Discrete dynamics by different concepts of majorization

For the description of complex dynamics of open systems an approach is given by different concepts of majorization (order structure). Discrete diffusion processes with both invariant object number and sink or source can be represented by the development of Young diagrams on lattices. As experimental example we investigated foam decay, dominated by sinks. The relevance of order structures for characterization of certain processes is discussed

Magnetic order in GdBiPt studied by x-ray resonant magnetic scattering

Rare earth (R) half-Heusler compounds, RBiPt, exhibit a wide spectrum of novel ground states. Recently, GdBiPt has been proposed as a potential antiferromagnetic topological insulator (AFTI). We have employed x-ray resonant magnetic scattering to elucidate the microscopic details of the magnetic structure in GdBiPt below T_N = 8.5 K. Experiments at the Gd L_2 absorption edge show that the Gd moments order in an antiferromagnetic stacking along the cubic diagonal [1 1 1] direction satisfying the requirement for an AFTI, where both time-reversal symmetry and lattice translational symmetry are broken, but their product is conserved.Comment: 4 pages, 4 figure

An approach to estimating prognosis using fractional polynomials in metastatic renal carcinoma

We present a prognostic model for metastatic renal cell carcinoma based on fractional polynomials. We retrospectively analysed 425 metastatic renal cell carcinoma patients treated with subcutaneous recombinant cytokine-based home therapies in consecutive trials. In our approach, we categorised a continuous prognostic index produced by the multivariable fractional polynomial (MFP) algorithm, using a strategy in which continuous predictors are kept continuous. The MFP algorithm selected five prognostic factors as significant at the 5% level in a multivariable model: lymph node metastases, liver metastases, bone metastases, age, C-reactive protein and neutrophils. The MFP model allowed us to divide patients into four risk groups achieving median overall survivals of 38 months (low risk), 23 months (low intermediate risk), 15 months (high intermediate risk) and 5.6 months (high risk). Our approach, based on categorising a continuous prognostic index produced by the MFP algorithm, allowed more flexibility in the determination of risk groups than traditional approaches

A new method to analyse the pace of child development: Cox regression validated by a bootstrap resampling procedure

<p>Abstract</p> <p>Background</p> <p>Various perinatal factors influencing neuromotor development are known from cross sectional studies. Factors influencing the age at which distinct abilities are acquired are uncertain. We hypothesized that the Cox regression model might identify these factors.</p> <p>Methods</p> <p>Neonates treated at Aachen University Hospital in 2000/2001 were identified retrospectively (n = 796). Outcome data, based on a structured interview, were available from 466 children, as were perinatal data. Factors possibly related to outcome were identified by bootstrap selection and then included into a multivariate Cox regression model. To evaluate if the parental assessment might change with the time elapsed since birth we studied five age cohorts of 163 normally developed children.</p> <p>Results</p> <p>Birth weight, gestational age, congenital cardiac disease and periventricular leukomalacia were related to outcome in the multivariate analysis (p < 0.05). Analysis of the control cohorts revealed that the parents' assessment of the ability of bladder control is modified by the time elapsed since birth.</p> <p>Conclusions</p> <p>Combined application of the bootstrap resampling procedure and multivariate Cox regression analysis effectively identifies perinatal factors influencing the age at which distinct abilities are acquired. These were similar as known from previous cross sectional studies. Retrospective data acquistion may lead to a bias because the parental memories change with time. This recommends applying this statistical approach in larger prospective trials.</p

First-Trimester Prediction of Gestational Diabetes Mellitus: Examining the Potential of Combining Maternal Characteristics and Laboratory Measures

OBJECTIVE Predictors of gestational diabetes mellitus (GDM) have been widely studied, but few studies have considered multiple measures. Our objective was to integrate several potential GDM predictors with consideration to both simple and novel measures and to determine the extent to which GDM can be predicted in the first trimester. RESEARCH DESIGN AND METHODS We identified first-trimester maternal samples from 124 women who developed GDM and 248 control subjects who did not. We gathered data on age, BMI, parity, race, smoking, prior GDM, family history of diabetes, and blood pressure. Using retrieved samples, we measured routine (lipids, high-sensitivity C-reactive protein, and gamma-glutamyltransferase) and novel (adiponectin, E-selectin, and tissue plasminogen activator [t-PA]) parameters. We determined independent predictors from stepwise regression analyses, calculated areas under the receiver-operating characteristic curves (AUC-ROC), and integrated discrimination improvement (IDI) for relevant models. RESULTS Compared with control subjects, women who subsequently developed GDM were older, had higher BMIs, were more likely to be of Asian origin, had a history of GDM or family history of type 2 diabetes, and had higher systolic blood pressure (P &lt; 0.05 for all). With regard biochemical measures, stepwise analyses identified only elevated t-PA and low HDL cholesterol levels as significant (P &lt;= 0.015) independent predictors of GDM beyond simple non-laboratory-based maternal measures. Their inclusion improved the AUC-ROC from 0.824 to 0.861 and IDI by 0.052 (0.017-0.115). CONCLUSIONS GDM can be usefully estimated from a mix of simple questions with potential for further improvement by specific blood measures (lipids and t-PA)

Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study

Background: There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model. Methods: Datasets were generated to resemble the skewed distributions seen in a motivating breast cancer example. Multivariate missing data were imposed on four covariates using four different mechanisms; missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR) and a combination of all three mechanisms. Five amounts of incomplete cases from 5% to 75% were considered. Complete case analysis (CC), single imputation (SI) and five multiple imputation (MI) techniques available within the R statistical software were investigated: a) data augmentation (DA) approach assuming a multivariate normal distribution, b) DA assuming a general location model, c) regression switching imputation, d) regression switching with predictive mean matching (MICE-PMM) and e) flexible additive imputation models. A Cox proportional hazards model was fitted and appropriate estimates for the regression coefficients and model performance measures were obtained. Results: Performing a CC analysis produced unbiased regression estimates, but inflated standard errors, which affected the significance of the covariates in the model with 25% or more missingness. Using SI, underestimated the variability; resulting in poor coverage even with 10% missingness. Of the MI approaches, applying MICE-PMM produced, in general, the least biased estimates and better coverage for the incomplete covariates and better model performance for all mechanisms. However, this MI approach still produced biased regression coefficient estimates for the incomplete skewed continuous covariates when 50% or more cases had missing data imposed with a MCAR, MAR or combined mechanism. When the missingness depended on the incomplete covariates, i.e. MNAR, estimates were biased with more than 10% incomplete cases for all MI approaches. Conclusion: The results from this simulation study suggest that performing MICE-PMM may be the preferred MI approach provided that less than 50% of the cases have missing data and the missing data are not MNAR

A Genome-Wide Comparative Evolutionary Analysis of Herpes Simplex Virus Type 1 and Varicella Zoster Virus

Herpes simplex virus type 1 (HSV-1) and varicella zoster virus (VZV) are closely related viruses causing lifelong infections. They are typically associated with mucocutaneous or skin lesions, but may also cause severe neurological or ophthalmic diseases, possibly due to viral- and/or host-genetic factors. Although these viruses are well characterized, genome-wide evolutionary studies have hitherto only been presented for VZV. Here, we present a genome-wide study on HSV-1. We also compared the evolutionary characteristics of HSV-1 with those for VZV. We demonstrate that, in contrast to VZV for which only a few ancient recombination events have been suggested, all HSV-1 genomes contain mosaic patterns of segments with different evolutionary origins. Thus, recombination seems to occur extremely frequent for HSV-1. We conclude by proposing a timescale for HSV-1 evolution, and by discussing putative underlying mechanisms for why these otherwise biologically similar viruses have such striking evolutionary differences

Examining the BMI-mortality relationship using fractional polynomials

<p>Abstract</p> <p>Background</p> <p>Many previous studies estimating the relationship between body mass index (BMI) and mortality impose assumptions regarding the functional form for BMI and result in conflicting findings. This study investigated a flexible data driven modelling approach to determine the nonlinear and asymmetric functional form for BMI used to examine the relationship between mortality and obesity. This approach was then compared against other commonly used regression models.</p> <p>Methods</p> <p>This study used data from the National Health Interview Survey, between 1997 and 2000. Respondents were linked to the National Death Index with mortality follow-up through 2005. We estimated 5-year all-cause mortality for adults over age 18 using the logistic regression model adjusting for BMI, age and smoking status. All analyses were stratified by sex. The multivariable fractional polynomials (MFP) procedure was employed to determine the best fitting functional form for BMI and evaluated against the model that includes linear and quadratic terms for BMI and the model that groups BMI into standard weight status categories using a deviance difference test. Estimated BMI-mortality curves across models were then compared graphically.</p> <p>Results</p> <p>The best fitting adjustment model contained the powers -1 and -2 for BMI. The relationship between 5-year mortality and BMI when estimated using the MFP approach exhibited a J-shaped pattern for women and a U-shaped pattern for men. A deviance difference test showed a statistically significant improvement in model fit compared to other BMI functions. We found important differences between the MFP model and other commonly used models with regard to the shape and nadir of the BMI-mortality curve and mortality estimates.</p> <p>Conclusions</p> <p>The MFP approach provides a robust alternative to categorization or conventional linear-quadratic models for BMI, which limit the number of curve shapes. The approach is potentially useful in estimating the relationship between the full spectrum of BMI values and other health outcomes, or costs.</p

Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK): Explanation and Elaboration

The REMARK “elaboration and explanation” guideline, by Doug Altman and colleagues, provides a detailed reference for authors on important issues to consider when designing, conducting, and analyzing tumor marker prognostic studies