title: Parameterization of a disease progression simulation model for sequentially treated metastatic human epidermal growth factor receptor 2 positive breast cancer patients.
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. Nonetheless, analytical and graphical tests can be performed to check the face validity of the reconstructed data. Additionally, sensitivity analyses can be conducted to test the impact of uncertainty surrounding the estimated parameters defining equations for transitional probabilities. Conclusion The results of this study can be used as input in model-based economic evaluations of sequential therapy for HER2+ mBC.
authors.affiliation: a College of Pharmacy and Pharmaceutical Sciences, Florida A&M University , Tallahassee , FL , USA ;
authors.forename: Vakaramoko
authors.initials: V
authors.lastname: Diaby
authors.affiliation: a College of Pharmacy and Pharmaceutical Sciences, Florida A&M University , Tallahassee , FL , USA ;
authors.forename: Askal A
authors.initials: AA
authors.lastname: Ali
authors.affiliation: b Virginia Commonwealth University School of Medicine , Richmond , VA , USA ;
authors.forename: Georges
authors.initials: G
authors.lastname: Adunlin
authors.affiliation: c University of Saint Joseph School of Pharmacy, Hartford Hospital Evidence-based Practice Center , Hartford , CT , USA ;
authors.forename: Christine G
authors.initials: CG
authors.lastname: Kohn
authors.affiliation: d Cleveland Clinic, Taussig Cancer Institute , Department of Solid Tumor Oncology , Cleveland , OH , USA.
authors.forename: Alberto J
authors.initials: AJ
authors.lastname: Montero
mesh_terms: D000971:Antineoplastic Combined Chemotherapy Protocols / Q000627:therapeutic use*; D001943:Breast Neoplasms / Q000188:drug therapy*; D003362:Cost-Benefit Analysis; D018450:Disease Progression; D018572:Disease-Free Survival; D005260:Female; D006801:Humans; D016015:Logistic Models; D018803:Models, Economic; D011336:Probability; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000378:metabolism
chemical_list: D018719:Erb-b2 Receptor Tyrosine Kinases; C508053:ERBB2 protein, human
keywords: Disease progression modeling; HER2-positive metastatic breast cancer; Individual patient data reconstruction; Sequential therapy; Transitional probabilities
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