Improved Dynamic Predictions from Joint Models of Longitudinal and Survival Data with Time-Varying Effects using P-splines

In the field of cardio-thoracic surgery, valve function is monitored over time after surgery. The motivation for our research comes from a study which includes patients who received a human tissue valve in the aortic position. These patients are followed prospectively over time by standardized echocardiographic assessment of valve function. Loss of follow-up could be caused by valve intervention or the death of the patient. One of the main characteristics of the human valve is that its durability is limited. Therefore, it is of interest to obtain a prognostic model in order for the physicians to scan trends in valve function over time and plan their next intervention, accounting for the characteristics of the data. Several authors have focused on deriving predictions under the standard joint modeling of longitudinal and survival data framework that assumes a constant effect for the coefficient that links the longitudinal and survival outcomes. However, in our case this may be a restrictive assumption. Since the valve degenerates, the association between the biomarker with survival may change over time. To improve dynamic predictions we propose a Bayesian joint model that allows a time-varying coefficient to link the longitudinal and the survival processes, using P-splines. We evaluate the performance of the model in terms of discrimination and calibration, while accounting for censoring

Penalized composite link models for aggregated spatial count data: a mixed model approach

Mortality data provide valuable information for the study of the spatial distri- bution of mortality risk, in disciplines such as spatial epidemiology and public health. However, they are frequently available in an aggregated form over irreg- ular geographical units, hindering the visualization of the underlying mortality risk. Also, it can be of interest to obtain mortality risk estimates on a finer spatial resolution, such that they can be linked to potential risk factors that are usually measured in a different spatial resolution. In this paper, we propose the use of the penalized composite link model and its mixed model representation. This model considers the nature of mortality rates by incorporating the population size at the finest resolution, and allows the creation of mortality maps at a finer scale, thus reducing the visual bias resulting from the spatial aggrega- tion within original units. We also extend the model by considering individual random effects at the aggregated scale, in order to take into account the overdis- persion. We illustrate our novel proposal using two datasets: female deaths by lung cancer in Indiana, USA, and male lip cancer incidence in Scotland counties. We also compare the performance of our proposal with the area-to-point Poisson kriging approach

A Checklist of the Vascular Flora of Allamakee County, Iowa

Based upon field and herbarium study, the vascular flora of Allamakee County is composed of 1040 taxa (species plus hybrids), including 46 endangered and 23 threatened Iowa species. This number represents approximately 50% of the species in the state flora and is the greatest number of taxa documented for a single Iowa county. The large and diverse flora reflects the diversity of topography and habitat types within the county. The study resulted in the addition of four taxa to the state flora (Conopholis americana, Dryopteris X triploidea, Equisetum X litorale, and Polygonum douglasii) and in the location of populations of two species previously considered extirpated within the state (Dryopteris intermedia and Ilex verticillata)

Penalized composite link mixed models for two-dimensional count data

Mortality data provide valuable information for the study of the spatial distribution of mortality risk, in disciplines such as spatial epidemiology, medical demography, and public health. However, they are often available in an aggregated form over irregular geographical units, hindering the visualization of the underlying mortality risk and the detection of meaningful patterns. Also, it could be of interest to obtain mortality risk estimates on a finer spatial resolution, such that they can be linked with potential risk factors — in a posterior correlation analysis — that are usually measured in a different spatial resolution than mortality data. In this paper, we propose the use of the penalized composite link model and its representation as a mixed model to deal with these issues. This model takes into account the nature of mortality rates by incorporating the population size at the finest resolution, and allows the creation of mortality maps at a desirable scale, reducing the visual bias resulting from the spatial aggregation within original units. We illustrate our proposal with the analysis of several datasets related with deaths by respiratory diseases, cardiovascular diseases, and lung cancer.MTM2011-28285-C02-02 MTM2014-52184-

Fast estimation of multidimensional adaptive P-spline models

A fast and stable algorithm for estimating multidimensional adaptive P-spline models is presented. We call it as Separation of Overlapping Penalties (SOP) as it is an extension of the Separation of Anisotropic Penalties (SAP) algorithm. SAP was originally derived for the estimation of the smoothing parameters of a multidimensional tensor product P-spline model with anisotropic penalties.MTM2014-55966-P MTM2014-52184-

On the estimation of variance parameters in non-standard generalised linear mixed models: Application to penalised smoothing

We present a novel method for the estimation of variance parameters in generalised linear mixed models. The method has its roots in Harville (1977)'s work, but it is able to deal with models that have a precision matrix for the random-effect vector that is linear in the inverse of the variance parameters (i.e., the precision parameters). We call the method SOP (Separation of Overlapping Precision matrices). SOP is based on applying the method of successive approximations to easy-to-compute estimate updates of the variance parameters. These estimate updates have an appealing form: they are the ratio of a (weighted) sum of squares to a quantity related to effective degrees of freedom. We provide the sufficient and necessary conditions for these estimates to be strictly positive. An important application field of SOP is penalised regression estimation of models where multiple quadratic penalties act on the same regression coefficients. We discuss in detail two of those models: penalised splines for locally adaptive smoothness and for hierarchical curve data. Several data examples in these settings are presented.MTM2014-55966-P MTM2014-52184-

Spatio-temporal adaptive penalized splines with application to Neuroscience

Data analysed here derive from experiments conducted to study neurons' activity in the visual cortex of behaving monkeys. We consider a spatio-temporal adaptive penalized spline (P-spline) approach for modelling the firing rate of visual neurons. To the best of our knowledge, this is the first attempt in the statistical literature for locally adaptive smoothing in three dimensions. Estimation is based on the Separation of Overlapping Penalties (SOP) algorithm, which provides the stability and speed we look for.MTM2014-55966-P MTM2014-52184-P RETICS, Oftared - RD12/0034/001

Checklist of the Vascular Flora of Lyon and Sioux Counties, Iowa

The combined vascular flora of Lyon and Sioux counties, Iowa, based upon field and herbarium study, is composed of 612 species, of which 454 species (74%) occur in both counties. The Lyon County vascular flora consists of 561 species, including 13 state endangered, 9 state threatened species, and 102 non-native species. The Sioux County vascular flora consists of 506 species, including 2 state threatened species and 106 non-native species. The floras are most notable for the presence of plants with floristic affinities to the Great Plains to the west of Iowa. They also have a very high percentage (18%-20%) of their floras comprised of non-native species, reflecting the intensity of human activities on the landscape