Determination of aqueous two-phase system phase-forming components in the presence of bovine serum albumin

In the current work, the quantification of different poly(ethylene glycol) (PEG)–potassium phosphate/sodium citrate aqueous two-phase system (ATPS) phase-forming components was investigated by using conductivity and refractive index measurements. For this purpose, refractive index and conductivity calibration curves were obtained for ATPS at different pH values in the presence of different bovine serum albumin (BSA) concentrations. Whereas BSA had no effect on the conductivity, it had a considerable effect on the refractive index. Finally, a convenient dilution of the samples prior to the ATPS constituent determination is needed to ensure no significant influence from BSA.     

Regression Calibration in Semiparametric Accelerated Failure Time Models

Summary In large cohort studies, it often happens that some covariates are expensive to measure and hence only measured on a validation set. On the other hand, relatively cheap but error‐prone measurements of the covariates are available for all subjects. Regression calibration (RC) estimation method ( Prentice, 1982 , Biometrika 69 , 331–342) is a popular method for analyzing such data and has been applied to the Cox model by Wang et al. (1997, Biometrics 53 , 131–145) under normal measurement error and rare disease assumptions. In this article, we consider the RC estimation method for the semiparametric accelerated failure time model with covariates subject to measurement error. Asymptotic properties of the proposed method are investigated under a two‐phase sampling scheme for validation data that are selected via stratified random sampling, resulting in neither independent nor identically distributed observations. We show that the estimates converge to some well‐defined parameters. In particular, unbiased estimation is feasible under additive normal measurement error models for normal covariates and under Berkson error models. The proposed method performs well in finite‐sample simulation studies. We also apply the proposed method to a depression mortality study.     

Uncertainty in allometric exponent estimation: a case study in scaling metabolic rate with body mass

Many factors could influence the allometric scaling exponent β estimation, but have not been explored systematically. We investigated the influences of three factors on the estimate of β based on a data set of 626 species of basal metabolic rate and mass in mammals. The influence of sampling error was tested by re-sampling with different sample sizes using a Monte Carlo method. Small random errors were introduced to measured data to examine their influence on parameter estimations. The influence of analysis method was also evaluated by applying nonlinear and linear regressions to the original data. Results showed that a relative large sample size was required to lower statistical inference errors. When sample size n was 10% of the base population size (n=63), 35% of the samples supported β=2/3, 39% supported β=3/4, and 15% rejected β=0.711, even though the base population had a β=0.711. The controversy surrounding the estimation of β in the literature could be partially attributable to such small sample sizes in many studies. Measurement errors in body mass and base metabolic rate, especially in body mass, could largely increase alpha and beta errors. Analysis methods also affected parameter estimations. Nonlinear regressions provided better estimates of the scaling exponent that were significantly higher than these commonly estimated by linear regressions. This study demonstrated the importance of the quantity and quality of data as well as analysis method in power law analysis, raising caution in interpreting power law results. Meta-data synthesis using data from independent studies seems to be a proper approach in the future, but caution should be taken to make sure that such measurements are made using similar protocols.     

Evaluating climatic and non-climatic influences on ion chemistry in natural and man-made lakes of Nebraska,USA

Conductivity and major ion chemistry data were analyzed for a suite of Nebraska (USA) natural lakes, reservoirs, sand pits, and barrow pits to evaluate the magnitude of climatic versus non-climatic influence on ionic concentration and composition. In both natural lakes and sand and barrow pits, conductivity is positively related to longitude and reflects decreasing effective moisture from east to west. Reservoirs showed no relationship between lake conductivity and location, probably because the reservoirs are very strongly influenced by groundwater and surface water inflow and have shorter residence times relative to the other lake types. At smaller spatial scales, conductivity among natural lakes is variable. Lakes that are at low elevation within a groundwater flow system were fresh, because of substantial input of fresh groundwater. In contrast, lakes at high elevation exhibited a wide range of conductivity, probably because of differences in the degree of connection to groundwater and surface to volume ratio impacts on evaporation rates. Differences also were evident among natural lakes in terms of their response to seasonal changes in precipitation. Sub-saline and saline lakes showed more seasonal variation in conductivity than freshwater lakes, and lakes in the more arid part of the state showed larger responses to precipitation change than those in areas to the east that receive higher precipitation.     

颧根与侧颅底重要结构的解剖学测量

目的：通过测量颧根与侧颅底各重要结构间的距离关系,为临床侧颅底外科手术治疗提供定位参考。方法：取成人颅骨标本50例（去颅盖标本8例,整颅42例）100侧,用游标卡尺、圆规和直尺测量颧根与侧颅底重要结构的距离。结果：实验测得左右侧颧根与外耳门前缘中点、乳突尖、茎突、翼突外侧板根部、舌下神经管外口、茎乳孔、颈静脉孔外缘、颈动脉管外口后缘、棘孔、卵圆孔、破裂孔的距离分别为22.30±2.84mm和22.02±3.27mm、40.37±3.21mm和40.56±3.54mm、32.53±2.78mm和32.92±2.68mm、35.13±3.14mm和35.19±2.74mm、49.29±2.88mm和48.98±2.87mm、32.92±2.44mm和33.05±2.61mm、35.15±2.86mm和34.68±3.13mm、33.17±2.78mm和33.17±2.72mm、28.83±2.62mm和28.68±2.63mm、31.15±2.76mm和31.49±2.73mm、43.67±3.32mm和44.15±3.02mm,左右侧数据无差异（P〉0.05）。结论：颧根可以作为侧颅底外科手术的定位标志,为直视条件下经颞下等入路的侧颅底外科手术提供解剖学依据。     

Measurement error and confidence intervals for ROC curves

Measurement error in a continuous test variable may bias estimates of the summary properties of receiver operating characteristics (ROC) curves. Typically, unbiased measurement error will reduce the diagnostic potential of a continuous test variable. This paper explores the effects of possibly heterogenous measurement error on estimated ROC curves for binormal test variables. Corrected estimators for specific points on the curve are derived under the assumption of known or estimated measurement variances for individual test results. These estimators and associated confidence intervals do not depend on normal assumptions for the distribution of the measurement error and are shown to be approximately unbiased for moderate size samples in a simulation study. An application from a study of emerging imaging modalities in breast cancer is used to demonstrate the new techniques.     

A longitudinal measurement error model with a semicontinuous covariate

Covariate measurement error in regression is typically assumed to act in an additive or multiplicative manner on the true covariate value. However, such an assumption does not hold for the measurement error of sleep-disordered breathing (SDB) in the Wisconsin Sleep Cohort Study (WSCS). The true covariate is the severity of SDB, and the observed surrogate is the number of breathing pauses per unit time of sleep, which has a nonnegative semicontinuous distribution with a point mass at zero. We propose a latent variable measurement error model for the error structure in this situation and implement it in a linear mixed model. The estimation procedure is similar to regression calibration but involves a distributional assumption for the latent variable. Modeling and model-fitting strategies are explored and illustrated through an example from the WSCS.     

On the effect of misclassification on bias of perfectly measured covariates in regression

This note clarifies under what conditions a naive analysis using a misclassified predictor will induce bias for the regression coefficients of other perfectly measured predictors in the model. An apparent discrepancy between some previous results and a result for measurement error of a continuous variable in linear regression is resolved. We show that similar to the linear setting, misclassification (even when not related to the other predictors) induces bias in the coefficients of the perfectly measured predictors, unless the misclassified variable and the perfectly measured predictors are independent. Conditional and asymptotic biases are discussed in the case of linear regression, and explored numerically for an example relating birth weight to the weight and smoking status of the mother.