tailieunhanh - Báo cáo y học: "Regression modelling in hospital epidemiology: a statistical note"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Critical Care giúp cho các bạn có thêm kiến thức về ngành y học đề tài: Regression modelling in hospital epidemiology: a statistical note. | Available online http content 12 5 427 Letter Regression modelling in hospital epidemiology a statistical note Martin Wolkewitz1 Jan Beyersmann1 Petra Gastmeier2 and Martin Schumacher1 11nstitute of Medical Biometry and Medical Informatics University Medical Center Freiburg Stefan-Meier-StraBe D-79104 Freiburg Germany institute of Hygiene and Environmental Medicine Charité - University Medicine Hindenburgdamm 27 12203 Berlin Germany Corresponding author Martin Wolkewitz wolke@ Published 4 September 2008 This article is online at http content 12 5 427 2008 BioMed Central Ltd Critical Care 2008 12 427 doi cc6991 See related commentary by Barnett and Graves http content 12 2 134 and see related research by Wolkewitz et al. http content 12 2 R44 Barnett and Graves 1 in their commentary on our report recently published in Critical Care 2 suggested that timediscrete methods should be used to address time-dependent risk factors and competing risks. In this letter we comment on two statements by those authors. First Barnett and Graves claim that An alternative method to the competing risks model is a multistate model. In fact a multistate model is not an alternative to modelling competing risks but a competing risks model is an example of a multistate model. This is explained in the tutorial by Putter and coworkers 3 . However competing risks only model the time to first event and the event type for example time to nosocomial infection NI or discharge death whatever comes first. To model subsequent events also more complex multistate models are needed. Barnett and Graves give an example in which discharge death events after NI are also modelled. However such a complex multistate model is implicitly used in a competing risks analysis when timedependent risk factors are included. For example in our report we also analyzed discharge and death as competing events and NI as a time-dependent covariate. .