Skip to main content
Top
Published in: Trials 1/2016

Open Access 01-12-2016 | Methodology

Type I error rates of multi-arm multi-stage clinical trials: strong control and impact of intermediate outcomes

Authors: Daniel J. Bratton, Mahesh K. B. Parmar, Patrick P. J. Phillips, Babak Choodari-Oskooei

Published in: Trials | Issue 1/2016

Login to get access

Abstract

Background

The multi-arm multi-stage (MAMS) design described by Royston et al. [Stat Med. 2003;22(14):2239–56 and Trials. 2011;12:81] can accelerate treatment evaluation by comparing multiple treatments with a control in a single trial and stopping recruitment to arms not showing sufficient promise during the course of the study. To increase efficiency further, interim assessments can be based on an intermediate outcome (I) that is observed earlier than the definitive outcome (D) of the study. Two measures of type I error rate are often of interest in a MAMS trial. Pairwise type I error rate (PWER) is the probability of recommending an ineffective treatment at the end of the study regardless of other experimental arms in the trial. Familywise type I error rate (FWER) is the probability of recommending at least one ineffective treatment and is often of greater interest in a study with more than one experimental arm.

Methods

We demonstrate how to calculate the PWER and FWER when the I and D outcomes in a MAMS design differ. We explore how each measure varies with respect to the underlying treatment effect on I and show how to control the type I error rate under any scenario. We conclude by applying the methods to estimate the maximum type I error rate of an ongoing MAMS study and show how the design might have looked had it controlled the FWER under any scenario.

Results

The PWER and FWER converge to their maximum values as the effectiveness of the experimental arms on I increases. We show that both measures can be controlled under any scenario by setting the pairwise significance level in the final stage of the study to the target level. In an example, controlling the FWER is shown to increase considerably the size of the trial although it remains substantially more efficient than evaluating each new treatment in separate trials.

