2019
DOI: 10.1002/psp4.12454
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Linking Tumor Growth Dynamics to Survival in Ipilimumab‐Treated Patients With Advanced Melanoma Using Mixture Tumor Growth Dynamic Modeling

Abstract: Early tumor assessments have been widely used to predict overall survival (OS), with potential application to dose selection and early go/no‐go decisions. Most published tumor dynamic models assume a uniform pattern of tumor growth dynamics (TGDs). We developed a mixture TGD model to characterize different patterns of longitudinal tumor sizes. Data from 688 patients with advanced melanoma who received ipilimumab 3 or 10 mg/kg every 3 weeks in a phase III study (NCT01515189) were used in a TGD‐OS analysis. The … Show more

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Cited by 16 publications
(24 citation statements)
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“…This article is protected by copyright. All rights reserved tumor types [16][17][18][19] for a variety of treatments. Leveraging tumor dynamics as a biomarker to predict OS in phase II trials with cancer immunotherapy (CIT) is not a novel concept, but longitudinal tumor response to CIT treatment may elicit different patterns compared to treatments with other mechanisms of action, such as delayed responses or increased tumor burden before regression [10,12,13,16].…”
Section: Accepted Articlementioning
confidence: 99%
“…This article is protected by copyright. All rights reserved tumor types [16][17][18][19] for a variety of treatments. Leveraging tumor dynamics as a biomarker to predict OS in phase II trials with cancer immunotherapy (CIT) is not a novel concept, but longitudinal tumor response to CIT treatment may elicit different patterns compared to treatments with other mechanisms of action, such as delayed responses or increased tumor burden before regression [10,12,13,16].…”
Section: Accepted Articlementioning
confidence: 99%
“…In multiple cancer types, the OS is correlated with the tumor dynamics such that the probability of survival decreases with the increase in tumor growth rate (g in Equation 3). [27][28][29][30][31][32][33][34][35][36][37] Other key determinants for survival are baseline prognostic factors specific for the cancer type. Drug exposure is evaluated as a covariate in the multivariate survival model.…”
Section: Tgi-os Modelingmentioning
confidence: 99%
“…To address this limitation, we implemented a mixture version of the Wang 3 model that allows for differences in parameter distributions in each mixture population, as well as a modified structural model in one of the subpopulations (the "no growth" subpopulation) in which the growth term is replaced by a term describing the steady-state value of tumor burden. 33 Bayesian information criterion (BIC) was used as model selection criteria. The improvement in the fit of the data with the mixture TGD model can be clearly illustrated by a comparison of goodness-of-fit plots for the mixture and nonmixture TGD models.…”
Section: Mixture Of Tumor Growth Dynamic Modeling Of Immuno-oncology mentioning
confidence: 99%