ONE-WAY MULTIVARIATE REPEATED MEASUREMENTS ANALYSIS OF VARIANCE MODEL

The one-way multivariate repeated measurements analysis of variance (1-way MRM ANOVA) model for complete data and the sphericity test are studied.

On the construction of balanced repeated measurements designs with good circular properties

Several fields,such as biological,medical,public health,agricultural sciences,etc.,require circular balanced repeated measurement designs with fewer unequal number of repeated measure-ments than the number of treatments.Also,the availability and high cost of experimental subjects in these fields prefer the design in fewer experimental units.However,balancing the carryover effects of the treatments in minimal experimental subjects is one of the problems in this case.In this paper,several new series of minimal circular nearly strongly balanced RMDs in periods of two and three different sizes are constructed.The proposed construction of designs has high efficiency and,therefore,can save the cost of experimentations due to a fewer exper-imental subjects.Most of the designs are very useful because of the unavailability of strongly balanced RMDs for these combinations of parameters.A list of sets of shifts for the construction of minimal circular nearly SBRMDs has also been mentioned in the Appendix.

The Use of Repeated Measures Analysis of Variance to Study the Effect of Phlegm-Heat Syndrome on Neurological Deficits in Patients with Stroke~

Objective： To explore the effect of phlegm-heat syndrome on the degree of neurological deficit and provide some data support for the correct recognition of the relationship between phlegm-heat syndrome and neurological deficits in stroke patients. Methods： Clinical information were collected on 294 patients with acute ischemic stroke （AIS） whose syndrome and National Institute of Health stroke scale （NIHSS） score were checked at baseline （within the first 3-day admission） and at 7, 14, 28, and 90 days after admission to our clinical research centre. We explored the effect of phlegm-heat syndrome on the degree of neurological deficit following stroke by applying a repeated measures analysis of variance. Results： Stroke patients with phlegm-heat syndrome had higher NIHSS score than patients without the syndrome （P〈0.01）, and there appeared to be a decrease in NIHSS score over time in all patients （P〈0.01）. Conduslons： Phlegm-heat syndrome in patients who have suffered stroke has an effect on the degree of neurological deficiency. Disappearance of phlegm-heat syndrome may improve the degree of neurological deficit observed in stroke patients.

Parameter Estimates in Random Intercept Mixed Effects Model for Repeated Measures

In this article the following random intercept mixed effects model will be considered： yij = vi =v^τijβ＋ εij,i=1,…,m;j=1,2,…,ni, where {vi} are i.i.d, random effects with mean α 2. 2 and finite variance σ^2 v, {εij} are i.i.d, random errors with finite variance ε^2 ε. Here we will estimate α,σ^2 v,σ^2 ε,β and study their large sample properties, such as strong consistency, strong convergence rates and asymptotic normality.

Association of handgrip strength with semen characteristics:a study with repeated measurements among healthy Chinese men

Accumulating epidemiological evidence shows that handgrip strength provides predictive potential in physical,mental,and reproductive health status.However,the associations between handgrip strength and semen characteristics have not been explored.We recruited 1382 eligible men at the Hubei Province Human Sperm Bank(Wuhan,China)who had their handgrip strength measured at recruitment and provided 6458 repeated semen specimens within a 6-month period.Semen characteristics,including semen volume,sperm motility parameters(immotility,nonprogressive motility,and progressive motility),and sperm concentration,were assessed.Mixed-effect models and restricted cubic spline functions were applied to investigate the relationship of handgrip strength with repeated measurements of semen characteristics.After adjusting for confounding factors,the mixed-effect models revealed that handgrip strength was positively associated with semen volume,sperm concentration,progressive motility,total motility,and total count(all P for trend<0.05).Compared to men in the lowest quartile,those in the highest quartile of handgrip strength had higher semen volume,sperm concentration,progressive motility,total motility,and total count,with measurements of 14.2%(95%confidence interval[CI]:5.9%-23.2%),19.5%(95%CI:7.3%-33.1%),9.5%(95%CI:3.4%-15.9%),8.8%(95%CI:3.2%-14.6%),and 36.4%(95%CI:18.9%-56.5%),respectively.These positive dose-response relationships were further confirmed in restricted cubic splines,where handgrip strength was modeled as a continuous variable.Handgrip strength,as an indicator of muscular function and strength,was positively associated with semen characteristics in a dose-dependentmanner.

