Statistical evaluation of toxicological bioassays - a review

The basic conclusions in almost all reports on new drug applications and in all publications in toxicology are based on statistical methods. However, serious contradictions exist in practice: designs with small samples sizes but use of asymptotic methods (i.e. constructed for larger sample sizes), statistically significant findings without biological relevance (and vice versa), proof of hazard vs. proof of safety, testing (e.g. no observed effect level) vs. estimation (e.g. benchmark dose), available statistical theory vs. related user-friendly software. In this review the biostatistical developments since about the year 2000 onwards are discussed, mainly structured for repeated-dose studies, mutagenicity, carcinogenicity, reproductive and ecotoxicological assays. A critical discussion is included on the unnecessarily conservative evaluation proposed in guidelines, the inadequate but almost always used proof of hazard approach, and the limitation of data-dependent decision-tree approaches

Tests for strict monotonic trend in bio-medical dose-response relationships (respective concentration-response or exposure-response relationships) -- a biostatistical perspective

Evidence of a global trend in dose-response dependencies is commonly used in bio-medicine and epidemiology, especially because this represents a causality criterion. However, conventional trend tests indicate a significant trend even when dependence is in the opposite direction for low doses when the high dose alone has a superior effect. Here we present a trend test for a strictly monotonic increasing (or decreasing) trend, evaluate selected sample data for it, and provide corresponding R code using CRAN packages.Comment: 11 Figures, 5 Table

Trend tests for the evaluation of exposure-response relationships in epidemiological exposure studies

One possibility for the statistical evaluation of trends in epidemiological exposure studies is the use of a trend test for data organized in a 2 × k contingency table. Commonly, the exposure data are naturally grouped or continuous exposure data are appropriately categorized. The trend test should be sensitive to any shape of the exposure-response relationship. Commonly, a global trend test only determines whether there is a trend or not. Once a trend is seen it is important to identify the likely shape of the exposure-response relationship. This paper introduces a best contrast approach and an alternative approach based on order-restricted information criteria for the model selection of a particular exposure-response relationship. For the simple change point alternative H1 : 1 =.= q <q+1 =. = k an appropriate approach for the identification of a global trend as well as for the most likely shape of that exposure-response relationship is characterized by simulation and demonstrated for real data examples. Power and simultaneous confidence intervals can be estimated as well. If the conditions are fulfilled to transform the exposure-response data into a 2 × k table, a simple approach for identification of a global trend and its elementary shape is available for epidemiologists

Robust designs in non-inferiority three arm clinical trials with presence of heteroscedasticity

In this paper, we describe an adjusted method to facilitate a non-inferiority trial by a three-arm robust design. Because local optimal designs derived in Hasler et al. [2007] require knowledge about the ratios of the population variances and are not necessarily robust with respect to possible misspecifications, a maximin approach is adopted. This method requires only the specification of an interval for the of variance ratios and yields robust and efficient designs. We demonstrate that a maximin optimal design only depends on the boundary points specified for the intervals of the variance ratios and receive numerical and analytical solutions. The derived designs are robust and very efficient for statistical analysis in non inferiority three arm trials. --maximin design,robust design,non-inferiority,three arm design,gold design trials,randomized clinical trial

A comparison study on modeling of clustered and overdispersed count data for multiple comparisons

Data collected in various scientific fields are count data. One way to analyze such data is to compare the individual levels of the factor treatment using multiple comparisons. However, the measured individuals are often clustered–e.g. according to litter or rearing. This must be considered when estimating the parameters by a repeated measurement model. In addition, ignoring the overdispersion to which count data is prone leads to an increase of the type one error rate. We carry out simulation studies using several different data settings and compare different multiple contrast tests with parameter estimates from generalized estimation equations and generalized linear mixed models in order to observe coverage and rejection probabilities. We generate overdispersed, clustered count data in small samples as can be observed in many biological settings. We have found that the generalized estimation equations outperform generalized linear mixed models if the variance-sandwich estimator is correctly specified. Furthermore, generalized linear mixed models show problems with the convergence rate under certain data settings, but there are model implementations with lower implications exists. Finally, we use an example of genetic data to demonstrate the application of the multiple contrast test and the problems of ignoring strong overdispersion