JOINT UCL-IDDI WORKSHOP
December 11, 2018
Louvain-la-Neuve, Belgium
Alternatives to the Proportional Hazards Modeling in Cancer Drug Development
We thank you for attending our workshop on Alternatives to the Proportional Hazards Modeling in Cancer Drug Development at the UNIVERSITE CATHOLIQUE DE LOUVAIN (UCL).
Presentations are available in the Abstracts Section
ABSTRACTS
| Utility of restricted mean survival time in oncology clinical trials Chen Hu, Ph. D. | Assistant Professor of Oncology, Radiation Oncology and Biostatistics| Sidney Kimmel Comprehensive Cancer Center, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, US For oncology clinical trials, time-to-event endpoints, such as overall survival or progression-free survival, are used widely as key endpoints and of great interest. While the classic log-rank test and Cox proportional hazards model have been considered as the “default” analysis methods for statistical inference and for quantifying treatment benefit, we have witnessed many challenges and issues when they cannot be readily or properly applied. Moreover, as patients naturally encounter multiple outcomes in their disease course, or are subject to competing risks, there is increasing need and interest in developing and applying alternative metrics and inference tools to assess the benefit-risk profile more efficiently and in a timely fashion. In recent years, restricted mean survival time (RMST) has gained growing interests as an important alternative metric to evaluate survival data. In the presentation, we will review and discuss these recent methodological developments, as well as their potential use and implications in oncology clinical trials and drug development.  DOWNLOAD PRESENTATION HERE |
| Time for a broader use of accelerated failure-time models in cancer clinical trials Tomasz Burzykowski, Ph. D. | Professor of Biostatistics and Statistical Bioinformatics | I-BioStat, Hasselt University, Diepenbeek, and IDDI, Louvain-la-Neuve, Belgium Currently, treatment effects on time-to-event endpoints in oncology trials are almost exclusively estimated by using the proportional hazards (PH) model. However, the PH assumption is quite restrictive. Moreover, there is an increasing body of evidence that it may not be tenable for, e.g., cancer immunotherapy. Accelerated failure-time (AFT) models offer an alternative to the PH model. In particular, in recent years important developments have taken place regarding, e.g., the estimation of the semi-parametric AFT model. In the presentation, these and other developments will be reviewed and discussed in the context of the use of AFT model as an alternative to the PH model in cancer clinical trials. DOWNLOAD PRESENTATION HERE |
| To use a cure model or not, is that the question? Catherine Legrand, Ph. D. | Associate Professor of Statistics and Biostatistics |ISBA-IMMAQ, UCL, Louvain-la-Neuve, Belgium Thanks to the advances in medical research, one can now reasonably expect, for some specific cancer types, a fraction of long-term survivors in clinical trials. The presence of short-and long-term survivors may lead to a violation of the proportional hazards assumption and therefore jeopardize the use of the popular Cox model. Furthermore, in such a situation, the proportion of «cured» patients becomes a crucial component of the assessment of patient benefit, and being able to distinguish a curative from a life-prolonging effect conveys important additional information in the evaluation of a new treatment. To address these issues, specific «cure models» have been proposed in the statistical literature. In the presentation, based on joint work with Aurélie Bertrand, we introduce the two main families of such models: mixture cure models and promotion time cure models and elaborate on how and when to use them, with a particular attention to their links with the proportional hazards assumption. DOWNLOAD PRESENTATION HERE |
VENUE
UCL – Auditorium SOCRATE (SOCR -240): Place Cardinal Mercier, 1348 Louvain-la-Neuve, Belgium. Click here for location on Google Maps
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