A covariate adjusted two-stage allocation design for binary responses in randomized clinical trials.
TL;DR: A two-stage allocation rule for binary response using the log-odds ratio within the Bayesian framework allowing the current allocation to depend on the covariate value of the current subject is developed.
Abstract: In the present work, we develop a two-stage allocation rule for binary response using the log-odds ratio within the Bayesian framework allowing the current allocation to depend on the covariate value of the current subject. We study, both numerically and theoretically, several exact and limiting properties of this design. The applicability of the proposed methodology is illustrated by using some data set. We compare this rule with some of the existing rules by computing various performance measures.
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TL;DR: A new class of procedures, covariate-adjusted response adaptive (CARA) randomization procedures that attempt to optimize both efficiency and ethical considerations, while maintaining randomization are advocated.
Abstract: There has been a split in the statistics community about the need for taking covariates into account in the design phase of a clinical trial. There are many advocates of using stratification and covariate-adaptive randomization to promote balance on certain known covariates. However, balance does not always promote efficiency or ensure more patients are assigned to the better treatment. We describe these procedures, including model-based procedures, for incorporating covariates into the design of clinical trials, and give examples where balance, efficiency and ethical considerations may be in conflict. We advocate a new class of procedures, covariate-adjusted response-adaptive (CARA) randomization procedures that attempt to optimize both efficiency and ethical considerations, while maintaining randomization. We review all these procedures, present a few new simulation studies, and conclude with our philosophy.
147 citations
Journal Article•
TL;DR: Evidence-based medicine (EBM) is a shift in medical paradigms and about solving clinical problems, acknowledging that intuition, unsystematic clinical experience, and pathophysiologic rationale are insufficient grounds for clinical decision-making.
Abstract: Evidence-based medicine (EBM) is a shift in medical paradigms and about solving clinical problems, acknowledging that intuition, unsystematic clinical experience, and pathophysiologic rationale are insufficient grounds for clinical decision-making. The importance of randomized trials has been created by the concept of the hierarchy of evidence in guiding therapy. Even though the concept of hierarchy of evidence is not absolute, in modern medicine, most researchers synthesizing the evidence may or may not follow the principles of EBM, which requires that a formal set of rules must complement medical training and common sense for clinicians to interpret the results of clinical research. N of 1 randomized controlled trials (RCTs) has been positioned as the top of the hierarchy followed by systematic reviews of randomized trials, single randomized trial, systematic review of observational studies, single observational study, physiologic studies, and unsystematic clinical observations. However, some have criticized that the hierarchy of evidence has done nothing more than glorify the results of imperfect experimental designs on unrepresentative populations in controlled research environments above all other sources of evidence that may be equally valid or far more applicable in given clinical circumstances. Design, implementation, and reporting of randomized trials is crucial. The biased interpretation of results from randomized trials, either in favor of or opposed to a treatment, and lack of proper understanding of randomized trials, leads to a poor appraisal of the quality. Multiple types of controlled trials include placebo-controlled and pragmatic trials. Placebo controlled RCTs have multiple shortcomings such as cost and length, which limit the availability for studying certain outcomes, and may suffer from problems of faulty implementation or poor generalizability, despite the study design which ultimately may not be the prime consideration when weighing evidence for treatment alternatives. However, in practical clinical trials, interventions compared in the trial are clinically relevant alternatives, participants reflect the underlying affected population with the disease, participants come from a heterogeneous group of practice settings and geographic locations, and endpoints of the trial reflect a broad range of meaningful clinical outcomes.
145 citations
TL;DR: 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.
Abstract: 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.
72 citations
Cites methods from "A covariate adjusted two-stage allo..."
...This approach was used to develop CARA procedures in the papers by Bandyopadhyay et al. (2007, 2010) and Biswas et al. (2010)....
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TL;DR: In this paper, the authors define admissible allocations, namely treatment assignments that cannot be simultaneously improved upon with respect to both a specific design criterion, reflecting the inferential properties of the experiment, and the proportion of patients assigned to the best treatment or treatments; they survey existing designs from this viewpoint.
Abstract: In recent years, several authors have investigated response-adaptive allocation rules for comparative clinical trials, in order to favour, at each stage of the trial, the treatment that appears to be best. In this paper, we define admissible allocations, namely treatment assignments that cannot be simultaneously improved upon with respect to both a specific design criterion, reflecting the inferential properties of the experiment, and the proportion of patients assigned to the best treatment or treatments; we survey existing designs from this viewpoint. We also suggest combining information and ethical considerations by taking a suitable weighted mean of two corresponding standardized criteria, with weights that depend on the actual treatment effects. This compound criterion leads to a locally optimal allocation that can be targeted by some response-adaptive randomization rule. The paper mainly deals with the case of two treatments, but the suggested methodology is shown to extend to more than two.
41 citations
TL;DR: A randomized two-stage adaptive Bayesian design is proposed and studied for allocation and comparison in a phase III clinical trial with survival time as treatment response and the applicability of the proposed methodology is illustrated.
Abstract: A randomized two-stage adaptive Bayesian design is proposed and studied for allocation and comparison in a phase III clinical trial with survival time as treatment response. Several exact and limiting properties of the design and the follow-up inference are studied, both numerically and theoretically, and are compared with a single-stage randomized procedure. The applicability of the proposed methodology is illustrated by using some real data.
21 citations
Cites methods from "A covariate adjusted two-stage allo..."
...Consequently, we propose to choose p by maximizing the utility defined by U (p) = UV + αUR for some known value of α. Atkinson and Biswas (2005) and Bandyopadhyay et al. (2007) used such an utility function for normally distributed treatment responses and binary treatment responses, but for…...
