The generalized linear model and extensions: a review and some biological and environmental applications

Paul, S. R.; Saha, Krishna K.

doi:10.1002/env.849

Cited by 26 publications

(14 citation statements)

References 17 publications

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“…Increased induction of micronuclei in PUBEC by increased 4-ABP concentrations without and in combination with 1 M B[a]P was investigated using a quasi-binomial model with the log link function accounting for overdispersion and variation between PUBEC (Paul and Saha 2007). Williams-type contrasts (Williams 1971) were used to detect a possible concentrations-related trend supposing a monotone trend for increasing 4-ABP concentrations, 1 M B[a]P without 4-ABP, and combinations of 1 M B[a]P and increasing 4-ABP concentrations (Hothorn et al 2008;Hothorn and Gerhard 2008).…”

Section: Discussionmentioning

confidence: 99%

Synergism of aromatic amines and benzo[a]pyrene in induction of Ah receptor-dependent genes

et al. 2008

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Aromatic amines have been shown to cause bladder cancer. However, epithelial cells of the urinary bladder, cells of origin of bladder cancer, may be exposed to numerous substances besides aromatic amines. In the present study, we analysed possible interactions between the aromatic amines 4-aminobiphenyl (4-ABP) as well as 2-naphthylamine (2-NA) and the polycyclic aromatic hydrocarbon benzo[a]pyrene (B[a]P). For this purpose we incubated primary porcine urinary bladder epithelial cells (PUBEC) with concentrations of 1 to 50 M 4-ABP with and without coexposure to B[a]P. As expected B[a]P increased mRNA expression of cytochrome P450 1A1 (CYP1A1), whereas 4-ABP had no eVect. However, when co-exposed 4-ABP enhanced the induction of CYP1A1 by B[a]P. This result was conWrmed by Western blot analysis of CYP1A1 protein expression. A similar eVect as for CYP1A1 was also observed for cyclooxygenase-2 (COX-2) and UDP-glucuronosyltransferase 1 (UGT1). Next, we studied co-exposures of 2-NA and B[a]P. Similar as for 4-ABP also 2-NA enhanced B[a]P-mediated induction of CYP1A1. Our results demonstrate that some aromatic amines may enhance the inXuence of B[a]P on Ah receptor-dependent genes.

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Section: Discussionmentioning

confidence: 99%

Synergism of aromatic amines and benzo[a]pyrene in induction of Ah receptor-dependent genes

et al. 2008

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“…It was observed that the variability between dishes can be well taken into account by a quasi-Poisson model developed for counts, where a dispersion parameter is estimated from the complete data [16]. This led to the agreement that a negative binomial generalised linear model (i.e.…”

Section: Recommended Statistical Data Analysismentioning

confidence: 82%

Two new approaches to improve the analysis of BALB/c 3T3 cell transformation assay data

Hoffmann¹,

Hothorn

Edler

et al. 2012

Mutation Research/Genetic Toxicology and Environmental Mutagene

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a b s t r a c tValidation activities of the BALB/c 3T3 cell transformation assay (CTA) -a test method used for the assessment of the carcinogenic potential of compounds -have revealed the need for statistical analysis tailored to specific features of BALB/c 3T3 CTA data. Whereas a standard statistical approach for the Syrian hamster embryo (SHE) CTA was considered sufficient, an international expert group was gathered by the European Centre for the Validation of Alternative Methods (ECVAM) to review commonly applied statistical approaches for BALB/c 3T3 CTA. As it was concluded that none of the commonly applied approaches is entirely appropriate, two novel statistical approaches were found to be recommended for the evaluation of BALB/c 3T3 CTA data accounting for possible non-monotone concentration-response relationship and variance heterogeneity: a negative binomial generalised linear model with William's-type downturnprotected trend tests and a normalisation of the data by a specific transformation allowing for application of a general linear model that estimates effects assuming a normal distribution with William's-type protected tests. Both approaches are described in this article and their performance and the quality of the results they generate is demonstrated using exemplary data. Our work confirmed that both approaches are suitable for the statistical analysis of BALB/c 3T3 CTA data and that each of them is superior to commonly used methods. Furthermore, a procedure dichotomising data into negatives and positives is proposed which allows re-testing in cases where inconclusive data are encountered. The scripts of the statistical evaluation programs written in R -a freely available statistical software -are appended including exemplary outputs (Appendix A).

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“…In the hierarchical generalized linear model setup, Lee and Nelder 29 propose the DEQL to estimate the mean and the dispersion parameters of the response variable. Omitting the detailed derivation here, we obtain the profile DEQL from Paul and Saha 16, apart from a constant, as The maximum DEQL estimates of µ and a are then obtained by maximizing D + , or, alternatively, by solving the estimating equations and simultaneously. Denote the DEQL estimate of a by .…”

Section: The Point Estimation Of Amentioning

confidence: 99%

“…For example, it is commonly used as an inverse measure of aggregation in biological count data 2, 6–12. Extensive work has been done on its point estimation in terms of bias and efficiency 13–17 and testing for over‐dispersion 18–21. However, little has been done on the construction of confidence intervals (CIs) of the over‐dispersion parameter.…”

Section: Introductionmentioning

confidence: 99%

Interval estimation of the over‐dispersion parameter in the analysis of one‐way layout of count data

Saha

2010

Statistics in Medicine

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The over-dispersion parameter is an important and versatile measure in the analysis of one-way layout of count data in biological studies. For example, it is commonly used as an inverse measure of aggregation in biological count data. Its estimation from finite data sets is a recognized challenge. Many simulation studies have examined the bias and efficiency of different estimators of the over-dispersion parameter for finite data sets (see, for example, Clark and Perry, Biometrics 1989; 45:309-316 and Piegorsch, Biometrics 1990; 46:863-867), but little attention has been paid to the accuracy of the confidence intervals (CIs) of it. In this paper, we first derive asymptotic procedures for the construction of confidence limits for the over-dispersion parameter using four estimators that are specified by only the first two moments of the counts. We also obtain closed-form asymptotic variance formulae for these four estimators. In addition, we consider the asymptotic CI based on the maximum likelihood (ML) estimator using the negative binomial model. It appears from the simulation results that the asymptotic CIs based on these five estimators have coverage below the nominal coverage probability. To remedy this, we also study the properties of the asymptotic CIs based on the restricted estimates of ML, extended quasi-likelihood, and double extended quasi-likelihood by eliminating the nuisance parameter effect using their adjusted profile likelihood and quasi-likelihoods. It is shown that these CIs outperform the competitors by providing coverage levels close to nominal over a wide range of parameter combinations. Two examples to biological count data are presented.

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The generalized linear model and extensions: a review and some biological and environmental applications

Cited by 26 publications

References 17 publications

Synergism of aromatic amines and benzo[a]pyrene in induction of Ah receptor-dependent genes

Synergism of aromatic amines and benzo[a]pyrene in induction of Ah receptor-dependent genes

Two new approaches to improve the analysis of BALB/c 3T3 cell transformation assay data

Interval estimation of the over‐dispersion parameter in the analysis of one‐way layout of count data

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