Time-dependent endpoints as predictors of overall survival in multiple myeloma

Félix, Jorge Vinícius Cestari; Aragão, Filipa; Almeida, José; Calado, F; Ferreira, Diana; Parreira, A; Rodrigues, Ricardo; Rijo, João

doi:10.1186/1471-2407-13-122

Cited by 21 publications

(31 citation statements)

References 86 publications

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“…Our findings suggest that the benefit of an extended PFS that was observed in the published clinical trials may lead to or be an indicator of an OS benefit, and provide evidence that PFS should be considered as a valid surrogate endpoint for OS in MM. A meta-analysis conducted by Félix et al [41] found that various time-based endpoints such as median PFS, TTP, or event-free survival predicted median OS, but they did not directly examine the relationship between PFS and OS based on treatment effects. If we consider validation of a surrogate endpoint as a 2-step process of (1) establishing that a surrogate endpoint predicts a final endpoint and (2) establishing that a relationship exists between the treatment effects on the surrogate and final endpoint [7], then this study can be considered as a second step in the process of validating PFS as a surrogate of OS in MM.…”

Section: Discussionmentioning

confidence: 99%

Relationship between Treatment Effects on Progression-Free Survival and Overall Survival in Multiple Myeloma: A Systematic Review and Meta-Analysis of Published Clinical Trial Data

et al. 2015

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Background: Demonstrating improved overall survival (OS) with new multiple myeloma (MM) treatments is becoming difficult because of extended survival, so progression-free survival (PFS) is commonly used as a surrogate endpoint for OS. We evaluated PFS as a potential surrogate for OS by examining whether observed treatment effects on PFS are positively associated with treatment effects on OS in MM. Methods: A systematic literature review identified 21 randomized control trials reporting hazard ratios (HRs) for treatment effects on PFS and OS. Pearson's r estimated the relationship between HRs (HR_PFS and HR_OS), and between log-transformed HRs (log(HR_PFS) and log(HR_OS)). R² values were estimated from linear regression models of the HR and the log(HR) relationships. Sensitivity and subgroup analyses examined the robustness of the HR findings. Results: Positive correlations were found between HR_PFS and HR_OS (r = 0.82; p < 0.0001) and between log(HR_PFS) and log(HR_OS) (r = 0.80; p < 0.0001). Linear regression models produced R² values of 0.67 and 0.63 when regressing HR_OS on HR_PFS, and log(HR_OS) on log(HR_PFS), respectively. Sensitivity analyses supported the HR findings. Conclusion: This analysis provides evidence for a positive association between treatment effects on PFS and OS. Studies involving patient level data are necessary to confirm whether PFS is a valid surrogate for OS in MM.

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Section: Discussionmentioning

confidence: 99%

Relationship between Treatment Effects on Progression-Free Survival and Overall Survival in Multiple Myeloma: A Systematic Review and Meta-Analysis of Published Clinical Trial Data

et al. 2015

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“…(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)26,32,33,35,36,38,(40)(41)(42)47,49,50) The literature suggests that the relationship between PFS and OS can be different within the same cancer trial depending on the treatment applied or the therapy selected.…”

Section: Challenges For Analysing Pfs As a Surrogate Of Osmentioning

confidence: 99%

“…2) Treatment line (14,21,22,25,26,28,33,34,36,40,41,49,52) In some cases the analysis cannot validate the surrogacy for first line therapy, as distinct 3) Year of the trial (11,13,15,16,18,19,22,28,(31)(32)(33)(34)40) The importance of the year in which the clinical trial was conducted or published was explained by the number of drugs available having increased (11 and because the criteria applied to measure progression have changed (e.g. RECIST published in 2000 was modified in 2010 to mRECIST (54)).…”

Section: ) Type Of Treatment And/or Therapymentioning

confidence: 99%

“…RECIST published in 2000 was modified in 2010 to mRECIST (54)). (10,11,16,18,21,26,32,34,39,52) As in Davis, Tappenden (1), the results from the validation of PFS as a surrogate point for OS vary substantially between cancer types. Six out of 16 articles for lung cancer conclude that PFS is not an appropriate surrogate for OS, and consistency does not improve when we consider the line of therapy and the phase of the clinical trial.…”