Conclusions

The proposed methods allow the PWER and FWER to be controlled in various MAMS designs, potentially increasing the uptake of the MAMS design in practice. The methods are also applicable in cases where the I and D outcomes are identical.
Literature
1.
go back to reference Royston P, Parmar MK, Qian W. Novel designs for multi-arm clinical trials with survival outcomes with an application in ovarian cancer. Stat Med. 2003; 22(14):2239–56.CrossRefPubMed Royston P, Parmar MK, Qian W. Novel designs for multi-arm clinical trials with survival outcomes with an application in ovarian cancer. Stat Med. 2003; 22(14):2239–56.CrossRefPubMed
2.
go back to reference Royston P, Barthel FM, Parmar MK, Choodari-Oskooei B, Isham V. Designs for clinical trials with time-to-event outcomes based on stopping guidelines for lack of benefit. Trials. 2011; 12:81.CrossRefPubMedPubMedCentral Royston P, Barthel FM, Parmar MK, Choodari-Oskooei B, Isham V. Designs for clinical trials with time-to-event outcomes based on stopping guidelines for lack of benefit. Trials. 2011; 12:81.CrossRefPubMedPubMedCentral
3.
go back to reference Bratton DJ, Phillips PPJ, Parmar MKB. A multi-arm multi-stage clinical trial design for binary outcomes with application to tuberculosis. Med Res Methodol. 2013; 13:139.CrossRef Bratton DJ, Phillips PPJ, Parmar MKB. A multi-arm multi-stage clinical trial design for binary outcomes with application to tuberculosis. Med Res Methodol. 2013; 13:139.CrossRef
4.
go back to reference Parmar MK, Barthel FM, Sydes M, Langley R, Kaplan R, Eisenhauer E, et al.Speeding up the evaluation of new agents in cancer. J Natl Cancer Inst. 2008; 100(17):1204–14.CrossRefPubMedPubMedCentral Parmar MK, Barthel FM, Sydes M, Langley R, Kaplan R, Eisenhauer E, et al.Speeding up the evaluation of new agents in cancer. J Natl Cancer Inst. 2008; 100(17):1204–14.CrossRefPubMedPubMedCentral
5.
go back to reference Phillips PPJ, Gillespie SH, Boeree M, Heinrich N, Aarnoutse R, McHugh T, et al.Innovative trial designs are practical solutions for improving the treatment of tuberculosis. J Infect Dis. 2012; 205(suppl 2):S250–7.CrossRefPubMed Phillips PPJ, Gillespie SH, Boeree M, Heinrich N, Aarnoutse R, McHugh T, et al.Innovative trial designs are practical solutions for improving the treatment of tuberculosis. J Infect Dis. 2012; 205(suppl 2):S250–7.CrossRefPubMed
6.
go back to reference Sydes MR, Parmar MK, James ND, Clarke NW, Dearnaley DP, Mason MD, et al.Issues in applying multi-arm multi-stage methodology to a clinical trial in prostate cancer: the MRC STAMPEDE trial. Trials. 2009; 10:39.CrossRefPubMedPubMedCentral Sydes MR, Parmar MK, James ND, Clarke NW, Dearnaley DP, Mason MD, et al.Issues in applying multi-arm multi-stage methodology to a clinical trial in prostate cancer: the MRC STAMPEDE trial. Trials. 2009; 10:39.CrossRefPubMedPubMedCentral
7.
go back to reference Choodari-Oskooei B, Parmar MKB, Royston P, Bowden J. Impact of lack-of-benefit stopping rules on treatment effect estimates of two-arm multi-stage (TAMS) trials with time to event outcome. Trials. 2013; 14:23.CrossRefPubMedPubMedCentral Choodari-Oskooei B, Parmar MKB, Royston P, Bowden J. Impact of lack-of-benefit stopping rules on treatment effect estimates of two-arm multi-stage (TAMS) trials with time to event outcome. Trials. 2013; 14:23.CrossRefPubMedPubMedCentral
8.
go back to reference Wason J, Magirr D, Law M, Jaki T. Some recommendations for multi-arm multi-stage trials. Stat Methods Med Res. 2016; 25(2):716–27.CrossRefPubMed Wason J, Magirr D, Law M, Jaki T. Some recommendations for multi-arm multi-stage trials. Stat Methods Med Res. 2016; 25(2):716–27.CrossRefPubMed
9.
go back to reference Bratton DJ, Choodari-Oskooei B, Royston P. A menu-driven facility for sample size calculation in multi-arm multi-stage randomised controlled trials with time-to-event outcomes: update. Stata J. 2015; 15(2):350–68. Bratton DJ, Choodari-Oskooei B, Royston P. A menu-driven facility for sample size calculation in multi-arm multi-stage randomised controlled trials with time-to-event outcomes: update. Stata J. 2015; 15(2):350–68.
10.
go back to reference Barthel FMS, Royston P, Parmar MKB. A menu-driven facility for sample-size calculation in novel multi-arm, multi-stage randomized controlled trials with a time-to-event outcome. Stata J. 2009; 9(4):505–23. Barthel FMS, Royston P, Parmar MKB. A menu-driven facility for sample-size calculation in novel multi-arm, multi-stage randomized controlled trials with a time-to-event outcome. Stata J. 2009; 9(4):505–23.
11.
go back to reference Wason JM, Jaki T. Optimal design of multi-arm multi-stage trials. Stat Med. 2012; 31(30):4269–79.CrossRefPubMed Wason JM, Jaki T. Optimal design of multi-arm multi-stage trials. Stat Med. 2012; 31(30):4269–79.CrossRefPubMed
13.
