Segmented regression in the presence of covariate measurement error in main study/validation study designs

This article considers the problem of segmented regression in the presence of covariate measurement error in main study/validation study designs. First, we derive a closed and interpretable form for the full likelihood. After that, we use the likelihood results to compute the bias of the estimated changepoint in the case when the measurement error is ignored. We find the direction of the bias in the estimated changepoint to be determined by the design distribution of the observed covariates, and the bias can be in either direction. We apply the methodology to data from a nutritional study that investigates the relation between dietary folate and blood serum homocysteine levels and find that the analysis that ignores covariate measurement error would have indicated a much higher minimum daily dietary folate intake requirement than is obtained in the analysis that takes covariate measurement error into account.     

Weighted normality-based estimator in correcting correlation coefficient estimation between incomplete nutrient measurements

Consider the problem of estimating the correlation between two nutrient measurements, such as the percent energy from fat obtained from a food frequency questionnaire (FFQ) and that from repeated food records or 24-hour recalls. Under a classical additive model for repeated food records, it is known that there is an attenuation effect on the correlation estimation if the sample average of repeated food records for each subject is used to estimate the underlying long-term average. This paper considers the case in which the selection probability of a subject for participation in the calibration study, in which repeated food records are measured, depends on the corresponding FFQ value, and the repeated longitudinal measurement errors have an autoregressive structure. This paper investigates a normality-based estimator and compares it with a simple method of moments. Both methods are consistent if the first two moments of nutrient measurements exist. Furthermore, joint estimating equations are applied to estimate the correlation coefficient and related nuisance parameters simultaneously. This approach provides a simple sandwich formula for the covariance estimation of the estimator. Finite sample performance is examined via a simulation study, and the proposed weighted normality-based estimator performs well under various distributional assumptions. The methods are applied to real data from a dietary assessment study.     

Precision and accuracy of acetabular size measures in fragmentary hominin pelves obtained using sphere‐fitting techniques

Hip joint diameter is highly correlated with body size in primates and so can potentially provide important information about the biology of fossil hominins. However, quantifying hip joint size has been difficult or impossible for many important but fragmentary specimens. New three‐dimensional technologies can be used to digitally fit spheres to the acetabular lunate surface, potentially allowing hip joint diameter estimates for incomplete joint surfaces. Here we evaluate the reliability of sphere‐fitting to incomplete lunate surfaces in silico using three‐dimensional polygonal models of extant anthropoid hipbones. Measurement error in lunate sphere‐fitting was assessed at the individual observer level, as well as between observers. Prediction error was also established for acetabular sphere size estimates for smaller divisions of the lunate surface. Sphere‐fitting techniques were then applied to undistorted regions of lunate surface in Plio‐Pleistocene hominin pelves, with a range of diameters constructed from extant error estimates. The results of this study indicate that digital sphere‐fitting techniques are precise and that the lunate does not need to be completely preserved to accurately infer hip dimensions, although some aspects of joint size and morphology can influence sphere size estimates. Joint diameter is strongly predicted by spheres fit to the cranial and caudal halves of the lunate in all anthropoids. We present new hip joint size estimates for a number of fossil hominins, and outline additional applications for digital sphere‐fitting as a morphometric technique. Am J Phys Anthropol 150:565–578, 2013. © 2013 Wiley Periodicals, Inc.     

On Estimation of Relative Risk in Case Control Studies

An estimator of relative risk in a case control study has been proposed in terms of observed cell frequencies and the probability of disease. The bias of the usual estimator i.e odds ratio as compared to the new estimator has been workedout. The expression of Mean Square Error of proposed estimator has been derived in situations where probability of disease is exactly known and when it is estimated through an independent survey. It has been observed that there is a serious error using odds ratio as an estimate of relative risk when probability of disease is not negligible. In such situations the proposed estimator can be used with advantage.     

Estimation of Relative Risk in Case Control Studies Through Logistic Regression Analysis

Multiple logistic regression analysis is used to estimate the relative risk in case control studies. The estimators obtained are valid when disease is rare. In this paper an estimator of relative risk in a case control study has been proposed using logistic regression results when the incidence of disease is not small. The bias of the usual estimator through logistic regression as compared to the new estimator has been worked out. The expression of Mean Square Error of proposed estimator has been derived in situations when the incidence of disease is known exactly as well as when estimated through an independent survey. It has been observed that there is a significant bias using the conventional estimator of relative risk when incidence of disease is high. In such situations the proposed estimator can be used with advantage.     

