Log-transformed Data Research Articles

Comparative Bioavailability of Sildenafil 50-mg Film-Coated Tablets and 50-mg Orally Disintegrating Films in Healthy Mexican Subjects: Results From a Randomized, Open-Label, Crossover Study.

An orally disintegrating film formulation of sildenafil 50 mg (CL Pharm Co, Ltd) was used in this study and compared to the market-available product film coated tablets (Viagra® , Pfizer, Mexico). The objective was to compare the pharmacokinetic properties of these products after a single-dose administration to 47 healthy male volunteers (aged 19-48 years) in a randomized, open-label, 2-way crossover study. Each subject received a single oral dose of 50 mg of sildenafil test or reference product administered under fasting conditions at each of the 2 study periods according to a crossover design. There was a 3-day washout period between drug administrations. Blood samples for pharmacokinetic analysis were collected predose and at different times postdosing. The maximum plasma concentration and area under the curve from administration to last observed concentration time of test and reference products were compared. Pharmacokinetic parameters shown to be within the confidence interval 80% to 125% for log-transformed data and Shuirmann and Anderson Hauck tests showed a high probability that area under the curve values for the test product were within 80% to 125% (P < .05). Adverse events occurred at similar rates for the 2 formulations (8 for each product), headache being the most prevalent. The results suggest that the 2 sildenafil formulations, orally disintegrating films and film-coated tablets, are similar in terms of bioavailability, making the test product a good alternative to treat erectile dysfunction and improving dosing convenience.

Changes in the Responsiveness of the Hypothalamic-Pituitary-Gonadal Axis to Kisspeptin-10 Administration during Pubertal Transition in Boys.

In human, no studies are available regarding changes in kisspeptin1 receptor (KISS1R) sensitivity during pubertal transition. In this study, healthy boys were classified into 5 Tanner stages of puberty (n = 5/stage). Human kisspeptin-10 was administered to boys at each Tanner stage and to adult men (n = 5) as an IV bolus for comparison. Serial blood samples were collected for 30 min pre- and 120 min post-kisspeptin injection periods at 30 min interval for measuring plasma LH and testosterone levels. There was insignificant effect of kisspeptin on LH and testosterone levels in boys of Tanner stages I–III. At Tanner stage IV, the effect of kisspeptin on plasma LH was insignificant. However, a paired t-test on a log-transformed data showed a significant (P < 0.05) increase in mean peak post-kisspeptin testosterone level. In Tanner stage V, a significant (P < 0.05) increase was observed in mean post-kisspeptin peak LH level as compared to the mean basal LH value. Post-kisspeptin plasma testosterone levels were also significantly (P < 0.05) increased as compared to the pre-kisspeptin level in Tanner stage V. Our data suggest that sensitivity of KISS1R on GnRH neurons with reference to LH stimulation in boys develops during the later part of puberty reaching to adult level at Tanner stage V. This trial is registered with WHO International Clinical Trial Registration ID NCT03286517.

Estimating body mass and composition from proximal femur dimensions using dual energy x-ray absorptiometry

Body mass prediction from the skeleton most commonly employs femoral head diameter (FHD). However, theoretical predictions and empirical data suggest the relationship between mass and FHD is strongest in young adults, that bone dimensions reflect lean mass better than body or fat mass and that other femoral measurements may be superior. Here, we generate prediction equations for body mass and its components using femoral head, neck and proximal shaft diameters and body composition data derived from dual-energy x-ray absorptiometry (DXA) scans of young adults (n = 155, 77 females and 78 males, mean age 22.7 ± 1.3 years) from the Andhra Pradesh Children and Parents Study, Hyderabad, India. Sex-specific regression of log-transformed data on femoral measurements predicted lean mass with smaller standard errors of estimate (SEEs) than body mass (12–14% and 16–17% respectively), while none of the femoral measurements were significant predictors of fat mass. Subtrochanteric mediolateral shaft diameter gave lower SEEs for lean mass in both sexes and for body mass in males than FHD, while FHD was a better predictor of body mass in women. Our results provide further evidence that lean mass is more closely related to proximal femur dimensions than body or fat mass and that proximal shaft diameter is a better predictor than FHD of lean but not always body mass. The mechanisms underlying these relationships have implications for selecting the most appropriate measurement and reference sample for estimating body or lean mass, which also depend on the question under investigation.

