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Multistate Modeling to Analyze Nosocomial Infection Data: An Introduction and Demonstration

  • Martin Wolkewitz (a1), Maja von Cube (a1) and Martin Schumacher (a1)



Multistate and competing risks models have become an established and adequate tool with which to quantify determinants and consequences of nosocomial infections. In this tutorial article, we explain and demonstrate the basics of these models to a broader audience of professionals in health care, infection control, and hospital epidemiology.


Using a publicly available data set from a cohort study of intensive care unit patients, we show how hospital infection data can be displayed and explored graphically and how simple formulas are derived under some simplified assumptions for illustrating the basic ideas behind multistate models. Only a few simply accessible values (event counts and patient days) and a pocket calculator are needed to reveal basic insights into cumulative risk and clinical outcomes of nosocomial infection in terms of mortality and length of stay.


We show how to use these values to perform basic multistate analyses in own data or to correct biased estimates in published data, as these values are often reported. We also show relationships between multistate-based hazard ratios and odds ratios, which are derived from the popular logistic regression model.


No sophisticated statistical software is required to apply a basic multistate model and to avoid typical pitfalls such as time-dependent or competing-risks bias.

Infect Control Hosp Epidemiol 2017;38:953–959


Corresponding author

Address correspondence to Martin Wolkewitz, PhD, Freiburg Center of Data Analysis and Modelling, Eckerstr 1, Room 105, 79104 Freiburg, Germany (


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Infection Control & Hospital Epidemiology
  • ISSN: 0899-823X
  • EISSN: 1559-6834
  • URL: /core/journals/infection-control-and-hospital-epidemiology
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