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1.
Mol Diagn Ther ; 28(3): 319-328, 2024 May.
Article En | MEDLINE | ID: mdl-38616205

OBJECTIVES: We evaluated the prognostic value of the neurotrophic tyrosine receptor kinase (NTRK) gene fusions by comparing the survival of patients with NTRK+ tumours with patients without NTRK+ tumours. METHODS: We used genomic and clinical registry data from the Center for Personalized Cancer Treatment (CPCT-02) study containing a cohort of cancer patients who were treated in Dutch clinical practice between 2012 and 2020. We performed a propensity score matching analysis, where NTRK+ patients were matched to NTRK- patients in a 1:4 ratio. We subsequently analysed the survival of the matched sample of NTRK+ and NTRK- patients using the Kaplan-Meier method and Cox regression, and performed an analysis of credibility to evaluate the plausibility of our result. RESULTS: Among 3556 patients from the CPCT-02 study with known tumour location, 24 NTRK+ patients were identified. NTRK+ patients were distributed across nine different tumour types: bone/soft tissue, breast, colorectal, head and neck, lung, pancreas, prostate, skin and urinary tract. NTRK fusions involving the NTRK3 gene (46%) and NTRK1 gene (33%) were most common. The survival analysis rendered a hazard ratio (HR) of 1.44 (95% CI 0.81-2.55) for NTRK+ patients. Using the point estimates of three prior studies on the prognostic value of NTRK fusions, our finding that the HR is > 1 was deemed plausible. CONCLUSIONS: NTRK+ patients may have an increased risk of death compared with NTRK- patients. When using historic control data to assess the comparative effectiveness of TRK inhibitors, the prognostic value of the NTRK fusion biomarker should therefore be accounted for.


Biomarkers, Tumor , Neoplasms , Receptor, trkA , Receptor, trkB , Receptor, trkC , Humans , Prognosis , Neoplasms/epidemiology , Neoplasms/genetics , Neoplasms/therapy , Survival Analysis , Receptor, trkC/analysis , Receptor, trkC/genetics , Receptor, trkA/analysis , Receptor, trkA/genetics , Receptor, trkB/analysis , Receptor, trkB/genetics , Male , Female , Middle Aged , Aged , Gene Fusion , Biomarkers, Tumor/analysis , Biomarkers, Tumor/genetics
2.
Value Health ; 19(4): 343-52, 2016 06.
Article En | MEDLINE | ID: mdl-27325326

BACKGROUND: In 2009, a new version of the EuroQol five-dimensional questionnaire (EQ-5D) was introduced with five rather than three answer levels per dimension. This instrument is known as the EQ-5D-5L. To make the EQ-5D-5L suitable for use in economic evaluations, societal values need to be attached to all 3125 health states. OBJECTIVES: To derive a Dutch tariff for the EQ-5D-5L. METHODS: Health state values were elicited during face-to-face interviews in a general population sample stratified for age, sex, and education, using composite time trade-off (cTTO) and a discrete choice experiment (DCE). Data were modeled using ordinary least squares and tobit regression (for cTTO) and a multinomial conditional logit model (for DCE). Model performance was evaluated on the basis of internal consistency, parsimony, goodness of fit, handling of left-censored values, and theoretical considerations. RESULTS: A representative sample (N = 1003) of the Dutch population participated in the valuation study. Data of 979 and 992 respondents were included in the analysis of the cTTO and the DCE, respectively. The cTTO data were left-censored at -1. The tobit model was considered the preferred model for the tariff on the basis of its handling of the censored nature of the data, which was confirmed through comparison with the DCE data. The predicted values for the EQ-5D-5L ranged from -0.446 to 1. CONCLUSIONS: This study established a Dutch tariff for the EQ-5D-5L on the basis of cTTO. The values represent the preferences of the Dutch population. The tariff can be used to estimate the impact of health care interventions on quality of life, for example, in context of economic evaluations.


Health Status Indicators , Quality of Life , Surveys and Questionnaires , Adolescent , Adult , Aged , Aged, 80 and over , Algorithms , Female , Humans , Interviews as Topic , Male , Middle Aged , Netherlands , Regression Analysis , Surveys and Questionnaires/standards , Young Adult
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