Quantitative and statistical analysis of differences in sensitivity between Long-Evans and Han/Wistar rats following long-term exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin.

In this study, differences in sensitivity between Long-Evans (L-E; dioxin sensitive) and Han/Wistar (H/W; dioxin resistant) rats following long-term exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) were statistically and quantitatively investigated. Sensitivity differences were analyzed by comparing benchmark doses (BMDs) for the two strains considering a number of toxicological endpoints including data on body and organ weights, hepatic foci, hepatic CYP1A1 induction, as well as tissue retinoid levels. Dose-response relationships for L-E and H/W rats, described by the Hill function, were assumed to be parallel, which was supported according to statistical analysis. It was concluded that L-E and H/W rats differed statistically in their response to TCDD treatment for most of the parameters investigated. Differences between the strains were most pronounced for hepatic foci; L-E rats were approximately 20-40 times more sensitive than H/W rats. For body and organ weight parameters, L-E rats were approximately 10-20 times more sensitive than H/W rats. For retinoid parameters and hepatic CYP1A1 induction, estimated differences between the strains were generally about 5-fold, and associated with a low uncertainty. In conclusion, the present study employs a dose-response modeling approach suitable for statistical evaluation of strain and species differences in sensitivity to chemical exposure. The study demonstrates quantitatively the differences in sensitivity between the L-E and H/W rat strains following long-term TCDD exposure.

Dose-Related Severity Sequence, and Risk-Based Integration, of Chemically Induced Health Effects.

Risk assessment of chemical hazards is typically based on single critical health effects. This work aims to expand the current approach by characterizing the dose-related sequence of the development of multiple (lower- to higher-order) toxicological health effects caused by a chemical. To this end a "reference point profile" is defined as the relation between benchmark doses for considered health effects, and a standardized severity score determined for these effects. For a given dose of a chemical or mixture the probability for exceeding the reference point profile, thereby provoking lower- to higher-order effects, can be assessed. The overall impact at the same dose can also be derived by integrating contributions across all health effects following severity-weighting. In its generalized form the new impact metric relates to the probability of response for the most severe health effects. Reference points (points of departure) corresponding to defined levels of response can also be estimated. The proposed concept, which is evaluated for dioxin-like chemicals, provides an alternative for characterizing the low-dose region below the reference point for a severe effect like cancer. The shape and variability of the reference point profile add new dimensions to risk assessment, which for example extends the characterization of chemical potency, and the concept of acceptable effect sizes for individual health effects. Based on the present data the method shows high stability at low doses/responses, and is also robust to differences in severity categorization of effects. In conclusion, the novel method proposed enables risk-based integration of multiple dose-related health effects. It provides a first step towards a more comprehensive characterization of chemical toxicity, and suggests a potential for improved low-dose risk assessment.

Influence diagnostics for count data under AB-BA crossover trials.

This paper aims to develop diagnostic measures to assess the influence of data perturbations on estimates in AB-BA crossover studies with a Poisson distributed response. Generalised mixed linear models with normally distributed random effects are utilised. We show that in this special case, the model can be decomposed into two independent sub-models which allow to derive closed-form expressions to evaluate the changes in the maximum likelihood estimates under several perturbation schemes. The performance of the new influence measures is illustrated by simulation studies and the analysis of a real dataset.

Identification of a critical dose level for risk assessment: developments in benchmark dose analysis of continuous endpoints.

The benchmark dose (BMD) method has been recommended to replace the no-observed-adverse-effect-level (NOAEL) approach in health risk assessment of chemical substances. In the present article, developments in BMD analysis from continuous experimental data are proposed. The suggested approach defines the BMD as the dose at which the slope of the S-shaped dose-response relationship changes the most in the low-dose region. This dose resides in a region where the sensitivity to chemical exposure may start to change noticeably. It is shown that the response (defined as a percent change relative to the magnitude, or size, of response) corresponding to the dose where the slope changes the most depends on the geometrical shape of the dose-response curve; the response becomes lower as the curve becomes more asymmetrical and threshold-like in the low-dose region. Given a symmetrical case, described by the Hill function, the response associated with the critical dose level becomes 21% (defined as a percent change relative to the magnitude, or size, of response). According to a limiting case of asymmetry and threshold-like characteristics, reflected by a Gompertz curve, the response corresponding to the dose of interest becomes as low as 7.3% (defined as a percent change relative to the magnitude, or size, of response). Use of a response in the range of 5-10% when estimating the BMD conservatively accounts for uncertainties associated with the proposed strategy, and may be appropriate in a risk assessment point of view. The present investigation also indicated that a BMD defined according to the suggested procedure may be estimated more precisely relative to BMDs defined under other approaches for continuous data.

ECT of major depressed patients in relation to biological and clinical variables: a brief overview.