go back to reference Magirr D, Jaki T, Whitehead J. A generalized Dunnett test for multi-arm multi-stage clinical studies with treatment selection. Biometrika. 2012; 99(2):494–501.CrossRef Magirr D, Jaki T, Whitehead J. A generalized Dunnett test for multi-arm multi-stage clinical studies with treatment selection. Biometrika. 2012; 99(2):494–501.CrossRef
14.
go back to reference Jaki T, Magirr D. Considerations on covariates and endpoints in multi-arm multi-stage clinical trials selecting all promising treatments. Stat Med. 2013; 32(7):1150–63.CrossRefPubMed Jaki T, Magirr D. Considerations on covariates and endpoints in multi-arm multi-stage clinical trials selecting all promising treatments. Stat Med. 2013; 32(7):1150–63.CrossRefPubMed
15.
go back to reference Dunnett CW. A multiple comparison procedure for comparing several treatments with a control. J Am Stat Assoc. 1955; 50(272):1096–121.CrossRef Dunnett CW. A multiple comparison procedure for comparing several treatments with a control. J Am Stat Assoc. 1955; 50(272):1096–121.CrossRef
16.
go back to reference Pocock SJ. Group sequential methods in the design and analysis of clinical trials. Biometrika. 1977; 64(2):191–9.CrossRef Pocock SJ. Group sequential methods in the design and analysis of clinical trials. Biometrika. 1977; 64(2):191–9.CrossRef
17.
go back to reference O’Brien PC, Fleming TR. A multiple testing procedure for clinical trials. Biometrics. 1979; 35(3):549–56.CrossRefPubMed O’Brien PC, Fleming TR. A multiple testing procedure for clinical trials. Biometrics. 1979; 35(3):549–56.CrossRefPubMed
18.
go back to reference Kunz CU, Friede T, Parsons N, Todd S, Stallard N. Data-driven treatment selection for seamless phase II/III trials incorporating early-outcome data. Pharm Stat. 2014; 13:238–46.CrossRefPubMedPubMedCentral Kunz CU, Friede T, Parsons N, Todd S, Stallard N. Data-driven treatment selection for seamless phase II/III trials incorporating early-outcome data. Pharm Stat. 2014; 13:238–46.CrossRefPubMedPubMedCentral
19.
go back to reference Kunz CU, Friede T, Parsons N, Todd S, Stallard N. A comparison of methods for treatment selection in seamless phase II/III clinical trials incorporating information on short-term endpoints. J Biopharm Stat. 2015; 25:170–89.CrossRefPubMedPubMedCentral Kunz CU, Friede T, Parsons N, Todd S, Stallard N. A comparison of methods for treatment selection in seamless phase II/III clinical trials incorporating information on short-term endpoints. J Biopharm Stat. 2015; 25:170–89.CrossRefPubMedPubMedCentral
21.
go back to reference Hughes MD. Multiplicity in Clinical Trials. Encyclopedia of Biostatistics. 2005; 5:3446–51. Hughes MD. Multiplicity in Clinical Trials. Encyclopedia of Biostatistics. 2005; 5:3446–51.
22.
go back to reference Freidlin B, Korn EL, Gray R, Martin A. Multi-arm clinical trials of new agents: some design considerations. Clin Cancer Res. 2008; 14(14):4368–71.CrossRefPubMed Freidlin B, Korn EL, Gray R, Martin A. Multi-arm clinical trials of new agents: some design considerations. Clin Cancer Res. 2008; 14(14):4368–71.CrossRefPubMed
23.
go back to reference Sydes MR, Parmar MK, Mason MD, Clarke NW, Amos C, Anderson J, et al.Flexible trial design in practice – stopping arms for lack-of-benefit and adding research arms mid-trial in STAMPEDE: a multi-arm multi-stage randomized controlled trial. Trials. 2012; 13(1):168.CrossRefPubMedPubMedCentral Sydes MR, Parmar MK, Mason MD, Clarke NW, Amos C, Anderson J, et al.Flexible trial design in practice – stopping arms for lack-of-benefit and adding research arms mid-trial in STAMPEDE: a multi-arm multi-stage randomized controlled trial. Trials. 2012; 13(1):168.CrossRefPubMedPubMedCentral
24.
go back to reference Cohen DR, Todd S, Gregory WM, Brown JM. Adding a treatment arm to an ongoing clinical trial: a review of methodology and practice. Trials. 2015; 16:179.CrossRefPubMedPubMedCentral Cohen DR, Todd S, Gregory WM, Brown JM. Adding a treatment arm to an ongoing clinical trial: a review of methodology and practice. Trials. 2015; 16:179.CrossRefPubMedPubMedCentral
26.
go back to reference Bratton DJ, Choodari-Oskooei B, Phillips PPJ, Sydes MR, Parmar MKB. Comments on ‘A modest proposal for dropping poor arms in clinical trials’ by Proschan and Dodd. Stat Med. 2015; 34:2678–9.CrossRefPubMed Bratton DJ, Choodari-Oskooei B, Phillips PPJ, Sydes MR, Parmar MKB. Comments on ‘A modest proposal for dropping poor arms in clinical trials’ by Proschan and Dodd. Stat Med. 2015; 34:2678–9.CrossRefPubMed
Metadata
Title
Type I error rates of multi-arm multi-stage clinical trials: strong control and impact of intermediate outcomes
Authors
Daniel J. Bratton
Mahesh K. B. Parmar
Patrick P. J. Phillips
Babak Choodari-Oskooei
Publication date
01-12-2016
Publisher
BioMed Central
Published in
Trials / Issue 1/2016
Electronic ISSN: 1745-6215
DOI
https://doi.org/10.1186/s13063-016-1382-5

Other articles of this Issue 1/2016

Trials 1/2016 Go to the issue