The heritability of premenstrual syndrome

We aimed to determine (1) the prevalence of premenstrual syndrome in a sample of twins and (2) the relative contribution of genes and environment in premenstrual syndrome. A group of 193 subjects inclusive of same gender twins (n = 176) and females from opposite sex twin sets (n = 17) entered the study. Heritability analysis used same gender twin data only. The probandwise concordance rate for the presence or absence of premenstrual syndrome was calculated and the heritability of premenstrual syndrome was assessed by a quantitative genetic model fitting approach using MX software. The prevalence of premenstrual syndrome was 43.0% and 46.8% in monozygotic and dizygotic twins, respectively. The probandwise concordance for premenstrual syndrome was higher in monozygotic (0.81) than in dizygotic twins (0.67), indicating a strong genetic effect. Quantitative genetic modeling found that a model comprising of additive genetic (A) and unique environment (E) factors provided the best fit (A: 95%, E: 5%). No association was found between premenstrual symptom and the following variables: belonging to the opposite gender twin set, birth weight, being breast fed and vaccination. These results established a clear genetic influence in premenstrual syndrome

Modelling multiscale aspects of colorectal cancer

Colorectal cancer (CRC) is responsible for nearly half a million deaths annually world-wide [11]. We present a series of mathematical models describing the dynamics of the intestinal epithelium and the kinetics of the molecular pathway most commonly mutated in CRC, the Wnt signalling network. We also discuss how we are coupling such models to build a multiscale model of normal and aberrant guts. This will enable us to combine disparate experimental and clinical data, to investigate interactions between phenomena taking place at different levels of organisation and, eventually, to test the efficacy of new drugs on the system as a whole

Comparison of some smoothing parameter selection methods in generalized estimating equation-smoothing spline

This paper considers performance of some smoothing parameter selection methods in Generalized Estimating Equation-Smoothing Spline for nonparametric regression with binary data. We evaluated eight methods, GCV given by Green and Silverman, GCV and AIC given by Ruppert et al, ACV and GACV given by Xiang and Wahba, AIC given by Chiou and Tsai, SCVD given by WU and Zhang and the last method is AIC*, modification of AlC given by Chiou and Tsai. Using simulation we found that for nonlinear systematic component (sinusoidal) AlC and AIC* of Chiou and Tsai are the best methods and the worst method is GCV of Green and Silverman. For linear systematic component, GCV of Green and Silverman is the best, while AlC and AlC* are the worst. Since in practical situation we do not know the form of the systematic component, hence we suggest the use of ACV and GACV of Xiang and Wahba or AIC of Ruppert et al, which give moderate results

Nonparametric regression for longitudinal binary data based on GEE-Smoothing Spline.

This paper considers nonparametric regression to analyze longitudinal binary data. In this paper we propose GEE-Smoothing spline and study the properties of the estimator such as the bias, consistency and efficiency. We use natural cubic spline with combination of generalized estimating equation proposed by Liang & Zeger (1986). We evaluated these properties through simulations and obtained that GEE-Smoothing spline has good properties. The percentage of acceptance of the hypothesis that the function is equal to the true function, using naive and sandwich variance estimators is also obtained. The bias of pointwise estimator is decreasing with increasing sample size. The pointwise estimator is also consistent even using incorrect correlation structure, and the most efficient estimate is obtained if the true correlation structure is used. Example of real data is presented with comparison of GEE with GEE-Smoothing spline

GEE-smoothing spline for semiparametric estimation of longitudinal binary data

This paper considers analyzing longitudinal data semiparametrically and proposing GEE-Smoothing spline in the estimation of the parametric and nonparametric components. Generalized estimating equation is used as the core of the estimation. Estimation of association or within subject correlation used method of moment suggested by Liang and Zeger (1986). In the estimation of nonparametric component, we used smoothing spline approach specifically the natural cubic spline. We show through simulation that GEE-Smoothing Spline has good properties. The bias of parametric and nonparametric estimators decrease with increasing sample size. These estimators are also consistent even though incorrect correlation structure is used. The most efficient estimator can be obtained if the correct correlation structure is used rather than ignore the dependency