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Semiparametric linear transformation models : effect measures, estimators and applications

Jan De Neve (UGent) , Olivier Thas (UGent) and Thomas Gerds
(2019) STATISTICS IN MEDICINE. 38(8). p.1484-1501
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Abstract
Semiparametric linear transformation models form a versatile class of regression models with the Cox proportional hazards model being the most well-known member. These models are well studied for right censored outcomes and are typically used in survival analysis. We consider transformation models as a tool for situations with uncensored continuous outcomes where linear regression is not appropriate. We introduce the probabilistic index as a uniform effect measure for the class of transformation models. We discuss and compare three estimators using a working Cox regression model: the partial likelihood estimator, an estimator based on binary generalized linear models and one based on probabilistic index model estimating equations. The latter has a superior performance in terms of bias and variance when the working model is misspecified. For the purpose of illustration, we analyze data that were collected at an urban alcohol and drug detoxification unit.
Keywords
probabilistic index, proportional hazard model, proportional odds model, semiparametric regression, MAXIMUM-LIKELIHOOD-ESTIMATION, PROBABILISTIC INDEX, REGRESSION-MODELS

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Citation

Please use this url to cite or link to this publication:

Chicago
De Neve, Jan, Olivier Thas, and Thomas Gerds. 2019. “Semiparametric Linear Transformation Models : Effect Measures, Estimators and Applications.” Statistics in Medicine 38 (8): 1484–1501.
APA
De Neve, J., Thas, O., & Gerds, T. (2019). Semiparametric linear transformation models : effect measures, estimators and applications. STATISTICS IN MEDICINE, 38(8), 1484–1501.
Vancouver
1.
De Neve J, Thas O, Gerds T. Semiparametric linear transformation models : effect measures, estimators and applications. STATISTICS IN MEDICINE. 2019;38(8):1484–501.
MLA
De Neve, Jan, Olivier Thas, and Thomas Gerds. “Semiparametric Linear Transformation Models : Effect Measures, Estimators and Applications.” STATISTICS IN MEDICINE 38.8 (2019): 1484–1501. Print.
@article{8584256,
  abstract     = {Semiparametric linear transformation models form a versatile class of regression models with the Cox proportional hazards model being the most well-known member. These models are well studied for right censored outcomes and are typically used in survival analysis. We consider transformation models as a tool for situations with uncensored continuous outcomes where linear regression is not appropriate. We introduce the probabilistic index as a uniform effect measure for the class of transformation models. We discuss and compare three estimators using a working Cox regression model: the partial likelihood estimator, an estimator based on binary generalized linear models and one based on probabilistic index model estimating equations. The latter has a superior performance in terms of bias and variance when the working model is misspecified. For the purpose of illustration, we analyze data that were collected at an urban alcohol and drug detoxification unit.},
  author       = {De Neve, Jan and Thas, Olivier and Gerds, Thomas},
  issn         = {0277-6715},
  journal      = {STATISTICS IN MEDICINE},
  language     = {eng},
  number       = {8},
  pages        = {1484--1501},
  title        = {Semiparametric linear transformation models : effect measures, estimators and applications},
  url          = {http://dx.doi.org/10.1002/sim.8078},
  volume       = {38},
  year         = {2019},
}

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