EFFECTS OF TETRAHYDROCANNABINOL ON SEXUAL DIMORPHISM OF RAT LIVER MIXED FUNCTION OXIDASE

Pregnant Sprague-Dawley rats were gavaged with vehicle (olive oil) or 37.5, 75, 150 or 300 mg/kg of (DELTA)('9)-Tetrahydrocannabinol (THC) on days 18 or 19 of gestation. Male offspring as well as a group of hypophysectomized rats (positive control) were sacrificed at 35 days of age, while females and hypophysectomized control were sacrificed at 36 days of age. The sex-differences in ethylmorphine-N-demethylase and aniline hydroxylase liver activities were evaluated.^ Ethylmorphine-N-demethylase activity showed a significant difference between males and females from control and 37.5, 75 and 150 mg/kg THC dosed groups. Female offspring exposed prenatally to 300 mg/kg THC had a significant increase (p < .01) in N-demethylation activity, while their male counterparts had similar enzyme activity to those found in the male groups from control and 37.5 to 150 mg/kg THC dosed. Moreover, the percent increase in the 300 mg/kg THC dosed females was similar to that detected in the hypophysectomized female rats (positive control). As expected no sex difference in aniline hydroxylase activity was detected in control as well as exposed groups, including the 300 mg/kg THC dosed group.^ It is concluded that (DELTA)('9)-Tetrahydrocannabinol administered once by gavage in days 18 or 19 of gestation alters the liver Mixed Function Oxidase (MFO) sexual dimorphism imprinting process of the rat. ^

Evaluation of the Hosmer -Lemeshow global goodness -of-fit statistic for mixed variable logistic regression models

The performance of the Hosmer-Lemeshow global goodness-of-fit statistic for logistic regression models was explored in a wide variety of conditions not previously fully investigated. Computer simulations, each consisting of 500 regression models, were run to assess the statistic in 23 different situations. The items which varied among the situations included the number of observations used in each regression, the number of covariates, the degree of dependence among the covariates, the combinations of continuous and discrete variables, and the generation of the values of the dependent variable for model fit or lack of fit.^ The study found that the $\rm\ C$g* statistic was adequate in tests of significance for most situations. However, when testing data which deviate from a logistic model, the statistic has low power to detect such deviation. Although grouping of the estimated probabilities into quantiles from 8 to 30 was studied, the deciles of risk approach was generally sufficient. Subdividing the estimated probabilities into more than 10 quantiles when there are many covariates in the model is not necessary, despite theoretical reasons which suggest otherwise. Because it does not follow a X$\sp2$ distribution, the statistic is not recommended for use in models containing only categorical variables with a limited number of covariate patterns.^ The statistic performed adequately when there were at least 10 observations per quantile. Large numbers of observations per quantile did not lead to incorrect conclusions that the model did not fit the data when it actually did. However, the statistic failed to detect lack of fit when it existed and should be supplemented with further tests for the influence of individual observations. Careful examination of the parameter estimates is also essential since the statistic did not perform as desired when there was moderate to severe collinearity among covariates.^ Two methods studied for handling tied values of the estimated probabilities made only a slight difference in conclusions about model fit. Neither method split observations with identical probabilities into different quantiles. Approaches which create equal size groups by separating ties should be avoided. ^