lcc: an R package to estimate the concordance correlation, Pearson correlation and accuracy over time.

BACKGROUND AND OBJECTIVE: Observational studies and experiments in medicine, pharmacology and agronomy are often concerned with assessing whether different methods/raters produce similar values over the time when measuring a quantitative variable. This article aims to describe the statistical package lcc, for are, that can be used to estimate the extent of agreement between two (or more) methods over the time, and illustrate the developed methodology using three real examples. METHODS: The longitudinal concordance correlation, longitudinal Pearson correlation, and longitudinal accuracy functions can be estimated based on fixed effects and variance components of the mixed-effects regression model. Inference is made through bootstrap confidence intervals and diagnostic can be done via plots, and statistical tests. RESULTS: The main features of the package are estimation and inference about the extent of agreement using numerical and graphical summaries. Moreover, our approach accommodates both balanced and unbalanced experimental designs or observational studies, and allows for different within-group error structures, while allowing for the inclusion of covariates in the linear predictor to control systematic variations in the response. All examples show that our methodology is flexible and can be applied to many different data types. CONCLUSIONS: The lcc package, available on the CRAN repository, proved to be a useful tool to describe the agreement between two or more methods over time, allowing the detection of changes in the extent of agreement. The inclusion of different structures for the variance-covariance matrices of random effects and residuals makes the package flexible for working with different types of databases.

A generalized transition model for grouped longitudinal categorical data.

Transition models are an important framework that can be used to model longitudinal categorical data. They are particularly useful when the primary interest is in prediction. The available methods for this class of models are suitable for the cases in which responses are recorded individually over time. However, in many areas, it is common for categorical data to be recorded as groups, that is, different categories with a number of individuals in each. As motivation we consider a study in insect movement and another in pig behaviou. The first study was developed to understand the movement patterns of female adults of Diaphorina citri, a pest of citrus plantations. The second study investigated how hogs behaved under the influence of environmental enrichment. In both studies, the number of individuals in different response categories was observed over time. We propose a new framework for considering the time dependence in the linear predictor of a generalized logit transition model using a quantitative response, corresponding to the number of individuals in each category. We use maximum likelihood estimation and present the results of the fitted models under stationarity and non-stationarity assumptions, and use recently proposed tests to assess non-stationarity. We evaluated the performance of the proposed model using simulation studies under different scenarios, and concluded that our modeling framework represents a flexible alternative to analyze grouped longitudinal categorical data.

Joint incorporation of randomised and observational evidence in estimating treatment effects.

In evidence-based medicine, randomised trials are regarded as a gold standard in estimating relative treatment effects. Nevertheless, a potential gain in precision is forfeited by ignoring observational evidence. We describe a simple estimator that combines treatment estimates from randomised and observational data and investigate its properties by simulation. We show that a substantial gain in estimation accuracy, compared with the estimator based solely on the randomised trial, is possible when the observational evidence has low bias and standard error. In the contrasting scenario where the observational evidence is inaccurate, the estimator automatically discounts its contribution to the estimated treatment effect. Meta-analysis extensions, combining estimators from multiple observational studies and randomised trials, are also explored.

Estimating average attributable fractions with confidence intervals for cohort and case-control studies.

Chronic diseases tend to depend on a large number of risk factors, both environmental and genetic. Average attributable fractions were introduced by Eide and Gefeller as a way of partitioning overall disease burden into contributions from individual risk factors; this may be useful in deciding which risk factors to target in disease interventions. Here, we introduce new estimation methods for average attributable fractions that are appropriate for both case-control designs and prospective studies. Confidence intervals, derived using Monte Carlo simulation, are also described. Finally, we introduce a novel approximation for the sample average attributable fraction that will ensure a computationally tractable approach when the number of risk factors is large. An R package, [Formula: see text], implementing the methods described in this manuscript can be downloaded from the CRAN repository.

Summarising censored survival data using the mean residual life function.

The mean residual life function provides a clear and simple summary of the effect of a treatment or a risk factor in units of time, avoiding hazard ratios or probability scales, which require careful interpretation. Estimation of the mean residual life is complicated by the upper tail of the survival distribution not being observed as, for example, patients may still be alive at the end of the follow-up period. Various approaches have been developed to estimate the mean residual life in the presence of such right censoring. In this work, a novel semi-parametric method that combines existing non-parametric methods and an extreme value tail model is presented, where the limited sample information in the tail (prior to study termination) is used to estimate the upper tail behaviour. This approach will be demonstrated with simulated and real-life examples.

BioconductorBuntu: a Linux distribution that implements a web-based DNA microarray analysis server.

SUMMARY: BioconductorBuntu is a custom distribution of Ubuntu Linux that automatically installs a server-side microarray processing environment, providing a user-friendly web-based GUI to many of the tools developed by the Bioconductor Project, accessible locally or across a network. System installation is via booting off a CD image or by using a Debian package provided to upgrade an existing Ubuntu installation. In its current version, several microarray analysis pipelines are supported including oligonucleotide, dual-or single-dye experiments, including post-processing with Gene Set Enrichment Analysis. BioconductorBuntu is designed to be extensible, by server-side integration of further relevant Bioconductor modules as required, facilitated by its straightforward underlying Python-based infrastructure. BioconductorBuntu offers an ideal environment for the development of processing procedures to facilitate the analysis of next-generation sequencing datasets. AVAILABILITY: BioconductorBuntu is available for download under a creative commons license along with additional documentation and a tutorial from (http://bioinf.nuigalway.ie).

