A simulation study using the hybrid statistic and the meta-analysis t-test for data with matched and unmatched subjects in the interim analysis of clinical trials

An interim analysis is usually applied in later phase II or phase III trials to find convincing evidence of a significant treatment difference that may lead to trial termination at an earlier point than planned at the beginning. This can result in the saving of patient resources and shortening of drug development and approval time. In addition, ethics and economics are also the reasons to stop a trial earlier. In clinical trials of eyes, ears, knees, arms, kidneys, lungs, and other clustered treatments, data may include distribution-free random variables with matched and unmatched subjects in one study. It is important to properly include both subjects in the interim and the final analyses so that the maximum efficiency of statistical and clinical inferences can be obtained at different stages of the trials. So far, no publication has applied a statistical method for distribution-free data with matched and unmatched subjects in the interim analysis of clinical trials. In this simulation study, the hybrid statistic was used to estimate the empirical powers and the empirical type I errors among the simulated datasets with different sample sizes, different effect sizes, different correlation coefficients for matched pairs, and different data distributions, respectively, in the interim and final analysis with 4 different group sequential methods. Empirical powers and empirical type I errors were also compared to those estimated by using the meta-analysis t-test among the same simulated datasets. Results from this simulation study show that, compared to the meta-analysis t-test commonly used for data with normally distributed observations, the hybrid statistic has a greater power for data observed from normally, log-normally, and multinomially distributed random variables with matched and unmatched subjects and with outliers. Powers rose with the increase in sample size, effect size, and correlation coefficient for the matched pairs. In addition, lower type I errors were observed estimated by using the hybrid statistic, which indicates that this test is also conservative for data with outliers in the interim analysis of clinical trials.^

Comparison of machine learning approaches and statistical methods for estimation of airborne chemical exposures

Accurate quantitative estimation of exposure using retrospective data has been one of the most challenging tasks in the exposure assessment field. To improve these estimates, some models have been developed using published exposure databases with their corresponding exposure determinants. These models are designed to be applied to reported exposure determinants obtained from study subjects or exposure levels assigned by an industrial hygienist, so quantitative exposure estimates can be obtained. ^ In an effort to improve the prediction accuracy and generalizability of these models, and taking into account that the limitations encountered in previous studies might be due to limitations in the applicability of traditional statistical methods and concepts, the use of computer science- derived data analysis methods, predominantly machine learning approaches, were proposed and explored in this study. ^ The goal of this study was to develop a set of models using decision trees/ensemble and neural networks methods to predict occupational outcomes based on literature-derived databases, and compare, using cross-validation and data splitting techniques, the resulting prediction capacity to that of traditional regression models. Two cases were addressed: the categorical case, where the exposure level was measured as an exposure rating following the American Industrial Hygiene Association guidelines and the continuous case, where the result of the exposure is expressed as a concentration value. Previously developed literature-based exposure databases for 1,1,1 trichloroethane, methylene dichloride and, trichloroethylene were used. ^ When compared to regression estimations, results showed better accuracy of decision trees/ensemble techniques for the categorical case while neural networks were better for estimation of continuous exposure values. Overrepresentation of classes and overfitting were the main causes for poor neural network performance and accuracy. Estimations based on literature-based databases using machine learning techniques might provide an advantage when they are applied to other methodologies that combine `expert inputs' with current exposure measurements, like the Bayesian Decision Analysis tool. The use of machine learning techniques to more accurately estimate exposures from literature-based exposure databases might represent the starting point for the independence from the expert judgment.^

Geochemistry of Late Paleocene thermal maximum sediments

The late Paleocene thermal maximum (LPTM) was a dramatic, short-term global warming event that occurred ~55 Ma. Warming of high-latitude surface waters and global deep waters during the LPTM has been well documented; however, current data suggest that subtropical and tropical sea surface temperatures (SSTs) did not change during the event. Conventional paradigms of global climate change, such as CO2-induced greenhouse warming, predict greater warming in the high latitudes than in the tropics or subtropics but, nonetheless, cannot account for the stable tropical/subtropical SSTs. We measured the stable isotope values of well-preserved late Paleocene to early Eocene planktonic foraminifera from South Atlantic Deep Sea Drilling Project (DSDP) Site 527 to evaluate the subtropical response to the climatic and environmental changes of the LPTM. Planktonic foraminiferal d18O values at Site 527 decrease by ~0.94 per mil from pre-LPTM to excursion values, providing the first evidence for subtropical warming during the LPTM. We estimate that subtropical South Atlantic SSTs warmed by at least ~1°-4°C, on the basis of possible changes in evaporation and precipitation. The new evidence for subtropical SST warming supports a greenhouse mechanism for global warming involving elevated atmospheric CO2 levels.

Annual layer thicknesses and age-depth (oldest estimate) of Derwael Ice Rise (IC12), Dronning Maud Land, East Antarctica

A 120 m-long ice core was drilled in 2012 on the Derwael Ice Rise, coastal Dronning Maud Land, East Antarctica. Water stable isotopes (d18O and dD) stratigraphy is supplemented by discontinuous major ion profiles and continuous electrical conductivity measurements. The base of the ice core is dated to AD 1759 ± 16, providing a climate proxy for the past ~250 years. This data set presents the core's annual layer thickness history in meters water equivalent for the oldest age-depth estimate before correction for the influence of ice deformation.

Early Oligocene continental climate ofthe Paleogene Basin in Hungary and Slovenia

This paper concentrates on the Early Oligocene palaeoclimate of the southern part of Eastern and Central Europe and gives a detailed climatological analysis, combined with leaf-morphological studies and modelling of the palaeoatmospheric CO2 level using stomatal and d13 C data. Climate data are calculated using the Coexistence Approach for Kiscellian floras of the Palaeogene Basin (Hungary and Slovenia) and coeval assemblages from Central and Southeastern Europe. Potential microclimatic or habitat variations are considered using morphometric analysis of fossil leaves from Hungarian, Slovenian and Italian floras. Reconstruction of CO2 is performed by applying a recently introduced mechanistic model. Results of climate analysis indicate distinct latitudinal and longitudinal climate patterns for various variables which agree well with reconstructed palaeogeography and vegetation. Calculated climate variables in general suggest a warm and frost-free climate with low seasonal variation of temperature. A difference in temperature parameters is recorded between localities from Central and Southeastern Europe, manifested mainly in the mean temperature of the coldest month. Results of morphometric analysis suggest microclimatic or habitat difference among studied floras. Extending the scarce information available on atmospheric CO2 levels during the Oligocene, we provide data for a well-defined time-interval. Reconstructed atmospheric CO2 levels agree well with threshold values for Antarctic ice sheet growth suggested by recent modelling studies. The successful application of the mechanistic model for the reconstruction of atmospheric CO2 levels raises new possibitities for future climate inference from macro-flora studies.