Date: 2018-04-27

Time: 15:30-16:30

Location: BURN 1205

Abstract:

To explore the impact of length-biased sampling on the evaluation of risk factors of nosocomial infections in point-prevalence studies. We used cohort data with full information including the exact date of the nosocomial infection and mimicked an artificial one-day prevalence study by picking a sample from this cohort study. Based on the cohort data, we studied the underlying multi-state model which accounts for nosocomial infection as an intermediate and discharge/death as competing events. Simple formulas are derived to display relationships between risk-, hazard- and prevalence odds ratios. Due to length-biased sampling, long-stay and thus sicker patients are more likely to be sampled. In addition, patients with nosocomial infections usually stay longer in hospital. We explored mechanisms which are -due to the design- hidden in prevalence data. In our example, we showed that prevalence odds ratios were usually less pronounced than risk odds ratios but more pronounced than hazard ratios. Thus, to avoid misinterpretation, knowledge of the mechanisms from the underlying multi-state model are essential for the interpretation of risk factors derived from point-prevalence data.

Speaker

Prof. Martin Wolkewitz, mathematician, is leading the division ‘Methods in Clinical Epidemiology’ which belongs to the Institute of Medical Biometry and Statistics, Freiburg, Germany. He is the group leader of seven mathematicians and two health economists. The main research interests of our group include: statistical modelling in clinical epidemiology, bias evaluation, health-care cost evaluation, risk communication, advanced survival analysis (such as multistate models, competing risk models), study designs with applications in hospital-acquired infections, antimicrobial resistance, influenza, oncology and society.