Finite mixture regression model with random effects: application to neonatal hospital length of stay

A two-component mixture regression model that allows simultaneously for heterogeneity and dependency among observations is proposed. By specifying random effects explicitly in the linear predictor of the mixture probability and the mixture components, parameter estimation is achieved by maximising the corresponding best linear unbiased prediction type log-likelihood. Approximate residual maximum likelihood estimates are obtained via an EM algorithm in the manner of generalised linear mixed model (GLMM). The method can be extended to a g-component mixture regression model with the component density from the exponential family, leading to the development of the class of finite mixture GLMM. For illustration, the method is applied to analyse neonatal length of stay (LOS). It is shown that identification of pertinent factors that influence hospital LOS can provide important information for health care planning and resource allocation. (C) 2002 Elsevier Science B.V. All rights reserved.

Modelling inpatient length of stay by a hierarchical mixture regression via the EM algorithm

The modelling of inpatient length of stay (LOS) has important implications in health care studies. Finite mixture distributions are usually used to model the heterogeneous LOS distribution, due to a certain proportion of patients sustaining-a longer stay. However, the morbidity data are collected from hospitals, observations clustered within the same hospital are often correlated. The generalized linear mixed model approach is adopted to accommodate the inherent correlation via unobservable random effects. An EM algorithm is developed to obtain residual maximum quasi-likelihood estimation. The proposed hierarchical mixture regression approach enables the identification and assessment of factors influencing the long-stay proportion and the LOS for the long-stay patient subgroup. A neonatal LOS data set is used for illustration, (C) 2003 Elsevier Science Ltd. All rights reserved.

Forecasting with serially correlated regression models

In this article we investigate the asymptotic and finite-sample properties of predictors of regression models with autocorrelated errors. We prove new theorems associated with the predictive efficiency of generalized least squares (GLS) and incorrectly structured GLS predictors. We also establish the form associated with their predictive mean squared errors as well as the magnitude of these errors relative to each other and to those generated from the ordinary least squares (OLS) predictor. A large simulation study is used to evaluate the finite-sample performance of forecasts generated from models using different corrections for the serial correlation.

A review of two journals found that articles using multivariable logistic regression frequently did not report commonly recommended assumptions

Background and Objective: To examine if commonly recommended assumptions for multivariable logistic regression are addressed in two major epidemiological journals. Methods: Ninety-nine articles from the Journal of Clinical Epidemiology and the American Journal of Epidemiology were surveyed for 10 criteria: six dealing with computation and four with reporting multivariable logistic regression results. Results: Three of the 10 criteria were addressed in 50% or more of the articles. Statistical significance testing or confidence intervals were reported in all articles. Methods for selecting independent variables were described in 82%, and specific procedures used to generate the models were discussed in 65%. Fewer than 50% of the articles indicated if interactions were tested or met the recommended events per independent variable ratio of 10: 1. Fewer than 20% of the articles described conformity to a linear gradient, examined collinearity, reported information on validation procedures, goodness-of-fit, discrimination statistics, or provided complete information on variable coding. There was no significant difference (P >.05) in the proportion of articles meeting the criteria across the two journals. Conclusion: Articles reviewed frequently did not report commonly recommended assumptions for using multivariable logistic regression. (C) 2004 Elsevier Inc. All rights reserved.

Better prediction of protein contact number using a support vector regression analysis of amino acid sequence

