Spike-and-Slab Priors for Function Selection in Structured Additive Regression Models

Structured additive regression provides a general framework for complex Gaussian and non-Gaussian regression models, with predictors comprising arbitrary combinations of nonlinear functions and surfaces, spatial effects, varying coefficients, random effects and further regression terms. The large flexibility of structured additive regression makes function selection a challenging and important task, aiming at (1) selecting the relevant covariates, (2) choosing an appropriate and parsimonious representation of the impact of covariates on the predictor and (3) determining the required interactions. We propose a spike-and-slab prior structure for function selection that allows to include or exclude single coefficients as well as blocks of coefficients representing specific model terms. A novel multiplicative parameter expansion is required to obtain good mixing and convergence properties in a Markov chain Monte Carlo simulation approach and is shown to induce desirable shrinkage properties. In simulation studies and with (real) benchmark classification data, we investigate sensitivity to hyperparameter settings and compare performance to competitors. The flexibility and applicability of our approach are demonstrated in an additive piecewise exponential model with time-varying effects for right-censored survival times of intensive care patients with sepsis. Geoadditive and additive mixed logit model applications are discussed in an extensive appendix

Modeling Exposure-Lag-Response Associations with Penalized Piece-wise Exponential Models

We present a novel approach for the flexible modeling of exposure-lag-response associations, i.e., time-to-event data where multiple past exposures are cumulatively associated with the hazard after a certain temporal delay. Our method is based on piece-wise exponential models and allows estimation of a wide variety of effects, including potentially smooth and time-varying effects as well as cumulative effects with leads and lags, taking advantage of the advanced inference methods that have recently been developed for generalized additive mixed models

A penalized framework for distributed lag non-linear models.

: Distributed lag non-linear models (DLNMs) are a modelling tool for describing potentially non-linear and delayed dependencies. Here, we illustrate an extension of the DLNM framework through the use of penalized splines within generalized additive models (GAM). This extension offers built-in model selection procedures and the possibility of accommodating assumptions on the shape of the lag structure through specific penalties. In addition, this framework includes, as special cases, simpler models previously proposed for linear relationships (DLMs). Alternative versions of penalized DLNMs are compared with each other and with the standard unpenalized version in a simulation study. Results show that this penalized extension to the DLNM class provides greater flexibility and improved inferential properties. The framework exploits recent theoretical developments of GAMs and is implemented using efficient routines within freely available software. Real-data applications are illustrated through two reproducible examples in time series and survival analysis.<br/

Modeling Exposure-Lag-Response Associations with Penalized Piece-wise Exponential Models

We present a novel approach for the flexible modeling of exposure-lag-response associations, i.e., time-to-event data where multiple past exposures are cumulatively associated with the hazard after a certain temporal delay. Our method is based on piece-wise exponential models and allows estimation of a wide variety of effects, including potentially smooth and time-varying effects as well as cumulative effects with leads and lags, taking advantage of the advanced inference methods that have recently been developed for generalized additive mixed models

Functional generalized additive models

We introduce the functional generalized additive model (FGAM), a novel regression model for association studies between a scalar response and a functional predictor. We model the link-transformed mean response as the integral with respect to t of F{X(t), t} where F(·, ·) is an unknown regression function and X(t) is a functional covariate. Rather than having an additive model in a finite number of principal components as by Müller and Yao (2008), our model incorporates the functional predictor directly and thus our model can be viewed as the natural functional extension of generalized additive models. We estimate F(·, ·) using tensor-product B-splines with roughness penalties. A pointwise quantile transformation of the functional predictor is also considered to ensure each tensor-product B-spline has observed data on its support. The methods are evaluated using simulated data and their predictive performance is compared with other competing scalar-on-function regression alternatives. We illustrate the usefulness of our approach through an application to brain tractography, where X(t) is a signal from diffusion tensor imaging at position, t, along a tract in the brain. In one example, the response is disease-status (case or control) and in a second example, it is the score on a cognitive test. The FGAM is implemented in R in the refund package. There are additional supplementary materials available online. © 2013 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America

Bayesian Functional Generalized Additive Models with Sparsely Observed Covariates

The functional generalized additive model (FGAM) was recently proposed in McLean et al. (2013) as a more flexible alternative to the common functional linear model (FLM) for regressing a scalar on functional covariates. In this paper, we develop a Bayesian version of FGAM for the case of Gaussian errors with identity link function. Our approach allows the functional covariates to be sparsely observed and measured with error, whereas the estimation procedure of McLean et al. (2013) required that they be noiselessly observed on a regular grid. We consider both Monte Carlo and variational Bayes methods for fitting the FGAM with sparsely observed covariates. Due to the complicated form of the model posterior distribution and full conditional distributions, standard Monte Carlo and variational Bayes algorithms cannot be used. The strategies we use to handle the updating of parameters without closed-form full conditionals should be of independent interest to applied Bayesian statisticians working with nonconjugate models. Our numerical studies demonstrate the benefits of our algorithms over a two-step approach of first recovering the complete trajectories using standard techniques and then fitting a functional regression model. In a real data analysis, our methods are applied to forecasting closing price for items up for auction on the online auction website eBay

Modeling Exposure-Lag-Response Associations with Penalized Piece-wise Exponential Models

We present a novel approach for the flexible modeling of exposure-lag-response associations, i.e., time-to-event data where multiple past exposures are cumulatively associated with the hazard after a certain temporal delay. Our method is based on piece-wise exponential models and allows estimation of a wide variety of effects, including potentially smooth and time-varying effects as well as cumulative effects with leads and lags, taking advantage of the advanced inference methods that have recently been developed for generalized additive mixed models

EGFR inhibitors identified as a potential treatment for chordoma in a focused compound screen.

