Rank Covariance Methods for the Analysis of Survival Data

A procedure for comparing survival times between several groups of patients through rank analysis of covariance was introduced by WOOLSON and LACHENBRUCH (1983). It is a modification of Quade' rank analysis of covariance procedure (1967) and can be used for the analysis of right-censored data. In this paper, two additional modifications of Quade' original test statistic are proposed and compared to the original modification introduced by Woolson and Lachenbruch. These statistics are compared to one another and to the score test from Cox' proportional hazards model by way of a limited Monte Carlo study. One of the statistics, Q_R2, is recommended for general use for the rank analysis of covariance of right-censored survivorship data.     

Multiple Imputation Approaches for the Analysis of Dichotomized Responses in Longitudinal Studies with Missing Data

Summary Often a binary variable is generated by dichotomizing an underlying continuous variable measured at a specific time point according to a prespecified threshold value. In the event that the underlying continuous measurements are from a longitudinal study, one can use the repeated‐measures model to impute missing data on responder status as a result of subject dropout and apply the logistic regression model on the observed or otherwise imputed responder status. Standard Bayesian multiple imputation techniques ( Rubin, 1987 , in Multiple Imputation for Nonresponse in Surveys) that draw the parameters for the imputation model from the posterior distribution and construct the variance of parameter estimates for the analysis model as a combination of within‐ and between‐imputation variances are found to be conservative. The frequentist multiple imputation approach that fixes the parameters for the imputation model at the maximum likelihood estimates and construct the variance of parameter estimates for the analysis model using the results of Robins and Wang (2000, Biometrika 87, 113–124) is shown to be more efficient. We propose to apply ( Kenward and Roger, 1997 , Biometrics 53, 983–997) degrees of freedom to account for the uncertainty associated with variance–covariance parameter estimates for the repeated measures model.     

Analysis of Covariance Models for Data From Observational Field Studies

Abstract We outline the features of a general class of statistical models (i.e., analysis of covariance [ANCOVA] models) that has proven to be effective for the analysis of data from observational studies. In observational studies, treatments are assigned by Nature in a decidedly nonrandom manner; consequently, many of the crucial assumptions and safeguards of the classic experimental design either fail or are absent. Hence, inferences (causal or associative) are more difficult to justify. Typically, investigators can expect the primary factors of interest, which are usually called environmental exposures rather than treatments, to be involved in complex interactions with each other and with other factors, and these factors will be confounded with still other factors. We provide examples illustrating the application of ANCOVA models to adjust for confounding factors and complex interactions, thereby providing relatively clean estimates of association between exposure and response. We summarize information on available software and supporting literature for implementing ANCOVA models for the analysis of cross-sectional and longitudinal observational field data. We conclude with a brief discussion of critical model fitting issues, including proper specification of the functional form of continuous covariates and problems associated with overfitted models and misspecified models that lack important covariates.     

Analysis of Covariance in Split Block Design

In this paper the analysis of covariance in the split block design with many concomitant variables is presented. The problems concerning the estimation of parametric functions and testing hypotheses are discussed. In the presentation of the model three kinds of regression coefficients for individual sources of variation are taken into consideration. It is shown that for every estimable function of fixed effects, the best linear unbiased estimator under the assumed model is the same as the best linear unbiased estimator under the model with covariance matrix equal to identity matrix multiplied by a positive constant. A variance of this estimator can be calculated by the method presented here. Test functions for standard hypotheses concerning fixed effects are obtained.     

Scheffy's Confidence Intervals in the Models of Analysis of Covariance

Scheffe's confidence intervals for linear functions of some subvectors of a vector of parameters are presented. The considered subvectors are such that covariance matrices of their estimators are known non-negative definite matrices multiplied by unknown positive constants. This property is characteristic of the least squares estimators of vectors of main and interaction effects in the analysis of covariance models of the following experimental designs: split-block, split-plot, completely randomized two-factor design and randomized complete block design. The formulas for confidence intervals for linear functions of vectors of main or interaction effects in the designs mentioned above are given in the paper. The practical example is given as an illustration.     

Bayesian discrimination with longitudinal data

The motivation for the methodological development is a double-blind clinical trial designed to estimate the effect of regular injection of growth hormone, with the purpose of identifying growth hormone abusers in sport. The data formed part of a multicentre investigation jointly sponsored by the European Union and the International Olympic Committee. The data are such that for each individual there is a matrix of marker variables by time point (nominally 8 markers at each of 7 time points). Data arise out of a double-blind trial in which individuals are given growth hormone at one of two dose levels or placebo daily for 28 days. Monitoring by means of blood samples is at 0, 21, 28, 30, 33, 42 and 84 days. We give a new method of Bayesian discrimination for multivariate longitudinal data. This involves a Kronecker product covariance structure for the time by measurements (markers) data on each individual. This structure is estimated by an empirical Bayes approach, using an ECM algorithm, within a Bayesian Gaussian discrimination model. In future one may have markers for an individual at one or more time points. The method gives probabilities that an individual is on placebo or on one of the two dose regimes.     

