New relevance and significance measures to replace p-values.

The p-value has been debated exorbitantly in the last decades, experiencing fierce critique, but also finding some advocates. The fundamental issue with its misleading interpretation stems from its common use for testing the unrealistic null hypothesis of an effect that is precisely zero. A meaningful question asks instead whether the effect is relevant. It is then unavoidable that a threshold for relevance is chosen. Considerations that can lead to agreeable conventions for this choice are presented for several commonly used statistical situations. Based on the threshold, a simple quantitative measure of relevance emerges naturally. Statistical inference for the effect should be based on the confidence interval for the relevance measure. A classification of results that goes beyond a simple distinction like "significant / non-significant" is proposed. On the other hand, if desired, a single number called the "secured relevance" may summarize the result, like the p-value does it, but with a scientifically meaningful interpretation.

First Experiences With a Wearable Multisensor in an Outpatient Glucose Monitoring Study, Part I: The Users' View.

BACKGROUND: Extensive past work showed that noninvasive continuous glucose monitoring with a wearable Multisensor device worn on the upper arm provides useful information about glucose trends to improve diabetes therapy in controlled and semicontrolled conditions. METHODS: To test previous findings also in uncontrolled in-clinic and outpatient conditions, a long-term study has been conducted to collect Multisensor and reference glucose data in a population of 20 type 1 diabetes subjects. A total of 1072 study days were collected and a fully on-line compatible algorithmic routine linking Multisensor data to glucose applied to estimate glucose trends noninvasively. The operation of a digital log book, daily semiautomated data transfer and at least 10 daily SMBG values were requested from the patient. RESULTS: Results showed that the Multisensor is capable of indicating glucose trends. It can do so in 9 out of 10 cases either correctly or with one level of discrepancy. This means that in 90% of all cases the Multisensor shows the glucose dynamic to rapidly increase or at least increase. CONCLUSIONS: The Multisensor and the algorithmic routine used in controlled conditions can track glucose trends in all patients, also in uncontrolled conditions. Training of the patient proved to be essential. The workload imposed on patients was significant and should be reduced in the next step with further automation. The feature of glucose trend indication was welcomed and very much appreciated by patients; this value creation makes a strong case for the justification of wearing a wearable.

First Experiences With a Wearable Multisensor Device in a Noninvasive Continuous Glucose Monitoring Study at Home, Part II: The Investigators' View.

BACKGROUND: Extensive past work showed that noninvasive continuous glucose monitoring with a wearable multisensor device worn on the upper arm provides useful information about glucose trends to improve diabetes therapy in controlled and semicontrolled conditions. METHOD: To test previous findings also in uncontrolled conditions, a long term at home study has been organized to collect multisensor and reference glucose data in a population of 20 type 1 diabetes subjects. A total of 1072 study days were collected and a fully on-line compatible algorithmic routine linking multisensor data to glucose applied to estimate glucose levels noninvasively. RESULTS: The algorithm used here calculates glucose values from sensor data and adds a constant obtained by a daily calibration. It provides point inaccuracy measured by a MARD of 35.4 mg/dL on test data. This is higher than current state-of-the-art minimally invasive devices, but still 86.9% of glucose rate points fall within the zone AR+BR. CONCLUSIONS: The multisensor device and the algorithmic routine used earlier in controlled conditions tracks glucose changes also in uncontrolled conditions, although with lower accuracy. The examination of learning curves suggests that obtaining more data would not improve the results. Therefore, further efforts would focus on the development of more complex algorithmic routines able to compensate for environmental and physiological confounders better.

The Effect of a Global, Subject, and Device-Specific Model on a Noninvasive Glucose Monitoring Multisensor System.

