PubMed Health⌕ Search

Biomedical subjects

K G Janardan

Publications and source records attributed to K G Janardan.

9 recordsLinked to original sources
PubMed1987

Designing batteries of short-term tests with largest inter-tier correlation.

Designs for optimal sets of short-term assays for detecting carcinogens have assumed independence between assays. This assumption can be relaxed if an "optimal" set of predictive assays is defined as one having outcomes with the highest absolute correlation with outcomes from the definitive test(s). A method of calculating this correlation is developed as a correlated Bernoulli model. We show that even when outcomes from predictive assays are interdependent, the set of outcomes from predictive assays can be significantly correlated with the set of outcomes from definitive tests. The magnitude of the correlation depends, in part, on the number of assays in the predictive tier and the decision criterion.

Carcinogens↗
PubMed1982

Estimating the proportion of mutagenic compounds in environmental samples.

A method for estimating the proportion of mutagens in a sample of N compounds is developed. For this procedure to be applicable, there must be a statistically significant correlation between the number of mutagens in the sample and the sample size N. Sample size is treated as a random variable. A sequential sampling scheme is considered. In the first stage, compounds are identified and classified as mutagens, nonmutagens, or untested, as reported in the literature. In the second stage, all untested compounds are tested for mutagenicity. Since data of this type are not generally available, estimates of the proportions of compounds tested (p), tested and mutagenic (p1), and untested but mutagenic (p2) are developed from existing complications. It is shown that there is a high, statistically significant correlation between the total number of mutagens in a sample and the sample size N. The proportion of mutagens in a sample for various values of p, p1, and p2 is tabulated.

Environmental Pollutants↗
PubMed1974

Statistical analysis of the recovery of coliform organisms on Gelman and Millipore membrane filters.

The recovery of coliform organisms on Gelman and Millipore membranes was analyzed by using both a model I (which assumes no error in the x variable) and model II (which allows errors in both the variables) regression analysis. The two models afford estimates of the slope which agree within their 95% confidence limits. Using equations derived in this paper, the model II confidence limits on the intercept are obtained. This range does not include the model I intercept limits, thereby demonstrating the differences between results from an incorrect (model I) and correct (model II) approach. In addition, fecal coliform show no differences in response to the two membranes, whereas total coliform exhibit higher recoveries on Gelman membranes.

Escherichia coli↗
PubMedDate not supplied

Multivariate statistical methods in toxicology. III. Specifying joint toxic interaction using multiple regression analysis.

Multiple regression is widely employed to study the contribution of components to the toxicologic effect of a mixture. Here, use is made of the fact that data obtained from standard curves of substances and from their mixtures are separable in regression analysis. Thus, under an assumption of additivity of responses, regression coefficients obtained for components in mixtures alone should be the same as for the individual substances. A t-test is developed such that nonsignificant t values support additivity, negative significant values support antagonism, and positive significant values support synergism. The results are applied to data on the mutagenicity of binary mixtures of azaserine, 4-nitroquinoline N-oxide, and 9-aminoacridine in TA 100 in the Ames assay.

4-Nitroquinoline-1-oxide↗