Basic parametric analysis for a multi-state model in hospital epidemiology
Abstract Background The extended illness-death model is a useful tool to study the risks and consequences of hospital-acquired infections (HAIs). The statistical quantities of interest are the transition-specific hazard rates and the transition probabilities as well as attributable mortality (AM) an...
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| Format: | Article |
| Language: | English |
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BMC
2017-07-01
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| Series: | BMC Medical Research Methodology |
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| Online Access: | http://link.springer.com/article/10.1186/s12874-017-0379-4 |
| _version_ | 1811193060408688640 |
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| author | Maja von Cube Martin Schumacher Martin Wolkewitz |
| author_facet | Maja von Cube Martin Schumacher Martin Wolkewitz |
| author_sort | Maja von Cube |
| collection | DOAJ |
| description | Abstract Background The extended illness-death model is a useful tool to study the risks and consequences of hospital-acquired infections (HAIs). The statistical quantities of interest are the transition-specific hazard rates and the transition probabilities as well as attributable mortality (AM) and the population-attributable fraction (PAF). In the most general case calculation of these expressions is mathematically complex. Methods When assuming time-constant hazards calculation of the quantities of interest is facilitated. In this situation the transition probabilities can be expressed in closed mathematical forms. The estimators for AM and PAF can be easily derived from these forms. Results In this paper, we show how to explicitly calculate all the transition probabilities of an extended-illness model with constant hazards. Using a parametric model to estimate the time-constant transition specific hazard rates of a data example, the transition probabilities, AM and PAF can be directly calculated. With a publicly available data example, we show how the approach provides first insights into principle time-dynamics and data structure. Conclusion Assuming constant hazards facilitates the understanding of multi-state processes. Even in a non-constant hazards setting, the approach is a helpful first step for a comprehensive investigation of complex data. |
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| format | Article |
| id | doaj.art-bbbc8f934a5443c88702323ab3c6a07b |
| institution | Directory Open Access Journal |
| issn | 1471-2288 |
| language | English |
| last_indexed | 2024-04-12T00:02:10Z |
| publishDate | 2017-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Research Methodology |
| spelling | doaj.art-bbbc8f934a5443c88702323ab3c6a07b2022-12-22T03:56:13ZengBMCBMC Medical Research Methodology1471-22882017-07-0117111210.1186/s12874-017-0379-4Basic parametric analysis for a multi-state model in hospital epidemiologyMaja von Cube0Martin Schumacher1Martin Wolkewitz2Institute for Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of FreiburgInstitute for Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of FreiburgInstitute for Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of FreiburgAbstract Background The extended illness-death model is a useful tool to study the risks and consequences of hospital-acquired infections (HAIs). The statistical quantities of interest are the transition-specific hazard rates and the transition probabilities as well as attributable mortality (AM) and the population-attributable fraction (PAF). In the most general case calculation of these expressions is mathematically complex. Methods When assuming time-constant hazards calculation of the quantities of interest is facilitated. In this situation the transition probabilities can be expressed in closed mathematical forms. The estimators for AM and PAF can be easily derived from these forms. Results In this paper, we show how to explicitly calculate all the transition probabilities of an extended-illness model with constant hazards. Using a parametric model to estimate the time-constant transition specific hazard rates of a data example, the transition probabilities, AM and PAF can be directly calculated. With a publicly available data example, we show how the approach provides first insights into principle time-dynamics and data structure. Conclusion Assuming constant hazards facilitates the understanding of multi-state processes. Even in a non-constant hazards setting, the approach is a helpful first step for a comprehensive investigation of complex data.http://link.springer.com/article/10.1186/s12874-017-0379-4Homogeneous Markov processTransition probabilitiyAttributable mortalityPopulation-attributable fractionNosocomial infection |
| spellingShingle | Maja von Cube Martin Schumacher Martin Wolkewitz Basic parametric analysis for a multi-state model in hospital epidemiology BMC Medical Research Methodology Homogeneous Markov process Transition probabilitiy Attributable mortality Population-attributable fraction Nosocomial infection |
| title | Basic parametric analysis for a multi-state model in hospital epidemiology |
| title_full | Basic parametric analysis for a multi-state model in hospital epidemiology |
| title_fullStr | Basic parametric analysis for a multi-state model in hospital epidemiology |
| title_full_unstemmed | Basic parametric analysis for a multi-state model in hospital epidemiology |
| title_short | Basic parametric analysis for a multi-state model in hospital epidemiology |
| title_sort | basic parametric analysis for a multi state model in hospital epidemiology |
| topic | Homogeneous Markov process Transition probabilitiy Attributable mortality Population-attributable fraction Nosocomial infection |
| url | http://link.springer.com/article/10.1186/s12874-017-0379-4 |
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