BACKGROUND: We study here the influence of different patients and the influence of different devices with the same patients on the signals and modeling of data from measurements from a noninvasive Multisensor glucose monitoring system in patients with type 1 diabetes. The Multisensor includes several sensors for biophysical monitoring of skin and underlying tissue integrated on a single substrate. METHOD: Two Multisensors were worn simultaneously, 1 on the upper left and 1 on the upper right arm by 4 patients during 16 study visits. Glucose was administered orally to induce 2 consecutive hyperglycemic excursions. For the analysis, global (valid for a population of patients), personal (tailored to a specific patient), and device-specific multiple linear regression models were derived. RESULTS: We find that adjustments of the model to the patients improves the performance of the glucose estimation with an MARD of 17.8% for personalized model versus a MARD of 21.1% for the global model. At the same time the effect of the measurement side is negligible. The device can equally well measure on the left or right arm. We also see that devices are equal in the linear modeling. Thus hardware calibration of the sensors is seen to be sufficient to eliminate interdevice differences in the measured signals. CONCLUSIONS: We demonstrate that the hardware of the 2 devices worn on the left and right arms are consistent yielding similar measured signals and thus glucose estimation results with a global model. The 2 devices also return similar values of glucose errors. These errors are mainly due to nonstationarities in the measured signals that are not solved by the linear model, thus suggesting for more sophisticated modeling approaches.

Three-dimensional, task-specific robot therapy of the arm after stroke: a multicentre, parallel-group randomised trial.

BACKGROUND: Arm hemiparesis secondary to stroke is common and disabling. We aimed to assess whether robotic training of an affected arm with ARMin--an exoskeleton robot that allows task-specific training in three dimensions-reduces motor impairment more effectively than does conventional therapy. METHODS: In a prospective, multicentre, parallel-group randomised trial, we enrolled patients who had had motor impairment for more than 6 months and moderate-to-severe arm paresis after a cerebrovascular accident who met our eligibility criteria from four centres in Switzerland. Eligible patients were randomly assigned (1:1) to receive robotic or conventional therapy using a centre-stratified randomisation procedure. For both groups, therapy was given for at least 45 min three times a week for 8 weeks (total 24 sessions). The primary outcome was change in score on the arm (upper extremity) section of the Fugl-Meyer assessment (FMA-UE). Assessors tested patients immediately before therapy, after 4 weeks of therapy, at the end of therapy, and 16 weeks and 34 weeks after start of therapy. Assessors were masked to treatment allocation, but patients, therapists, and data analysts were unmasked. Analyses were by modified intention to treat. This study is registered with ClinicalTrials.gov, number NCT00719433. FINDINGS: Between May 4, 2009, and Sept 3, 2012, 143 individuals were tested for eligibility, of whom 77 were eligible and agreed to participate. 38 patients assigned to robotic therapy and 35 assigned to conventional therapy were included in analyses. Patients assigned to robotic therapy had significantly greater improvements in motor function in the affected arm over the course of the study as measured by FMA-UE than did those assigned to conventional therapy (F=4.1, p=0.041; mean difference in score 0.78 points, 95% CI 0.03-1.53). No serious adverse events related to the study occurred. INTERPRETATION: Neurorehabilitation therapy including task-oriented training with an exoskeleton robot can enhance improvement of motor function in a chronically impaired paretic arm after stroke more effectively than conventional therapy. However, the absolute difference between effects of robotic and conventional therapy in our study was small and of weak significance, which leaves the clinical relevance in question. FUNDING: Swiss National Science Foundation and Bangerter-Rhyner Stiftung.

Data processing for noninvasive continuous glucose monitoring with a multisensor device.

BACKGROUND: Impedance spectroscopy has been shown to be a candidate for noninvasive continuous glucose monitoring in humans. However, in addition to glucose, other factors also have effects on impedance characteristics of the skin and underlying tissue. METHOD: Impedance spectra were summarized through a principal component analysis and relevant variables were identified with Akaike's information criterion. In order to model blood glucose, a linear least-squares model was used. A Monte Carlo simulation was applied to examine the effects of personalizing models. RESULTS: The principal component analysis was able to identify two major effects in the impedance spectra: a blood glucose-related process and an equilibration process related to moisturization of the skin and underlying tissue. With a global linear least-squares model, a coefficient of determination (R²) of 0.60 was achieved, whereas the personalized model reached an R² of 0.71. The Monte Carlo simulation proved a significant advantage of personalized models over global models. CONCLUSION: A principal component analysis is useful for extracting glucose-related effects in the impedance spectra of human skin. A linear global model based on Solianis Multisensor data yields a good predictive power for blood glucose estimation. However, a personalized linear model still has greater predictive power.