Background: Protein tertiary structure can be partly characterized via each amino acid's contact number measuring how residues are spatially arranged. The contact number of a residue in a folded protein is a measure of its exposure to the local environment, and is defined as the number of C-beta atoms in other residues within a sphere around the C-beta atom of the residue of interest. Contact number is partly conserved between protein folds and thus is useful for protein fold and structure prediction. In turn, each residue's contact number can be partially predicted from primary amino acid sequence, assisting tertiary fold analysis from sequence data. In this study, we provide a more accurate contact number prediction method from protein primary sequence. Results: We predict contact number from protein sequence using a novel support vector regression algorithm. Using protein local sequences with multiple sequence alignments (PSI-BLAST profiles), we demonstrate a correlation coefficient between predicted and observed contact numbers of 0.70, which outperforms previously achieved accuracies. Including additional information about sequence weight and amino acid composition further improves prediction accuracies significantly with the correlation coefficient reaching 0.73. If residues are classified as being either contacted or non-contacted, the prediction accuracies are all greater than 77%, regardless of the choice of classification thresholds. Conclusion: The successful application of support vector regression to the prediction of protein contact number reported here, together with previous applications of this approach to the prediction of protein accessible surface area and B-factor profile, suggests that a support vector regression approach may be very useful for determining the structure-function relation between primary sequence and higher order consecutive protein structural and functional properties.

Predictive regression equations and clinical uses of peripheral pulse timing characteristics in children

Studies have shown that increased arterial stiffening can be an indication of cardiovascular diseases like hypertension. In clinical practice, this can be detected by measuring the blood pressure (BP) using a sphygmomanometer but it cannot be used for prolonged monitoring. It has been established that pulse wave velocity (PWV) is a direct measure of arterial stiffening but its usefulness is hampered by the absence of non-invasive techniques to estimate it. Pulse transit time (PTT) is a simple and non-invasive method derived from PWV. However, limited knowledge of PTT in children is found in the present literature. The aims of this study are to identify independent variables that confound PTT measure and describe PTT regression equations for healthy children. Therefore, PTT reference values are formulated for future pathological studies. Fifty-five Caucasian children (39 male) aged 8.4 +/- 2.3 yr (range 5-12 yr) were recruited. Predictive equations for PTT were obtained by multiple regressions with age, vascular path length, BP indexes and heart rate. These derived equations were compared in their PWV equivalent against two previously reported equations and significant agreement was obtained (p < 0.05). Findings herein also suggested that PTT can be useful as a continuous surrogate BP monitor in children.

Regression to the mean: what it is and how to deal with it

Background Regression to the mean (RTM) is a statistical phenomenon that can make natural variation in repeated data look like real change. It happens when unusually large or small measurements tend to be followed by measurements that are closer to the mean. Methods We give some examples of the phenomenon, and discuss methods to overcome it at the design and analysis stages of a study. Results The effect of RTM in a sample becomes more noticeable with increasing measurement error and when follow-up measurements are only examined on a sub-sample selected using a baseline value. Conclusions RTM is a ubiquitous phenomenon in repeated data and should always be considered as a possible cause of an observed change. Its effect can be alleviated through better study design and use of suitable statistical methods.

Hypermethylation of the 5 ' CpG island of the gene encoding the serine protease Testisin promotes its loss in testicular tumorigenesis

The Testisin gene (PRSS21) encodes a glycosylphosphatidylinositol (GPI)-linked serine protease that exhibits testis tissue-specific expression. Loss of Testisin has been implicated in testicular tumorigenesis, but its role in testis biology and tumorigenesis is not known. Here we have investigated the role of CpG methylation in Testisin gene inactivation and tested the hypothesis that Testisin may act as a tumour suppressor for testicular tumorigenesis. Using sequence analysis of bisulphite-treated genomic DNA, we find a strong relationship between hypermethylation of a 385 bp 50 CpG rich island of the Testisin gene, and silencing of the Testisin gene in a range of human tumour cell lines and in 100% (eight/eight) of testicular germ cell tumours. We show that treatment of Testisin-negative cell lines with demethylating agents and/or a histone deacetylase inhibitor results in reactivation of Testisin gene expression, implicating hypermethylation in Testisin gene silencing. Stable expression of Testisin in the Testisin-negative Tera-2 testicular cancer line suppressed tumorigenicity as revealed by inhibition of both anchorage-dependent cell growth and tumour formation in an SCID mouse model of testicular tumorigenesis. Together, these data show that loss of Testisin is caused, at least in part, by DNA hypermethylation and histone deacetylation, and suggest a tumour suppressor role for Testisin in testicular tumorigenesis.

