Literature DB >> 12486752

Improved up-and-down designs for phase I trials.

Anastasia Ivanova1, Aliakbar Montazer-Haghighi, Sri Gopal Mohanty, Stephen D Durham.   

Abstract

We consider several designs from the family of up-and-down rules for the sequential allocation of dose levels to subjects in a dose-response study. We show that an up-and-down design can be improved by using more information than the most recent response. For example, the k-in-a-row rule uses up to the k most recent responses. We introduce a new design, the Narayana rule, which uses a local estimate of the probability of toxicity calculated from all previous responses. For the Narayana rule, as the sample size gets large, the probability of assignment goes to zero for dose levels not among the two (or three) closest to the target. Different estimators of the target dose are compared. We find that the isotonic regression estimator is superior to other estimators for small to moderate sample sizes. Copyright 2003 John Wiley & Sons, Ltd.

Entities:  

Mesh:

Substances:

Year:  2003        PMID: 12486752     DOI: 10.1002/sim.1336

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  20 in total

1.  Adaptive dose finding based on t-statistic for dose-response trials.

Authors:  Anastasia Ivanova; James A Bolognese; Inna Perevozskaya
Journal:  Stat Med       Date:  2008-05-10       Impact factor: 2.373

2.  Phase I trial of carboplatin and etoposide in combination with panobinostat in patients with lung cancer.

Authors:  Ahmad A Tarhini; Haris Zahoor; Brian McLaughlin; William E Gooding; John C Schmitz; Jill M Siegfried; Mark A Socinski; Athanassios Argiris
Journal:  Anticancer Res       Date:  2013-10       Impact factor: 2.480

3.  Dose finding for continuous and ordinal outcomes with a monotone objective function: a unified approach.

Authors:  Anastasia Ivanova; Se Hee Kim
Journal:  Biometrics       Date:  2008-05-13       Impact factor: 2.571

4.  Bayesian Optimal Interval Design: A Simple and Well-Performing Design for Phase I Oncology Trials.

Authors:  Ying Yuan; Kenneth R Hess; Susan G Hilsenbeck; Mark R Gilbert
Journal:  Clin Cancer Res       Date:  2016-07-12       Impact factor: 12.531

5.  Phase 1/2 study of rilotumumab (AMG 102), a hepatocyte growth factor inhibitor, and erlotinib in patients with advanced non-small cell lung cancer.

Authors:  Ahmad A Tarhini; Imran Rafique; Theofanis Floros; Phu Tran; William E Gooding; Liza C Villaruz; Timothy F Burns; David M Friedland; Daniel P Petro; Mariya Farooqui; Jose Gomez-Garcia; Autumn Gaither-Davis; Sanja Dacic; Athanassios Argiris; Mark A Socinski; Laura P Stabile; Jill M Siegfried
Journal:  Cancer       Date:  2017-05-04       Impact factor: 6.860

6.  Up-and-down designs for phase I clinical trials.

Authors:  Suyu Liu; Chunyan Cai; Jing Ning
Journal:  Contemp Clin Trials       Date:  2013-07-13       Impact factor: 2.226

7.  Comparison of Isotonic Designs for Dose-Finding.

Authors:  Anastasia Ivanova; Nancy Flournoy
Journal:  Stat Biopharm Res       Date:  2009-02-01       Impact factor: 1.452

8.  An adaptive design for identifying the dose with the best efficacy/tolerability profile with application to a crossover dose-finding study.

Authors:  Anastasia Ivanova; Ken Liu; Ellen Snyder; Duane Snavely
Journal:  Stat Med       Date:  2009-10-30       Impact factor: 2.373

9.  The rapid enrollment design for Phase I clinical trials.

Authors:  Anastasia Ivanova; Yunfei Wang; Matthew C Foster
Journal:  Stat Med       Date:  2016-02-01       Impact factor: 2.373

10.  A comparative study of Bayesian optimal interval (BOIN) design with interval 3+3 (i3+3) design for phase I oncology dose-finding trials.

Authors:  Yanhong Zhou; Ruobing Li; Fangrong Yan; J Jack Lee; Ying Yuan
Journal:  Stat Biopharm Res       Date:  2020-09-14       Impact factor: 1.452

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.