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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
de Mello-Sampayo, F. (2014). The Timing and Probability of Treatment Switch under Cost Uncertainty: An Application to Patients with Gastrointestinal Stromal Tumor. Value in Health. 17 (2), 215-222
Exportar Referência (IEEE) 
F. D. Sampayo,  "The Timing and Probability of Treatment Switch under Cost Uncertainty: An Application to Patients with Gastrointestinal Stromal Tumor", in Value in Health, vol. 17, no. 2, pp. 215-222, 2014
Exportar BibTeX 
@article{sampayo2014_1714687646880,
	author = "de Mello-Sampayo, F.",
	title = "The Timing and Probability of Treatment Switch under Cost Uncertainty: An Application to Patients with Gastrointestinal Stromal Tumor",
	journal = "Value in Health",
	year = "2014",
	volume = "17",
	number = "2",
	doi = "10.1016/j.jval.2013.12.008",
	pages = "215-222",
	url = "http://dx.doi.org/10.1016/j.jval.2013.12.008"
}
Exportar RIS 
TY  - JOUR
TI  - The Timing and Probability of Treatment Switch under Cost Uncertainty: An Application to Patients with Gastrointestinal Stromal Tumor
T2  - Value in Health
VL  - 17
IS  - 2
AU  - de Mello-Sampayo, F.
PY  - 2014
SP  - 215-222
SN  - 1098-3015
DO  - 10.1016/j.jval.2013.12.008
UR  - http://dx.doi.org/10.1016/j.jval.2013.12.008
AB  - Background: Cost fluctuations render the outcome of any treatment switch uncertain, so that decision makers might have to wait for more information before optimally switching treatments, especially when the incremental cost per quality-adjusted life year (QALY) gained cannot be fully recovered later on Objective: To analyze the timing of treatment switch under cost uncertainty. Methods: A dynamic stochastic model for the optimal timing of a treatment switch is developed and applied to a problem in medical decision taking, i.e. to patients with unresectable gastrointestinal stromal tumour (GIST). Results: the theoretical model suggests that cost uncertainty reduces expected net benefit. In addition, cost volatility discourages switching treatments. the stochastic model also illustrates that as technologies become less cost competitive, the cost uncertainty becomes more dominant. With limited substitutability, higher quality of technologies will increase the demand for those technologies disregarding the cost uncertainty. The results of the empirical application suggest that the first-line treatment may be the better choice when considering lifetime welfare. Conclusions: Under uncertainty and irreversibility, low-risk patients must begin the second-line treatment as soon as possible, which is precisely when the second-line treatment is least valuable. As the costs of reversing current treatment impacts fall, it becomes more feasible to provide the option-preserving treatment to these low-risk individuals later on.
ER  -