A Bayesian multi-risks survival (MRS) model in the presence of double censorings

Semi-competing risks data include the time to a nonterminating event and the time to a terminating event, while competing risks data include the time to more than one terminating event. Our work is motivated by a prostate cancer study, which has one nonterminating event and two terminating events with both semi-competing risks and competing risks present as well as two censoring times. In this paper, we propose a new multi-risks survival (MRS) model for this type of data. In addition, the proposed MRS model can accommodate noninformative right-censoring times for nonterminating and terminating events. Properties of the proposed MRS model are examined in detail. Theoretical and empirical results show that the estimates of the cumulative incidence function for a nonterminating event may be biased if the information on a terminating event is ignored. A Markov chain Monte Carlo sampling algorithm is also developed. Our methodology is further assessed using simulations and also an analysis of the real data from a prostate cancer study. As a result, a prostate-specific antigen velocity greater than 2.0 ng/mL per year and higher biopsy Gleason scores are positively associated with a shorter time to death due to prostate cancer.     

Combining parametric and nonparametric models to estimate treatment effects in observational studies

Performing causal inference in observational studies requires we assume confounding variables are correctly adjusted for. In settings with few discrete-valued confounders, standard models can be employed. However, as the number of confounders increases these models become less feasible as there are fewer observations available for each unique combination of confounding variables. In this paper, we propose a new model for estimating treatment effects in observational studies that incorporates both parametric and nonparametric outcome models. By conceptually splitting the data, we can combine these models while maintaining a conjugate framework, allowing us to avoid the use of Markov chain Monte Carlo (MCMC) methods. Approximations using the central limit theorem and random sampling allow our method to be scaled to high-dimensional confounders. Through simulation studies we show our method can be competitive with benchmark models while maintaining efficient computation, and illustrate the method on a large epidemiological health survey.     

Physiotherapy students’ perceptions of ageing – Playing narratives as a pedagogical innovation

Introduction and objectivesThere is a considerable lack of structure in training health professionals in geriatrics. The narratives can promote a collaborative reflection on different topics and might be used as a pedagogic strategy for undergraduate health students. This study aimed to explore the adoption of new perspectives on ageing after the implementation of dynamic narratives in the first graduate year for physiotherapy students.Materials and methodsAn exploratory qualitative study was performed. Participants were included if they were: ≥18 years old, physiotherapy students and agreed to participate. Physiotherapy students (n = 44) were recruited from the School of Health Sciences, Polytechnic Institute of Leiria. Two gaming sessions were performed to help students, as narrators, express their visions and ways to deal with the geriatrics field. Students’ perspectives about ageing at baseline (T1) and after the narratives experience (T2) were collected by answering the following question: What is your perception regarding ageing? Two evaluators were involved in qualitative data analysis by performing: (i) an individual analysis of themes/subthemes and a discussion meeting to evaluate discrepancies and to reach a consensus.ResultsNegative perceptions about ageing were referred 39 times at T1 (most of them related to restriction; deterioration subthemes). There were no negative perceptions registered at T2. Positive perceptions increased at T2, from n = 39 to n = 52, and three new subthemes emerged (beginning of something, fighting ageism, challenge).ConclusionThis study demonstrated the potential of narrative-based experiences as a desirable pedagogic methodology (board games-oriented) for geriatric education in undergraduate health students.     