Assessment of empirical and regression methods for infilling missing streamflow data in Little Ruaha catchment Tanzania

Water resources and engineering projects are of major importance due to great demand of water and power supplies, irrigation needs, drought mitigation and flood control. In order to plan and design these projects, complete and reliable hydrological datasets are required. Missing data can severely compromise data quality and utility. The Rufiji Water Basin Office has developed and kept a database of daily streamflow records from 1950s to-date for at least 87 gauging stations. While the majority of the records are complete, data checks revealed significant gaps. Some stations are experiencing large gaps of up to 19 consecutive years. There is no dedicated study that looked at assessment of suitable methods for infilling such gaps.This study therefore made a contribution by appraising empirical and regression rainfall runoff based methods in the Little Ruaha River catchment, a sub-catchment of the Great Ruaha River sub-basin both within the Rufiji River Basin. The methods employed included simple linear regression (with untransformed and log-transformed data), multiple linear regression (with untransformed and log-transformed data), rainfall-runoff relationship using double mass curve technique, flow duration matching and drainage-area ratio. In addition, rainfall runoff modeling using HBV-Light was done for further comparison. With exception to the rainfall-runoff relationship and HBV-Light model, all other methods relied upon data transfer from donor stations (upstream & downstream station(s)) for infilling a downstream/upstream station. Data quality and consistency checks were performed, and performances of infilling methods were evaluated based on three performance criteria namely Nash-Sutcliffe efficiency coefficient (NSE), Coefficient of determination (R2) and standard error of estimate (SE) during calibration and validation periods. Four gauging stations (2 each upstream and downstream) were separately used to infill artificially created gaps to the target station. Overall, the calibration and validation daily results at 1KA21A indicated that the flow duration matching technique and multiple linear regression methods performed better than other methods with NSE (71%; 93%) and NSE (55%; 75%) respectively. These results have a potential for wide application in other basins of Tanzania for hydrological analysis and water resource management, where missing data is very common.

Chemometric Methods to Predict of Pb in Urban Soil from Port Pirie, South Australia, using Spectrally Active of Soil Carbon

ABSTRACTA total of 73 soil samples were initially analyzed for lead (Pb) concentration as an indicator of the environment impact of smelter activity in the Port Pirie, South Australia. Chemometric techniques were used to assess the ability of near-infrared (NIR) reflectance spectroscopy to predict soil Pb using spectrally active soil characteristics such as soil carbon (C). The result indicated a strong linear relationship between log-transformed data of soil Pb and spectral reflectance in the range between 500 and 612 nm with R2 = 0.54 and a low root-mean-square error (RMSEv = 0.38) for the validation mode with an acceptable ratio of performance to deviation and ratio of error range (1.6 and 7.7, respectively). This study suggested that NIR spectroscopy based on auxiliary spectrally active components is a rapid and noninvasive assessment technique and has the ability to determine Pb contamination in urban soil to be useful in environmental health risk assessment.

Bioequivalence Between Two Tablets of Lacosamide in Healthy Subjects Under Fasting Condition

Bioavailability from different formulations of lacosamide 200 mg film-coated tablets was compared in a bioequivalence study under fasting conditions. The study was single dose, randomized, open label, two-period crossover, with Brazilian males and females healthy subjects. Blood samples were taken during 72 h and plasmatic concentrations were determined using a validated HPLC-MS/MS method. Confidence intervals (CI90%) for the peak plasma concentration (Cmax) and area under the concentration-time curve (AUC0-t) were determined by calculating log-transformed data. The ratios and 90% CI for the geometric mean test/reference ratios were 96.17% (89.30-103.56%) for Cmax and 98.69% (96.71-100.72%) for AUC0-t. Under fasting condition, the test (lacosamide 200 mg film-coated tablets, Monte Verde S.A.) and reference (Vimpat® 200 mg film-coated tablets, Aesica Pharmaceuticals GmbH) formulations were considered bioequivalent since the 90% CIs for the geometric mean test/reference ratios were within ANVISA’s predetermined range of 80% to 125%.

