Dynamic Treatment Regimen Estimation via Regression-Based Techniques: Introducing R Package DTRreg

被引:21
|
作者
Wallace, Michael P. [1 ]
Moodie, Erica E. M. [2 ]
Stephens, David A. [2 ]
机构
[1] Univ Waterloo, Waterloo, ON, Canada
[2] McGill Univ, Montreal, PQ, Canada
来源
JOURNAL OF STATISTICAL SOFTWARE | 2017年 / 80卷 / 02期
基金
加拿大自然科学与工程研究理事会;
关键词
adaptive treatment strategies; dynamic treatment regimens; dynamic weighted ordinary least squares; G-estimation; personalized medicine; TREATMENT RULES;
D O I
10.18637/jss.v080.i02
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Personalized medicine, whereby treatments are tailored to a specific patient rather than a general disease or condition, is an area of growing interest in the fields of biostatistics, epidemiology, and beyond. Dynamic treatment regimens (DTRs) are an integral part of this framework, allowing for personalized treatment of patients with long-term conditions while accounting for both their present circumstances and medical history. The identification of the optimal DTR in any given context, however, is a non-trivial problem, and so specialized methodologies have been developed for that purpose. Here we introduce the R package DTRreg which implements two regression-based approaches: G-estimation and dynamic weighted ordinary least squares regression. We outline the theory underlying these methods, discuss the implementation of DTRreg and demonstrate its use with hypothetical and real-world inspired simulated datasets.
引用
收藏
页码:1 / 20
页数:20
相关论文
共 21 条
  • [1] Variable selection in regression-based estimation of dynamic treatment regimes
    Bian, Zeyu
    Moodie, Erica E. M.
    Shortreed, Susan M.
    Bhatnagar, Sahir
    [J]. BIOMETRICS, 2023, 79 (02) : 988 - 999
  • [2] Regression-based estimation of dynamic asset pricing models
    Adrian, Tobias
    Crump, Richard K.
    Moench, Emanuel
    [J]. JOURNAL OF FINANCIAL ECONOMICS, 2015, 118 (02) : 211 - 244
  • [3] Evaluation of soft computing and regression-based techniques for the estimation of evaporation
    Singh, Aparajita
    Singh, R. M.
    Kumar, A. R. Senthil
    Kumar, Ashish
    Hanwat, Subodh
    Tripathi, V. K.
    [J]. JOURNAL OF WATER AND CLIMATE CHANGE, 2021, 12 (01) : 32 - 43
  • [4] Quantile Regression-Based Estimation of Dynamic Statistical Contingency Fuel
    Kang, Lei
    Hansen, Mark
    [J]. TRANSPORTATION SCIENCE, 2021, 55 (01) : 257 - 273
  • [5] Outcome regression-based estimation of conditional average treatment effect
    Lu Li
    Niwen Zhou
    Lixing Zhu
    [J]. Annals of the Institute of Statistical Mathematics, 2022, 74 : 987 - 1041
  • [6] Outcome regression-based estimation of conditional average treatment effect
    Li, Lu
    Zhou, Niwen
    Zhu, Lixing
    [J]. ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS, 2022, 74 (05) : 987 - 1041
  • [7] Estimation of aboveground biomass from PolSAR and PolInSAR using regression-based modelling techniques
    Mukhopadhyay, Ritwika
    Kumar, Shashi
    Aghababaei, Hossein
    Kulshrestha, Anurag
    [J]. GEOCARTO INTERNATIONAL, 2022, 37 (14) : 4181 - 4207
  • [8] Model Assessment in Dynamic Treatment Regimen Estimation via Double Robustness
    Wallace, Michael P.
    Moodie, Erica E. M.
    Stephens, David A.
    [J]. BIOMETRICS, 2016, 72 (03) : 855 - 864
  • [9] GENERALIZATION ERROR BOUNDS OF DYNAMIC TREATMENT REGIMES IN PENALIZED REGRESSION-BASED LEARNING
    Oh, Eun Jeong
    Qian, Min
    Cheung, Ying Kuen
    [J]. ANNALS OF STATISTICS, 2022, 50 (04): : 2047 - 2071
  • [10] DTR: An R Package for Estimation and Comparison of Survival Outcomes of Dynamic Treatment Regimes
    Tang, Xinyu
    Melguizo, Maria
    [J]. JOURNAL OF STATISTICAL SOFTWARE, 2015, 65 (07): : 1 - 28