Multimorbidity and risk among patients with established cardiovascular disease: a cohort study.

BACKGROUND: Most patients managed in primary care have more than one condition. Multimorbidity presents challenges for the patient and the clinician, not only in terms of the process of care, but also in terms of management and risk assessment. AIM: To examine the effect of the presence of chronic kidney disease and diabetes on mortality and morbidity among patients with established cardiovascular disease. DESIGN OF STUDY: Retrospective cohort study. SETTING: Random selection of 35 general practices in the west of Ireland. METHOD: A practice-based sample of 1609 patients with established cardiovascular disease was generated in 2000-2001 and followed for 5 years. The primary endpoint was death from any cause and the secondary endpoint was a cardiovascular composite endpoint that included death from a cardiovascular cause or any of the following cardiovascular events: myocardial infarction, heart failure, peripheral vascular disease, or stroke. RESULTS: Risk of death from any cause was significantly increased in patients with increased multimorbidity (P<0.001), as was the risk of the cardiovascular composite endpoint (P<0.001). Patients with cardiovascular disease and diabetes had a similar survival pattern to those with cardiovascular disease and chronic kidney disease, but experienced more cardiovascular events. CONCLUSION: Level of multimorbidity is an independent predictor of prognosis among patients with established cardiovascular disease. In such patients, the presence of chronic kidney disease carries a similar mortality risk to diabetes. Multimorbidity may be a useful factor in prioritising management of patients in the community with significant cardiovascular risk.

Enthalpies of formation and bond dissociation energies of lower alkyl hydroperoxides and related hydroperoxy and alkoxy radicals.

The enthalpies of formation and bond dissociation energies, D(ROO-H), D(RO-OH), D(RO-O), D(R-O 2) and D(R-OOH) of alkyl hydroperoxides, ROOH, alkyl peroxy, RO, and alkoxide radicals, RO, have been computed at CBS-QB3 and APNO levels of theory via isodesmic and atomization procedures for R = methyl, ethyl, n-propyl and isopropyl and n-butyl, tert-butyl, isobutyl and sec-butyl. We show that D(ROO-H) approximately 357, D(RO-OH) approximately 190 and D(RO-O) approximately 263 kJ mol (-1) for all R, whereas both D(R-OO) and D(R-OOH) strengthen with increasing methyl substitution at the alpha-carbon but remain constant with increasing carbon chain length. We recommend a new set of group additivity contributions for the estimation of enthalpies of formation and bond energies.

Chronic kidney disease and mortality and morbidity among patients with established cardiovascular disease: a West of Ireland community-based cohort study.

BACKGROUND: The importance of chronic kidney disease as an independent risk factor for morbidity and mortality in patients with cardiovascular disease in the community is not widely recognized. METHODS: A retrospective cohort study based in the West of Ireland followed a randomized practice-based sample of patients with cardiovascular disease. A database of 1609 patients with established cardiovascular disease was established in 2000. This was generated from a randomized sample of 35 general practices in the West of Ireland. The primary endpoint was a cardiovascular composite endpoint, which included death from a cardiovascular cause or any of the cardiovascular events of myocardial infarction (MI), heart failure, peripheral vascular disease and stroke. The secondary endpoint was death from any cause. RESULTS: Of the original community-based cohort of 1609 patients with cardiovascular disease, 1272 (79%) had one or more serum creatinine measurements during the study period and 31 (1.9%) patients were lost to follow-up. Median follow-up was 2.90 years (SD 1.47) and the risk of the cardiovascular composite endpoint (total of 219 events) was significantly increased in those patients with reduced estimated glomerular filtration rate (GFR) [log rank (Mantel-Cox) 26.74, P<0.001] as was the risk of death from any cause (total of 214 deaths) [Log Rank (Mantel-Cox) 56.97, P<0.001]. On the basis of the proportional hazards model, while adjusting for other significant covariates, reduced estimated GFR was associated with a significant increase in risk of the primary and secondary outcomes (P<0.01). For every 10 ml decrement in estimated GFR there was a corresponding 20% increase in hazard of the cardiovascular composite endpoint and a 33% increase in hazard of death from any cause. CONCLUSIONS: Estimated GFR appears to discriminate prognosis between patients with established cardiovascular disease. These results emphasise the importance of recognising chronic kidney disease as a significant risk factor in patients with cardiovascular disease in the community.

Modeling diarrhea disease in children less than 5 years old.

PURPOSE: Identification of the temporal pattern of diarrhea disease in children less than 5 years of age in Rio de Janeiro City (1995-1998) to provide support for decisions about prevention and control of the disease. METHODS: The weekly counts of hospitalizations and deaths due to diarrhea disease were analyzed separately. An initial generalized linear model (GLM) was derived using variables related to weather and month. Displays of fitted generalized additive models (GAM) including a spline smoothed function of time suggested additional predictors that were used to obtain new models. RESULTS: The initial models did not properly account for the observed cyclical pattern of the data. Graphical displays of the GAM model show a nonhomogeneous decline and annual cycles. Stepwise fitting of GLMs with two factors (cycle and season), and a time trend, showed that the full three-way interaction model was required. Plots of the residuals from the death model suggested a mixture of distributions while the residuals from the hospitalization model were approximately normal. CONCLUSIONS: The same general pattern for both time series was found by graphical inspection and fitting of appropriate GLMs. This study provides some additional evidence that severe cases of diarrhea disease may be attributed to rotavirus.