IS THE PARTIAL MANTEL TEST INADEQUATE?

Abstract.— A publication by Raufaste and Rousset (2001) questioned the effectiveness of the partial Mantel test, a nonparametric statistical test for association among three distance matrices. By repeated simulations, we calculated the cumulative density functions of the null-hypothesis probability of no correlation, within an explicit model of causal relationships. Results do not support the criticism: in conditions of moderate correlation between the independent matrices, the actual error rate is closely associated to the intended type-I error a.     

A flexible approach to measurement error correction in case-control studies

SUMMARY: We investigate the use of prospective likelihood methods to analyze retrospective case-control data where some of the covariates are measured with error. We show that prospective methods can be applied and the case-control sampling scheme can be ignored if one adequately models the distribution of the error-prone covariates in the case-control sampling scheme. Indeed, subject to this, the prospective likelihood methods result in consistent estimates and information standard errors are asymptotically correct. However, the distribution of such covariates is not the same in the population and under case-control sampling, dictating the need to model the distribution flexibly. In this article, we illustrate the general principle by modeling the distribution of the continuous error-prone covariates using the skewnormal distribution. The performance of the method is evaluated through simulation studies, which show satisfactory results in terms of bias and coverage. Finally, the method is applied to the analysis of two data sets which refer, respectively, to a cholesterol study and a study on breast cancer.     

A new method for dealing with measurement error in explanatory variables of regression models

We introduce a new method, moment reconstruction, of correcting for measurement error in covariates in regression models. The central idea is similar to regression calibration in that the values of the covariates that are measured with error are replaced by "adjusted" values. In regression calibration the adjusted value is the expectation of the true value conditional on the measured value. In moment reconstruction the adjusted value is the variance-preserving empirical Bayes estimate of the true value conditional on the outcome variable. The adjusted values thereby have the same first two moments and the same covariance with the outcome variable as the unobserved "true" covariate values. We show that moment reconstruction is equivalent to regression calibration in the case of linear regression, but leads to different results for logistic regression. For case-control studies with logistic regression and covariates that are normally distributed within cases and controls, we show that the resulting estimates of the regression coefficients are consistent. In simulations we demonstrate that for logistic regression, moment reconstruction carries less bias than regression calibration, and for case-control studies is superior in mean-square error to the standard regression calibration approach. Finally, we give an example of the use of moment reconstruction in linear discriminant analysis and a nonstandard problem where we wish to adjust a classification tree for measurement error in the explanatory variables.     

Design-based mapping of tree attributes by 3P sampling

The estimation of individual values (marks) in a finite population of units (e.g., trees) scattered onto a survey region is considered under 3P sampling. For each unit, the mark is estimated by means of an inverse distance weighting interpolator. Conditions ensuring the design-based consistency of maps are considered under 3P sampling. A computationally simple mean squared error estimator is adopted. Because 3P sampling involves the prediction of marks for each unit in the population, prediction errors rather than marks can be interpolated. Then, marks are estimated by the predictions plus the interpolated errors. If predictions are good, prediction errors are more smoothed than raw marks so that the procedure is likely to better meet consistency requirements. The purpose of this paper is to provide theoretical and empirical evidence on the effectiveness of the interpolation based on prediction errors to prove that the proposed strategy is a tool of general validity for mapping forest stands.     

