Adaptive Designs for Interim Dose Selection

Li, Gang; Zhu, Junhong; Ouyang, Soo Peter; Xie, John; Deng, Ling; Law, Gordon

doi:10.1198/sbr.2009.0042

Cited by 9 publications

(9 citation statements)

References 15 publications

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“…The issue of whether the unconditional procedure strongly controls the family-wise error rate is yet to be addressed. Liu and Peldger [7], Koenig et al [4], and Li et al [6] also considered other approaches, which allow more than one winner to be selected. Stallard and Friede [13] provided a conservative procedure for the multi-winner case.…”

Section: Introductionmentioning

confidence: 97%

Interim Treatment Selection in Drug Development

Wang

Ouyang³

2009

Stat Biosci

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We consider hypothesis testing in a clinical trial with an interim treatment selection. Recently, unconditional and conditional procedures for selecting one treatment as the winner have been proposed when the mean responses are approximately normal. In this paper, we generalize both procedures to multi-winner cases. The distributions of the test statistics are obtained and step-down approaches are proposed. We prove that both unconditional and conditional procedures strongly control the family-wise error rate. We give a brief discussion on power comparisons.

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Section: Introductionmentioning

confidence: 97%

Interim Treatment Selection in Drug Development

Wang

Ouyang³

2009

Stat Biosci

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“…At the end of the second stage, inference comparing the selected treatment and the control is conducted using the data from both stages. More recently, other two-stage designs were also proposed (Li et al, 2009;Wang et al, 2011). Depending on the ranking results at the end of the first stage, these designs allow the selection of more than one dose to be carried to the second stage.…”

Section: Introductionmentioning

confidence: 98%

An Adaptive Staggered Dose Design for a Normal Endpoint

Menon

Chang

2014

Journal of Biopharmaceutical Statistics

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In a clinical trial where several doses are compared to a control, a multi-stage design that combines both the selection of the best dose and the confirmation of this selected dose is desirable. An example is the two-stage drop-the-losers or pick-the-winner design, where inferior doses are dropped after interim analysis. Selection of target dose(s) can be based on ranking of observed effects, hypothesis testing with adjustment for multiplicity, or other criteria at interim stages. A number of methods have been proposed and have made significant gains in trial efficiency. However, many of these designs started off with all doses with equal allocation and did not consider prioritizing the doses using existing dose-response information. We propose an adaptive staggered dose procedure that allows explicit prioritization of doses and applies error spending scheme that favors doses with assumed better responses. This design starts off with only a subset of the doses and adaptively adds new doses depending on interim results. Using simulation, we have shown that this design performs better in terms of increased statistical power than the drop-the-losers design given strong prior information of dose response.

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“…This concept was considered by Box and Jenkins (1962) and studied in earlier literature report, including those on group sequential design (Amitage, 1958; Jennison and Turnbull, 1999; Stein, 1945) and adaptive randomization (Pocock and Simon, 1975; Zelen, 1969). Recently, there has been a great deal of renewed interest in this area, and numerous papers were published in sample size reestimation (Bauer and Köhne, 1994; Cui et al, 1999; Chow and Chang, 2007; Fisher, 1998; Liu and Chi, 2001; Proschan and Hunsberger, 1995), adaptive treatment selection (Li et al, 2009; Luo et al, 2010; Stallard and Friede, 2008), population enrichment (Wang et al, 2007) and covariate adjustment and response adaptive designs (Hu and Rosenberger, 2003; Zhang et al, 2007). A comprehensive publications can be found in the draft guidance on adaptive design recently published by the U.S. Food and Drug Administration (FDA).…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we formulate a MPP framework for clinical trials and derive a general approach for treatment effect estimation following an adaptive clinical trial design based on the martingale structure of the MPP. We illustrate the method via a two stage adaptive selection design that complements the work by Sampson and Sill (2005, 2009), Stallard and Friede (2008), and Li et al (2009). It can be viewed as an extension of Luo et al (2010), which includes both clinical trial and numerical examples but based on binomial distribution only.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Treatment Effect Following a Clinical Trial with Adaptive Design

Luo¹,

Li²,

Shih³

et al. 2012

Journal of Biopharmaceutical Statistics

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Parameter estimation following an adaptive design or group sequential design has been extremely challenging due to potential random high from its face value estimate. In this paper, we introduce a new framework to model clinical trial data flow based on a marked point process (MPP). The MPP model allows us to use methods of stochastic calculus for analyses of any adaptive clinical trial. As an example, we apply this method to a two stage treatment selection design and derive a procedure to estimate the treatment effect. Numerical examples will be used to evaluate the performance of the proposed procedure.

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Adaptive Designs for Interim Dose Selection

Cited by 9 publications

References 15 publications

Interim Treatment Selection in Drug Development

Interim Treatment Selection in Drug Development

An Adaptive Staggered Dose Design for a Normal Endpoint

Estimation of Treatment Effect Following a Clinical Trial with Adaptive Design

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