On Analysis of Proportions in Tables with Spoiled Data

This paper discusses analysis of dispersion tests in tables with spoiled data, that is, where some cells are missing. BENNETT (1987) gave a method for estimating the missing cells in a two-way table and illustrated it with a simple example. This paper uses GLIM to estimate the models and indicates that the General Linear Model performs the computations without need to estimate the missing values. The binomial error model also may be used for this problem, and is the more natural approach to the problem.     

Multiscale Adaptive Marginal Analysis of Longitudinal Neuroimaging Data with Time‐Varying Covariates

Summary Neuroimaging data collected at repeated occasions are gaining increasing attention in the neuroimaging community due to their potential in answering questions regarding brain development, aging, and neurodegeneration. These datasets are large and complicated, characterized by the intricate spatial dependence structure of each response image, multiple response images per subject, and covariates that may vary with time. We propose a multiscale adaptive generalized method of moments (MA‐GMM) approach to estimate marginal regression models for imaging datasets that contain time‐varying, spatially related responses and some time‐varying covariates. Our method categorizes covariates into types to determine the valid moment conditions to combine during estimation. Further, instead of assuming independence of voxels (the components that make up each subject’s response image at each time point) as many current neuroimaging analysis techniques do, this method “adaptively smoothes” neuroimaging response data, computing parameter estimates by iteratively building spheres around each voxel and combining observations within the spheres with weights. MA‐GMM’s development adds to the few available modeling approaches intended for longitudinal imaging data analysis. Simulation studies and an analysis of a real longitudinal imaging dataset from the Alzheimer’s Disease Neuroimaging Initiative are used to assess the performance of MA‐GMM. Martha Skup, Hongtu Zhu, and Heping Zhang for the Alzheimer’s Disease Neuroimaging Initiative.     

Investigations on the Similarity of the Structure of Two Covariance Matrices for Some Blood Pressure Data

We have two sample covariance matrices of size p × p, where p is the number of variables denoting measurements of arterial blood pressure recorded in various positions and methods for women and men. The hypothesis on equality of the two covariance matrices was rejected and we want to find, why this happened and in which covariances the two matrices differ. In § 2 we explore the primary (observed) data looking for outliers and deviations from normality. We do it by drawing χ² plots. In § 3 we look for common principal components. We use here Flury's method. We find that the data for blood pressure systolic might have a common principal axes system (P≈?0.0866), while for blood pressure diastolic this is dubious (p≈?0.0112). The variances of the first principal component found for woman are a little larger than those for men, and this fact explains the rejection of the classical test ∑₁ = ∑₂ on the equality of the two covariance matrices. After applying a common rotation to the two covariance matrices we reduce them to a nearly diagonal form. Looking at the non diagonal elements we see which covariances do not fit into the model. In § 4 we repeat the calculations from § 3 for covariance matrices calculated from the same data using robust methods.     

Covariance Analysis of Chlorophyll Distribution in the Sundays River Estuary,Eastern Cape

Summary

An analysis of covariance performed on chlorophyll a distribution data from the Sundays River estuary identified five persistent water masses with significantly different chlorophyll a contents. These corresponded to different hydrodynamic regions within the estuary. The relationship between salinity and chlorophyll a was used to identify a transition zone between the low-salinity upper estuary and the brackish riverine inflow. Chlorophyll a concentrations ranged from <6 μg 1^?1 near the mouth to >100 μg 1^?1 in the middle and upper reaches. High variance of chlorophyll a data in this region was the result of temporal chlorophyll a fluctuations in two time scales. The first related to the daily ebb/tide and the second to the lunar spring/neap tidal cycle. Unlike its importance in larger and deeper estuaries, light played only a minor role in the horizontal distribution of chlorophyll a. The presence of a chlorophyll a maximum in the turbid low salinity region is explained in terms of the relatively constant photic depth/mixed depth ratio along the estuary and the high residence time of water in this region.     

动物学定量与定性资料分析中的常见错误

合理选择正确的统计方法是提高科研工作质量和可信度的关键.然而有些科研工作所采用的统计方法往往不合理,甚至是错误的.例如,常常忽视定量资料中t-检验和方差分析的前提条件--正态性、方差齐性.对单因素多水平的定量资料直接使用组间两两比较的t-检验;对定性资料中列联表的分析一律通用Pearson χ~2检验,包括对Pearson χ~2检验结果的理解出现偏差,从而做出错误的推论.本文对国内一些动物学研究论文中所采用的具体统计方法提出辨析.     

