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A comparison of two frameworks for multi-state modelling, applied to outcomes after hospital admissions with COVID-19.
Jackson, Christopher H; Tom, Brian Dm; Kirwan, Peter D; Mandal, Sema; Seaman, Shaun R; Kunzmann, Kevin; Presanis, Anne M; De Angelis, Daniela.
  • Jackson CH; 47959MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
  • Tom BD; 47959MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
  • Kirwan PD; 47959MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
  • Mandal S; Public Health England, London, UK.
  • Seaman SR; Public Health England, London, UK.
  • Kunzmann K; 47959MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
  • Presanis AM; 47959MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
  • De Angelis D; 47959MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
Stat Methods Med Res ; 31(9): 1656-1674, 2022 09.
Article in English | MEDLINE | ID: covidwho-1932991
ABSTRACT
We compare two multi-state modelling frameworks that can be used to represent dates of events following hospital admission for people infected during an epidemic. The methods are applied to data from people admitted to hospital with COVID-19, to estimate the probability of admission to intensive care unit, the probability of death in hospital for patients before and after intensive care unit admission, the lengths of stay in hospital, and how all these vary with age and gender. One modelling framework is based on defining transition-specific hazard functions for competing risks. A less commonly used framework defines partially-latent subpopulations who will experience each subsequent event, and uses a mixture model to estimate the probability that an individual will experience each event, and the distribution of the time to the event given that it occurs. We compare the advantages and disadvantages of these two frameworks, in the context of the COVID-19 example. The issues include the interpretation of the model parameters, the computational efficiency of estimating the quantities of interest, implementation in software and assessing goodness of fit. In the example, we find that some groups appear to be at very low risk of some events, in particular intensive care unit admission, and these are best represented by using 'cure-rate' models to define transition-specific hazards. We provide general-purpose software to implement all the models we describe in the flexsurv R package, which allows arbitrarily flexible distributions to be used to represent the cause-specific hazards or times to events.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: Stat Methods Med Res Year: 2022 Document Type: Article Affiliation country: 09622802221106720

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: Stat Methods Med Res Year: 2022 Document Type: Article Affiliation country: 09622802221106720