Literature DB >> 26040620

Adjusting for the Confounding Effects of Treatment Switching-The BREAK-3 Trial: Dabrafenib Versus Dacarbazine.

Nicholas R Latimer1, Keith R Abrams2, Mayur M Amonkar2, Ceilidh Stapelkamp2, R Suzanne Swann2.   

Abstract

BACKGROUND: Patients with previously untreated BRAF V600E mutation-positive melanoma in BREAK-3 showed a median overall survival (OS) of 18.2 months for dabrafenib versus 15.6 months for dacarbazine (hazard ratio [HR], 0.76; 95% confidence interval, 0.48-1.21). Because patients receiving dacarbazine were allowed to switch to dabrafenib at disease progression, we attempted to adjust for the confounding effects on OS.
MATERIALS AND METHODS: Rank preserving structural failure time models (RPSFTMs) and the iterative parameter estimation (IPE) algorithm were used. Two analyses, "treatment group" (assumes treatment effect could continue until death) and "on-treatment observed" (assumes treatment effect disappears with discontinuation), were used to test the assumptions around the durability of the treatment effect.
RESULTS: A total of 36 of 63 patients (57%) receiving dacarbazine switched to dabrafenib. The adjusted OS HRs ranged from 0.50 to 0.55, depending on the analysis. The RPSFTM and IPE "treatment group" and "on-treatment observed" analyses performed similarly well.
CONCLUSION: RPSFTM and IPE analyses resulted in point estimates for the OS HR that indicate a substantial increase in the treatment effect compared with the unadjusted OS HR of 0.76. The results are uncertain because of the assumptions associated with the adjustment methods. The confidence intervals continued to cross 1.00; thus, the adjusted estimates did not provide statistically significant evidence of a treatment benefit on survival. However, it is clear that a standard intention-to-treat analysis will be confounded in the presence of treatment switching-a reliance on unadjusted analyses could lead to inappropriate practice. Adjustment analyses provide useful additional information on the estimated treatment effects to inform decision making. IMPLICATIONS FOR PRACTICE: Treatment switching is common in oncology trials, and the implications of this for the interpretation of the clinical effectiveness and cost-effectiveness of the novel treatment are important to consider. If patients who switch treatments benefit from the experimental treatment and a standard intention-to-treat analysis is conducted, the overall survival advantage associated with the new treatment could be underestimated. The present study applied established statistical methods to adjust for treatment switching in a trial that compared dabrafenib and dacarbazine for metastatic melanoma. The results showed that this led to a substantially increased estimate of the overall survival treatment effect associated with dabrafenib. ©AlphaMed Press.

Entities:  

Keywords:  BRAF V600E mutation-positive melanoma; Dabrafenib; Dacarbazine; Overall survival; Survival analysis

Mesh:

Substances:

Year:  2015        PMID: 26040620      PMCID: PMC4492231          DOI: 10.1634/theoncologist.2014-0429

Source DB:  PubMed          Journal:  Oncologist        ISSN: 1083-7159


  14 in total

1.  Adjusting for differential proportions of second-line treatment in cancer clinical trials. Part I: structural nested models and marginal structural models to test and estimate treatment arm effects.

Authors:  Takuhiro Yamaguchi; Yasuo Ohashi
Journal:  Stat Med       Date:  2004-07-15       Impact factor: 2.373

2.  NRAS mutation status is an independent prognostic factor in metastatic melanoma.

Authors:  John A Jakob; Roland L Bassett; Chaan S Ng; Jonathan L Curry; Richard W Joseph; Gladys C Alvarado; Michelle L Rohlfs; Jessie Richard; Jeffrey E Gershenwald; Kevin B Kim; Alexander J Lazar; Patrick Hwu; Michael A Davies
Journal:  Cancer       Date:  2011-12-16       Impact factor: 6.860

3.  Overall survival: patient outcome, therapeutic objective, clinical trial end point, or public health measure?

Authors:  Everardo D Saad; Marc Buyse
Journal:  J Clin Oncol       Date:  2012-03-05       Impact factor: 44.544

4.  Distinct sets of genetic alterations in melanoma.

Authors:  John A Curtin; Jane Fridlyand; Toshiro Kageshita; Hetal N Patel; Klaus J Busam; Heinz Kutzner; Kwang-Hyun Cho; Setsuya Aiba; Eva-Bettina Bröcker; Philip E LeBoit; Dan Pinkel; Boris C Bastian
Journal:  N Engl J Med       Date:  2005-11-17       Impact factor: 91.245

Review 5.  A method for the analysis of randomized trials with compliance information: an application to the Multiple Risk Factor Intervention Trial.