Interobserver error in grassland vegetation surveys: sources and implications

人类观测误差是植被测量中不可避免的一个问题。我们量化了与高草草原植被长期监测相关的观测者间误差的四个组成部分：忽略误 差、误识别误差、谨慎误差和估计误差。由于观察者会产生误差，我们还评估了地块大小与伪周转率的关系，以及对比了物种组成和丰度的伪变化与四年间植被变化之间的关系。这项研究是在美国堪萨斯州的高草草原国家保护区进行的。监测点包括10个地块，每个地块由一系列的四个嵌套框架(0.01, 0.1, 1和10 m²)组成。在每个嵌套框架中记录了所有的草本物种，并且在10 m²的空间尺度下，视觉估计了7个覆盖类别内的叶面覆盖。总共调查了300个地块(30个地点)，并随机选择28个地块重新进行测量以评估观测者的误差。所有的调查由四名观测者分两组完成。研究结果表明，在10 m²空间尺度上，由忽略误差引起的伪周转率平均为18.6%，而由误识别误差和谨慎误差引起的伪周转率平均值分别为1.4%和0.6%。尽管由重新定位引起的误差可能也起一定的作用，由忽略误差导致的伪周转率随样地面积的减小而增 加。物种组成在四年期间的变化(排除潜在的误识别误差和谨慎误差)为30.7%，其中包括由忽略误差和实际变化引起的伪周转率。18.6%的忽略误差表明四年期间的实际变化只有12.1%。对于估计误差，26.2%会记录为不同的覆盖等级。在四年的时间内，46.9%的记录显示了不同的覆盖等级，这表明两个时间段间覆盖率变化的56%是由于观测者误差造成的。     

Identification of novel loci influencing refractive error in East Asian populations using an extreme phenotype design

The global “myopia boom” has raised significant international concerns. Despite a higher myopia prevalence in Asia, previous large-scale genome-wide association studies (GWASs) were mostly based on European descendants. Here, we report a GWAS of spherical equivalent (SE) in 1852 Chinese Han individuals with extreme SE from Guangzhou (631 < ?6.00D and 574 > 0.00D) and Wenzhou (593 < ?6.00D and 54 > ?1.75D), followed by a replication study in two independent cohorts with totaling 3538 East Asian individuals. The discovery GWAS and meta-analysis identify three novel loci, which show genome-wide significant associations with SE, including 1q25.2 FAM163A, 10p11.22 NRP1/PRAD3, and 10p11.21 ANKRD30A/MTRNR2L7, together explaining 3.34% of SE variance. 10p11.21 is successfully replicated. The allele frequencies of all three loci show significant differences between major continental groups (P < 0.001). The SE reducing (more myopic) allele of rs10913877 (1q25.2 FAM163A) demonstrates the highest frequency in East Asians and much lower frequencies in Europeans and Africans (EAS = 0.60, EUR = 0.20, and AFR = 0.18). The gene-based analysis additionally identifies three novel genes associated with SE, including EI24, LHX5, and ARPP19. These results provide new insights into myopia pathogenesis and indicate the role of genetic heterogeneity in myopia epidemiology among different ethnicities.     

On the Estimation of the Nearest Neighbour Distribution,G(t), for Point Processes

A new method is proposed for estimating G(t), the distribution function of the distance from an object to its nearest neighbour in a spatial point process. The new method makes more complete use of the information available and has a smaller mean squared error than that of the existing alternatives. The method appears equally effective with random, clustered and regular patterns.     

Measurement error: Inter-and Intraobserver Variability. An Empiric Study

The intra- and inter-observer measurement error variability was studied using univariate and multivariate statistical tests. Eleven skeletal variables of four individuals each in four Primate species were measured ten times by three different researchers, using six different tools. An average measurement error of 0.52 mm. was obtained. Univariate statistics showed significant differences among reseachers. A multivariate discriminant analysis could also discriminate them. The measurement error may be either systematic or random, and depends not only on the researcher, but also on the tool used, the variable measured, and on the magnitude of the variable. The technique of Measurement Replication is proposed in order to reduce the measurement error, specially when compairing small samples or when trying to find small average differences between populations. The replication technique also reduces the standard deviation of the population sample.     

Covariate measurement error adjustment for matched case-control studies

We propose a conditional scores procedure for obtaining bias-corrected estimates of log odds ratios from matched case-control data in which one or more covariates are subject to measurement error. The approach involves conditioning on sufficient statistics for the unobservable true covariates that are treated as fixed unknown parameters. For the case of Gaussian nondifferential measurement error, we derive a set of unbiased score equations that can then be solved to estimate the log odds ratio parameters of interest. The procedure successfully removes the bias in naive estimates, and standard error estimates are obtained by resampling methods. We present an example of the procedure applied to data from a matched case-control study of prostate cancer and serum hormone levels, and we compare its performance to that of regression calibration procedures.     

Set-up uncertainty during postmastectomy radiotherapy with Segmented Photon Beams Technique

Aim

To verify the reproducibility of patients irradiated after mastectomy on the immobilization system designed and manufactured for our hospital and to compare the Internal Protocol (IP) with the modified-No Action Level Protocol.