A Primer to Molecular Phylogenetic Analysis in Plants

Reconstructing a tree of life by inferring evolutionary history is an important focus of evolutionary biology. Phylogenetic reconstructions also provide useful information for a range of scientific disciplines such as botany, zoology, phylogeography, archaeology and biological anthropology. Until the development of protein and DNA sequencing techniques in the 1960s and 1970s, phylogenetic reconstructions were based on fossil records and comparative morphological/physiological analyses. Since then, progress in molecular phylogenetics has compensated for some of the shortcomings of phenotype-based comparisons. Comparisons at the molecular level increase the accuracy of phylogenetic inference because there is no environmental influence on DNA/peptide sequences and evaluation of sequence similarity is not subjective. While the number of morphological/physiological characters that are sufficiently conserved for phylogenetic inference is limited, molecular data provide a large number of datapoints and enable comparisons from diverse taxa. Over the last 20 years, developments in molecular phylogenetics have greatly contributed to our understanding of plant evolutionary relationships. Regions in the plant nuclear and organellar genomes that are optimal for phylogenetic inference have been determined and recent advances in DNA sequencing techniques have enabled comparisons at the whole genome level. Sequences from the nuclear and organellar genomes of thousands of plant species are readily available in public databases, enabling researchers without access to molecular biology tools to investigate phylogenetic relationships by sequence comparisons using the appropriate nucleotide substitution models and tree building algorithms. In the present review, the statistical models and algorithms used to reconstruct phylogenetic trees are introduced and advances in the exploration and utilization of plant genomes for molecular phylogenetic analyses are discussed.     

Phylogenetic eigenvectors and nonstationarity in the evolution of theropod dinosaur skulls

Despite the long‐standing interest in nonstationarity of both phenotypic evolution and diversification rates, only recently have methods been developed to study this property. Here, we propose a methodological expansion of the phylogenetic signal‐representation (PSR) curve based on phylogenetic eigenvectors to test for nonstationarity. The PSR curve is built by plotting the coefficients of determination R² from phylogenetic eigenvector regression (PVR) models increasing the number of phylogenetic eigenvectors against the accumulated eigenvalues. The PSR curve is linear under a stationary model of trait evolution (i.e. the Brownian motion model). Here we describe the distribution of shifts in the models R² and used a randomization procedure to compare observed and simulated shifts along the PSR curve, which allowed detecting nonstationarity in trait evolution. As an applied example, we show that the main evolutionary pattern of variation in the theropod dinosaur skull was nonstationary, with a significant shift in evolutionary rates in derived oviraptorosaurs, an aberrant group of mostly toothless, crested, birdlike theropods. This result is also supported by a recently proposed Bayesian‐based method (AUTEUR). A significant deviation between Ceratosaurus and Limusaurus terminal branches was also detected. We purport that our new approach is a valuable tool for evolutionary biologists, owing to its simplicity, flexibility and comprehensiveness.     

Kupffer Cell Isolation for Nanoparticle Toxicity Testing

The large majority of in vitro nanotoxicological studies have used immortalized cell lines for their practicality. However, results from nanoparticle toxicity testing in immortalized cell lines or primary cells have shown discrepancies, highlighting the need to extend the use of primary cells for in vitro assays. This protocol describes the isolation of mouse liver macrophages, named Kupffer cells, and their use to study nanoparticle toxicity. Kupffer cells are the most abundant macrophage population in the body and constitute part of the reticulo-endothelial system (RES), responsible for the capture of circulating nanoparticles. The Kupffer cell isolation method reported here is based on a 2-step perfusion method followed by purification on density gradient. The method, based on collagenase digestion and density centrifugation, is adapted from the original protocol developed by Smedsrød et al. designed for rat liver cell isolation and provides high yield (up to 14 x 10⁶ cells per mouse) and high purity (>95%) of Kupffer cells. This isolation method does not require sophisticated or expensive equipment and therefore represents an ideal compromise between complexity and cell yield. The use of heavier mice (35-45 g) improves the yield of the isolation method but also facilitates remarkably the procedure of portal vein cannulation. The toxicity of functionalized carbon nanotubes f-CNTs was measured in this model by the modified LDH assay. This method assesses cell viability by measuring the lack of structural integrity of Kupffer cell membrane after incubation with f-CNTs. Toxicity induced by f-CNTs can be measured consistently using this assay, highlighting that isolated Kupffer cells are useful for nanoparticle toxicity testing. The overall understanding of nanotoxicology could benefit from such models, making the nanoparticle selection for clinical translation more efficient.     

