Phase 3 adaptive trial design options in treatment of complicated urinary tract infection.

New antimicrobial drugs for treatment of complicated urinary tract infection (cUTI) are generally assessed in randomized, double-blind, noninferiority clinical trials. Robust historical data for the active comparator inform on treatment effect estimation, yet typically do not substitute for the active comparator data in the proposed trial. We report design options for a phase 3 trial of cUTI using a Bayesian hierarchical model and historical data from 2 well-executed phase 3 registrational trials of doripenem. The methodology is directly applicable to other phase 3 noninferiority settings. In addition to the research design application, we provide a novel methodology for assessing the robustness of type I error control. The model borrows heavily from the prior data when the current active comparator parameter estimate approximated the historical estimate. In contrast, the model had restricted borrowing when the 2 estimates were very different. The alternative trial design, with or without the inclusion of futility stopping criteria, provides a framework for future cUTI phase 3 trials.

Comparing methods for analyzing overdispersed count data in aquatic toxicology.

Endpoints in aquatic toxicity tests can be measured using a variety of measurement scales including dichotomous (survival), continuous (growth) and count (number of young). A distribution is assumed for an endpoint and analyses proceed accordingly. In certain situations, the assumed distribution may be incorrect and this may lead to incorrect statistical inference. The present study considers the analysis of count effects, here motivated by the Ceriodaphnia dubia reproduction study. While the Poisson probability model is a common starting point, this distribution assumes that the mean and variance are the same. This will not be the case if there is some extraneous source of variability in the system, and in this case, the variability may exceed the mean. A computer simulation study was used to examine the impact of overdispersion or outliers on the analysis of count data. Methods that assumed Poisson or negative binomially distributed outcomes were compared to methods that accommodated this potential overdispersion using quasi-likelihood (QL) or generalized linear mixed models (GLMM). If the data were truly Poisson, the adjusted methods still performed at nominal type I error rates. In the cases of overdispersed counts, the Poisson assumed methods resulted in rejection rates that exceeded nominal levels and standard errors for regression coefficients that were too narrow. The negative binomial methods worked best in the case when the data were, in fact, negative binomial but did not maintain nominal characteristics in other situations. In general, the QL and GLMM methods performed reasonably based on the present study, although all procedures suffered some impact in the presence of potential outliers. In particular, the QL is arguably preferred because it makes fewer assumptions than the GLMM and performed well over the range of conditions considered.

Comparing methods for analyzing overdispersed binary data in aquatic toxicology.

Historically, death is the most commonly studied effect in aquatic toxicity tests. These tests typically employ a gradient of concentrations and exposure with more than one organism in a series of replicate chambers in each concentration. Whereas a binomial distribution commonly is employed for such effects, variability may exceed that predicted by binomial probability models. This additional variability could result from heterogeneity in the probabilities across the chambers in which the organisms are housed and subsequently exposed to concentrations of toxins. Incorrectly assuming a binomial distribution for the statistical analysis may lead to incorrect statistical inference. We consider the analysis of grouped binary data, here motivated by the study of survival. We use a computer simulation study to examine the impact of overdispersion or outliers on the analysis of binary data. We compare methods that assume binomial or generalizations that accommodate this potential overdispersion. These generalizations include adjusting the standard probit model for clustering/correlation or using alternative estimation methods, generalized estimating equations, or generalized linear mixed models (GLMM). When data were binomial or overdispersed binomial, none of the models exhibited any significant bias when estimating regression coefficients. When the data were truly binomial, the probit model controlled type I errors, as did the Donald and Donner method and the GLMM method. When data were overdispersed, the probit model no longer controlled type I error, and the standard errors were too small. In general, the Donald and Donner and the GLMM methods performed reasonably based on this study, although all procedures suffered some impact in the presence of potential outliers.

Model-averaged benchmark concentration estimates for continuous response data arising from epidemiological studies.

Worker populations often provide data on adverse responses associated with exposure to potential hazards. The relationship between hazard exposure levels and adverse response can be modeled and then inverted to estimate the exposure associated with some specified response level. One concern is that this endpoint may be sensitive to the concentration metric and other variables included in the model. Further, it may be that the models yielding different risk endpoints are all providing relatively similar fits. We focus on evaluating the impact of exposure on a continuous response by constructing a model-averaged benchmark concentration from a weighted average of model-specific benchmark concentrations. A method for combining the estimates based on different models is applied to lung function in a cohort of miners exposed to coal dust. In this analysis, we see that a small number of the thousands of models considered survive a filtering criterion for use in averaging. Even after filtering, the models considered yield benchmark concentrations that differ by a factor of 2 to 9 depending on the concentration metric and covariates. The model-average BMC captures this uncertainty, and provides a useful strategy for addressing model uncertainty.

