LMS方法及其在流量数据中的应用

LMS模型是分析生长发育最常用的方法之一。本文详细阐述了LMS模型的构造原理；基于流量数据提出了费希尔信息矩阵与惩罚贝叶斯后验对数似然算法两种模型算法；利用中国健康营养调查（CHNS）1989-2011年中9年的流量数据，以所提出的LMS曲线算法为基础，通过计算BMI（Body Mass Index）绘制生长发育曲线研究我国青少年儿童生长发育情况和中年人健康问题。研究结果表明：1989-2011年间，我国0~18岁年龄段青少年儿童BMI中位数提高5%左右，生长发育高峰期有提前趋势；中年人群BMI中位数提高了10%左右，2000年后55周岁以上中年人体质差异有增大趋势。     

Explaining Trends in Body Mass Index Using Demographic Counterfactuals

The United States is experiencing a major public health problem relating to increasing levels of excess body fat. This paper is about the relationship in the United States between trends in the distribution of body mass index (BMI), including trends in overweight and obesity, and demographic change. We provide estimates of the counterfactual distribution of BMI that would have been observed in 2003–2008 had demographics remained fixed at 1980 values, roughly the beginning of the period of increasing overweight and obesity. We find that changes in demographics are partly responsible for the changes in the population distribution of BMI and are capable of explaining about 8.6% of the increase in the combined rate of overweight and obesity among women and about 7.2% of the increase among men. We also use demographic projections to predict a BMI distribution and corresponding rates of overweight and obesity for 2050.     

Fitting bivariate multilevel models to assess long-term changes in body mass index and cigarette smoking

Using data from the National Health interview Survey from 1997 to 2006, we present a multilevel analysis of change in body mass index (BMI) and number of cigarettes smoked per day in the USA. Smoking and obesity are the leading causes of preventable mortality and morbidity in the USA and most parts of the developed world. A two-stage bivariate model of changes in obesity and number of cigarette smoked per day is proposed. At the within subject stage, an individual's BMI status and the number of cigarette smoked per day are jointly modeled as a function of an individual growth trajectory plus a random error. At the between-subject stage, the parameters of the individual growth trajectories are allowed to vary as a function of differences between subjects with respect to demographic and behavioral characteristics and with respect to the four regions of the USA (Northeast, West, South and North central). Our two-stage modeling techniques are more informative than standard regression because they characterize both group-level (nomothetic) and individual-level (idiographic) effects, yielding a more complete understanding of the phenomena under study.     

On the eve of birth control. II

The results of a 1981 sample survey on family planning in Poland show that the average birth ratio for couples married in 1975 is 2.1 (2.0 in the cities and 2.5 in rural areas). With a much lower mortality rate, this minimal reproduction ratio still provides the fairly high natural population growth which is characteristic of all industrialized nations. This birth ratio is maintained by almost universal use of various birth control methods after 6-7 years of marriage, i.e., an average family with 2 children is planned and created by means of contraceptives and abortions. The family planning situation in Poland is discussed for women who: 1) plan to have more children, 2) might change their decision not to have more children, and 3) made a final decision not to have more children. The reasons for these decisions are thoroughly analyzed. Particular attention is paid to the extent to which couples are able to reach their reproductive objectives. The analysis indicates that many couples would have had more children if the husband received a higher salary, housing conditions were better, and the mother could quit her job for at least some period of time. Birth control is used because most couples are satisfied with having 2 children and consider their reproductive objectives fulfilled.     

Acute Middle Ear Infection in Small Children: A Bayesian Analysis Using Multiple Time Scales

The study is based on a sample of 965 children living in Oulu region (Finland), who were monitored for acute middle ear infections from birth to the age of two years. We introduce a nonparametrically defined intensity model for ear infections, which involves both fixed and time dependent covariates, such as calendar time, current age, length of breast-feeding time until present, or current type of day care. Unmeasured heterogeneity, which manifests itself in frequent infections in some children and rare in others and which cannot be explained in terms of the known covariates, is modelled by using individual frailty parameters. A Bayesian approach is proposed to solve the inferential problem. The numerical work is carried out by Monte Carlo integration (Metropolis-Hastings algorithm).     