Authors:  S D Mark; J M Robins
Journal:  Control Clin Trials       Date:  1993-04

6.  Mutations of the BRAF gene in human cancer.

Authors:  Helen Davies; Graham R Bignell; Charles Cox; Philip Stephens; Sarah Edkins; Sheila Clegg; Jon Teague; Hayley Woffendin; Mathew J Garnett; William Bottomley; Neil Davis; Ed Dicks; Rebecca Ewing; Yvonne Floyd; Kristian Gray; Sarah Hall; Rachel Hawes; Jaime Hughes; Vivian Kosmidou; Andrew Menzies; Catherine Mould; Adrian Parker; Claire Stevens; Stephen Watt; Steven Hooper; Rebecca Wilson; Hiran Jayatilake; Barry A Gusterson; Colin Cooper; Janet Shipley; Darren Hargrave; Katherine Pritchard-Jones; Norman Maitland; Georgia Chenevix-Trench; Gregory J Riggins; Darell D Bigner; Giuseppe Palmieri; Antonio Cossu; Adrienne Flanagan; Andrew Nicholson; Judy W C Ho; Suet Y Leung; Siu T Yuen; Barbara L Weber; Hilliard F Seigler; Timothy L Darrow; Hugh Paterson; Richard Marais; Christopher J Marshall; Richard Wooster; Michael R Stratton; P Andrew Futreal
Journal:  Nature       Date:  2002-06-09       Impact factor: 49.962

7.  Adjusting for treatment switching in randomised controlled trials - A simulation study and a simplified two-stage method.

Authors:  Nicholas R Latimer; K R Abrams; P C Lambert; M J Crowther; A J Wailoo; J P Morden; R L Akehurst; M J Campbell
Journal:  Stat Methods Med Res       Date:  2014-11-21       Impact factor: 3.021

8.  Adjusting overall survival for treatment switches: commonly used methods and practical application.

Authors:  Claire Watkins; Xin Huang; Nicholas Latimer; Yiyun Tang; Elaine J Wright
Journal:  Pharm Stat       Date:  2013-10-18       Impact factor: 1.894

9.  Assessing methods for dealing with treatment switching in randomised controlled trials: a simulation study.

Authors:  James P Morden; Paul C Lambert; Nicholas Latimer; Keith R Abrams; Allan J Wailoo
Journal:  BMC Med Res Methodol       Date:  2011-01-11       Impact factor: 4.615

10.  Adjusting survival time estimates to account for treatment switching in randomized controlled trials--an economic evaluation context: methods, limitations, and recommendations.

Authors:  Nicholas R Latimer; Keith R Abrams; Paul C Lambert; Michael J Crowther; Allan J Wailoo; James P Morden; Ron L Akehurst; Michael J Campbell
Journal:  Med Decis Making       Date:  2014-01-21       Impact factor: 2.583

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  10 in total

Review 1.  Drug Combinations as the New Standard for Melanoma Treatment.

Authors:  Marta Polkowska; Edyta Czepielewska; Małgorzata Kozłowska-Wojciechowska
Journal:  Curr Treat Options Oncol       Date:  2016-12

Review 2.  The Treatment of Advanced Melanoma: Therapeutic Update.

Authors:  Alessia Villani; Luca Potestio; Gabriella Fabbrocini; Giancarlo Troncone; Umberto Malapelle; Massimiliano Scalvenzi
Journal:  Int J Mol Sci       Date:  2022-06-07       Impact factor: 6.208

3.  Adjusting for treatment switching in the METRIC study shows further improved overall survival with trametinib compared with chemotherapy.

Authors:  Nicholas R Latimer; Helen Bell; Keith R Abrams; Mayur M Amonkar; Michelle Casey
Journal:  Cancer Med       Date:  2016-01-27       Impact factor: 4.452

4.  Network Meta-analysis of Progression-Free Survival and Overall Survival in First-Line Treatment of BRAF Mutation-Positive Metastatic Melanoma.

Authors:  Jordan Amdahl; Lei Chen; Thomas E Delea
Journal:  Oncol Ther       Date:  2016-09-27

5.  Two-stage estimation to adjust for treatment switching in randomised trials: a simulation study investigating the use of inverse probability weighting instead of re-censoring.

Authors:  N R Latimer; K R Abrams; U Siebert
Journal:  BMC Med Res Methodol       Date:  2019-03-29       Impact factor: 4.615

6.  Causal inference for long-term survival in randomised trials with treatment switching: Should re-censoring be applied when estimating counterfactual survival times?

Authors:  N R Latimer; I R White; K R Abrams; U Siebert
Journal:  Stat Methods Med Res       Date:  2018-06-25       Impact factor: 3.021

7.  Trends in the crossover of patients in phase III oncology clinical trials in the USA.

Authors:  Justin Yeh; Shruti Gupta; Sunny J Patel; Vamsi Kota; Achuta K Guddati
Journal:  Ecancermedicalscience       Date:  2020-11-13

8.  Evaluating how clear the questions being investigated in randomised trials are: systematic review of estimands.

Authors:  Suzie Cro; Brennan C Kahan; Sunita Rehal; Anca Chis Ster; James R Carpenter; Ian R White; Victoria R Cornelius
Journal:  BMJ       Date:  2022-08-23

Review 9.  Resistance to Targeted Therapy and RASSF1A Loss in Melanoma: What Are We Missing?

Authors:  Stephanie McKenna; Lucía García-Gutiérrez
Journal:  Int J Mol Sci       Date:  2021-05-12       Impact factor: 5.923

10.  Adjusting Overall Survival Estimates for Treatment Switching in Metastatic, Castration-Sensitive Prostate Cancer: Results from the LATITUDE Study.

Authors:  Susan Feyerabend; Fred Saad; Nolen Joy Perualila; Suzy Van Sanden; Joris Diels; Tetsuro Ito; Peter De Porre; Karim Fizazi
Journal:  Target Oncol       Date:  2019-12       Impact factor: 4.493

  10 in total

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