Evaluating Personalized Medicine in Multi-marker Multi-treatment Clinical Trials: Accounting for Heterogeneity
The assessment of the added value when matching the right treatment to the right population based on a molecular profile raises numerous statistical issues. Due to the low prevalence of potential molecular predictive factors of response to treatment as we
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Abstract The assessment of the added value when matching the right treatment to the right population based on a molecular profile raises numerous statistical issues. Due to the low prevalence of potential molecular predictive factors of response to treatment as well as of the existence of many types of histology in oncology, it is often impossible to carry out a separate trial for each histology and molecular profile combination. Instead, several contemporary randomized clinical trials investigate the efficacy of algorithms that combine multiple treatments with multiple molecular markers. Some of them focus on a single histology, whereas other are histology-agnostic and test whether selecting the treatment based on biology is superior to selecting the treatment based on histology. Several important sources of variability are induced by these types of trials. When this variability also concerns the treatment effect, the statistical properties of the design may be strongly compromised. In this chapter, using the randomized SHIVA trial evaluating personalized medicine in patients with advanced cancers as example, we present strengths and pitfalls of designs and various analysis tools. In particular, we illustrate the lack of power in the case of an algorithm being partially erroneous, the necessity to use randomized trials compared to designs where the patient is his or (her) own control, and propose a modeling approach to account for heterogeneity in treatment effects at the analysis step. Keywords Treatment algorithm
PFS ratio Randomized mixed effect model
X. Paoletti (&) S. Michiels Service de Biostatistique et Epidémiologie, Gustave Roussy & INSERM CESP U1018 - OncoStat, Gustave Roussy Cancer Center, Université Paris Sud Saclay, 114 Rue Ed Vaillant, 94805 Villejuif Cedex, France e-mail: [email protected] S. Michiels e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2017 S. Matsui and J. Crowley (eds.), Frontiers of Biostatistical Methods and Applications in Clinical Oncology, DOI 10.1007/978-981-10-0126-0_9
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1 Introduction Building on recent advances in biology and biotechnology, most new agents in oncology are designed to target molecular alterations or immunologic specificities involved in carcinogenesis. Anti-tumor activity is expected only in the presence of the matching molecular alterations or markers, which play the role of predictive factors of increased treatment benefit. Tumour genetics is increasingly being claimed as the main source of variability in the treatment effect compared to histology. Nevertheless, molecularly targeted agents (MTAs) have been assessed to date according to tumor location and histology before considering the molecular target. For instance, trastuzumab was first developed in breast cancer patients over-expressing HER2 before anti-tumor activity in advanced/metastatic stomach cancers with the same target was demonstrated [2]. This approach, that allows a fine description of the activity of new agen
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