Testicular development of Zebu bulls after chronic treatment with a gonadotropin-releasing hormone agonist

The objective was to compare testis characteristics of Zebu bulls treated with the GnRH agonist, deslorelin, at different times and for different durations during their development. An additional objective was to determine the usefulness of a stain for the transcription factor GATA-binding protein 4 (GATA-4) as a specific marker for Sertoli cell nuclei in cattle. Bulls (54) were allocated to nine groups (n = 6) and received s.c. deslorelin implants as follows: G1 = from birth to 3 mo of age; G2 = from 3 to 6 mo; G3 = from 6 to 9 mo; G4 = from 9 to 12 mo; G5 = from birth to 15 mo; G6 = from 3 to 15 mo; G7 = from 6 to 15 mo; G8 = from 12 to 15 mo; and G9 (control) = no implant. Bulls were castrated at 19 mo of age. Paraffin sections (10 mu m) were subjected to quantitative morphometry and GATA-4 immunohistochemistry. At castration, all bulls in the control group (6/6) had attained puberty (scrotal circumference ! 28 cm), whereas a smaller proportion (P < 0.05) had reached puberty in G2 (2/5) and G6 (1/ 6). Bulls in G2 and G6 also had a lesser (P < 0.05) testis weight compared with the control group. Total volume of seminiferous epithelium and total daily sperm production in G2 and G6 were only half that observed in the control group. Spermatids were observed in less than 50% of seminiferous tubules in G2, G6, and G7 compared with 82% in the control group (P < 0.05). Staining for GATA-4 was specific for and abundant in the Sertoli cell nucleus in both pre- and postpubertal bulls, and no other cell nucleus inside the seminiferous tubule was positive for GATA-4. Total number of Sertoli cells was not affected by treatment (P = 0.45), but nuclear volume was smaller in G2 and G6 (P < 0.05) compared with the control group. In conclusion, treatment of Zebu bulls with deslorelin had no apparent beneficial effect on testis development and delayed puberty when treatment was initiated at 3 mo of age. Staining for GATA-4 was a useful method for identifying and quantifying Sertoli cell nuclei in both pre- and postpubertal bulls.

Forecasting Helicoverpa populations in Australia: A comparison of regression based models and a bio-climatic based modeling approach

Long-term forecasts of pest pressure are central to the effective management of many agricultural insect pests. In the eastern cropping regions of Australia, serious infestations of Helicoverpa punctigera (Wallengren) and H. armigera (Hübner)(Lepidoptera: Noctuidae) are experienced annually. Regression analyses of a long series of light-trap catches of adult moths were used to describe the seasonal dynamics of both species. The size of the spring generation in eastern cropping zones could be related to rainfall in putative source areas in inland Australia. Subsequent generations could be related to the abundance of various crops in agricultural areas, rainfall and the magnitude of the spring population peak. As rainfall figured prominently as a predictor variable, and can itself be predicted using the Southern Oscillation Index (SOI), trap catches were also related to this variable. The geographic distribution of each species was modelled in relation to climate and CLIMEX was used to predict temporal variation in abundance at given putative source sites in inland Australia using historical meteorological data. These predictions were then correlated with subsequent pest abundance data in a major cropping region. The regression-based and bioclimatic-based approaches to predicting pest abundance are compared and their utility in predicting and interpreting pest dynamics are discussed.

Genetic basis of human testicular germ cell cancer: insights from the fruitfly and mouse

The prevalence of tumours of the germ line is increasing in the male population. This complex disease has a complex aetiology. We examine the contribution of genetic mutations to the development of germ line tumours in this review. In particular, we concentrate on fly and mouse experimental systems in order to demonstrate that mutations in some conserved genes cause pathologies typical of certain human germ cell tumours, whereas other mutations elicit phenotypes that are unique to the experimental model. Despite these experimental systems being imperfect, we show that they are useful models of human testicular germ cell tumourigenesis.

