Parameterization of a disease progression simulation model for sequentially treated metastatic human epidermal growth factor receptor 2 positive breast cancer patients

Current Medical Research and Opinion
Vakaramoko DiabyAlberto J Montero

Abstract

Background The objective of this study is twofold: 1) to propose a simulation model for HER2+ metastatic breast cancer (mBC) which could further be used to assess the overall cost-effectiveness of the treatment sequences that would maximize survival of patients, and 2) to estimate transitional probabilities between treatment lines required to parameterize the simulation model, in the absence of individual patient data (IPD). Methods Individual patient data (IPD) were reconstructed for treatment lines composing four treatment sequences. Parametric models were tested to select the model that best fits the IPD. The transitional probability equations, used for disease progression modeling, were obtained by substituting the parameters of the general equation for transitional probabilities by the parameters estimated from fitted distributions. Results The log-logistic model best fitted the reconstructed data for progression-free and overall survival curves for each line of treatment. The shapes and scales of the log-logistic models were used to develop the transitional probability equations for the HER2+ mBC simulation model. The estimation of the transitional probabilities depends heavily on the accuracy of the IPD reconstruction. N...Continue Reading

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Citations

Feb 2, 2018·Journal of Oncology Pharmacy Practice : Official Publication of the International Society of Oncology Pharmacy Practitioners·Henry Wc LeungShyh-Yau Wang
Jul 10, 2017·Breast Cancer Research and Treatment·Xavier Ghislain Léon Victor PouwelsManuela A Joore
May 19, 2019·Journal for Immunotherapy of Cancer·Howard L KaufmanWendy K D Selig
Jun 30, 2021·European Journal of Cancer : Official Journal for European Organization for Research and Treatment of Cancer (EORTC) [and] European Association for Cancer Research (EACR)·Shai GilboaTomer Meirson

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