Assessing the distribution of parameters in models of ligand-receptor interaction: To log or not to log

Research output: Contribution to journalArticleResearchpeer-review

Abstract

It is quite common to see experimental data analysed according to a variety of models of ligand-receptor interaction. Often, parameters derived from such models are compared statistically. The most commonly employed statistical analyses contain explicit assumptions about the underlying distributions of the model parameters being compared, yet the validity of these assumptions is not often ascertained. In this article, Arthur Christopoulos describes a general approach to Monte Carlo simulation of data, and outlines how the analysis of such simulated data may be used to address the question of the distribution of model parameters. The results of such an exercise can guide the researcher to the appropriate choice of statistical test or data transform. Copyright (C) 1998 Elsevier Science Ltd.

Original languageEnglish
Pages (from-to)351-357
Number of pages7
JournalTrends in Pharmacological Sciences
Volume19
Issue number9
DOIs
Publication statusPublished - 1 Sep 1998
Externally publishedYes

Cite this

@article{61275771e6864ec681125a88073d2747,
title = "Assessing the distribution of parameters in models of ligand-receptor interaction: To log or not to log",
abstract = "It is quite common to see experimental data analysed according to a variety of models of ligand-receptor interaction. Often, parameters derived from such models are compared statistically. The most commonly employed statistical analyses contain explicit assumptions about the underlying distributions of the model parameters being compared, yet the validity of these assumptions is not often ascertained. In this article, Arthur Christopoulos describes a general approach to Monte Carlo simulation of data, and outlines how the analysis of such simulated data may be used to address the question of the distribution of model parameters. The results of such an exercise can guide the researcher to the appropriate choice of statistical test or data transform. Copyright (C) 1998 Elsevier Science Ltd.",
author = "Arthur Christopoulos",
year = "1998",
month = "9",
day = "1",
doi = "10.1016/S0165-6147(98)01240-1",
language = "English",
volume = "19",
pages = "351--357",
journal = "Trends in Pharmacological Sciences",
issn = "0165-6147",
publisher = "Elsevier",
number = "9",

}

Assessing the distribution of parameters in models of ligand-receptor interaction : To log or not to log. / Christopoulos, Arthur.

In: Trends in Pharmacological Sciences, Vol. 19, No. 9, 01.09.1998, p. 351-357.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Assessing the distribution of parameters in models of ligand-receptor interaction

T2 - To log or not to log

AU - Christopoulos, Arthur

PY - 1998/9/1

Y1 - 1998/9/1

N2 - It is quite common to see experimental data analysed according to a variety of models of ligand-receptor interaction. Often, parameters derived from such models are compared statistically. The most commonly employed statistical analyses contain explicit assumptions about the underlying distributions of the model parameters being compared, yet the validity of these assumptions is not often ascertained. In this article, Arthur Christopoulos describes a general approach to Monte Carlo simulation of data, and outlines how the analysis of such simulated data may be used to address the question of the distribution of model parameters. The results of such an exercise can guide the researcher to the appropriate choice of statistical test or data transform. Copyright (C) 1998 Elsevier Science Ltd.

AB - It is quite common to see experimental data analysed according to a variety of models of ligand-receptor interaction. Often, parameters derived from such models are compared statistically. The most commonly employed statistical analyses contain explicit assumptions about the underlying distributions of the model parameters being compared, yet the validity of these assumptions is not often ascertained. In this article, Arthur Christopoulos describes a general approach to Monte Carlo simulation of data, and outlines how the analysis of such simulated data may be used to address the question of the distribution of model parameters. The results of such an exercise can guide the researcher to the appropriate choice of statistical test or data transform. Copyright (C) 1998 Elsevier Science Ltd.

UR - http://www.scopus.com/inward/record.url?scp=0032436491&partnerID=8YFLogxK

U2 - 10.1016/S0165-6147(98)01240-1

DO - 10.1016/S0165-6147(98)01240-1

M3 - Article

VL - 19

SP - 351

EP - 357

JO - Trends in Pharmacological Sciences

JF - Trends in Pharmacological Sciences

SN - 0165-6147

IS - 9

ER -