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Stochastic ordering under conditional modelling of extreme values: drug induced liver injury

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Stochastic ordering under conditional modelling of extreme values : drug induced liver injury. / Papastathopoulos, Ioannis; Tawn, Jonathan.

In: Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol. 64, No. 2, 02.2015, p. 299-317.

Research output: Contribution to journalJournal article

Harvard

Papastathopoulos, I & Tawn, J 2015, 'Stochastic ordering under conditional modelling of extreme values: drug induced liver injury', Journal of the Royal Statistical Society: Series C (Applied Statistics), vol. 64, no. 2, pp. 299-317. https://doi.org/10.1111/rssc.12074

APA

Papastathopoulos, I., & Tawn, J. (2015). Stochastic ordering under conditional modelling of extreme values: drug induced liver injury. Journal of the Royal Statistical Society: Series C (Applied Statistics), 64(2), 299-317. https://doi.org/10.1111/rssc.12074

Vancouver

Papastathopoulos I, Tawn J. Stochastic ordering under conditional modelling of extreme values: drug induced liver injury. Journal of the Royal Statistical Society: Series C (Applied Statistics). 2015 Feb;64(2):299-317. https://doi.org/10.1111/rssc.12074

Author

Papastathopoulos, Ioannis ; Tawn, Jonathan. / Stochastic ordering under conditional modelling of extreme values : drug induced liver injury. In: Journal of the Royal Statistical Society: Series C (Applied Statistics). 2015 ; Vol. 64, No. 2. pp. 299-317.

Bibtex

@article{4a817de944b24c44891f3a006a6389b7,
title = "Stochastic ordering under conditional modelling of extreme values: drug induced liver injury",
abstract = "Drug-induced liver injury (DILI) is a major public health issue and of serious concern for the pharmaceutical industry. Early detection of signs of a drug's potential for DILI is vital for pharmaceutical companies' evaluation of new drugs. A combination of extreme values of liver-specific variables indicate potential DILI (Hy's law). We estimate the probability of joint extreme elevations of laboratory variables by using the conditional approach to multivariate extremes which concerns the distribution of a random vector given an extreme component. We extend the current model to include the assumption of stochastically ordered survival curves and construct a hypothesis test for ordered tail dependence between doses, which is a pattern that is potentially triggered by DILI. The model proposed is applied to safety data from a phase 3 clinical trial of a drug that has been linked to liver toxicity.",
keywords = "Conditional dependence, Drug toxicity, Liver injury, Multivariate extremes, Safety data, Stochastic ordering",
author = "Ioannis Papastathopoulos and Jonathan Tawn",
year = "2015",
month = feb,
doi = "10.1111/rssc.12074",
language = "English",
volume = "64",
pages = "299--317",
journal = "Journal of the Royal Statistical Society: Series C (Applied Statistics)",
issn = "0035-9254",
publisher = "Wiley-Blackwell",
number = "2",

}

RIS

TY - JOUR

T1 - Stochastic ordering under conditional modelling of extreme values

T2 - drug induced liver injury

AU - Papastathopoulos, Ioannis

AU - Tawn, Jonathan

PY - 2015/2

Y1 - 2015/2

N2 - Drug-induced liver injury (DILI) is a major public health issue and of serious concern for the pharmaceutical industry. Early detection of signs of a drug's potential for DILI is vital for pharmaceutical companies' evaluation of new drugs. A combination of extreme values of liver-specific variables indicate potential DILI (Hy's law). We estimate the probability of joint extreme elevations of laboratory variables by using the conditional approach to multivariate extremes which concerns the distribution of a random vector given an extreme component. We extend the current model to include the assumption of stochastically ordered survival curves and construct a hypothesis test for ordered tail dependence between doses, which is a pattern that is potentially triggered by DILI. The model proposed is applied to safety data from a phase 3 clinical trial of a drug that has been linked to liver toxicity.

AB - Drug-induced liver injury (DILI) is a major public health issue and of serious concern for the pharmaceutical industry. Early detection of signs of a drug's potential for DILI is vital for pharmaceutical companies' evaluation of new drugs. A combination of extreme values of liver-specific variables indicate potential DILI (Hy's law). We estimate the probability of joint extreme elevations of laboratory variables by using the conditional approach to multivariate extremes which concerns the distribution of a random vector given an extreme component. We extend the current model to include the assumption of stochastically ordered survival curves and construct a hypothesis test for ordered tail dependence between doses, which is a pattern that is potentially triggered by DILI. The model proposed is applied to safety data from a phase 3 clinical trial of a drug that has been linked to liver toxicity.

KW - Conditional dependence

KW - Drug toxicity

KW - Liver injury

KW - Multivariate extremes

KW - Safety data

KW - Stochastic ordering

U2 - 10.1111/rssc.12074

DO - 10.1111/rssc.12074

M3 - Journal article

VL - 64

SP - 299

EP - 317

JO - Journal of the Royal Statistical Society: Series C (Applied Statistics)

JF - Journal of the Royal Statistical Society: Series C (Applied Statistics)

SN - 0035-9254

IS - 2

ER -