Two-Stage Adaptive Design for Clinical Trials with Survival Data

Li, Gang; Shih, Weichung J.; Wang, Yining

doi:10.1081/bip-200062293

Cited by 35 publications

(31 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, suppose that it is desirable to set a 0 = 0.3, this rule then restricts a 1 = 0.013, more than half of the overall a, and definitely more than 0.005, what we would like to spend for the first stage. Li et al (2002Li et al ( , 2005, when discussing the conditional error functions of Proschan and Hunsberger (1995), have pointed out this fact and proposed a method that enables us to specify a 0 and a 1 independently, thus makes the design even more flexible.…”

mentioning

confidence: 97%

Plan to be Flexible: A Commentary on Adaptive Designs

Shih

2006

Biometrical J

View full text Add to dashboard Cite

This is a discussion of the following three papers appearing in this special issue on adaptive designs: 'Nested repeated confidence intervals and switching between noninferiority and superiority' by Joachim Hartung and Guido Knapp; 'Confirmatory Seamless Phase II/III Clinical trials with Hypotheses Selection at Interim: General Concepts' by Frank Bretz, Heinz Schmidli, Franz König, Amy Racine and Willi Maurer; and 'Confirmatory Seamless Phase Il/III Clinical Trials with Hypotheses Selection at Interim: Applications and Practical Considerations' by Heinz Schmidli, Frank Bretz, Amy Racine and Willi Maurer.

show abstract

mentioning

confidence: 97%

Plan to be Flexible: A Commentary on Adaptive Designs

Shih

2006

Biometrical J

View full text Add to dashboard Cite

show abstract

“…Chow, Chang, and Pong [15] examined the impact of population shift due to protocol amendments. Li et al, [12] studied a two-stage adaptive design with a survival endpoint, while Hommel et al [16] studied a two-stage adaptive design with correlated data. An adaptive design with a bivariate-endpoint was studied by Todd [17] Tsiatis and Mehta [18] showed that there exists a more powerful group sequential design for any adaptive design with sample size adjustment, For illustration purpose, in what follows, we will introduce the method based on sum of p-values (MSP) by Chang [2,19].…”

Section: Analysis For Category I Adaptive Designsmentioning

confidence: 99%

“…In practice, many interesting methods for Category I designs are available in the literature. These methods include (1) Fisher's criterion for combining independent p-values [6][7][8], (2) weighted test statistics [9] (3) the conditional error function approach [10,11] and (4) conditional power approaches [12].…”

Section: Analysis For Category I Adaptive Designsmentioning

confidence: 99%

Analysis of Two-Stage Adaptive Seamless Trial Design

M¹

2015

Pharm Anal Acta

View full text Add to dashboard Cite

In the past decade, adaptive design methods in clinical research have attracted much attention because it offers the principal investigators (1) potential flexibility for identifying clinical benefit of a test treatment under investigation, but efficiency for speeding up the development process. One of the most commonly considered adaptive designs is probably a two-stage seamless (e.g., phase I/II or phase II/III) adaptive design. The two-stage seamless adaptive designs can be classified into four categories depending upon study objectives and study endpoints at different stages. These categories include (I) design with same study objectives and study endpoints at different stages, (II) designs with same study objectives but different study endpoints at different stages, (III) designs with different study objectives but same study endpoints at different stages, and (IV) designs with different study objectives and different study endpoints at different stages. In this article, an overview of statistical methods for analysis of these different types of two-stage designs is provided. In addition, a case study concerning the evaluation of a test treatment for treating hepatitis C infected patients utilizing type (IV) trial design is presented.

show abstract

“…In adaptive trial design, at the time of the interim analysis: (1) if a "large" treatment effect is observed, the study can be terminated with the rejection of the null hypothesis; (2) if a "small" treatment effect is observed the study is terminated while declaring the treatment futile; or (3) if there is an intermediate treatment effect, the study is continued where the number of patients in the second stage is determined as a function of the observed treatment effect from the first stage (the study sample size is not specified until after the interim analysis). Methods to implement this "two stage" approach have been developed for trials with continuous 17 and time-to-event 19 outcomes.…”

Section: Howard Nonconventional Clinical Trial Designs 805mentioning

confidence: 99%

Nonconventional Clinical Trial Designs

Howard

2007

Stroke

View full text Add to dashboard Cite

Abstract-Statistical sciences have recently made advancements that allow improved precision or reduced sample size in clinical research studies. Herein, we review 4 of the more promising: (1) improvements in approaches for dose selection trials, (2) approaches for sample size adjustment, (3) selection of study end point and associated statistical methods, and (4) frequentist versus Bayesian statistical methods. Whereas each of these holds the opportunity for more efficient trials, each are associated with the need for more stringent assumptions or increased complexity in the interpretation of results. The opportunities for these promising approaches, and their associated "costs," are reviewed. iostatistics and clinical trial methodology continues to evolve and offer opportunities for more efficient clinical trials, directly translating into increased statistical power, smaller sample sizes, or both. These advancements, however, come at the cost of additional statistical assumptions, logistical challenges to the conduct of the trials, or increased complexity in the interpretation of results.There are many recent statistical advances, and the selection of which to include herein is clearly a subjective decision. Four areas warrant discussion in the stroke arena: (1) improvements in dose selection trials, (2) approaches for adapting the sample size during the conduct of the trial, (3) selection of scales for end points, and (4) Bayesian statistical methods. Improvements in Approaches for Dose Selection TrialsSelection of the appropriate dose is central to the success of compounds in Phase III trials. The fundamental assumption underlying dosing studies is that both efficacy and the risk of "safety end points" (toxicity) increases with dose, and therefore the "optimal" dose is the highest potential dose that has a low rate of safety events. For purposes of acute stroke trials, safety is often defined as having an acceptable rate of hemorrhage or death: for example, a hemorrhage/death rate of 10% or less. Until recently, the "3ϩ3" algorithm was the most commonly used algorithm for selection of the appropriate dose for acute stroke treatment compounds. This algorithm evaluates the first 3 patients at the lowest step of potential doses and adjusts the dose for subsequent patients as a function of the number of observed "safety events" 1 : (1) if there are no safety events, the dose is increased to the next level for the next 3 patients; (2) if there is exactly 1 safety event, then an additional 3 patients are evaluated at the current dose level, then: (a) the dose is increased if there are no additional safety events (ie, 1 of 6 patients with safety events); (b) the dose is decreased or the process terminated if there are 1 or more events in the second cohort or patients (ie, 2 or more safety events in 6 patients.); (c) if there are 2 or more events then the dose is decreased or the process terminated.This process is repeated until a dose is revisited twice, and then the procedure is terminated. Importantly, this algor...

show abstract

Two-Stage Adaptive Design for Clinical Trials with Survival Data

Cited by 35 publications

References 22 publications

Plan to be Flexible: A Commentary on Adaptive Designs

Plan to be Flexible: A Commentary on Adaptive Designs

Analysis of Two-Stage Adaptive Seamless Trial Design

Nonconventional Clinical Trial Designs

Contact Info

Product

Resources

About