Additive mixed models with Dirichlet process mixture and P-spline priors

Longitudinal data often require a combination of flexible time trends and individual-specific random effects. For example, our methodological developments are motivated by a study on longitudinal body mass index profiles of children collected with the aim to gain a better understanding of factors driving childhood obesity. The high amount of nonlinearity and heterogeneity in these data and the complexity of the data set with a large number of observations, long longitudinal profiles and clusters of observations with specific deviations from the population model make the application challenging and prevent the application of standard growth curve models. We propose a fully Bayesian approach based on Markov chain Monte Carlo simulation techniques that allows for the semiparametric specification of both the trend function and the random effects distribution. Bayesian penalized splines are considered for the former, while a Dirichlet process mixture (DPM) specification allows for an adaptive amount of deviations from normality for the latter. The advantages of such DPM prior structures for random effects are investigated in terms of a simulation study to improve the understanding of the model specification before analyzing the childhood obesity data.     

Predicting body fat using weight–height indices

While body fat is the most accurate measure of obesity, its measurement requires special equipment that can be costly and time consuming to operate. Attention has thus typically focused on the easier to calculate body mass index (BMI). However, the ability of BMI to accurately identify obesity has been increasingly questioned. This paper focuses attention on whether more general body mass indices are appropriate measures of body fat. Using a data set of body fat, height, and weight measurements, general models are estimated which nest a wide variety of weight–height indices as special cases. In the absence of a race and gender categorisation, the conventional BMI was found to be the appropriate index with which to predict body fat. When such a categorisation was made, however, the BMI was never selected as the appropriate index. In general, predicted female body fat was some 10 kg higher than that of a male of identical build and predicted % body fat was over 11 percentage points higher, but age effects were smaller for females. Considerable racial differences in predicted body fat were found for males, but such differences were less marked for females. The implications of this finding for interpreting recent research on the effect of obesity on health, society, and economic factors are considered.     

A pooled Bayes test of independence for sparse contingency tables from small areas

We study the association between bone mineral density (BMD) and body mass index (BMI) when contingency tables are constructed from the several U.S. counties, where BMD has three levels (normal, osteopenia and osteoporosis) and BMI has four levels (underweight, normal, overweight and obese). We use the Bayes factor (posterior odds divided by prior odds or equivalently the ratio of the marginal likelihoods) to construct the new test. Like the chi-squared test and Fisher's exact test, we have a direct Bayes test which is a standard test using data from each county. In our main contribution, for each county techniques of small area estimation are used to borrow strength across counties and a pooled test of independence of BMD and BMI is obtained using a hierarchical Bayesian model. Our pooled Bayes test is computed by performing a Monte Carlo integration using random samples rather than Gibbs samples. We have seen important differences among the pooled Bayes test, direct Bayes test and the Cressie-Read test that allows for some degree of sparseness, when the degree of evidence against independence is studied. As expected, we also found that the direct Bayes test is sensitive to the prior specifications but the pooled Bayes test is not so sensitive. Moreover, the pooled Bayes test has competitive power properties, and it is superior when the cell counts are small to moderate.     

Multilevel multivariate modelling of childhood growth, numbers of growth measurements and adult characteristics

Summary.  A general latent normal model for multilevel data with mixtures of response types is extended in the case of ordered responses to deal with variates having a large number of categories and including count data. An example is analysed by using repeated measures data on child growth and adult measures of body mass index and glucose. Applications are described that are concerned with the flexible prediction of adult measurements from collections of growth measurements and for studying the relationship between the number of measurement occasions and growth trajectories.     

Variation in height acceleration in the Fels growth data

The Fels growth data record at half-yearly intervals the heights of children from birth to adulthood, and are the basis for pediatricians' growth charts used throughout North America. Aspects of human growth are the subject of a large medical and statistical literature. This paper uses smoothing splines to study the variation in height acceleration. By use of a functional version of principal-components analysis, we find that variation in the acceleration curve is essentially three-dimensional in nature. Evidence for a small growth spurt between the ages of six and eight, reported for data collected in Switzerland, is examined, and little support is found for the existence of this phenomenon in the Fels data.     