Pharmacologic and Pharmacodynamic Equivalence of 2 Formulations of Orlistat

We sought to establish the bioequivalence of 2 weight-loss aids: orlistat 27-mg chewable tablet and orlistat 60-mg capsule, measured pharmacodynamically as percentage fecal fat excretion. Two open-label, single-center, randomized, 3-period, 3-treatment crossover studies were conducted in adults with body mass index 25-33kg/m2 . For each 9-day treatment period, subjects received orlistat 27-mg chewable tablet, 60-mg capsule, or 120-mg capsules (2 60-mg capsules) 3 times daily; a 2-day washout separated treatments. Primary bioequivalence analyses were based on 2 1-sided tests of the 90% CI of the ratio of geometric means using log-transformed data (study 1) and by the dose-scale method to calculate bias-corrected and accelerated 90% CI of relative bioavailability (f) using nontransformed data (study 2). Bioequivalence was established if 90% CIs fell within 0.80-1.25. In total, 48 and 144 subjects were randomized in study 1 and study 2, respectively. Bioequivalence between the formulations was established in both studies: study 1 ratio of geometric means of percentage fecal fat excretion was 0.96 (2 1-sided tests, 90% CI 0.87-1.06); study 2-point estimate of f was 1.09 (bias-corrected and accelerated 90% CI 0.98-1.22). Tolerability of the 27-mg tablet was consistent with the 60-mg capsule; mild gastrointestinal effects were most common.

Speckle Noise Reduction of Medical Imaging via Logistic Density in Redundant Wavelet Domain

In the digital world, artificial intelligence tools and machine learning algorithms are widely applied in analysis of medical images for identifying diseases and make diagnoses; for example, to make recognition and classification. Speckle noises affect all medical imaging systems. Therefore, reduction in corrupting speckle noises is very important, since it deteriorates the quality of the medical images and makes tasks such as recognition and classification difficult. Most existing denoising algorithms have been developed for the additive white Gaussian noise (AWGN). However, AWGN is not a speckle noise. Therefore, this work presents a novel speckle noise removal algorithm within the framework of Bayesian estimation and wavelet analysis. This research focuses on noise reduction by the Bayesian with wavelet-based method because it provides good efficiency in noise reduction and spends short time in processing. The subband decomposition of a logarithmically transformed image is best described by a family of heavy-tailed densities such as Logistic distribution. Then, this research proposes the maximum a posteriori (MAP) estimator assuming Logistic random vectors for each parent-child wavelet co-efficient of noise-free log-transformed data and log-normal density for speckle noises. Moreover, a redundant wavelet transform, i.e., the cycle-spinning method, is applied in our proposed methods. In our experiments, our proposed methods give promising denoising results.

Scaling of work and power in a locomotor muscle of a frog.

Muscle work and power are important determinants of movement performance in animals. How these muscle properties scale determines, in part, the scaling of performance during movements, such as jump height or distance. Muscle-mass-specific work is predicted to remain constant across a range of scales, assuming geometric similarity, while muscle-mass-specific power is expected to decrease with increasing scale. We tested these predictions by examining muscle morphology and contractile properties of plantaris muscles from frogs ranging in mass from 1.28 to 20.60g. Scaling of muscle work and power was examined using both linear regression on log10-transformed data (LR) and non-linear regressions on untransformed data (NLR). Results depended on the method of regression not because of large changes in scaling slopes, but because of changing levels of statistical significance using corrections for multiple tests, demonstrating the importance of careful consideration of statistical methods when analyzing patterns of scaling. In LR, muscle-mass-specific work decreased with increasing scale, but an accompanying positive allometry of muscle mass predicts constant movement performance at all scales. These relationships were non-significant in NLR, though scaling with geometric similarity also predicts constant jump performance across scales, because of proportional increases in available muscle energy and body mass. Both intrinsic shortening velocity and muscle-mass-specific power were positively allometric in both types of analysis. Nonetheless, scale accounts for little variation in contractile properties overall over the range examined, indicating that other sources of intraspecific variation may be more important in determining muscle performance and its effects on movement.

Bayesian statistical models to estimate EQ-5D utility scores from EORTC QLQ data in myeloma.

