Semiparametric linear transformation models: Effect measures, estimators, and applications



Publication Details

De Neve, J., Thas, O. & Gerds, T. A. (2019). Semiparametric linear transformation models: Effect measures, estimators, and applications. Statistics in Medicine, 38 (8), 1484-1501.


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.

Please refer to publisher version or contact your library.



Link to publisher version (DOI)