Section: ) Type Of Treatment And/or Therapymentioning

confidence: 99%

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Challenges and Methodologies in Using Progression Free Survival as a Surrogate for Overall Survival in Oncology

Hernández-Villafuerte¹,

Fischer

Latimer

2018

Int J Technol Assess Health Care

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Objectives: A primary outcome in oncology trials is overall survival (OS). However, to estimate OS accurately requires a sufficient number of patients to have died, which may take a long time. If an alternative endpoint is sufficiently highly correlated with OS, it can be used as a surrogate. Progression-free survival (PFS) is the surrogate most often used in oncology, but does not always satisfy the correlation conditions for surrogacy.We analyse the methodologies used when extrapolating from PFS to OS. Methods: A wide range of methods has been used to determine the appropriateness of surrogates.While usually adhering to reporting standards, the standard of scholarship appears sometimes to be questionable and the reporting of results often haphazard. Conclusion:Standards of analysis and reporting PFS to OS surrogate studies should be improved by increasing the rigour of statistical reporting and by agreeing to a minimum set of reporting guidelines. Moreover, the use of IPD to assess surrogacy should increase.

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“…However, many bivariate failure time distributions are not well understood or widely used, and the dependence relationship are not intuitively expressed in general bivariate distributions. Alternatively, we will propose to use a survival regression model with TTP as a time‐dependent covariate to explicitly express the association of progression with the risk of death because the risk for death will usually increase upon cancer progression. We assume TTP distributed as T p ∼ f p ( t p | θ p ) and OS distributed as T d ∼ f d ( t d | θ d ) before disease progression and as

T_{d}^{*} \sim f_{d}^{*} (t_{d} - t_{p} | θ_{d}^{*})

after disease progression at t p , where

f_{d}^{*}

could be the same as or different from f d .…”

Section: Data Structure and Statistical Modelmentioning

confidence: 99%

Predicting analysis time in events‐driven clinical trials using accumulating time‐to‐event surrogate information

Wang¹,

Yu³

et al. 2015

Pharmaceutical Statistics

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For clinical trials with time-to-event endpoints, predicting the accrual of the events of interest with precision is critical in determining the timing of interim and final analyses. For example, overall survival (OS) is often chosen as the primary efficacy endpoint in oncology studies, with planned interim and final analyses at a pre-specified number of deaths. Often, correlated surrogate information, such as time-to-progression (TTP) and progression-free survival, are also collected as secondary efficacy endpoints. It would be appealing to borrow strength from the surrogate information to improve the precision of the analysis time prediction. Currently available methods in the literature for predicting analysis timings do not consider utilizing the surrogate information. In this article, using OS and TTP as an example, a general parametric model for OS and TTP is proposed, with the assumption that disease progression could change the course of the overall survival. Progression-free survival, related both to OS and TTP, will be handled separately, as it can be derived from OS and TTP. The authors seek to develop a prediction procedure using a Bayesian method and provide detailed implementation strategies under certain assumptions. Simulations are performed to evaluate the performance of the proposed method. An application to a real study is also provided. Copyright © 2015 John Wiley & Sons, Ltd.

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Time-dependent endpoints as predictors of overall survival in multiple myeloma

Cited by 21 publications

References 86 publications

Relationship between Treatment Effects on Progression-Free Survival and Overall Survival in Multiple Myeloma: A Systematic Review and Meta-Analysis of Published Clinical Trial Data

Relationship between Treatment Effects on Progression-Free Survival and Overall Survival in Multiple Myeloma: A Systematic Review and Meta-Analysis of Published Clinical Trial Data

Challenges and Methodologies in Using Progression Free Survival as a Surrogate for Overall Survival in Oncology

Predicting analysis time in events‐driven clinical trials using accumulating time‐to‐event surrogate information

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