Simple Sequential Boundaries for Treatment Selection in Multi‐Armed Randomized Clinical Trials with a Control
ABSTRACT In situations when many regimens are possible candidates for a large phase III study, but too few resources are available to evaluate each relative to the standard, conducting a multi-armed randomized selection trial is a useful strategy to remove inferior treatments from further consideration. When the study has a relatively quick endpoint such as an imaging-based lesion volume change in acute stroke patients, frequent interim monitoring of the trial is ethically and practically appealing to clinicians. In this article, I propose a class of sequential selection boundaries for multi-armed clinical trials, in which the objective is to select a treatment with a clinically significant improvement upon the control group, or to declare futility if no such treatment exists. The proposed boundaries are easy to implement in a blinded fashion, and can be applied on a flexible monitoring schedule in terms of calendar time. Design calibration with respect to prespecified levels of confidence is simple, and can be accomplished when the response rate of the control group is known only up to an interval. One of the proposed methods is applied to redesign a selection trial with an imaging endpoint in acute stroke patients, and is compared to an optimal two-stage design via simulations: The proposed method imposes smaller sample size on average than the two-stage design; this advantage is substantial when there is in fact a superior treatment to the control group.
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- "Schaid, Wieand and Therneau (1990) proposed a similar 2-stage design for time-to-event outcomes that allows more than one E j to move forward to stage 2, allows termination of the trial in stage 1 for either futility or superiority, and does pairwise comparisons with the possibility of concluding that more than one E j provides an improvement over S. Many extensions of these designs have been proposed, including designs with more than two stages (Stallard and Todd, 2003; Stallard and Friede, 2008), a design that continues accrual between stages and uses the stage 1 data to determine the stage 2 sample size adaptively (Lin and Pledger, 2005), and an algorithm for computing decision boundaries (Cheung, 2008). "
ABSTRACT: The problem of comparing several experimental treatments to a standard arises frequently in medical research. Various multi-stage randomized phase II/III designs have been proposed that select one or more promising experimental treatments and compare them to the standard while controlling overall Type I and Type II error rates. This paper addresses phase II/III settings where the joint goals are to increase the average time to treatment failure and control the probability of toxicity while accounting for patient heterogeneity. We are motivated by the desire to construct a feasible design for a trial of four chemotherapy combinations for treating a family of rare pediatric brain tumors. We present a hybrid two-stage design based on two-dimensional treatment effect parameters. A targeted parameter set is constructed from elicited parameter pairs considered to be equally desirable. Bayesian regression models for failure time and the probability of toxicity as functions of treatment and prognostic covariates are used to define two-dimensional covariate-adjusted treatment effect parameter sets. Decisions at each stage of the trial are based on the ratio of posterior probabilities of the alternative and null covariate-adjusted parameter sets. Design parameters are chosen to minimize expected sample size subject to frequentist error constraints. The design is illustrated by application to the brain tumor trial design.Journal of Statistical Planning and Inference 04/2012; 142(4):944-955. DOI:10.1016/j.jspi.2011.10.016 · 0.60 Impact Factor
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- "While this approach apparently solves the missing data problem, the interpretation of such a composite outcome is not always clear. Finally, in trials where a non-trivial number of subjects may die within a few months of diagnosis (e.g., patients with advanced pancreatic cancer), it may be appropriate to consider death or short-term progression-free survival as the primary endpoint and monitor the trial based on binary outcomes; see Cheung (2008). This approach is recommended only when there are reasons to believe the experimental treatments will provide a substantial improvement in survival; otherwise, the sample size requirement will prove to be prohibitive for a phase II trial. "
ABSTRACT: The primary objective of Phase II cancer trials is to evaluate the potential efficacy of a new regimen in terms of its antitumor activity in a given type of cancer. Due to advances in oncology therapeutics and heterogeneity in the patient population, such evaluation can be interpreted objectively only in the presence of a prospective control group of an active standard treatment. This paper deals with the design problem of Phase II selection trials in which several experimental regimens are compared to an active control, with an objective to identify an experimental arm that is more effective than the control or to declare futility if no such treatment exists. Conducting a multi-arm randomized selection trial is a useful strategy to prioritize experimental treatments for further testing when many candidates are available, but the sample size required in such a trial with an active control could raise feasibility concerns. In this study, we extend the sequential probability ratio test for normal observations to the multi-arm selection setting. The proposed methods, allowing frequent interim monitoring, offer high likelihood of early trial termination, and as such enhance enrollment feasibility. The termination and selection criteria have closed form solutions and are easy to compute with respect to any given set of error constraints. The proposed methods are applied to design a selection trial in which combinations of sorafenib and erlotinib are compared to a control group in patients with non-small-cell lung cancer using a continuous endpoint of change in tumor size. The operating characteristics of the proposed methods are compared to that of a single-stage design via simulations: The sample size requirement is reduced substantially and is feasible at an early stage of drug development.Journal of Biopharmaceutical Statistics 02/2009; 19(3):494-508. DOI:10.1080/10543400902802425 · 0.72 Impact Factor
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ABSTRACT: In a Phase II trial, we may randomize patients to multiple arms of experimental therapies and evaluate their efficacy to determine if any of them is worthy of a large scale Phase III trial. Usually the primary objective of such a study is to identify experimental therapies that are efficacious compared to a historical control. Each arm is independently evaluated using a standard design a for single-arm Phase II trial, e.g., Simon's optimal or minimax design. When more than one arm is accepted through such a randomized trial, we may want to select the winner(s) among them. There are methods for between-arm comparisons in the literature, but most of them have drawbacks. They have a large false selection (type I error) probability when the competing arms have a small difference in efficacy, or the statistical tests used in the selection procedure do not properly reflect the small sample sizes and multi-stage design of the trials. In this paper, we propose between-arm comparison methods for selection in randomized Phase II trials addressing these issues.Journal of Biopharmaceutical Statistics 02/2009; 19(3):456-68. DOI:10.1080/10543400902802391 · 0.72 Impact Factor