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References
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Book•
01 Jan 1992
TL;DR: In this article, the authors present an analysis of experiments with both qualitative and quantitative factors: Blocking response surface designs, restricted region designs, failure of the experiment and design augmentation, and discrimination between models.
Abstract: Part I. Fundamentals Introduction Some key ideas Experimental strategies The choice of a model Models and least squares Criteria for a good experiment Standard designs The analysis of experiments Part II. Theory and applications Optimum design theory Criteria of optimality Experiments with both qualitative and quantitative factors Blocking response surface designs Restricted region designs Failure of the experiment and design augmentation Non-linear models Optimum Bayesian design Discrimination between models Composite design criteria Further topics.
1,437 citations
TL;DR: This book is a searching analysis of the fundamental principles of the theory of probability and of the particular judgments involved in its application to concrete problems and is in agreement with the views expressed by Dr. Wrinch and the present reviewer.
Abstract: DR. KEYNES'S book is a searching analysis of the fundamental principles of the theory of probability and of the particular judgments involved in its application to concrete problems. He adopts the view that knowledge may be relevant to our rational belief of a proposition without amounting to complete proof or disproof of it, and treats the probability as a measure of this relevance. NO.Otherwise he does not attempt to define “probability,” regarding it as a concept intelligible without further definition. In this respect, as in several others, he is in agreement with the views expressed by Dr. Wrinch and the present reviewer (Philosophical Magazine, vol. 38, 1919, pp. 715-31), and some comparison of the two presentations may not be out of place. A Treatise on Probability By J. M. Keynes. Pp. xi + 466. (London: Macmillan and Co., Ltd., 1921.) 18s. net.
1,390 citations
Book•
01 Jan 1983
TL;DR: This paper presents alternative Approaches to the Design and Analysis of Sequential Clinical Trials, and some examples of implementation ofSequential Methods: Some Examples.
Abstract: Clinical Trials. Allocating Patients to Treatments. Measurement of Treatment Difference. The Design of a Sequential Trial Using the Boundaries Approach. The Analysis of a Sequential Trial. Alternative Approaches to the Design and Analysis of Sequential Clinical Trials. Prognostic Factors. The Comparison of More than Two Treatments. Implementation of Sequential Methods: Some Examples. References. Appendix. Index.
662 citations
Book•
11 Jul 2002
TL;DR: In this paper, the effects of bias bias bias on the allocation of treatment allocation in clinical trials are discussed, including the effect of unobserved covariates Selection bias Randomization as a Basis for Inference Inference for Stratified, Blocked, and Covariate-Adjusted Analyses Randomization in Practice Response-Adaptive Randomization Inference For Response Adaptive Rondomization Response Adaptation Randomization is used in Clinical Trials.
Abstract: Preface Randomization and the Clinical Trial Issues in the Design of Clinical Trials Randomization for Balancing Treatment Assignments Balancing on Known Covariates The Effects of Unobserved Covariates Selection Bias Randomization as a Basis for Inference Inference for Stratified, Blocked, and Covariate-Adjusted Analyses Randomization in Practice Response-Adaptive Randomization Inference for Response-Adaptive Rondomization Response-Adaptive Randomization in Practice Some Useful results in Large Sample Theory Large Sample Inference for Complete and Restricted Randomization Large sample Inference for Response-Adaptive Randomization Author Index Subject Index
374 citations
TL;DR: In this paper, the authors used optimum design theory to provide a procedure of the biased coin type for an arbitrary number of treatments in the presence, or absence, of prognostic factors, and the results of the paper can be used for the sequential construction of DA-optimum designs in which randomization is not introduced by the experimenter.
Abstract: Patients in a clinical trial arrive sequentially and are assigned to one of t treatments. This assignment should maintain a balance between the numbers receiving each treatment, yet should be sufficiently random to avoid any suspicion of conscious or unconscious cheating. To balance these requirements Efron (1971) introduced biased coin designs for the comparison of two treatments in which allocation of the treatment is determined probabilistically, but with a bias towards the underrepresented treatment. One disadvantage of Efron's scheme is that it does not include balance over covariates or prognostic factors which may affect the response of the patient to the treatment. Biased coin schemes which do force balance over both treatments and prognostic factors are given by Pocock & Simon (1975) and Efron (1980). The properties of the designs have been elucidated by numerical studies and they are now being increasingly used in clinical trials. Reviews of the literature and discussions of the practical implications of the designs are given by Pocock (1979) and Simon (1979). However, the designs suffer from the disadvantage that they rely on arbitrary functions to achieve the desired balance. The procedures thus lack a firm theoretical framework. An alternative approach in the presence of prognostic factors (Begg & Iglewicz, 1980) uses optimum design theory to suggest a deterministic design criterion, which is then modified for computational convenience. In this paper I use optimum design theory to provide a procedure of the biased coin type for an arbitrary number of treatments in the presence, or absence, of prognostic factors. This has the theoretical advantage of obviating dependence on a series of arbitrary functions. The necessary optimum design theory is presented in ? 2 and, in ? 3, applied to biased coin experiments. The simplest case, that of two treatments in the absence of prognostic factors, is studied in ? 4. The extension to three or more treatments is in ? 5, followed, in ? 6, by the allowance for prognostic factors. Although, in the biased coin designs, allocation of treatments is made probabilistically, the results of the paper can be used for the sequential construction of DA-optimum designs in which randomization is not introduced by the experimenter. All the designs mentioned so far are sequential, but not adaptive. In the useful distinction made by Pericchi (1981), they are data-dependent but not outcomedependent: each allocation depends on the previous allocations and prognostic factors,
245 citations