ArticleHealth care management science2008
Challenging the role of calibration, validation and sensitivity analysis in relation to models of health care processes.
Article in Health care management science, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Team Size and Composition in Home Healthcare: Quantitative Insights and Six Model-Based Principles.Healthcare (Basel, Switzerland) · 2023Article
- Modeling the demand for long-term care services under uncertain information.Health care management science · 2012Article
- Scheduling admissions and reducing variability in bed demand.Health care management science · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many applications of Operational Research in the context of health care involve processes of calibration, validation and sensitivity analysis. Indeed these processes seem to have such an elevated status that their absence is often regarded as a marker that a study is somehow substandard. Undoubtedly this may be the case, however there may also be circumstances where it is perfectly reasonable not to use such methods. This paper concerns general principles underlying mathematical modelling, particularly in contexts where data for calibration are either poor quality or non-existent. The discussion challenges the view that modelling should necessarily be subject to formulaic calibration, validation and sensitivity analysis processes in an attempt to achieve or establish 'accuracy'. Some models are used purely to deduce the logical consequences of a set of beliefs and in this context, the need for validation is at best questionable. If calibration and sensitivity analysis are to be carried out, there is a need to be clear about what the objective is in such analyses.
Indexed as
Identifiers
18581827What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.