INFERENCE FOR REPEATED MEASURES MODELS UNDER HETEROSCEDASTICITY

This paper considers a widely used mixed effects model in repeated measures under het- eroscedasticity. Hypotheses of the equality of the fixed effects and the simultaneous confidence intervals for all pair-wise differences are discussed. A generalized F-test has been proposed to test the equality of the fixed effects in the model, but simulation results for evaluating its performance have not been shown in the literature. Moreover, the generalized F-test cannot be used to deduce the simultaneous confidence intervals for all pair-wise differences of the fixed effects. The authors propose two new p-values to test the hypotheses of equality of the fixed effects and simultaneous confidence intervals of the differences of the effects based on the generalized pivotal quantities derived in this paper. The authors also compare the empirical performances of the proposed tests and the generalized F-test. The type I error rates and powers of these tests are evaluated using the Monte Carlo simulation. The simulation studies show that the generalized F-test does not perform well in terms of type I error rate under various sample size and parameter combinations. However, the type I error probabilities of the proposed tests are always close to the nominal value. It can also be seen that the simultaneous confidence intervals perform well.

Empirical likelihood estimation in multivariate mixture models with repeated measurements

Multivariate mixtures are encountered in situations where the data are repeated or clustered measurements in the presence of heterogeneity among the observations with unknown proportions.In such situations,the main interest may be not only in estimating the component parameters,but also in obtaining reliable estimates of the mixing proportions.In this paper,we propose an empirical likelihood approach combined with a novel dimension reduction procedure for estimating parameters of a two-component multivariate mixture model.The performance of the new method is compared to fully parametric as well as almost nonparametric methods used in the literature.

A comparative study of models for the different covariance structure analysis of repeated measurement data

In repeated measurement data, the variables are not independent, and a certain auto- correlation typically exists between different levels of repeated measurement factors. The random error is composed of at least two parts, i.e. the individual random effect and the intra-individual multi-repeated measurement effect. Traditional statistical analysis methods （such as the t-test and the one-way analysis of variance） are not applicable. The linear mixed model has been widely applied for the analysis and design of repeated measurement data. This paper focuses on medical examples and describes the selection of a covariance structure for the linear mixed model of repeated measurement in the modeling of different variance-eovariance structures. By selecting different covariance structures, we can perform the parameter estimation and statistical test for the fixed effect of repeated measurement data, the parameters of random effects, and the covari- ance matrix. The results are analyzed and compared to provide a reference for applying the linear mixed model of repeated measurement to medical research.

Early performance of Pinus radiata provenances in the earthquakeravaged dry river valley area of Sichuan, southwest China

A provenance experiment involving five native provenances and an Australian landrace of Pinus radiata （D. Don） was established over three sites in the dry river valley area of Sichuan, southwest China in 2004 in order to select the most suitable provenance for environmental planting on the dry, steep and degraded slopes to reduce soil erosion. Although with much lower soil moisture supply and mean minimum temperatures in winter compared to P. radiata provenance trials estab- lished elsewhere in the world, these sites are within the working limits of the species defined by previous climate modelling and matching. Be- cause of the difficult site conditions and severe natural disturbances after the experiment was established, mortality was high across the three sites in comparison to provenance trials in other countries.

Elemental and proximate analysis of coal by x-ray fluorescence assisted laser-induced breakdown spectroscopy

Although laser-induced breakdown spectroscopy(LIBS),as a fast on-line analysis technology,has great potential and competitiveness in the analysis of chemical composition and proximate analysis results of coal in thermal power plants,the measurement repeatability of LIBS needs to be further improved due to the difficulty in controlling the stability of the generated plasmas at present.In this paper,we propose a novel x-ray fluorescence(XRF) assisted LIBS method for high repeatability analysis of coal quality,which not only inherits the ability of LIBS to directly analyze organic elements such as C and H in coal,but also uses XRF to make up for the lack of stability of LIBS in determining other inorganic ash-forming elements.With the combination of elemental lines in LIBS and XRF spectra,the principal component analysis and the partial least squares are used to establish the prediction model and perform multi-elemental and proximate analysis of coal.Quantitative analysis results show that the relative standard deviation(RSD) of C is 0.15%,the RSDs of other elements are less than 4%,and the standard deviations of calorific value,ash content,sulfur content and volatile matter are 0.11 MJ kg,0.17%,0.79% and 0.41%respectively,indicating that the method has good repeatability in determination of coal quality.This work is helpful to accelerate the development of LIBS in the field of rapid measurement of coal entering the power plant and on-line monitoring of coal entering the furnace.