Profile Analysis of Variance as a Tool for Analyzing Correlated Responses in Experimental Ecology

In ecological studies experiments are often designed in which the variables to be compared are not statistically independent. Examples include repeated measures of the same response by the same individual at different times, measurement of several traits on the same individual, and measurements taken from two or more types of organisms present together in the same experimental unit (e.g.) plot, cage, pond, etc.). This type of design violates several assumptions of the standard analysis of variance. These assumptions are examined and profile analysis, a modification of the standard analysis of variance which does not depend upon these assumptions, is presented. Simple instructions for performing profile analysis of variance using two common statistical packages for mainframe computers are provided in an appendix.     

中药治疗亚急性甲状腺炎疗效Me ta分析

目的：评价中药治疗甲状腺功能亢进症的有效性及安全性。方法：计算机检索关于中药治疗甲亢的人体临床试验研究文献,选择从1980年1月至2014年1月符合要求的随机对照试验（RCT）和临床对照试验（CCT）的文章,并按排除原则进行筛选,利用Cochrane手册进行文献质量评价,利用RevMan 5．2软件进行Meta分析。结果：共纳入16篇随机对照试验,所有文献入组患者合计702例,有效661例,对照组合计590例,有效462例。Meta分析结果显示,中药治疗亚急性甲状腺炎与单纯西药治疗比较总有效率（治愈率＋好转率）的差异有统计学意义[OR＝0．22,95％CI（0．15,0．33）,P＜0．01]。结论：采用中药治疗亚急性甲状腺炎的总有效率和治愈率明显优于单纯使用西药。     

Safety evaluation of the consumption of high dose milk fat globule membrane in healthy adults: a double-blind,randomized controlled trial with parallel group design

Consumption of milk fat globule membrane (MFGM) in combination with habitual exercise suppresses age-associated muscle loss. The effects of high dose MFGM, however, are not known. A double-blind, randomized controlled trial with parallel group design was conducted to evaluate the safety of consuming high dose MFGM tablets. The subjects were 32 healthy adult men and women. Subjects were given 5 times the recommended daily intake of the tablets containing 6.5 g of MFGM or whole milk powder for 4 weeks. Stomach discomfort and diarrhea were observed; however, these symptoms were transitory and slight and were not related to consumption of the test tablets. In addition, there were no clinically significant changes in anthropometric measurements or blood tests. Total degree of safety assessed by the physicians of all subjects was “safe.” These findings suggest that consumption of the tablets containing 6.5 g MFGM for 4 weeks is safe for healthy adults.     

An Analysis of Variance of Nominal Data

This paper considers a common problem in analysis of variance where the responses to a set of treatments are nominal (i.e. are recorded in frequencies) with no underlying metric. Reasoning by analogy from standard analysis of variance of a two-way classification we develop chi-square tests for significance of treatments and interactions. Two tests are proposed for interaction and their asymptotic properties are studied.     

On the Impact of Parametric Assumptions and Robust Alternatives for Longitudinal Data Analysis

Models for longitudinal data are employed in a wide range of behavioral, biomedical, psychosocial, and health‐care‐related research. One popular model for continuous response is the linear mixed‐effects model (LMM). Although simulations by recent studies show that LMM provides reliable estimates under departures from the normality assumption for complete data, the invariable occurrence of missing data in practical studies renders such robustness results less useful when applied to real study data. In this paper, we show by simulated studies that in the presence of missing data estimates of the fixed effect of LMM are biased under departures from normality. We discuss two robust alternatives, the weighted generalized estimating equations (WGEE) and the augmented WGEE (AWGEE), and compare their performances with LMM using real as well as simulated data. Our simulation results show that both WGEE and AWGEE provide valid inference for skewed non‐normal data when missing data follows the missing at random, the most popular missing data mechanism for real study data.     

Pulse Wave Velocity Testing in the Baltimore Longitudinal Study of Aging

Carotid-femoral pulse wave velocity is considered the gold standard for measurements of central arterial stiffness obtained through noninvasive methods¹. Subjects are placed in the supine position and allowed to rest quietly for at least 10 min prior to the start of the exam. The proper cuff size is selected and a blood pressure is obtained using an oscillometric device. Once a resting blood pressure has been obtained, pressure waveforms are acquired from the right femoral and right common carotid arteries. The system then automatically calculates the pulse transit time between these two sites (using the carotid artery as a surrogate for the descending aorta). Body surface measurements are used to determine the distance traveled by the pulse wave between the two sampling sites. This distance is then divided by the pulse transit time resulting in the pulse wave velocity. The measurements are performed in triplicate and the average is used for analysis.