Copper tolerance in fathead minnows: I. The role of genetic and nongenetic factors.

Swim performances of male and female fathead minnows (Pimephales promelas) from three different suppliers were determined before and after an 8- to 9-d exposure to 175 microg/L copper (Cu). The reduction in swim performance (delta) due to the Cu exposure varied widely among individual fish, but was surprisingly consistent from one supplier to the next and between males and females. Genetic analysis of the individuals revealed significant correlations between delta and genotypic variation at the glucosephosphate isomerase-1, phosphoglucomutase-1, and lactate dehydrogenase-2 enzyme loci. Based upon delta, the most Cu-resistant fathead minnows were bred together, as were the most Cu-susceptible individuals and two groups of unselected minnows. Larvae produced by each group of adults were subjected to a survival test. The median lethal concentration (LC50) for larvae produced by Cu-resistant adults was significantly greater than the LC50s for the control groups. Surprisingly, the LC50 for the larvae produced by Cu-susceptible adults was also significantly greater than the LC50s for the control groups, but not significantly different from the larvae produced by Cu-resistant parents. While Cu tolerance has a genetic component in fathead minnows, the Cu tolerance of larval fish appears to be influenced by nongenetic as well as genetic factors.

Effects of Urotensin II Receptor Antagonist, GSK1440115, in Asthma.

BACKGROUND: Urotensin II (U-II) is highly expressed in the human lung and has been implicated in regulating respiratory physiology in preclinical studies. Our objective was to test antagonism of the urotensin (UT) receptor by GSK1440115, a novel, competitive, and selective inhibitor of the UT receptor, as a therapeutic strategy for the treatment of asthma. METHODS: Safety, tolerability, and pharmacokinetics (PK) of single doses of GSK1440115 (1-750 mg) were assessed in a Phase I, placebo controlled study in 70 healthy subjects. In a Phase Ib study, 12 asthmatic patients were randomized into a two-period, single-blind crossover study and treated with single doses of 750 mg GSK1440115 or placebo and given a methacholine challenge. RESULTS: Administration of GSK1440115 was safe and well-tolerated in healthy subjects and asthmatic patients. In both studies, there was a high degree of variability in the observed PK following oral dosing with GSK1440115 at all doses. There was a marked food effect in healthy subjects at the 50 mg dose. In the presence of food at the 750 mg dose, the time to maximal concentration was between 2 and 6 h and the terminal half-life was short at approximately 2 h. All asthmatic patients maintained greater than the predicted concentration levels necessary to achieve predicted 96% receptor occupancy for ≥3 h (between 4 and 7 h post-dose). There were no apparent trends or relationships between the systemic plasma exposure of GSK1440115 and pharmacodynamic endpoints, PC20 after methacholine challenge and FEV1, in asthmatics. CONCLUSION: While GSK1440115 was safe and well-tolerated, it did not induce bronchodilation in asthmatics, or protect against methacholine-induced bronchospasm, suggesting that acute UT antagonism is not likely to provide benefit as an acute bronchodilator in this patient population.

Deposition and cycling of sulfur controls mercury accumulation in Isle Royale fish.

Mercury contamination of fish is a global problem. Consumption of contaminated fish is the primary route of methylmercury exposure in humans and is detrimental to health. Newly mandated reductions in anthropogenic mercury emissions aim to reduce atmospheric mercury deposition and thus mercury concentrations in fish. However, factors other than mercury deposition are important for mercury bioaccumulation in fish. In the lakes of Isle Royale, U.S.A., reduced rates of sulfate deposition since the Clean Air Act of 1970 have caused mercury concentrations in fish to decline to levels that are safe for human consumption, even without a discernible decrease in mercury deposition. Therefore, reductions in anthropogenic sulfur emissions may provide a synergistic solution to the mercury problem in sulfate-limited freshwaters.

Model uncertainty and risk estimation for experimental studies of quantal responses.

Experimental animal studies often serve as the basis for predicting risk of adverse responses in humans exposed to occupational hazards. A statistical model is applied to exposure-response data and this fitted model may be used to obtain estimates of the exposure associated with a specified level of adverse response. Unfortunately, a number of different statistical models are candidates for fitting the data and may result in wide ranging estimates of risk. Bayesian model averaging (BMA) offers a strategy for addressing uncertainty in the selection of statistical models when generating risk estimates. This strategy is illustrated with two examples: applying the multistage model to cancer responses and a second example where different quantal models are fit to kidney lesion data. BMA provides excess risk estimates or benchmark dose estimates that reflects model uncertainty.