Modeling population and subject-specific growth in a latent trait measured by multiple instruments over time using a hierarchical Bayesian framework

Psychometric growth curve modeling techniques are used to describe a person’s latent ability and how that ability changes over time based on a specific measurement instrument. However, the same instrument cannot always be used over a period of time to measure that latent ability. This is often the case when measuring traits longitudinally in children. Reasons may be that over time some measurement tools that were difficult for young children become too easy as they age resulting in floor effects or ceiling effects or both. We propose a Bayesian hierarchical model for such a scenario. Within the Bayesian model we combine information from multiple instruments used at different age ranges and having different scoring schemes to examine growth in latent ability over time. The model includes between-subject variance and within-subject variance and does not require linking item specific difficulty between the measurement tools. The model’s utility is demonstrated on a study of language ability in children from ages one to ten who are hard of hearing where measurement tool specific growth and subject-specific growth are shown in addition to a group level latent growth curve comparing the hard of hearing children to children with normal hearing.KEYWORDS: Bayesian hierarchical models, psychometric modeling, language ability, growth curve modeling, longitudinal analysis     

Measurement and analysis of demographic processes: the characteristics of generation

Since it would take too long (100 years) to ascertain all demographic data about a given age group, i.e., all those born in a given year, these data are determined hypothetically by measuring the various characteristics of persons of all ages in a given time period (1-2 years). Also needed is an indicator of the population as a whole; cumulative coefficients are used for this purpose. One of these is the overall coefficient of births, meaning the number of children a women would have over her whole period of fertility if she had the precise number of children at each period in her life as other women of that age. An analogous indicator is used for measuring mortality--the average life expectancy of a person at each stage in his life. The crude coefficient of population reproduction represents the number of girls to which each woman will give birth between the ages of 15 and 50. This has to be corrected by the number of those who will not live to reproductive age. The result is the corrected coefficient of reproduction of the female population. This coefficient is often thought to reflect the population's growth prospects; if less than unity, therefore, the population will not reproduce itself. This is an incorrect interpretation. The impact of immigration and emigration on the population must also be incorportated. In addition to the above hypothetical indicators, we must also develop real population indicators. Techniques must also be employed to evaluate the reliability of these demographic indicators.     

A statistical model of tracking

A method is proposed to model individual patterns of growth over time by linear combinations of optimally chosen weighted orthogonal vectors. The goal is to distinguish individuals who track from nontrackers. Nontrackers are defined as those who follow different, usually more complex, growth patterns than trackers. Thus, nontrackers require more vectors than do trackers in modeling their longitudinal observations. A method of specifying the class-specific vectors and individual weights is demonstrated. When the proportion of nontrackers in the population is small, a modified form of the Akaike maximum entropy criterion is used to select the number of vectors appopriate for each person and also to classify each person into a tracking category. When the proportion of nontrackers is large, the modified Akaike criterion together with scatterplots of the growth curve weights are needed to distinguish trackers from nontrackers. The apprach is illustrated with longitudinal observations of height measured in an epidemiologic survey of children.     

Child health,breast-feeding,and survival in Malaysia: a random-effects logit approach

This study examines the possibility that estimation of the effect of breast-feeding on infant survival is affected by selection bias, in that children who are healthier at birth may be more likely to be breast-fed. Data are from the 1976 Malaysian Family Life Survey. "Ordinary logit models for breast-feeding and survival are estimated, and the results suggest that selection is indeed present. For example, children of higher birth weight appear to be more likely to be breast-fed and likely to survive. In addition, weight at birth and the duration of breast-feeding appear to be linked." Using birth weight as an indicator for the child's health, the authors conclude that "the direct influence of breast-feeding on survival remains of overwhelming importance even after corrections for selection bias are made."     

Design priorities and disciplinary perspectives: the case of the US National Children's Study

Summary.  The National Children's Study (NCS), which was undertaken in 2000 by collaboration between several US federal government agencies, is one of the largest and boldest longitudinal studies of children's health ever undertaken. One of the key design issues has been the nature of the NCS sample. The paper describes the nature of the choices and the reasons for the decision that the NCS be based on a national probability sample. Designed as a study of the environmental influences on children's health and development, the NCS is expected to identify, enrol and follow about 100000 children from their birth to the age of 21 years. A broad definition of relevant environments of interest, and a full partnership between government, university and medical scientists, introduces considerable challenges in the design of the study.     