Eye movement evidence in investigative identification based on experiments

Investigative identification is a routine criminal investigative procedure,the results of which can be used as evidence in litigation.However,some suspects often deny their involvement in the case,and some witnesses may withhold information or misrepresent it,all of which may lead to a miscarriage of justice.This study created a stressful environment and conducted a simulated crime experiment to explore whether eye movement data can be an effective feature for distinguishing perpetrators,innocents,and insiders.The eye movement features—such as the total fixation duration,number of fixations,and first fixation duration—within an area of interest were collected from 83 participants sorted into informed,involved,and innocent groups.The results revealed the following:(1)compared with the object and scene stimuli,subjects with different identities were more likely to exhibit significant differences in eye movement data for the involved and irrelevant portraits.The total fixation duration and the number of fixations can provide a reference for judging whether someone is involved in a case,and the first fixation duration effect was not obvious.(2)Using machine learning algorithms to predict subjects’identities through eye movement features,it was demonstrated that the involved portrait-object-scene model had the best predictive effect.(3)Multiple algorithmic models were used to distinguish subjects’identities,and the highest accuracy of 92.7%was achieved for the informed×innocent group,88%for the innocent×suspect group(including the informed and involved groups),and 84.5%for the involved group.The eye movement analysis method can provide a reference for criminal investigators to distinguish between the perpetrator,insider,and innocent,and offer a novel approach to determining the direction of further investigation and uncovering and verifying case clues.

Analysis on the Effect of Acupuncture in Treating Cervical Spondylosis with Different Syndrome Types

Objective:To observe the clinical effect of acupuncture in treating cervical spondylosis with different syndrome types.Methods:One hundred and seventeen patients were randomized into the treated group (59 cases),treated with normal acupuncture,and the control group(58 cases),treated with sham acupuncture, operated once every other day,9 times in total(in 18 days) as one therapeutic course,and a succeeding 3-month follow-up study was carried out after terminating the therapy.The efficacy of treatment was eva...

Evaluation of neuro-intensive care unit performance in China： predicting outcomes of Simplified Acute Physiology Score II or Glasgow Coma Scale

Background Severity scoring systems are useful tools for measuring the severity of the disease and its outcome. This pilot study was to verify and compare the prognostic performance of the Simplified Acute Physiology Score II （SAPS II） and Glasgow Coma Scale （GCS） in neuro-intensive care unit （N-ICU） patients. Methods A total of 1684 patients consecutively admitted to the N-ICU at Xuanwu Hospital between January 1, 2005 and December 31, 2011 were enrolled in this study. The data-base included admission data, at 24-, 48-, and 72-hour SAPS II and GCS. Repeated measure data analysis of variance, Logistic regression analysis, the Hosmer-Lemeshow goodness-of-fit statistic, and the area under the receiver operating characteristic were used to evaluate the performance. Results There was a significant difference between the SAPS II or GCS score at four time points （F=16.110, P=0.000 or F=8.108, P=0.000）. The SAPS II scores or GCS score at four time points interacted with the outcomes with significant difference （F=116.771, P=0.000 or F=65.316, P=0.000）. Calibration of the SAPS II or GCS score at each time point on all patients was good. The percentage of a risk estimate prediction corresponding to observed mortality was also good. The 72-hour score have the greatest consistency. Discriminations of the SAPS II or GCS score at each time were all satisfactory. The 72-hour score had the greatest discriminative power. The cut-off value was 33 （sensitivity of 85.2% and specificity of 74.3%） and 6 （sensitivity of 70.6% and specificity of 65.0%）. The SAPS II at each time point on all patients showed better calibration, consistency and discrimination than GCS. The binary Logistic regression analysis identified physiological variables, GCS, age, and disease category as significant independent risk factors of death. After the two variables including underlying disease and type of admission were excluded, we built the simplified SAPS II model. A correlation was suggested between the simplified SAPS II score at each time point and outcome, regardless of the diagnosis. Conclusions The GCS scoring system tends to be a little weaker in the predictive power than the SAPS II scoring system in this Chinese cohort of N-ICU patients. The advantage of SAPS II scoring system still exists that it dose not need to take into account the diagnosis or diseases categories, even in the special N-ICU. The simplified SAPS II scoring system is considered a new idea for the estimation of effectiveness.

Weighted estimating equation： modified GEE in longitudinal data analysis

The method of generalized estimating equations （GEE） introduced by K. Y. Liang and S. L. Zeger has been widely used to analyze longitudinal data. Recently, this method has been criticized for a failure to protect against misspecification of working correlation models, which in some cases leads to loss of efficiency or infeasibility of solutions. In this paper, we present a new method named as ＇weighted estimating equations （WEE）＇ for estimating the correlation parameters. The new estimates of correlation parameters are obtained as the solutions of these weighted estimating equations. For some commonly assumed correlation structures, we show that there exists a unique feasible solution to these weighted estimating equations regardless the correlation structure is correctly specified or not. The new feasible estimates of correlation parameters are consistent when the working correlation structure is correctly specified. Simulation results suggest that the new method works well in finite samples.