Chordoma is a rare malignant bone tumour with a poor prognosis and limited therapeutic options. We undertook a focused compound screen (FCS) against 1097 compounds on three well-characterized chordoma cell lines; 154 compounds were selected from the single concentration screen (1 µm), based on their growth-inhibitory effect. Their half-maximal effective concentration (EC50 ) values were determined in chordoma cells and normal fibroblasts. Twenty-seven of these compounds displayed chordoma selective cell kill and 21/27 (78%) were found to be EGFR/ERBB family inhibitors. EGFR inhibitors in clinical development were then studied on an extended cell line panel of seven chordoma cell lines, four of which were sensitive to EGFR inhibition. Sapitinib (AstraZeneca) emerged as the lead compound, followed by gefitinib (AstraZeneca) and erlotinib (Roche/Genentech). The compounds were shown to induce apoptosis in the sensitive cell lines and suppressed phospho-EGFR and its downstream pathways in a dose-dependent manner. Analysis of substituent patterns suggested that EGFR-inhibitors with small aniline substituents in the 4-position of the quinazoline ring were more effective than inhibitors with large substituents in that position. Sapitinib showed significantly reduced tumour growth in two xenograft mouse models (U-CH1 xenograft and a patient-derived xenograft, SF8894). One of the resistant cell lines (U-CH2) was shown to express high levels of phospho-MET, a known bypass signalling pathway to EGFR. Neither amplifications (EGFR, ERBB2, MET) nor mutations in EGFR, ERBB2, ERBB4, PIK3CA, BRAF, NRAS, KRAS, PTEN, MET or other cancer gene hotspots were detected in the cell lines. Our findings are consistent with the reported (p-)EGFR expression in the majority of clinical samples, and provide evidence for exploring the efficacy of EGFR inhibitors in the treatment of patients with chordoma and studying possible resistance mechanisms to these compounds in vitro and in vivo. © 2016 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland

Costs of Reproduction and Terminal Investment by Females in a Semelparous Marsupial

Evolutionary explanations for life history diversity are based on the idea of costs of reproduction, particularly on the concept of a trade-off between age-specific reproduction and parental survival, and between expenditure on current and future offspring. Such trade-offs are often difficult to detect in population studies of wild mammals. Terminal investment theory predicts that reproductive effort by older parents should increase, because individual offspring become more valuable to parents as the conflict between current versus potential future offspring declines with age. In order to demonstrate this phenomenon in females, there must be an increase in maternal expenditure on offspring with age, imposing a fitness cost on the mother. Clear evidence of both the expenditure and fitness cost components has rarely been found. In this study, we quantify costs of reproduction throughout the lifespan of female antechinuses. Antechinuses are nocturnal, insectivorous, forest-dwelling small (20–40 g) marsupials, which nest in tree hollows. They have a single synchronized mating season of around three weeks, which occurs on predictable dates each year in a population. Females produce only one litter per year. Unlike almost all other mammals, all males, and in the smaller species, most females are semelparous. We show that increased allocation to current reproduction reduces maternal survival, and that offspring growth and survival in the first breeding season is traded-off with performance of the second litter in iteroparous females. In iteroparous females, increased allocation to second litters is associated with severe weight loss in late lactation and post-lactation death of mothers, but increased offspring growth in late lactation and survival to weaning. These findings are consistent with terminal investment. Iteroparity did not increase lifetime reproductive success, indicating that terminal investment in the first breeding season at the expense of maternal survival (i.e. semelparity) is likely to be advantageous for females

The sacral chordoma margin

[Objective]: Aim of the manuscript is to discuss how to improve margins in sacral chordoma. [Background]: Chordoma is a rare neoplasm, arising in half cases from the sacrum, with reported local failure in >50% after surgery. [Methods]: A multidisciplinary meeting of the “Chordoma Global Consensus Group” was held in Milan in 2017, focusing on challenges in defining and achieving optimal margins in chordoma with respect to surgery, definitive particle radiation therapy (RT) and medical therapies. This review aims to report on the outcome of the consensus meeting and to provide a summary of the most recent evidence in this field. Possible new ways forward, including on-going international clinical studies, are discussed. [Results]: En-bloc tumor-sacrum resection is the cornerstone of treatment of primary sacral chordoma, aiming to achieve negative microscopic margins. Radical definitive particle therapy seems to offer a similar outcome compared to surgery, although confirmation in comparative trials is lacking; besides there is still a certain degree of technical variability across institutions, corresponding to different fields of treatment and different tumor coverage. To address some of these questions, a prospective, randomized international study comparing surgery versus definitive high-dose RT is ongoing. Available data do not support the routine use of any medical therapy as (neo)adjuvant/cytoreductive treatment. [Conclusion]: Given the significant influence of margins status on local control in patients with primary localized sacral chordoma, the clear definition of adequate margins and a standard local approach across institutions for both surgery and particle RT is vital for improving the management of these patients