Problems with using the normal distribution--and ways to improve quality and efficiency of data analysis.

BACKGROUND: The gaussian or normal distribution is the most established model to characterize quantitative variation of original data. Accordingly, data are summarized using the arithmetic mean and the standard deviation, by mean ± SD, or with the standard error of the mean, mean ± SEM. This, together with corresponding bars in graphical displays has become the standard to characterize variation. METHODOLOGY/PRINCIPAL FINDINGS: Here we question the adequacy of this characterization, and of the model. The published literature provides numerous examples for which such descriptions appear inappropriate because, based on the "95% range check", their distributions are obviously skewed. In these cases, the symmetric characterization is a poor description and may trigger wrong conclusions. To solve the problem, it is enlightening to regard causes of variation. Multiplicative causes are by far more important than additive ones, in general, and benefit from a multiplicative (or log-) normal approach. Fortunately, quite similar to the normal, the log-normal distribution can now be handled easily and characterized at the level of the original data with the help of both, a new sign, x/, times-divide, and notation. Analogous to mean ± SD, it connects the multiplicative (or geometric) mean mean * and the multiplicative standard deviation s* in the form mean * x/s*, that is advantageous and recommended. CONCLUSIONS/SIGNIFICANCE: The corresponding shift from the symmetric to the asymmetric view will substantially increase both, recognition of data distributions, and interpretation quality. It will allow for savings in sample size that can be considerable. Moreover, this is in line with ethical responsibility. Adequate models will improve concepts and theories, and provide deeper insight into science and life.

Characteristics of a multisensor system for non invasive glucose monitoring with external validation and prospective evaluation.

The Multisensor Glucose Monitoring System (MGMS) features non invasive sensors for dielectric characterisation of the skin and underlying tissue in a wide frequency range (1 kHz-100 MHz, 1 and 2 GHz) as well as optical characterisation. In this paper we describe the results of using an MGMS in a miniaturised housing with fully integrated sensors and battery. Six patients with Type I Diabetes Mellitus (age 44±16 y; BMI 24.1±1.3 kg/m(2), duration of diabetes 27±12 y; HbA1c 7.3±1.0%) wore a single Multisensor at the upper arm position and performed a total of 45 in-clinic study days with 7 study days per patient on average (min. 5 and max. 10). Glucose changes were induced either orally or by i.v. glucose administration and the blood glucose was measured routinely. Several prospective data evaluation routines were applied to evaluate the data. The results are shown using one of the restrictive data evaluation routines, where measurements from the first 22 study days were used to train a linear regression model. The global model was then prospectively applied to the data of the remaining 23 study days to allow for an external validation of glucose prediction. The model application yielded a Mean Absolute Relative Difference of 40.8%, a Mean Absolute Difference of 51.9 mg dL(-1), and a correlation of 0.84 on average per study day. The Clarke error grid analyses showed 89.0% in A+B, 4.5% in C, 4.6% in D and 1.9% in the E region. Prospective application of a global, purely statistical model, demonstrates that glucose variations can be tracked non invasively by the MGMS in most cases under these conditions.

Non-invasive glucose monitoring in patients with Type 1 diabetes: a Multisensor system combining sensors for dielectric and optical characterisation of skin.

