by dynamic programming, in the simplified normal mean case with known variance and prespecified alternative, and superior to the existing two-stage designs and also to adaptive group-sequential designs when the alternative or nuisance parameters are unknown or misspecified.

Stochastic segmentation models for array-based comparative genomic hybridization data analysis.

Array-based comparative genomic hybridization (array-CGH) is a high throughput, high resolution technique for studying the genetics of cancer. Analysis of array-CGH data typically involves estimation of the underlying chromosome copy numbers from the log fluorescence ratios and segmenting the chromosome into regions with the same copy number at each location. We propose for the analysis of array-CGH data, a new stochastic segmentation model and an associated estimation procedure that has attractive statistical and computational properties. An important benefit of this Bayesian segmentation model is that it yields explicit formulas for posterior means, which can be used to estimate the signal directly without performing segmentation. Other quantities relating to the posterior distribution that are useful for providing confidence assessments of any given segmentation can also be estimated by using our method. We propose an approximation method whose computation time is linear in sequence length which makes our method practically applicable to the new higher density arrays. Simulation studies and applications to real array-CGH data illustrate the advantages of the proposed approach.

A combined superiority and non-inferiority approach to multiple endpoints in clinical trials.

Treatment comparisons in clinical trials often involve multiple endpoints. By making use of bootstrap tests, we develop a new non-parametric approach to multiple-endpoint testing that can be used to demonstrate non-inferiority of a new treatment for all endpoints and superiority for some endpoint when it is compared to an active control. It is shown that this approach does not incur a large multiplicity cost in sample size to achieve reasonable power and that it can incorporate complex dependencies in the multivariate distributions of all outcome variables for the two treatments via bootstrap resampling.

Confidence intervals for survival quantiles in the Cox regression model.

Median survival times and their associated confidence intervals are often used to summarize the survival outcome of a group of patients in clinical trials with failure-time endpoints. Although there is an extensive literature on this topic for the case in which the patients come from a homogeneous population, few papers have dealt with the case in which covariates are present as in the proportional hazards model. In this paper we propose a new approach to this problem and demonstrate its advantages over existing methods, not only for the proportional hazards model but also for the widely studied cases where covariates are absent and where there is no censoring. As an illustration, we apply it to the Stanford Heart Transplant data. Asymptotic theory and simulation studies show that the proposed method indeed yields confidence intervals and bands with accurate coverage errors.

Modified Haybittle-Peto group sequential designs for testing superiority and non-inferiority hypotheses in clinical trials.

In designing an active controlled clinical trial, one sometimes has to choose between a superiority objective (to demonstrate that a new treatment is more effective than an active control therapy) and a non-inferiority objective (to demonstrate that it is no worse than the active control within some pre-specified non-inferiority margin). It is often difficult to decide which study objective should be undertaken at the planning stage when one does not have actual data on the comparative advantage of the new treatment. By making use of recent advances in the theory of efficient group sequential tests, we show how this difficulty can be resolved by a flexible group sequential design that can adaptively choose between the superiority and non-inferiority objectives during interim analyses. While maintaining the type I error probability at a pre-specified level, the proposed test is shown to have power advantage and/or sample size saving over fixed sample size tests for either only superiority or non-inferiority, and over other group sequential designs in the literature.

A new approach to modeling covariate effects and individualization in population pharmacokinetics-pharmacodynamics.

By combining Laplace's approximation and Monte Carlo methods to evaluate multiple integrals, this paper develops a new approach to estimation in nonlinear mixed effects models that are widely used in population pharmacokinetics and pharmacodynamics. Estimation here involves not only estimating the model parameters from Phase I and II studies but also using the fitted model to estimate the concentration versus time curve or the drug effects of a subject who has covariate information but sparse measurements. Because of its computational tractability, the proposed approach can model the covariate effects nonparametrically by using (i) regression splines or neural networks as basis functions and (ii) AIC or BIC for model selection. Its computational and statistical advantages are illustrated in simulation studies and in Phase I trials.