It is well documented that the modelling of health-related quality of life data is difficult as the distribution of such data is often strongly right/left skewed and it includes a significant percentage of observations at one. The objective of this study is to develop a series of two-part models (TPMs) that deal with these issues. Data from the UK Medical Research Council Myeloma IX trial were used to examine the relationship between the European Organization for Research and Treatment of Cancer (EORTC) QLQ-C30/QLQ-MY20 scores and the European QoL-5 Dimensions (EQ-5D) utility score. Four different TPMs were developed. The models fitted included TPM with normal regression, TPM with normal regression with variance a function of participant characteristics, TPM with log-transformed data, and TPM with gamma regression and a log link. The cohort of 1839 patients was divided into 75% derivation sample, to fit the different models, and 25% validation sample to assess the predictive ability of these models by comparing predicted and observed mean EQ-5D scores in the validation set, unadjusted R2 , and root mean square error. Predictive performance in the derivation dataset depended on the criterion used, with R2 /adjusted-R2 favouring the TPM with normal regression and mean predicted error favouring the TPM with gamma regression. The TPM with gamma regression performs best within the validation dataset under all criteria. TPM regression models provide flexible approaches to estimate mean EQ-5D utility weights from the EORTC QLQ-C30/QLQ-MY20 for use in economic evaluation.

Morphometric panel regression equations for predicting body mass in immature humans.

Predicting body mass is a frequent objective of several anthropological subdisciplines, but there are few published methods for predicting body mass in immature humans. Because most reference samples are composed of adults, predicting body mass outside the range of adults requires extrapolation, which may reduce the accuracy of predictions. Prediction equations developed from a sample of immature humans would reduce extrapolation for application to small-bodied target individuals, and should have utility in multiple predictive contexts. Here, we present two novel body mass prediction equations derived from 3468 observations of stature and bi-iliac breadth from a large sample of immature humans (n = 173) collected in the Harpenden Growth Study. Prediction equations were generated using raw and natural log-transformed data and modeled using panel regression, which accounts for serial autocorrelation of longitudinal observations. Predictive accuracy was gauged with a global sample of human juveniles (n = 530 age- and sex-specific annual means) and compared to the performance of the adult morphometric prediction equation previously identified as most accurate for human juveniles. While the raw data panel equation is only slightly more accurate than the adult equation, the logged data panel equation generates very accurate body mass predictions across both sexes and all age classes of the test sample (mean absolute percentage prediction error = 2.47). The logged data panel equation should prove useful in archaeological, forensic, and paleontological contexts when predictor variables can be measured with confidence and are outside the range of modern adult humans.

Estimation of Standard Deviation for a Log-Transformed Variable Based on Summary Statistics in the Original Scale

ABSTRACTClinical study endpoints, including some biomarkers, are frequently analyzed after a log transformation. To calculate study power for detecting a between-treatment difference in the log scale, an estimate of the standard deviation of the log-transformed variable is needed. Often, though, only summary statistics in the original scale including arithmetic means with corresponding standard deviations or sample medians and interquartile ranges are found in the literature. In the absence of individual subjects' log-transformed data for directly calculating the sample standard deviation in the log scale, alternative approaches should be applied. This article presents methods for estimating the standard deviation of a log-transformed variable via the arithmetic means and standard deviations or medians and interquartile ranges of the untransformed variable. It further presents methods for constructing the corresponding confidence intervals. A meta-analysis approach, combining data from all sources for more robust estimation, is also discussed. Simulations and examples are provided to assess the performances of these estimates.

Analysing seasonal variation and forecasting the output of beverage industry in Pakistan using Demetra+

The study analyses the seasonal variation and forecasts the output of the beverage industry in Khyber Pakhtunkhwa (KPK) province of Pakistan, using a seven-year (month-wise) data from July 2007 to April 2014. Tramo Seats version of Demetra+ software has been used on log-transformed data with the distinct feature of automatic model identification to capture seasonal differences in output of the beverage industry in KPK. Results suggest wider fluctuations in the original series as compared to seasonal adjusted series which was mainly due to seasonal component in the time series instead of the irregular component; thus, confirming the principle of canonical decomposition of the data set in question. Results of non-parametric tests revealed a strong and stable seasonality pattern in the output of the beverage industry. Further, a significant difference between original and seasonal adjusted series has also been observed in the output. The results have implications for policy planners both at micro as well as macro level.