In vivo variations of blood glucose (BG) are affecting the biophysical characteristics (e.g. dielectric and optical) of skin and underlying tissue (SAUT) at various frequencies. However, the skin impedance spectra for instance can also be affected by other factors, perturbing the glucose related information, factors such as temperature, skin moisture and sweat, blood perfusion as well as body movements affecting the sensor-skin contact. In order to be able to correct for such perturbing factors, a Multisensor system was developed including sensors to measure the identified factors. To evaluate the quality of glucose monitoring, the Multisensor was applied in 10 patients with Type 1 diabetes. Glucose was administered orally to induce hyperglycaemic excursions at two different study visits. For analysis of the sensor signals, a global multiple linear regression model was derived. The respective coefficients of the variables were determined from the sensor signals of this first study visit (R(2)=0.74, MARD=18.0%--mean absolute relative difference). The identical set of modelling coefficients of the first study visit was re-applied to the test data of the second study visit to evaluate the predictive power of the model (R(2)=0.68, MARD=27.3%). It appears as if the Multisensor together with the global linear regression model applied, allows for tracking glucose changes non-invasively in patients with diabetes without requiring new model coefficients for each visit. Confirmation of these findings in a larger study group and under less experimentally controlled conditions is required for understanding whether a global parameterisation routine is feasible.

Critical evaluation of PAH source apportionment tools using data from the Swiss soil monitoring network.

In this study a large dataset on the polycyclic aromatic hydrocarbon (PAH) content of Swiss soils was analysed to evaluate two source apportionment tools, i.e., characteristic PAH ratios/molecular markers and a linear mixing model. Population density and total organic carbon (TOC) content were identified by a multiple regression model as independently and positively influencing the PAH concentrations in Swiss background soil. Specifically, TOC was more strongly positively correlated with the sum of light PAH (naphthalene to phenanthrene) than with the sum of heavy PAH (anthracene to benzo[ghj]perylene), whereas population density was more strongly positively correlated with the sum of heavy PAH than with light PAH. In addition, the sum of the heavy PAH as well as the total sum correlated negatively with sample site altitude. It is therefore hypothesised that heavy PAH are less mobile, whereas light PAH were closer to equilibrium with TOC in the soil. Similar results were found for polychlorinated biphenyls (PCB). The characteristic ratios and molecular markers pointed to pyrogenic origin of PAH in Swiss background soil but did not allow for further differentiation of individual fuel contributions, even though attempts to take environmental fractionation processes into account were made. The comparison of three soil profiles identified with a linear mixing model from the pattern of 16 PAH with >300 PAH emission profiles from the literature suggested urban dust, wood combustion and binders from asphalt as PAH sources. However, also here, environmental fractionation processes probably obscured source characteristic PAH ratios and fingerprints, which thus need to be interpreted with caution.

Organic pollutants in compost and digestate. Part 1. Polychlorinated biphenyls, polycyclic aromatic hydrocarbons and molecular markers.

In Europe, 9.3 x 10(6) t(dry weight (dw)) of compost and digestate are produced per year. Most of this is applied to agricultural land, which can lead to considerable inputs of organic pollutants, such as polychlorinated biphenyls (PCB) and polycyclic aromatic hydrocarbons (PAH) to soil. This paper presents an inventory of the pollutant situation in source-separated composts, digestates and presswater in Switzerland by a detailed analysis of over 70 samples. PCB concentrations ( summation PCB 28, 52, 101, 118, 138, 153, 180) were significantly higher in urban (median: 30 microg kg(-1)dw, n = 52) than in rural samples (median: 14 microg kg(-1)dw, n = 16). Together with low concentrations in general, this points to aerial deposition on compost input material as the major contamination pathway. Enantiomeric fractions of atropisometric PCB were close to racemic. Median PAH concentration was 3010 microg kg(-1)dw( summation 15PAH, n = 69), and one quarter of the samples exhibited concentrations above the relevant Swiss guide value for compost (4000 microg kg(-1)dw). The levels were influenced by the treatment process (digestate > compost), the season of input material collection (spring-summer > winter > autumn), the particle size (coarse-grained > fine-grained), and maturity (mature > less mature). The main source of PAH in compost was pyrogenic, probably influenced mainly by liquid fossil fuel combustion and some asphalt abrasion, as suggested by multiple linear regression. This study, together with a companion paper reporting on other organic contaminates including emerging compound classes, provides a starting point for a better risk-benefit estimation of the application of compost and digestate to agricultural soil in Switzerland.

