Complexity in applying spatial analysis to describe heterogeneous air-trapping in thoracic imaging data

In this paper we consider a novel approach to analyzing medical images by applying a concept typically employed in geospatial studies. For certain diseases, such as asthma, there is a relevant distinction between the heterogeneity of constriction in airways for patients compared to healthy individuals. In order to describe such heterogeneities quantitatively, we utilize spatial correlation in the realm of lung computer tomography (CT). Specifically, we apply the approximate profile-likelihood estimator (APLE) to simulated lung air-trapping data selected based on potential interest to pulmonologists, and we explore reference values obtainable through this statistic. Results indicate that APLE values are independent of air-trapping values, and can provide useful insight into spatial patterns of these values within the lungs in situations where other common metrics, such as the coefficient of variation, reveal little. The APLE relies on a neighborhood weights matrix to define spatial relatedness of considered regions, and among a few weight structures explored, a working optimal choice seems to be one based on the inverse distance squared between regions of interest. The application yields a new method to help analyze the degree of heterogeneity in lung CT images, which can be generalized to other medical images as well.     

A non‐linear model of fecundability,postpartum amenorrhea,and sterility*

This paper proposes a convolution model of fecundability, controling for the effects of postpartum amenorrhea and unobserved heterogeneity in fecundability. Simulation analysis was used to assess the validity and reliability of estimates derived from the model. Analysis showed that the model captured the mean and standard deviation of age at the onset of sterility in simulated populations where sterility followed either a Gompertz, a gamma, or a lognormal distribution. The model performed well when sterility was specified by either a lognormal or a gamma distribution. The model also accurately estimated fecundability and postpartum amenorrhea. Next, the model was found to fit data from 17th and 18th century French Canadian birth histories. In this French Canadian sample the mean age at sterility was found to be 46.3 years using a gamma model. The decline in fecundability was almost linear after age 30. Thus, fecundability at age 40 had declined to about one‐third of that observed at age 30. Variability in individual fecundability was quite high. For example, women with fecundability one standard deviation above the mean had about 2.3 times as high fecundability as women one standard deviation below the mean.     

Exploring unobserved heterogeneity in perinatal and neonatal mortality risks: The case of an Italian sharecropping community, 1900–39

Previous researchers have found that traditional determinants explain only a limited part of the variation in perinatal and infant mortality at the family level. In the study reported in this paper, we explored the factors that make the perinatal/neonatal death risk more heterogeneous across families. We estimated logistic regressions with cluster random effects at the maternal level, using data from the Italian village of Granarolo from 1900 to 1939. We estimated the effects of selected predictors on perinatal/neonatal mortality and unexplained inter-family variation. We found that non-rural skilled and lower-skilled workers experienced higher perinatal and neonatal mortality risks. Unexplained heterogeneity at the maternal level was lower for women living in sharecropper families than for those in landless labourer and non-rural worker families. Unexplained perinatal and neonatal mortality components were also due to socio-economic differences and were not necessarily related only to maternal biological features or shared genetic frailty.     

Patient-specific dose finding in phase I clinical trials

This article proposes an extension of the continual reassessment method to determine the maximum tolerated dose (MTD) in the presence of patients' heterogeneity in phase I clinical trials. To start with a simple case, we consider the covariate as a binary variable representing two groups of patients. A logistic regression model is used to establish the dose–response relationship and the design is based on the Bayesian framework. Simulation studies for six plausible dose–response scenarios show that the proposed design is likely to determine the MTD more accurately than the design that does not take covariate into consideration.     

文化比较视域下的中国古典叙事诗研究

文学呈现于世界的最早形式,无疑是诗歌。中西方古典叙事诗是在各自独立的社会背景下形成的两大审美文化系统,它们在功能上有很大的差别。这些差别主要表现在社会功用、思维方式、语言表达等方面。中国古典叙事诗强调的是＂抒情＂和＂言志＂;西方主要讲＂模仿＂和＂认知＂。这种不同恰恰说明了人类文化的多元化,透过这种不同的文学现象,在经济全球化的语境下,我们可以更多地去了解不同民族在特定时期的形象化历史。     

Testing recapture model MT for departure from homogeneity

This paper presents a method of testing for animal heterogeneity when the capture effort is not constant from occasion to occasion. The basic set of statistics used is a lower triangular matrix whose (I,k)th element is the proportion of those animals caught on the next (i+1)the occasion who have been caught k times until now. Both theoretical and simulation evidence is presented that the new procedure is superior to methods previously suggested. The method may breakdown for certain kinds of behavioural response.     

Prior distributions for variance parameters in a sparse-event meta-analysis of a few small trials

In rare diseases, typically only a small number of patients are available for a randomized clinical trial. Nevertheless, it is not uncommon that more than one study is performed to evaluate a (new) treatment. Scarcity of available evidence makes it particularly valuable to pool the data in a meta-analysis. When the primary outcome is binary, the small sample sizes increase the chance of observing zero events. The frequentist random-effects model is known to induce bias and to result in improper interval estimation of the overall treatment effect in a meta-analysis with zero events. Bayesian hierarchical modeling could be a promising alternative. Bayesian models are known for being sensitive to the choice of prior distributions for between-study variance (heterogeneity) in sparse settings. In a rare disease setting, only limited data will be available to base the prior on, therefore, robustness of estimation is desirable. We performed an extensive and diverse simulation study, aiming to provide practitioners with advice on the choice of a sufficiently robust prior distribution shape for the heterogeneity parameter. Our results show that priors that place some concentrated mass on small τ values but do not restrict the density for example, the Uniform(−10, 10) heterogeneity prior on the log(τ²) scale, show robust 95% coverage combined with less overestimation of the overall treatment effect, across varying degrees of heterogeneity. We illustrate the results with meta-analyzes of a few small trials.     

Analysis of covariance as an alternative approach to the analysis of non-orthogonal lay-outs

Though the analysis of two, three, and four-way non-orthogonal lay-outs is available in literature, it involves tedious calculations, and experimenters refrain using higher-way non-orthogonal lay-outs where computer facilities are not available. This paper will show that the analysis of a n-way lay-out can be considered as the analysis of a (n-1) lay-out with covariance and this approach is expected to minimize the calculations when the analysis has to be carried through desk calculators.     

Seasonality Tests

This article modifies and extends the test against nonstationary stochastic seasonality proposed by Canova and Hansen. A simplified form of the test statistic in which the nonparametric correction for serial correlation is based on estimates of the spectrum at the seasonal frequencies is considered and shown to have the same asymptotic distribution as the original formulation. Under the null hypothesis, the distribution of the seasonality test statistics is not affected by the inclusion of trends, even when modified to allow for structural breaks, or by the inclusion of regressors with nonseasonal unit roots. A parametric version of the test is proposed, and its performance is compared with that of the nonparametric test using Monte Carlo experiments. A test that allows for breaks in the seasonal pattern is then derived. It is shown that its asymptotic distribution is independent of the break point, and its use is illustrated with a series on U.K. marriages. A general test against any form of permanent seasonality, deterministic or stochastic, is suggested and compared with a Wald test for the significance of fixed seasonal dummies. It is noted that tests constructed in a similar way can be used to detect trading-day effects. An appealing feature of the proposed test statistics is that under the null hypothesis, they all have asymptotic distributions belonging to the Cramér–von Mises family.