Background

Application of forward IMRT techniques requires a good reproducibility of patient positioning. To minimize the set-up error, an effective immobilization system is important.

Materials and methods

The study was performed for two groups of 65 each. In the first group, portal images for anterior field were taken in 1–3 fractions and, subsequently, three times a week. In this group, the mNAL protocol was used. In the second group, the IP was used. The portal images from the anterior field and from the gantry 0 were taken during the 1–3 and 10 fractions. In both groups, image registration was performed off-line. For each group the systematic and random errors and PTV margin were calculated.

Results

In the first group the value of the population systematic errors and random errors were 1.6 ± 1.6 mm for the left–right, and 1.5 ± 1.7 mm for the cranial–caudal directions, respectively, 1.7 ± 1.3 mm, and 1.9 ± 1.3 mm for the second group. The PTV margins for the left–right and cranial–caudal directions were 5.1 and 4.9 mm for the first group and 5.4 and 6.4 mm for the second group.

Conclusions

For patients immobilized with our support device treated according to the mNAL protocol or IP, a good set-up reproducibility was obtained. Implementation of IP limits the number of required images.     

非正态分布预测模型误差的估计

提出了模型预测误差在其分布为非正态分布时的区间估计方法,研究了模型预测误差的估计问题,给出了应用实例.     

On the Performance of Some Classification Rules for Qualitative Data for Simulated Underlying Distributions

Two linear functions for discriminating with qualitative variables (Fisher's linear discriminant function and the independence rule) are compared with the general multinomial procedure, a rule based on Lancaster's definition of higher order interactions and the quadratic discriminant function. The evaluation of these functions is carried out within Monte Carlo experiments. Various types of underlying distributions generated by a special algorithm are used.     

Influence of IMU position and orientation placement errors on ground reaction force estimation

Wearable inertial measurement units (IMU) have been proposed to estimate GRF outside of specialized laboratories, however the precise influence of sensor placement error on accuracy is unknown. We investigated the influence of IMU position and orientation placement errors on GRF estimation accuracy. Methods: Kinematic data from twelve healthy subjects based on marker trajectories were used to simulate 1848 combinations of sensor position placement errors (range ± 100 mm) and orientation placement errors (range ± 25°) across eight body segments (trunk, pelvis, left/right thighs, left/right shanks, and left/right feet) during normal walking trials for baseline cases when a single sensor was misplaced and for the extreme cases when all sensors were simultaneously misplaced. Three machine learning algorithms were used to estimate GRF for each placement error condition and compared with the no placement error condition to evaluate performance. Results: Position placement errors for a single misplaced IMU reduced vertical GRF (VGRF), medio-lateral GRF (MLGRF), and anterior-posterior GRF (APGRF) estimation accuracy by up to 1.1%, 2.0%, and 0.9%, respectively and for all eight simultaneously misplaced IMUs by up to 4.9%, 6.0%, and 4.3%, respectively. Orientation placement errors for a single misplaced IMU reduced VGRF, MLGRF, and APGRF estimation accuracy by up to 4.8%, 7.3%, and 1.5%, respectively and for all eight simultaneously misplaced IMUs by up to 20.8%, 23.4%, and 12.3%, respectively. Conclusion: IMU sensor misplacement, particularly orientation placement errors, can significantly reduce GRF estimation accuracy and thus measures should be taken to account for placement errors in implementations of GRF estimation via wearable IMUs.     

发展性阅读障碍的产生机制——从行为到遗传研究

发展性阅读障碍(developmental dyslexia,DD)是一种特殊的学习障碍,探索DD的产生机制有助于寻求DD儿童的鉴别和治疗方法.目前拼音文宁国家对DD的产生机制研究很多,结果也很丰富,但是很多观点还不一致.汉语DD研究起步较晚,各方面还不够深入和完善.简述了DD在认知、脑神经和基因方面的研究进展,对拼音文字DD与汉语DD的研究结果进行了对比,以揭示不同语言文字系统下DD者的认知神经差异.研究认为,应该大力加强对汉语DD的探究,这样不但为中国DD儿童的诊治提供理论基础,也可以为DD的语种特异性问题提供科学依据.