Statistical models for numbers of implantation sites and embryonic deaths in mice

McCaughran, Donald A.; Arnold, D.W.

doi:10.1016/0041-008x(76)90139-3

Cited by 32 publications

(8 citation statements)

References 3 publications

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“…To test the equality of several group means a normal theory analysis of variance procedure is employed on the transformed data Yij and Y'i after replacing c by its maximum likelihood estimate (Bliss & Owen, 1958;McCaughran & Arnold, 1976). To test the equality of several group means a normal theory analysis of variance procedure is employed on the transformed data Yij and Y'i after replacing c by its maximum likelihood estimate (Bliss & Owen, 1958;McCaughran & Arnold, 1976).…”

Section: However Anscombe Points Out That the Former Is More Effectimentioning

confidence: 99%

Analysis of One-Way Layout of Count Data with Negative Binomial Variation

Barnwal

Paul

1988

Biometrika

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JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Biometrika Trust is collaborating with JSTOR to digitize, preserve and extend access to Biometrika. SUMMARY Two C(a) statistics (Neyman, 1959) for testing the equality of the means of several groups of count data in the presence of a common dispersion parameter are derived. The performance of the two C(a) statistics, the likelihood ratio statistic and two more statistics based on transformed data (Anscombe, 1948) is then compared in terms of size and power by using Monte Carlo simulations. The C(a) statistics are recommended. The C (a) statistics for testing the equality of the dispersion parameters of several groups of count data are also derived. Two applications are given. Some key words: Common dispersion parameter; C(a) statistic; Equality of means; Equality of dispersion parameters; Negative binomial distribution.

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Section: However Anscombe Points Out That the Former Is More Effectimentioning

confidence: 99%

Analysis of One-Way Layout of Count Data with Negative Binomial Variation

Barnwal

Paul

1988

Biometrika

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“…<..1 (4 ) Expression (4) is a two-parameter alternative to Williams' betabinomial model. If needed, one could certainly employ a third or higherorder approximation to P (x .. ) , 1) but it has been our experience that A few comments are in order regarding inequality (5) and Table II. First of all, the upper bound is clearly less restrictive than the lower bound; this is desirable since in most (but not all) situations one would expect p. to be positive. 1 Secondly, both bounds become closer to zero as n.. increases, so that, in practice, the largest n.. in a given…”

Section: )mentioning

confidence: 99%

“…with the most restrictive and governing set of bounds. Finally, sample-based bounds using (5) should be imposed to insure that estimates of p. and 8. (e.g., those obtained by maximum…”

Section: )mentioning

confidence: 99%

The Use of a Correlated Binomial Model for the Analysis of Certain Toxicological Experiments

Kupper¹,

Haseman²

1978

Biometrics

236

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In certain toxicological experiments with laboratory animals, the outcome of interest is the occurrence of dead or malformed fetuses in a 'litter. Previous investigations have shown that the simple onep arameter binomial and Poisson models generally provide poor fits to this type of binary data. In this paper, a type of correlated binom ial model is proposed for use in this situation. First, the model is described in detail and is shown to have certain theoretical advantages over a beta-binomial model proposed by Williams [8]. These two-parameter models are then contrasted as to goodness of fit to some real~life data. Finally, numerical examples are given in which likelihood ratio tests based on these models are employed to assess the significance of treatment-control differences.

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“…(6)(7)(8)(9) In particular, the mean number of implants per pregnant mouse in one control database ranged from 9.50 to 13.74, depending on the route of adminstration and the strain of mouse, while the variance was much smaller, 2.78 to 8.50. (8) These values suggest biological constraints on the number of implants per female, which are not accounted for in standard parametric distributions for count data, such as the Poisson or negative binomial.…”

Section: Introductionmentioning

confidence: 98%

Assessing Overall Risk in Reproductive Experiments

Dunson

2000

Risk Analysis

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Toxicologists are often interested in assessing the joint effect of an exposure on multiple reproductive endpoints, including early loss, fetal death, and malformation. Exposures that occur prior to mating or extremely early in development can adversely affect the number of implantation sites or fetuses that form within each dam and may even prevent pregnancy. A simple approach for assessing overall adverse effects in such studies is to consider fetuses or implants that fail to develop due to exposure as missing data. The missing data can be imputed, and standard methods for the analysis of quantal response data can then be used for quantitative risk assessment or testing. In this article, a new bias-corrected imputation procedure is proposed and evaluated. The procedure is straightforward to implement in standard statistical packages and has excellent operating characteristics when used in combination with a marginal model fit with generalized estimating equations. The methods are applied to data from a reproductive toxicity study of Nitrofurazone conducted by the National Toxicology Program.

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Statistical models for numbers of implantation sites and embryonic deaths in mice

Cited by 32 publications

References 3 publications

Analysis of One-Way Layout of Count Data with Negative Binomial Variation

Analysis of One-Way Layout of Count Data with Negative Binomial Variation

The Use of a Correlated Binomial Model for the Analysis of Certain Toxicological Experiments

Assessing Overall Risk in Reproductive Experiments

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