Respiratory responses during affective picture viewing.

Previous research has demonstrated covariation of physiological responding with judgments of valence and arousal. However, until now links between these affective dimensions and respiratory measures have not been extensively investigated. In this study, eight picture series of different affective valence and arousal level were shown to 30 subjects, while respiration, skin conductance level (SCL), heart rate (HR) and affective judgments were measured. With increasing pleasantness, inspiratory time lengthened, mean inspiratory flow decreased and thoracic breathing increased. With increasing arousal, inspiratory time and total breath duration shortened and mean inspiratory flow, minute ventilation, thoracic breathing and electrodermal activity increased. These findings confirm the importance of arousal in respiratory responding, but also indicate a modulatory role of affective valence. We propose that the arousal effects reflect energy mobilization in preparation to act, and that the valence effects might be a manifestation of an attention bias toward negative stimuli.

Statistical procedures to test for linearity and estimate threshold doses for tumor induction with nonlinear dose-response relationships in bioassays for carcinogenicity.

Sublinear shapes of the dose-response curve in the low-dose range of toxicity testing are often postulated to be indicative of a no-effect threshold. We present statistical procedures to test sublinear dose responses in bioassays for carcinogenicity against the hypothesis of linearity and estimate a lower confidence limit for the dose at the postulated breakpoint. First, a control tumor incidence of 0 is assumed. Tumor incidence at dose 1 is allowed to range from 0 to 4 tumor-bearing animals (TBAs) in groups of 50 animals, dose 2 is assumed to result in a tumor incidence of 5-25 TBAs. The null hypothesis of a linear dose response is tested by (i) the likelihood ratio (LR) test and (ii) the minimum chi(2) (MC) method. Validation by simulation showed the MC method to be more conservative than the LR test. At the 5% level with MC, the following observed numbers of TBAs for the dose sequence 0-1-2 resulted in rejection of the hypothesis of linearity: 0-0-6, 0-1-10, 0-2-13, 0-3-16, 0-4-18. Second, the analysis was adapted to allow for a control tumor incidence of 0-4 TBAs/50 and a tumor incidence of 0-10 TBAs/50 at dose 1, and the minimum number of TBAs at dose 2 to reject linearity at the 5% level was calculated. Third, a program is made available to analyze data derived from protocols that include nonstandard dose span and group size. Internet access to the respective statistics software and source file is provided. Examples for nasal tumor induction by formaldehyde and for the induction of renal adenocarcinoma by ochratoxin A are shown. The proposed analysis may be useful to test sublinear sections of the dose response for the possibility of a threshold for carcinogens and to define dose levels that could be used as a starting point for setting exposure standards.

Das Dispersionsmuster von Teichmuscheln (Anodonta) im Zürichsee.

The dispersion pattern of mussels belonging to the genus Anodonta has been studied in a shallow area of Lake Zurich. Starting in July 1970, mussels were counted six times at monthly intervals on a grid of 800 square units of 0.5 m2 each. The mean (mussels per m2) varied between months from 1.3 (in July and December) to 2.2 (in November). Different parts of the grid contained unequal numbers of mussels, which is explained by nonhomogeneousness of the substratum.The observed frequency distribution of number of mussels per unit showed a close fit to an negative binomial distribution. For further interpretation the index of dispersion as proposed by Morisita (1959) was computed, first using single units and thereafter combinations of 2, 4, ... units. From the slopes of the lines obtained (Fig. 3) it is concluded that the mussels form small aggregations, within which their distribution is random. Aggregation is most pronounced in July, during a season when male mussels are known to release their sperm.