title: A case-cohort approach for multi-state models in hospital epidemiology.
abstract: Analysing the determinants and consequences of hospital-acquired infections involves the evaluation of large cohorts. Infected patients in the cohort are often rare for specific pathogens, because most of the patients admitted to the hospital are discharged or die without such an infection. Death and discharge are competing events to acquiring an infection, because these individuals are no longer at risk of getting a hospital-acquired infection. Therefore, the data is best analysed with an extended survival model - the extended illness-death model. A common problem in cohort studies is the costly collection of covariate values. In order to provide efficient use of data from infected as well as uninfected patients, we propose a tailored case-cohort approach for the extended illness-death model. The basic idea of the case-cohort design is to only use a random sample of the full cohort, referred to as subcohort, and all cases, namely the infected patients. Thus, covariate values are only obtained for a small part of the full cohort. The method is based on existing and established methods and is used to perform regression analysis in adapted Cox proportional hazards models. We propose estimation of all cause-specific cumulative hazards and transition probabilities in an extended illness-death model based on case-cohort sampling. As an example, we apply the methodology to infection with a specific pathogen using a large cohort from Spanish hospital data. The obtained results of the case-cohort design are compared with the results in the full cohort to investigate the performance of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.
authors: Institute for Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Germany.|Freiburg Center of Data Analysis and Modelling, Albert-Ludwigs University Freiburg, Freiburg., Germany.
authors: Maja
authors: M
authors: von Cube
authors: Institute for Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Germany.|Freiburg Center of Data Analysis and Modelling, Albert-Ludwigs University Freiburg, Freiburg., Germany.
authors: Martin
authors: M
authors: Schumacher
authors: Universitat Autonoma de Barcelona, Hospital Vall d'Hebron, Barcelona, ES, Spain.
authors: Mercedes
authors: M
authors: Palomar-Martinez
authors: Service of Intensive Care Medicine, Hospital de Galdakao-Usansolo, Bizkaia, ES, Spain.
authors: Pedro
authors: P
authors: Olaechea-Astigarraga
authors: Service of Intensive Care Medicine, Parc de Salut Mar, Barcelona, ES, Spain.
authors: Francisco
authors: F
authors: Alvarez-Lerma
authors: Institute for Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Germany.|Freiburg Center of Data Analysis and Modelling, Albert-Ludwigs University Freiburg, Freiburg., Germany.
authors: Martin
authors: M
authors: Wolkewitz
mesh_terms: D016022:Case-Control Studies; D003428:Cross Infection / Q000453:epidemiology* / Q000401:mortality; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data; D016013:Likelihood Functions; D015233:Models, Statistical; D010343:Patient Admission / Q000706:statistics & numerical data; D010351:Patient Discharge / Q000706:statistics & numerical data; D016016:Proportional Hazards Models; D012044:Regression Analysis; D013030:Spain / Q000453:epidemiology; D013223:Statistics as Topic / Q000379:methods; D013997:Time Factors
keywords: Cox proportional hazards model; case-cohort design; cumulative hazard function; multi-state models; transition probability