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Joint forces for making clinical prediction models contribute to science

   
@article{ATM31528,
	author = {Junfeng Wang and Yue Li},
	title = {Joint forces for making clinical prediction models contribute to science},
	journal = {Annals of Translational Medicine},
	volume = {8},
	number = {4},
	year = {2019},
	keywords = {},
	abstract = {Recently, the ATM journal has published a special report “In-depth mining of clinical data: the construction of clinical prediction model with R” (1), which is a series of methodology of clinical prediction model (CPM) construction. We read the 96 pages long article with great interest, especially because this is a statistical paper written by a group of clinicians. We are very glad to see clinicians are interested in statistics and predictive modelling, and they have reasonable knowledge in the development and validation of CPMs.},
	issn = {2305-5847},	url = {https://atm.amegroups.org/article/view/31528}
}