The knowledge that spontaneous or induced convulsions can improve mental disorders has been present for several centuries. electroconvulsive therapy (ECT) has undergone fundamental changes since its introduction, and in the last 15-20 years there has been a legitimate renewal of interest for this therapy. Today the indications for use of ECT seem well codified, and its technique and practices have evolved considerably. It is now firmly established as an important and effective method of treating certain severe forms of depression. However, still very little is known about the mechanism of ECT. In this paper, first, we will give a short overview as to how far we have got concerning ECT in relation to various clinical and biological variables. Second, we will describe ECT in relation to electroencephalographic (EEG) technique and clinical outcome as well as give some proposals as to how to go on with the data analysis of EEG. In conclusion, the superior effect of ECT compared to other antidepressives in severe depression may depend on neurochemical and neurobiological cascade effects initiated by repeated treatments. Above all, ECT offers a unique experimental opportunity to study how neuromodulation of the major transmitter systems may be involved in brain dynamics and alteration of connectivity.

Dose-response modeling and benchmark calculations from spontaneous behavior data on mice neonatally exposed to 2,2',4,4',5-pentabromodiphenyl ether.

In this paper the benchmark dose (BMD) method was introduced for spontaneous behavior data observed in 2-, 5-, and 8-month-old male and female C57Bl mice exposed orally on postnatal day 10 to different doses of 2,2',4,4',5-pentabromodiphenyl ether (PBDE 99). Spontaneous behavior (locomotion, rearing, and total activity) was in the present work quantified in terms of a fractional response defined as the cumulative response after 20 min divided by the cumulative response produced over the whole 1-h test period. The fractional response contains information about the time-response profile (which differs between the treatment groups) and has appropriate statistical characteristics. In the analysis, male and female mice could be characterized by a common dose-response model (i.e., they responded equally to the exposure to PBDE 99). As a primary approach, the BMD was defined as the dose producing a 5 or 10% change in the mean fractional response. According to the Hill model, considering a 10% change the lower bound of the BMD for rearing, locomotion, and total activity was 1.2, 0.85, and 0.31 mg PBDE 99/kg body weight, respectively. A probability-based procedure for BMD modeling was also considered. Using this methodology, the BMD was defined as corresponding to an excess risk of 5 or 10% of falling below cutoff points representing adverse levels of fractional response.

A new method to compare statistical tree growth curves: the PL-GMANOVA model and its application with dendrochronological data.

Growth curves are monotonically increasing functions that measure repeatedly the same subjects over time. The classical growth curve model in the statistical literature is the Generalized Multivariate Analysis of Variance (GMANOVA) model. In order to model the tree trunk radius (r) over time (t) of trees on different sites, GMANOVA is combined here with the adapted PL regression model Q = A · T+E, where for b ≠ 0 : Q = Ei[-b · r]-Ei[-b · r1] and for b = 0 : Q  = Ln[r/r1], A =  initial relative growth to be estimated, T = t-t1, and E is an error term for each tree and time point. Furthermore, Ei[-b · r]  = ∫(Exp[-b · r]/r)dr, b = -1/TPR, with TPR being the turning point radius in a sigmoid curve, and r1 at t1 is an estimated calibrating time-radius point. Advantages of the approach are that growth rates can be compared among growth curves with different turning point radiuses and different starting points, hidden outliers are easily detectable, the method is statistically robust, and heteroscedasticity of the residuals among time points is allowed. The model was implemented with dendrochronological data of 235 Pinus montezumae trees on ten Mexican volcano sites to calculate comparison intervals for the estimated initial relative growth A. One site (at the Popocatépetl volcano) stood out, with A being 3.9 times the value of the site with the slowest-growing trees. Calculating variance components for the initial relative growth, 34% of the growth variation was found among sites, 31% among trees, and 35% over time. Without the Popocatépetl site, the numbers changed to 7%, 42%, and 51%. Further explanation of differences in growth would need to focus on factors that vary within sites and over time.

On the efficiency of bootstrap method into the analysis contingency table.

The bootstrap method is a computer intensive statistical method that is widely used in performing nonparametric inference. Categorical data analysis, in particular the analysis of contingency tables, is commonly used in applied field. This work considers nonparametric bootstrap tests for the analysis of contingency tables. There are only a few research papers which exploit this field. The p-values of tests in contingency tables are discrete and should be uniformly distributed under the null hypothesis. The results of this article show that corresponding bootstrap versions work better than the standard tests. Properties of the proposed tests are illustrated and discussed using Monte Carlo simulations. This article concludes with an analytical example that examines the performance of the proposed tests and the confidence interval of the association coefficient.

Does noise reduction matter for curve fitting in growth curve models?

