Search

This report describes the results of a survey designed to evaluate the contribution of F1 neurobehavioral testing to hazard identification and characterization in safety assessment studies. (To review the details of the distributed survey, please see the supplementary data for this article on the journal's Web site.) The survey provided information about studies completed in industrial laboratories in the United States, Europe, and Japan since 1990 on 174 compounds. The types of compounds included were pharmaceutical (81%), agricultural (7%), industrial (1%), or were undefined (10%). Information collected included the intended use of the test agent, general study design and methodology, the types and characteristics of F1 behavioral evaluations, and the frequency with which agents affected neurobehavioral parameters in comparison to other F0 and F1 generation parameters. F1 general toxicology parameters such as mortality, pre- and postweaning body weight, and food intake were assessed in most studies and were affected more frequently than other parameters by the test agents. F1 behavioral parameters were assessed less consistently across studies, and were less frequently affected by the agents tested. Although affected by agents less often than general toxicology parameters, F1 behavioral parameters along with other parameters defined the no-observed-effect level (NOEL) in 17/113 (15%) of studies and solely defined the NOEL in 3/113 (2.6%) of studies. Thus, F1 behavioral parameters sometimes improved on the standard toxicological measures of hazard identification. While not detecting agent effects as readily as some measures, the F1 behavioral parameters provide information about agent effects on specialized functions of developing offspring not provided by other standard measures of toxicity. The survey results emphasize the need for further research into the methods of behavioral assessment as well as the mechanisms underlying the neurobehavioral alterations.

Background: Postoperative air leaks are a major cause of morbidity after lung resections. This study was designed to evaluate the efficacy and safety of a new synthetic, bioresorbable surgical sealant in preventing air leaks after pulmonary resection., Methods: In a multicenter trial, 172 patients undergoing thoracotomy were randomized intraoperatively in a 2:1 ratio to receive surgical sealant applied to sites at risk for air leak after standard methods of lung closure (treatment group) or to have standard lung closure only (control group). The primary outcome variable was the percentage of patients free of air leakage throughout hospitalization. Secondary outcome variables were the control of air leaks intraoperatively and the time to postoperative air leak cessation. Time to chest tube removal, time to hospital discharge, and safety outcomes were also evaluated., Results: Air leaks were identified before randomization in 89 of 117 patients in the treatment group and in 39 of 55 patients in the control group. Application of the sealant resulted in control of air leaks in 92% of treated patients (p < or = 0.001). A significantly higher percentage of treated patients than control patients remained free of air leaks during hospitalization (39% versus 11%, p < or =0.001). The mean times to last observable air leak were 30.9 hours in the treatment group and 52.3 hours in the control group (p = 0.006). In the treatment group, trends were observed for reduced time to chest tube removal and earlier discharge. No significant difference was identified in postoperative morbidity and mortality between the two groups., Conclusions: Air leaks after lung resection occur in most patients. The application of this novel surgical sealant appears to be effective and safe in preventing postoperative air leaks.

We propose the use of Gaussian mixed models to analyse statistically 24 hour ambulatory blood pressure data from clinical trials. We develop specific models and apply them to data from a clinical study that compares two angiotensin-converting enzyme inhibitors. We investigate and discuss computing issues related to the implementation of such methods. We conclude that this methodology provides a sophisticated but practical approach to the analysis of such data.

Bioequivalence trials are carried out to compare two or more formulations of a drug containing the same active ingredient, in order to determine whether the different formulation give rise to comparable blood levels. We consider the 2 x 2 changeover experiment the compares two formulations, one of which is considered the standard. For a single univariate characteristic of the plasma concentration--time curve, a criterion for bioequivalence is proposed based on the posterior probability that the difference in formulation means is less than a specific percentage of the mean of the standard. The sensitivity of this posterior probability to alternative priors is investigated. Differences in carry-over effects can be incorporated within the Bayesian framework without restoring to the "all-or-nothing" approach implied by a preliminary test. The use of sequential experimentation is discussed.

The interpretation of statistically significant findings in a carcinogenicity study is difficult, in part because of the large number of statistical tests conducted. Some scientists who believe that the false positive rates in these experiments are unreasonably large often suggest that the use of multiple control groups will provide important insight into the operational false positive rates. The purpose of this paper is 2-fold: to present results from two carcinogenicity studies with dual control groups, and to present and illustrate a new graphical technique potentially useful in the analysis and interpretation of tumor data from carcinogenicity studies. The experimental data analyzed show that statistically significant differences between identically treated groups will occur with regular frequency. Such data, however, do not provide strong evidence of extrabinomial variation in tumor rates. The p-value plot is advocated as a graphical method that can be used to assess visually the ensemble of p values for neoplasm data from an entire study. This technique is then illustrated using several examples. Through computer simulation, we present p-value plots generated with and without treatment effects present. On average, the plots look substantially different depending on the presence or absence of an effect. We also evaluate decision rules motivated by the p-value plots. Such rules appear to have good power to detect treatment effects (i.e., have low false negative rates) while still controlling false positive rates.

Bayesian methods are presented for assessing bioequivalence for studies in which a new formulation and a standard are administered simultaneously, and for Latin square designs which compare two or more new formulations to a standard. Two examples illustrate the application of the methods.