Analysing seasonal variation and forecasting the output of beverage industry in Pakistan using Demetra+

The study analyses the seasonal variation and forecasts the output of the beverage industry in Khyber Pakhtunkhwa (KPK) province of Pakistan, using a seven-year (month-wise) data from July 2007 to April 2014. Tramo Seats version of Demetra+ software has been used on log-transformed data with the distinct feature of automatic model identification to capture seasonal differences in output of the beverage industry in KPK. Results suggest wider fluctuations in the original series as compared to seasonal adjusted series which was mainly due to seasonal component in the time series instead of the irregular component; thus, confirming the principle of canonical decomposition of the data set in question. Results of non-parametric tests revealed a strong and stable seasonality pattern in the output of the beverage industry. Further, a significant difference between original and seasonal adjusted series has also been observed in the output. The results have implications for policy planners both at micro as well as macro level.

Adaptive Optimal Designs for Dose-Finding Studies with Time-to-Event Outcomes

We consider optimal design problems for dose-finding studies with censored Weibull time-to-event outcomes. Locally D-optimal designs are investigated for a quadratic dose–response model for log-transformed data subject to right censoring. Two-stage adaptive D-optimal designs using maximum likelihood estimation (MLE) model updating are explored through simulation for a range of different dose–response scenarios and different amounts of censoring in the model. The adaptive optimal designs are found to be nearly as efficient as the locally D-optimal designs. A popular equal allocation design can be highly inefficient when the amount of censored data is high and when the Weibull model hazard is increasing. The issues of sample size planning/early stopping for an adaptive trial are investigated as well. The adaptive D-optimal design with early stopping can potentially reduce study size while achieving similar estimation precision as the fixed allocation design.

Symmetric Dimethylarginine in Cats with Hypertrophic Cardiomyopathy and Diabetes Mellitus.

BackgroundSymmetric dimethylarginine (SDMA) has been increasingly used as a marker of early chronic kidney disease (CKD) in cats, but little is known about the influence of comorbidities on SDMA in this species.HypothesisHypertrophic cardiomyopathy (HCM) and diabetes mellitus (DM), independently of CKD, are associated with changes in serum SDMA.AnimalsNinety‐four cats (17 with CKD, 40 with HCM, 17 with DM, and 20 healthy controls).MethodsCase‐control study. Clinical examination, echocardiography, ECG, blood pressure, CBC, biochemistry, thyroxine, and SDMA measurement were performed. Urinalysis was performed in controls and cats with CKD and DM. Analysis of variance was used to compare overall differences in the log‐transformed SDMA data among groups. A random forest algorithm was applied to explore which clinical and other factors influenced serum SDMA.ResultsMedian (range) serum SDMA for the renal group (positive control) was 19 (10–93) μg/dL, whereas for the control group (negative control), it was 10 (5–15) μg/dL. For the cardiac and diabetic groups, serum SDMA was 9 (4–24) μg/dL and 7 (3–11) μg/dL, respectively. The renal group had significantly higher SDMA concentrations and the diabetic group significantly lower SDMA concentrations compared to all other groups.Conclusions and Clinical ImportanceSerum SDMA concentrations in cats with HCM were not significantly different from those of healthy control cats. Cats with DM, however, had significantly lower SDMA concentrations than controls, a finding that needs further investigation and should be kept in mind when evaluating renal function of cats with this endocrinopathy.

Bioequivalence Between Two Prolonged Release Tablets of Darifenacin in Healthy Subjects Under Fasting and Fed Conditions

Bioavailability in different formulations of darifenacin 15 mg prolonged release tablets was compared in two bioequivalence studies, one under fasting conditions and the other after a standard breakfast. Both studies were single dose, randomized, open label, two-period crossover, with Brazilian males and females healthy subjects. Blood samples were taken during 72 h and plasmatic concentrations were determined using a validated UPLC-MS/MS method. Confidence intervals (CI90%) for the peak plasma concentration (Cmax) and area under the concentration-time curve (AUC0-t) were determined by calculating log-transformed data. In the fast study, the ratios and 90% CI for the geometric mean was 94.82% (86.08-104.46%) for Cmax and 92.49% (84.68-101.03%) for AUC0-t. In the fed study, the ratios and 90% CI for the geometric mean was 94.41% (85.96-103.70%) for Cmax and 94.37% (88.79-100.31%) for AUC0-t. Under fast and fed conditions, the test (darifenacin 15 mg prolonged release tablets, Zodiac Produtos FarmacAauticos S.A.) and reference (Enablex® 15 mg prolonged release tablets, Novartis BiociAancias S.A.) formulations were considered bioequivalent since the 90% CIs for the geometric mean test/reference ratios were within a predetermined range of 80% to 125% according to FDA and to ANVISA.

