Statistical methods for assessing environmental effects on human genetic disorders

Walter W Piegorsch, J. A. Taylor

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Methods are presented for assessing interactions and other effects between genetic and environmental factors for human disease or cancer susceptibility. Statistical estimation and testing approaches are based on a simple multinomial sampling model for the case-control sampling scenario. It is noted that logistic regression methods can facilitate computation of likelihood-based statistics in this setting. Additional models for collapsibility over genotypes within the genetic factor are considered. Monte Carlo comparisons show that the method appears to retain nominal significance levels at total sample sizes above 100. At smaller sample sizes, a goodness-of-fit statistic is suggested for testing the interactive effect between the genetic and environmental factors. -Authors

Original languageEnglish (US)
Pages (from-to)369-384
Number of pages16
JournalEnvironmetrics
Volume3
Issue number4
StatePublished - 1992
Externally publishedYes

Fingerprint

environmental effect
Statistical method
Environmental impact
Disorder
Statistical methods
Environmental Factors
Statistics
Sampling
Testing
environmental factor
Collapsibility
Logistics
Statistical Estimation
Case-control
Significance level
Small Sample Size
sampling
Logistic Regression
Goodness of fit
Genotype

ASJC Scopus subject areas

  • Environmental Science(all)
  • Environmental Chemistry
  • Statistics and Probability

Cite this

Statistical methods for assessing environmental effects on human genetic disorders. / Piegorsch, Walter W; Taylor, J. A.

In: Environmetrics, Vol. 3, No. 4, 1992, p. 369-384.

Research output: Contribution to journalArticle

@article{bd3c4629e1a84677aea776ed152cc5b6,
title = "Statistical methods for assessing environmental effects on human genetic disorders",
abstract = "Methods are presented for assessing interactions and other effects between genetic and environmental factors for human disease or cancer susceptibility. Statistical estimation and testing approaches are based on a simple multinomial sampling model for the case-control sampling scenario. It is noted that logistic regression methods can facilitate computation of likelihood-based statistics in this setting. Additional models for collapsibility over genotypes within the genetic factor are considered. Monte Carlo comparisons show that the method appears to retain nominal significance levels at total sample sizes above 100. At smaller sample sizes, a goodness-of-fit statistic is suggested for testing the interactive effect between the genetic and environmental factors. -Authors",
author = "Piegorsch, {Walter W} and Taylor, {J. A.}",
year = "1992",
language = "English (US)",
volume = "3",
pages = "369--384",
journal = "Environmetrics",
issn = "1180-4009",
publisher = "John Wiley and Sons Ltd",
number = "4",

}

TY - JOUR

T1 - Statistical methods for assessing environmental effects on human genetic disorders

AU - Piegorsch, Walter W

AU - Taylor, J. A.

PY - 1992

Y1 - 1992

N2 - Methods are presented for assessing interactions and other effects between genetic and environmental factors for human disease or cancer susceptibility. Statistical estimation and testing approaches are based on a simple multinomial sampling model for the case-control sampling scenario. It is noted that logistic regression methods can facilitate computation of likelihood-based statistics in this setting. Additional models for collapsibility over genotypes within the genetic factor are considered. Monte Carlo comparisons show that the method appears to retain nominal significance levels at total sample sizes above 100. At smaller sample sizes, a goodness-of-fit statistic is suggested for testing the interactive effect between the genetic and environmental factors. -Authors

AB - Methods are presented for assessing interactions and other effects between genetic and environmental factors for human disease or cancer susceptibility. Statistical estimation and testing approaches are based on a simple multinomial sampling model for the case-control sampling scenario. It is noted that logistic regression methods can facilitate computation of likelihood-based statistics in this setting. Additional models for collapsibility over genotypes within the genetic factor are considered. Monte Carlo comparisons show that the method appears to retain nominal significance levels at total sample sizes above 100. At smaller sample sizes, a goodness-of-fit statistic is suggested for testing the interactive effect between the genetic and environmental factors. -Authors

UR - http://www.scopus.com/inward/record.url?scp=0027068501&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0027068501&partnerID=8YFLogxK

M3 - Article

VL - 3

SP - 369

EP - 384

JO - Environmetrics

JF - Environmetrics

SN - 1180-4009

IS - 4

ER -