Semiparametric estimation of regression quantiles with application to standardizing weight for height and age in US children

The appropriate interpretation of measurements often requires standardization for concomitant factors. For example, standardization of weight for both height and age is important in obesity research and in failure-to-thrive research in children. Regression quantiles from a reference population afford one intuitive and popular approach to standardization. Current methods for the estimation of regression quantiles can be classified as nonparametric with respect to distributional assumptions or as fully parametric. We propose a semiparametric method where we model the mean and variance as flexible regression spline functions and allow the unspecified distribution to vary smoothly as a function of covariates. Similarly to Cole and Green, our approach provides separate estimates and summaries for location, scale and distribution. However, similarly to Koenker and Bassett, we do not assume any parametric form for the distribution. Estimation for either cross-sectional or longitudinal samples is obtained by using estimating equations for the location and scale functions and through local kernel smoothing of the empirical distribution function for standardized residuals. Using this technique with data on weight, height and age for females under 3 years of age, we find that there is a close relationship between quantiles of weight for height and age and quantiles of body mass index (BMI=weight/height²) for age in this cohort.     

Holistic trajectories: a study of combined employment, housing and family careers by using multiple-sequence analysis

Summary.  Social science applications of sequence analysis have thus far involved the development of a typology on the basis of an analysis of one or two variables which have had a relatively low number of different states. There is a yet unexplored potential for sequence analysis to be applied to a greater number of variables and thereby a much larger state space. The development of a typology of employment experiences, for example, without reference to data on changes in housing, marital and family status is arguably inadequate. The paper demonstrates the use of sequence analysis in the examination of multivariable combinations of status as they change over time and shows that this method can provide insights that are difficult to achieve through other analytic methods. The data that are examined here provide support to intuitive understandings of clusters of common experiences which are both life course specific and related to socio-economic factors. Housing tenure is found to be of key importance in understanding the holistic trajectories that are examined. This suggests that life course trajectories are sharply differentiated by experience of social housing.     

Evolution of recurrent asthma event rate over time in frailty models

Summary. To model the time evolution of the event rate in recurrent event data a crucial role is played by the timescale that is used. Depending on the timescale selected the interpretation of the time evolution will be entirely different, both in parametric and semiparametric frailty models. The gap timescale is more appropriate when studying the recurrent event rate as a function of time since the last event, whereas the calendar timescale keeps track of actual time. We show both timescales in action on data from an asthma prevention trial in young children. The frailty model is further extended to include both timescales simultaneously as this might be most relevant in practice.     

Bias in the last observation carried forward method under informative dropout

Subject dropout is an inevitable problem in longitudinal studies. It makes the analysis challenging when the main interest is the change in outcome from baseline to endpoint of study. The last observation carried forward (LOCF) method is a very common approach for handling this problem. It assumes that the last measured outcome is frozen in time after the point of dropout, an unrealistic assumption given any time trends. Though existence and direction of the bias can sometimes be anticipated, the more important statistical question involves the actual magnitude of the bias and this requires computation. This paper provides explicit expressions for the exact bias in the LOCF estimates of mean change and its variance when the longitudinal data follow a linear mixed-effects model with linear time trajectories. General dropout patterns are considered that may depend on treatment group, subject-specific trajectories and follow different time to dropout distributions. In our case studies, the magnitude of bias for mean change estimators linearly increases as time to dropout decreases. The bias depends heavily on the dropout interval. The variance term is always underestimated.     

具有不同转换机制的非线性模型理论与应用研究

近几年来,非线性时间序列模型被广泛地应用于经济学领域,尤其是其中的STAR模型能很好的描述很多经济变量的运行轨迹。本文正是对于STAR模型中转换函数的不同函数形式形成的模型进行讨论,除了常见的LSTAR和ESTAR模型外,本文还提出了令转换函数为三角函数的的TSTAR模型,并且对于这三种STAR模型的检验和模型的选择作了一些探讨。