An MLE method for finding LKB NTCP model parameters using Monte Carlo uncertainty estimates

被引：2
|
作者：
Carolan, Martin [1 ,2 ]
Oborn, Brad [1 ,2 ]
Foo, Kerwyn [3 ]
Haworth, Annette [4 ]
Gulliford, Sarah [5 ,6 ]
Ebert, Martin [7 ,8 ]
机构：
[1] Wollongong Hosp, Illawarra Canc Care Ctr, Wollongong, NSW 2500, Australia
[2] Univ Wollongong, Ctr Med Radiat Phys, Wollongong, NSW 2522, Australia
[3] Royal Prince Alfred Hosp, Dept Radiat Oncol, Camperdown, NSW 2050, Australia
[4] Peter MacCallum Canc Ctr, Melbourne, Vic, Australia
[5] Inst Canc Res, Joint Dept Phys, Sutton, Surrey, England
[6] Royal Marsden Natl Hlth Serv Fdn Trust, Sutton, Surrey, England
[7] Sir Charles Gairdner Hosp, Nedlands, WA 6009, Australia
[8] Univ Western Australia, Sch Phys, Crawley, WA 6009, Australia
来源：
XVII INTERNATIONAL CONFERENCE ON THE USE OF COMPUTERS IN RADIATION THERAPY (ICCR 2013) | 2014年 / 489卷
基金：
澳大利亚国家健康与医学研究理事会;
关键词：
COMPLICATION PROBABILITY-MODELS;
D O I：
10.1088/1742-6596/489/1/012087
中图分类号：
O59 [应用物理学];
学科分类号：
摘要：
The aims of this work were to establish a program to fit NTCP models to clinical data with multiple toxicity endpoints, to test the method using a realistic test dataset, to compare three methods for estimating confidence intervals for the fitted parameters and to characterise the speed and performance of the program.
引用
收藏
页数：6
相关论文
共 50 条
  • [1] Obtaining Uncertainty Estimates Compatible with Estimates of Monte Carlo Method
    Zakharov, Igor
    Botsiura, Olesia
    Neyezhmakov, Pavel
    [J]. 2019 PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON MEASUREMENT (MEASUREMENT 2019), 2019, : 47 - 50
  • [2] Uncertainty quantification for chromatography model parameters by Bayesian inference using sequential Monte Carlo method
    Yamamoto, Yota
    Yajima, Tomoyuki
    Kawajiri, Yoshiaki
    [J]. CHEMICAL ENGINEERING RESEARCH & DESIGN, 2021, 175 : 223 - 237
  • [3] Uncertainty analysis of hec-hms model parameters using monte carlo simulation
    Shamsudin, Supiah
    Dan'azumi, Salisu
    Ab Rahman, Azmi
    [J]. International Journal of Modelling and Simulation, 2011, 31 (04): : 279 - 286
  • [4] On using the Monte Carlo method to calculate uncertainty intervals
    Willink, R.
    [J]. METROLOGIA, 2006, 43 (06) : L39 - L42
  • [5] A Sequential Monte Carlo Approach for MLE in a Plant Growth Model
    Trevezas, Samis
    Cournede, Paul-Henry
    [J]. JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS, 2013, 18 (02) : 250 - 270
  • [6] A Sequential Monte Carlo Approach for MLE in a Plant Growth Model
    Samis Trevezas
    Paul-Henry Cournède
    [J]. Journal of Agricultural, Biological, and Environmental Statistics, 2013, 18 : 250 - 270
  • [7] Uncertainty Evaluation by Monte Carlo Method
    Rachakonda, P.
    Ramnath, V.
    Pandey, V. S.
    [J]. MAPAN-JOURNAL OF METROLOGY SOCIETY OF INDIA, 2019, 34 (03): : 295 - 298
  • [8] Uncertainty Evaluation by Monte Carlo Method
    P. Rachakonda
    V. Ramnath
    V. S. Pandey
    [J]. MAPAN, 2019, 34 : 295 - 298
  • [9] Application of the Monte Carlo Method to Evaluate the Impact of Uncertainty of Model Parameters on the Time of Concrete Cover Damage
    Krykowski, Tomasz
    [J]. 5TH WORLD MULTIDISCIPLINARY CIVIL ENGINEERING-ARCHITECTURE-URBAN PLANNING SYMPOSIUM (WMCAUS), 2020, 960
  • [10] An Uncertainty Model for Strain Gages Using Monte Carlo Methodology
    Haslbeck, Matthias
    Boettcher, Joerg
    Braml, Thomas
    [J]. SENSORS, 2023, 23 (21)
12345