Adaptive assignment versus balanced randomization in clinical trials: A decision analysis

Berry, Donald A.; Eick, Stephen G.

doi:10.1002/sim.4780140302

Cited by 170 publications

(124 citation statements)

References 20 publications

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“…10,11 If this probability crosses a pre-specified boundary, the inferior arm is shut down before the maximal sample size is reached. However, it may reopen if further analyses indicate that results with the open arm(s) are deteriorating such that the probability that this arm(s) is superior has decreased.…”

Section: Adaptive Randomizationmentioning

confidence: 99%

Do commonly used clinical trial designs reflect clinical reality?

Estey¹

2009

Haematologica

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These trials typically randomize approximately 400 patients between two therapies. [1][2][3] This relatively large number is required to detect relatively small improvements with a false positive rate less than 5% (p<0.05) and a false negative rate less than 20% (80% power). For example, the trials in references 1-3 targeted increases in median event-free survival (EFS) or survival of 6-12 months, in 2-year EFS or survival of from 10% to 20% and in complete remission (CR) rate from 50% to 65%. Consider the relevance of a 6-month improvement in survival to an otherwise healthy 65-year old man with untreated acute myeloid leukemia (AML). Such a patient might expect to live another 15 years if he did not have AML but only another one half-year if he is randomized to a standard treatment arm. In such a case, he only retains 0.5/15 (3%) of his normally remaining life expectancy. If he is randomized to the investigational arm and it is successful, he gains another half-year and now retains 1/15 (7%) of his life expectancy.While statistically significant, I doubt many patients would consider this result medically significant. Hence, the targeted improvement does not reflect clinical reality. The choice of a false positive rate of 0.05 but a false negative rate of 0.20 implies a preference for more protection against a false positive than a false negative result. This is quite sensible when satisfactory treatment exists for the disease in question, and hence, replacement of this standard with a falsely positive new therapy is particularly undesirable. However, because there is no satisfactory treatment for most patients with AML, the medical risk of a false positive is much less. Indeed, the near universal choice of p=0.05 and power=80%, regardless of the disease in question, ignores the reality that diseases vary considerably in curability. Consequently, phase III AML trials should perhaps seek more clinically meaningful improvements and permit higher p values. Although this formulation would result in loss of power to detect relatively small advances, I question whether leukemia therapeutics advances in such small increments. In particular, it would appear that quantum therapeutic advances This paper contends that commonly used clinical trial designs do not reflect clinical reality as viewed by patients or physicians. Specifically, randomized phase III designs focus on improvements that are more significant statistically than medically and put an emphasis on avoiding a false positive result that is more appropriate for diseases that are curable, in contrast to acute leukemias. The resultant large sample sizes needed for each treatment restrict the trial to one or two new treatments, although historical reality suggests the difficulty in knowing, without clinical data, whether these are the best of several new treatments. The p value-based statistics discourage use of data from previous patients in the trial to inform treatment of subsequent patients, contravening patients' assumptions. Standard phase II trials focus o...

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Section: Adaptive Randomizationmentioning

confidence: 99%

Do commonly used clinical trial designs reflect clinical reality?

Estey¹

2009

Haematologica

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“…A workshop on 'Early Stopping Rules in Cancer Clinical Trials' included Bayesian approaches [151][152][153][154]. Other papers on clinical trials covered dose-finding [155][156][157][158][159][160], monitoring phase I studies [161], screening treatments prior to phase II evaluation [162], sample size for phase II [163], selecting treatments for phase III evaluation [164,165], monitoring phase II trials [166] bioequivalence [167,168], sample sizes for equivalence trials [169], two-period cross-over allowing for baseline [170], randomization [171], adaptive assignment [172], monitoring of trials [173,174], replication of evidence [175], reporting of clinical trials [176] and general commentaries [177]. In the regulatory context, European Notes for Guidance were published [178], which stated 'Although this Note for Guidance is written largely from the classical (frequentist) viewpoint, the use of Bayesian or other well-argued approaches is quite acceptable'.…”

Section: Clinical Trialsmentioning

confidence: 99%

Bayesian statistics in medicine: a 25 year review

2006

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SUMMARYThis review examines the state of Bayesian thinking as Statistics in Medicine was launched in 1982, reflecting particularly on its applicability and uses in medical research. It then looks at each subsequent five-year epoch, with a focus on papers appearing in Statistics in Medicine, putting these in the context of major developments in Bayesian thinking and computation with reference to important books, landmark meetings and seminal papers. It charts the growth of Bayesian statistics as it is applied to medicine and makes predictions for the future. From sparse beginnings, where Bayesian statistics was barely mentioned, Bayesian statistics has now permeated all the major areas of medical statistics, including clinical trials, epidemiology, meta-analyses and evidence synthesis, spatial modelling, longitudinal modelling, survival modelling, molecular genetics and decision-making in respect of new technologies.

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“…We denote Z i , i = 1, 2,...,N, as the response from the ith patient, which is either 1 for a success or 0 for a failure. As noted by Berry and Eick [3], N depends on the prevalence of the disease and is normally unknown to us.…”

Section: Introduction In Our Ethics Paper (Pullman and Wangmentioning

confidence: 99%

“…Such an objective is particularly desirable from the subject/patients' perspective. Following Berry and Eick [3], we maximize W π (P A ,P B ) = E π (Z 1 + Z 2 + ··· + Z N | P A ,P B ), conditionally on P A , P B , and N, or unconditionally, where π is a strategy for allocating treatments to these N patients. Any optimal strategy π * which maximizes W π (P A ,P B ) is characterized by the dynamic programming equation which states that the current patient is offered the best treatment under the current information, given that all future patients are treated optimally.…”

Section: Introduction In Our Ethics Paper (Pullman and Wangmentioning

confidence: 99%

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Play‐the‐winner rule and adaptive designs of clinical trials

Wang

Pullman

2001

International Journal of Mathematics and Mathematical Sciences

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Abstract. In another paper, we have argued that the traditional randomized design of clinical trials is ethically infeasible in desperate medical situations and adaptive designs are morally required. We have also argued that in such situations, the appropriate designs must satisfy what we call the "Principle of interchangeability." In this statistics paper, we show that the statistical model of bandit processes satisfies this principle of interchangeability. Moreover, we demonstrate that when such a model is used as an adaptive design, the total regret of successes lost is smaller when compared with simple randomization. We illustrate the results by the simple deterministic play-the-winner design.2000 Mathematics Subject Classification. 62K05, 62L05.

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Adaptive assignment versus balanced randomization in clinical trials: A decision analysis

Cited by 170 publications

References 20 publications

Do commonly used clinical trial designs reflect clinical reality?

Do commonly used clinical trial designs reflect clinical reality?

Bayesian statistics in medicine: a 25 year review

Play‐the‐winner rule and adaptive designs of clinical trials

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