© 2018 Elsevier Inc. Purpose: To explore the impact of length-biased sampling on the evaluation of risk factors of nosocomial infections (NIs) in point-prevalence studies. Methods: We used cohort data with full information including the exact date of the NI and mimicked an artificial 1-day prevalence study by picking a sample from this cohort study. Based on the cohort data, we studied the underlying multistate model which accounts for NI as an intermediate and discharge/death as competing events. Simple formulas are derived to display relationships between risk, hazard, and prevalence odds ratios. Results: Due to length-biased sampling, long stay and thus sicker patients are more likely to be sampled. In addition, patients with NIs usually stay longer in hospital. We explored mechanisms that 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. Conclusions: Thus, to avoid misinterpretation, knowledge of the mechanisms from the underlying multistate model is essential for the interpretation of risk factors derived from point-prevalence data.
- Cohort study
- Competing events
- Multi-state models
- Study design
Wolkewitz, M., Mandel, M., Palomar-Martinez, M., Alvarez-Lerma, F., Olaechea-Astigarraga, P., & Schumacher, M. (2018). Methodological challenges in using point-prevalence versus cohort data in risk factor analyses of nosocomial infections. Annals of Epidemiology, 28(7), 475-480.e1. https://doi.org/10.1016/j.annepidem.2018.03.017