In this paper, we discuss the efficiency of noise reduction for curve fitting in nonlinear growth curve models. We use singular spectrum analysis as a nonlinear-nonparametric denoising method. A set of longitudinal measurements is used in considering the performance of the method. We also use artificially generated data sets with and without noise for the purpose of validation of the results obtained in this study. The results show that noise reduction is important for curve fitting in growth curve models and also, that the singular spectrum analysis technique can be used as a powerful tool for noise reduction in longitudinal measurements.

Seizure (Ictal)--EEG characteristics in subgroups of depressive disorder in patients receiving electroconvulsive therapy (ECT)--a preliminary study and multivariate approach.

OBJECTIVES: Examine frequency distributions of ictal EEG after ECT stimulation in diagnostic subgroups of depression. METHODS: EEG registration was consecutively monitored in 33 patients after ECT stimulation. Patients were diagnosed according to DSM IV and subdivided into: (1) major depressive disorder with psychotic features (n = 7), (2) unipolar depression (n = 20), and (3) bipolar depression (n = 6). RESULTS: Results indicate that the diagnostically subgroups differ in their ictal EEG frequency spectrumml: (1) psychotic depression has a high occurrence of delta and theta waves, (2) unipolar depression has high occurrence of delta, theta and gamma waves, and (3) bipolar depression has a high occurrence of gamma waves. A linear discriminant function separated the three clinical groups with an accuracy of 94%. CONCLUSION: Psychotic depressed patients differ from bipolar depression in their frequency based on probability distribution of ictal EEG. Psychotic depressed patients show more prominent slowing of EEG than nonpsychotic depressed patients. Thus the EEG results may be supportive in classifying subgroups of depression already at the start of the ECT treatment.

Signatures of depression in non-stationary biometric time series.

This paper is based on a discussion that was held during a special session on models of mental disorders, at the NeuroMath meeting in Stockholm, Sweden, in September 2008. At this occasion, scientists from different countries and different fields of research presented their research and discussed open questions with regard to analyses and models of mental disorders, in particular depression. The content of this paper emerged from these discussions and in the presentation we briefly link biomarkers (hormones), bio-signals (EEG) and biomaps (brain-maps via EEG) to depression and its treatments, via linear statistical models as well as nonlinear dynamic models. Some examples involving EEG-data are presented.

Comparing experimental designs for benchmark dose calculations for continuous endpoints.

The BMD (benchmark dose) method that is used in risk assessment of chemical compounds was introduced by Crump (1984) and is based on dose-response modeling. To take uncertainty in the data and model fitting into account, the lower confidence bound of the BMD estimate (BMDL) is suggested to be used as a point of departure in health risk assessments. In this article, we study how to design optimum experiments for applying the BMD method for continuous data. We exemplify our approach by considering the class of Hill models. The main aim is to study whether an increased number of dose groups and at the same time a decreased number of animals in each dose group improves conditions for estimating the benchmark dose. Since Hill models are nonlinear, the optimum design depends on the values of the unknown parameters. That is why we consider Bayesian designs and assume that the parameter vector has a prior distribution. A natural design criterion is to minimize the expected variance of the BMD estimator. We present an example where we calculate the value of the design criterion for several designs and try to find out how the number of dose groups, the number of animals in the dose groups, and the choice of doses affects this value for different Hill curves. It follows from our calculations that to avoid the risk of unfavorable dose placements, it is good to use designs with more than four dose groups. We can also conclude that any additional information about the expected dose-response curve, e.g., information obtained from studies made in the past, should be taken into account when planning a study because it can improve the design.

Benchmark calculations in risk assessment using continuous dose-response information: the influence of variance and the determination of a cut-off value.

A benchmark dose (BMD) is the dose of a chemical that corresponds to a predetermined increase in the response (the benchmark response, BMR) of a health effect. In this article, a method (the hybrid approach) for benchmark calculations from continuous dose-response information is investigated. In the formulation of the methodology, a cut-off value for an adverse health effect has to be determined. It is shown that the influence of variance on the hybrid model depends on the choice of determination of the cut-off point. If the cut-off value is determined as corresponding to a specified tail proportion of the control distribution, P(0), the BMD becomes biased upward when the variance is biased upward. On the contrary, if the cut-off value is directly determined to some level of the continuous response variable, the BMD becomes biased upward when the variance is biased downward. A simulation study was also performed in which the accuracy and precision of the BMD was compared for the two ways of determining the cut-off value. In general, considering BMRs of 1, 5, and 10% (additional risk) the precision of the BMD became higher when the cut-off value was estimated by specifying P(0), relative to the case with a direct determination. Use of the square-root of the maximum-likelihood estimator of the variance in BMD estimation may provide a bias that is reflected by the cut-off formulation (downward bias if specifying P(0), and upward bias if specifying the cut-off, c, directly). This feature may be reduced if an unbiased estimator of the standard deviation is used in the calculations.