Sample size calculations based on a difference in medians for positively skewed outcomes in health care studies

BackgroundIn healthcare research, outcomes with skewed probability distributions are common. Sample size calculations for such outcomes are typically based on estimates on a transformed scale (e.g. log) which may sometimes be difficult to obtain. In contrast, estimates of median and variance on the untransformed scale are generally easier to pre-specify. The aim of this paper is to describe how to calculate a sample size for a two group comparison of interest based on median and untransformed variance estimates for log-normal outcome data.MethodsA log-normal distribution for outcome data is assumed and a sample size calculation approach for a two-sample t-test that compares log-transformed outcome data is demonstrated where the change of interest is specified as difference in median values on the untransformed scale. A simulation study is used to compare the method with a non-parametric alternative (Mann-Whitney U test) in a variety of scenarios and the method is applied to a real example in neurosurgery.ResultsThe method attained a nominal power value in simulation studies and was favourable in comparison to a Mann-Whitney U test and a two-sample t-test of untransformed outcomes. In addition, the method can be adjusted and used in some situations where the outcome distribution is not strictly log-normal.ConclusionsWe recommend the use of this sample size calculation approach for outcome data that are expected to be positively skewed and where a two group comparison on a log-transformed scale is planned. An advantage of this method over usual calculations based on estimates on the log-transformed scale is that it allows clinical efficacy to be specified as a difference in medians and requires a variance estimate on the untransformed scale. Such estimates are often easier to obtain and more interpretable than those for log-transformed outcomes.

Background levels of some trace elements in calcareous soils of the Hamedan Province, Iran

Natural concentrations of trace elements are necessary for assessing trace elements contamination affected by anthropogenic activities in soils. This study was conducted to establish background concentrations of cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb) and zinc (Zn) in 50 unpolluted native soils with a total of 100 soil samples (surface and subsurface soil layers) in Hamedan province of Iran. Different methods have been used for the evaluation of background limits, and the relationships between soil properties and trace elements concentrations and among trace element concentrations have been analyzed. Values from Geometric Mean (GM) procedure had the lowest background limit, while the GM×GSD2 (geometric standard deviation) method produces the highest background limits. The percentage of outliers in Median+2MAD (median absolute deviation) ranged from 4.0 to 16.0% for natural data and from 0.0 to 12% for log-transformed data. The smallest outlier percentage was for Cd and Cr, and it was higher for Co, Cu, and Zn. The obtained geochemical baseline concentrations (mgkg−1) based on Median+2MAD were (log-transformed data) Cd 1.36, Co 15.42, Cr (natural data) 36.69, Cu 29.99, Fe 25.79 (gkg−1), Mn 368.68, Ni (natural data) 53.59, Pb 39.60, Zn 103.80. Carbonate calcium had significant negative correlations with Co, Cr, Cu, Fe, and Mn, indicating that trace elements are not included in the crystal structure of secondary carbonate minerals; consequently, accumulation of CaCO3 causes a dilution effect in the soils. The content of clay, silt and organic matter had significant role in the accumulation of Mn, Ni, and Pb in the soils of the studied area. Significant correlation was found among Co, Cr, Cu, Fe, Mn, and Ni and between Zn with Cd, Cr, Cu, Mn, and Ni. Factor analysis of trace elements explored six factors attributing 64.1% of the total variability. According to the results found by upper whisker method, correlation analysis and factor analysis, studied trace elements contaminated none of the soil samples, natural accumulation processes influenced their contents in soils and origin of trace elements may be lithogenic, but their sources may be different.

Correct Use of Percent Coefficient of Variation (%CV) Formula for Log-Transformed Data

Correct Use of Percent Coefficient of Variation (%CV) Formula for Log-Transformed Data