Literature DB >> 20406744

Trends in the application of dynamic allocation methods in multi-arm cancer clinical trials.

Gregory R Pond1, Patricia A Tang, Stephen A Welch, Eric X Chen.   

Abstract

BACKGROUND: Dynamic allocation (DA) methods which attempt to balance baseline prognostic factors between treatment arms, can be used in multi-arm clinical trials to sequentially allocate patients to treatment. Although some experts express concern regarding the validity of inference from trials using DA, others believe DA methods produce more credible results.
PURPOSE: A review of published multi-arm cancer clinical trials was conducted to explore the frequency of DA use in oncology.
METHODS: Multi-arm phase III clinical trials of at least 100 patients per arm, published in 13 major oncology journals from 1995-2005 were manually reviewed. Information about reported use of DA methods, or randomization via random permuted blocks (PB), was extracted along with trial characteristics.
RESULTS: Of 476 published clinical trials, 112 (23.5%) reported using some form of DA method, while 103 (21.6%) reported using PB methods. Most trials (403 or 84.7%) reported stratifying on at least one baseline factor. The mean number of stratification factors was 2.70 per trial, and 78.6% of DA trials reported 3 or more stratification factors compared with 30.2% of non-DA trials (p < 0.001). The frequency of DA use increased over time, with 20.2%, 21.3%, 25.8%, 28.8% and 38.9% of trials reported use in 1995-2001, 2002, 2003, 2004, and 2005, respectively. Use of DA methods was more frequently reported in trials involving an academic co-operative group (28.4% vs. 13.8%), however, no difference was observed between industry-funded and other-funded trials (24.0% vs. 23.2%) or geographical region (19.7% of North American trials, 26.2% of European trials and 21.7% of multinational/other trials). LIMITATIONS: As a retrospective analysis, the true frequency of DA use is likely underreported. Few trials gave complete details of the allocation method used, thus it is possible some manuscripts reported incorrect allocation methods. Journals were selected which were assumed to publish most large, multi-arm clinical trials in cancer from 1995-2005, however, some trials were likely reported in journals other than what was reviewed.
CONCLUSIONS: DA methods are frequently used in multi-arm cancer clinical trials. The use of DA appears to becoming more common over time and are used more frequently when an academic cooperative group is involved. No relationship between industry funded trials or geographic region and allocation method was observed. Clinical Trials 2010; 7: 227-234. http://ctj.sagepub.com.

Entities:  

Mesh:

Year:  2010        PMID: 20406744     DOI: 10.1177/1740774510368301

Source DB:  PubMed          Journal:  Clin Trials        ISSN: 1740-7745            Impact factor:   2.486


  5 in total

1.  Quantitative comparison of randomization designs in sequential clinical trials based on treatment balance and allocation randomness.

Authors:  Wenle Zhao; Yanqiu Weng; Qi Wu; Yuko Palesch
Journal:  Pharm Stat       Date:  2011-05-05       Impact factor: 1.894

Review 2.  Block urn design - a new randomization algorithm for sequential trials with two or more treatments and balanced or unbalanced allocation.

Authors:  Wenle Zhao; Yanqiu Weng
Journal:  Contemp Clin Trials       Date:  2011-08-22       Impact factor: 2.226

3.  Impact of minimal sufficient balance, minimization, and stratified permuted blocks on bias and power in the estimation of treatment effect in sequential clinical trials with a binary endpoint.

Authors:  Steven D Lauzon; Wenle Zhao; Paul J Nietert; Jody D Ciolino; Michael D Hill; Viswanathan Ramakrishnan
Journal:  Stat Methods Med Res       Date:  2021-11-29       Impact factor: 2.494

4.  Statistical issues in the use of dynamic allocation methods for balancing baseline covariates.

Authors:  G R Pond
Journal:  Br J Cancer       Date:  2011-05-03       Impact factor: 7.640

5.  Optimized design and analysis of preclinical intervention studies in vivo.

Authors:  Teemu D Laajala; Mikael Jumppanen; Riikka Huhtaniemi; Vidal Fey; Amanpreet Kaur; Matias Knuuttila; Eija Aho; Riikka Oksala; Jukka Westermarck; Sari Mäkelä; Matti Poutanen; Tero Aittokallio
Journal:  Sci Rep       Date:  2016-08-02       Impact factor: 4.379

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.