Role of β-adrenergic receptors in the anti-obesity and anti-diabetic effects of zinc-α2-glycoprotien (ZAG)

Objectives: The goal of the current study is to determine whether the β-adrenoreceptor (β-AR) plays a role in the anti-obesity and anti-diabetic effects of zinc-α2-glycoprotein (ZAG). Material and methods: This has been investigated in CHO-K1 cells transfected with the human β1-, β2-, β3-AR and in ob/ob mice. Cyclic AMP assays were carried out along with binding studies. Ob/ob mice were treated with ZAG and glucose transportation and insulin were examined in the presence or absence of propranolol. Results: ZAG bound to the β3-AR with higher affinity (Kd 46 ± 1 nM) than the β2-AR (Kd 71 ± 3 nM) while there was no binding to the β1-AR, and this correlated with the increases in cyclic AMP in CHO-K1 cells transfected with the various β-AR and treated with ZAG. Treatment of ob/ob mice with ZAG increased protein expression of β3-AR in gastrocnemius muscle, and in white and brown adipose tissues, but had no effect on expression of β1- and β2-AR. A reduction of body weight was seen and urinary glucose excretion, increase in body temperature, reduction in maximal plasma glucose and insulin levels in the oral glucose tolerance test, and stimulation of glucose transport into skeletal muscle and adipose tissue, were completely attenuated by the non-specific β-AR antagonist propranolol. Conclusion: The results suggest that the effects of ZAG on body weight and insulin sensitivity in ob/ob mice are manifested through a β-3AR, or possibly a β2-AR.     

A comparison of machine learning algorithms and covariate balance measures for propensity score matching and weighting

Propensity score matching (PSM) and propensity score weighting (PSW) are popular tools to estimate causal effects in observational studies. We address two open issues: how to estimate propensity scores and assess covariate balance. Using simulations, we compare the performance of PSM and PSW based on logistic regression and machine learning algorithms (CART; Bagging; Boosting; Random Forest; Neural Networks; naive Bayes). Additionally, we consider several measures of covariate balance (Absolute Standardized Average Mean (ASAM) with and without interactions; measures based on the quantile‐quantile plots; ratio between variances of propensity scores; area under the curve (AUC)) and assess their ability in predicting the bias of PSM and PSW estimators. We also investigate the importance of tuning of machine learning parameters in the context of propensity score methods. Two simulation designs are employed. In the first, the generating processes are inspired to birth register data used to assess the effect of labor induction on the occurrence of caesarean section. The second exploits more general generating mechanisms. Overall, among the different techniques, random forests performed the best, especially in PSW. Logistic regression and neural networks also showed an excellent performance similar to that of random forests. As for covariate balance, the simplest and commonly used metric, the ASAM, showed a strong correlation with the bias of causal effects estimators. Our findings suggest that researchers should aim at obtaining an ASAM lower than 10% for as many variables as possible. In the empirical study we found that labor induction had a small and not statistically significant impact on caesarean section.     

Semi‐parametric analysis of overdispersed count and metric data with varying follow‐up times: Asymptotic theory and small sample approximations

Count data are common endpoints in clinical trials, for example magnetic resonance imaging lesion counts in multiple sclerosis. They often exhibit high levels of overdispersion, that is variances are larger than the means. Inference is regularly based on negative binomial regression along with maximum‐likelihood estimators. Although this approach can account for heterogeneity it postulates a common overdispersion parameter across groups. Such parametric assumptions are usually difficult to verify, especially in small trials. Therefore, novel procedures that are based on asymptotic results for newly developed rate and variance estimators are proposed in a general framework. Moreover, in case of small samples the procedures are carried out using permutation techniques. Here, the usual assumption of exchangeability under the null hypothesis is not met due to varying follow‐up times and unequal overdispersion parameters. This problem is solved by the use of studentized permutations leading to valid inference methods for situations with (i) varying follow‐up times, (ii) different overdispersion parameters, and (iii) small sample sizes.