Plasma delipidation process induces rapid regression of atherosclerosis and mobilisation of adipose tissue

Cholesterol is a major component of atherosclerotic plaques. Cholesterol accumulation within the arterial intima and atherosclerotic plaques is determined by the difference of cellular cholesterol synthesis and/or influx from apo B-containing lipoproteins and cholesterol efflux. In humans, apo A-I Milano infusion has led to rapid regression of atherosclerosis in coronary arteries. We hypothesised that a multifunctional plasma delipidation process (PDP) would lead to rapid regression of experimental atherosclerosis and probably impact on adipose tissue lipids. In hyperlipidemic animals, the plasma concentrations of cholesterol, triglyceride and phospholipid were, respectively, 6-, 157-, and 18-fold higher than control animals, which consequently resulted in atherosclerosis. PDP consisted of delipidation of plasma with a mixture of butanol-diisopropyl ether (DIPE). PDP removed considerably more lipid from the hyperlipidemic animals than in normolipidemic animals. PDP treatment of hyperlipidemic animals markedly reduced intensity of lipid staining materials in the arterial wall and led to dramatic reduction of lipid in the adipose tissue. Five PDP treatments increased apolipoprotein A1 concentrations in all animals. Biochemical and hematological parameters were unaffected during PDP treatment. These results show that five PDP treatments led to marked reduction in avian atherosclerosis and removal of lipid from adipose tissue. PDP is a highly effective method for rapid regression of atherosclerosis.

Multi-level zero-inflated Poisson regression modelling of correlated count data with excess zeros

Count data with excess zeros relative to a Poisson distribution are common in many biomedical applications. A popular approach to the analysis of such data is to use a zero-inflated Poisson (ZIP) regression model. Often, because of the hierarchical Study design or the data collection procedure, zero-inflation and lack of independence may occur simultaneously, which tender the standard ZIP model inadequate. To account for the preponderance of zero counts and the inherent correlation of observations, a class of multi-level ZIP regression model with random effects is presented. Model fitting is facilitated using an expectation-maximization algorithm, whereas variance components are estimated via residual maximum likelihood estimating equations. A score test for zero-inflation is also presented. The multi-level ZIP model is then generalized to cope with a more complex correlation structure. Application to the analysis of correlated count data from a longitudinal infant feeding study illustrates the usefulness of the approach.

Predicting residue-wise contact orders in proteins by support vector regression

Background: The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. Results: We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. Conclusion: The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences.

Testicular germ cell tumors exhibit evidence of hormone dependence

The aim of this investigation was to test the hypothesis that testicular germ cell tumors (TGCTs) are hormone-dependent cancers. Human TGCT cells were implanted in the left testis of male severe combined immunodeficient mice receiving either no treatment or hormone manipulation treatment [blockade of gonadotropin-releasing hormone secretion and/or signaling using leuprolide or leuprolide plus exogenous testosterone]. Real-time RT-PCR analysis was used to determine the expression profiles of hormone pathway-associated genes. Tumor burden was significantly smaller in mice receiving both leuprolide and testosterone. Real-time RTPCR analysis of follicle-stimulating hormone (FSH) receptor, luteinizing hormone (LH) receptor and P450 aromatase revealed changes in expression in normal testis tissue related to presence of xenograft tumors and manipulation of hormone levels but a complete absence of expression of these genes in tumor cells themselves. This was confirmed in human specimens of TGCT. Reduced TGCT growth in vivo was associated with significant downregulation of LH receptor and P450 aromatase expression in normal testes. In conclusion, manipulation of hormone levels influenced the growth of TGCT in vivo, while the presence of xenografted tumors influenced the expression of hormone-related genes in otherwise untreated animals. Human TGCTs, both in the animal model and in clinical specimens, appear not to express receptors for FSH or LH. Similarly, expression of the P450 aromatase gene is absent in TGCTs. Impaired estrogen synthesis and/or signaling may be at least partly responsible for inhibition of TGCT growth in the animal model. (c) 2005 Wiley-Liss, Inc.