GAMLSS for high-dimensional data – a flexible approach based on boosting

Generalized additive models for location, scale and shape (GAMLSS) are a popular semi-parametric modelling approach that, in contrast to conventional GAMs, regress not only the expected mean but every distribution parameter (e.g. location, scale and shape) to a set of covariates. Current fitting procedures for GAMLSS are infeasible for high-dimensional data setups and require variable selection based on (potentially problematic) information criteria. The present work describes a boosting algorithm for high-dimensional GAMLSS that was developed to overcome these limitations. Specifically, the new algorithm was designed to allow the simultaneous estimation of predictor effects and variable selection. The proposed algorithm was applied to data of the Munich Rental Guide, which is used by landlords and tenants as a reference for the average rent of a flat depending on its characteristics and spatial features. The net-rent predictions that resulted from the high-dimensional GAMLSS were found to be highly competitive while covariate-specific prediction intervals showed a major improvement over classical GAMs

Boosted Beta regression.

Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1). Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures

FGF-Receptors and PD-L1 in Anaplastic and Poorly Differentiated Thyroid Cancer: Evaluation of the Preclinical Rationale

Background: Treatment options for poorly differentiated (PDTC) and anaplastic (ATC) thyroid carcinoma are unsatisfactory and prognosis is generally poor. Lenvatinib (LEN), a multi-tyrosine kinase inhibitor targeting fibroblast growth factor receptors (FGFR) 1-4 is approved for advanced radioiodine refractory thyroid carcinoma, but response to single agent is poor in ATC. Recent reports of combining LEN with PD-1 inhibitor pembrolizumab (PEM) are promising. Materials and Methods: Primary ATC (n=93) and PDTC (n=47) tissue samples diagnosed 1997-2019 at five German tertiary care centers were assessed for PD-L1 expression by immunohistochemistry using Tumor Proportion Score (TPS). FGFR 1-4 mRNA was quantified in 31 ATC and 14 PDTC with RNAscope in-situ hybridization. Normal thyroid tissue (NT) and papillary thyroid carcinoma (PTC) served as controls. Disease specific survival (DSS) was the primary outcome variable. Results: PD-L1 TPS≥50% was observed in 42% of ATC and 26% of PDTC specimens. Mean PD-L1 expression was significantly higher in ATC (TPS 30%) than in PDTC (5%; p<0.01) and NT (0%, p<0.001). 53% of PDTC samples had PD-L1 expression ≤5%. FGFR mRNA expression was generally low in all samples but combined FGFR1-4 expression was significantly higher in PDTC and ATC compared to NT (each p<0.001). No impact of PD-L1 and FGFR 1-4 expression was observed on DSS. Conclusion: High tumoral expression of PD-L1 in a large proportion of ATCs and a subgroup of PDTCs provides a rationale for immune checkpoint inhibition. FGFR expression is low thyroid tumor cells. The clinically observed synergism of PEM with LEN may be caused by immune modulation

FGF-21 levels in polyuria-polydipsia syndrome

The pathomechanism of primary polydipsia is poorly understood. Recent animal data reported a connection between fibroblast growth factor 21 (FGF-21) and elevated fluid intake independently of hormonal control by the hormone arginine-vasopressin (AVP) and osmotic stimulation. We therefore compared circulating FGF-21 levels in patients with primary polydipsia to patients with AVP deficiency (central diabetes insipidus) and healthy volunteers. In this prospective cohort study, we analyzed FGF-21 levels of 20 patients with primary polydipsia, 20 patients with central diabetes insipidus and 20 healthy volunteers before and after stimulation with hypertonic saline infusion targeting a plasma sodium level ≥150 mmol/L. The primary outcome was the difference in FGF-21 levels between the three groups. Baseline characteristics were similar between the groups except for patients with central diabetes insipidus being heavier. There was no difference in baseline FGF-21 levels between patients with primary polydipsia and healthy volunteers (122 pg/mL (52,277) vs 193 pg/mL (48,301), but higher levels in patients with central diabetes insipidus were observed (306 pg/mL (114,484); P = 0.037). However, this was not confirmed in a multivariate linear regression analysis after adjusting for age, sex, BMI and smoking status. Osmotic stimulation did not affect FGF-21 levels in either group (difference to baseline: primary polydipsia −23 pg/mL (−43, 22); central diabetes insipidus 17 pg/mL (−76, 88); healthy volunteers −6 pg/mL (−68, 22); P = 0.45). To conclude, FGF-21 levels are not increased in patients with primary polydipsia as compared to central diabetes insipidus or healthy volunteers. FGF-21 therefore does not seem to be causal of elevated fluid intake in these patients

FGF-21 levels in polyuria-polydipsia syndrome

The pathomechanism of primary polydipsia is poorly understood. Recent animal data reported a connection between fibroblast growth factor 21 (FGF-21) and elevated fluid intake independently of hormonal control by the hormone arginine-vasopressin (AVP) and osmotic stimulation. We therefore compared circulating FGF-21 levels in patients with primary polydipsia to patients with AVP deficiency (central diabetes insipidus) and healthy volunteers. In this prospective cohort study, we analyzed FGF-21 levels of 20 patients with primary polydipsia, 20 patients with central diabetes insipidus and 20 healthy volunteers before and after stimulation with hypertonic saline infusion targeting a plasma sodium level >= 150 mmol/L. The primary outcome was the difference in FGF-21 levels between the three groups. Baseline characteristics were similar between the groups except for patients with central diabetes insipidus being heavier. There was no difference in baseline FGF-21 levels between patients with primary polydipsia and healthy volunteers (122 pg/mL (52,277) vs 193 pg/mL (48,301), but higher levels in patients with central diabetes insipidus were observed (306 pg/mL (114,484);P=0.037). However, this was not confirmed in a multivariate linear regression analysis after adjusting for age, sex, BMI and smoking status. Osmotic stimulation did not affect FGF-21 levels in either group (difference to baseline: primary polydipsia -23 pg/mL (-43, 22);central diabetes insipidus 17 pg/mL (-76, 88);healthy volunteers -6 pg/mL (-68, 22);P=0.45). To conclude, FGF-21 levels are not increased in patients with primary polydipsia as compared to central diabetes insipidus or healthy volunteers. FGF-21 therefore does not seem to be causal of elevated fluid intake in these patients

Boosting the discriminatory power of sparse survival models via optimization of the concordance index and stability selection

Background When constructing new biomarker or gene signature scores for time-to-event outcomes, the underlying aims are to develop a discrimination model that helps to predict whether patients have a poor or good prognosis and to identify the most influential variables for this task. In practice, this is often done fitting Cox models. Those are, however, not necessarily optimal with respect to the resulting discriminatory power and are based on restrictive assumptions. We present a combined approach to automatically select and fit sparse discrimination models for potentially high-dimensional survival data based on boosting a smooth version of the concordance index (C-index). Due to this objective function, the resulting prediction models are optimal with respect to their ability to discriminate between patients with longer and shorter survival times. The gradient boosting algorithm is combined with the stability selection approach to enhance and control its variable selection properties. Results The resulting algorithm fits prediction models based on the rankings of the survival times and automatically selects only the most stable predictors. The performance of the approach, which works best for small numbers of informative predictors, is demonstrated in a large scale simulation study: C-index boosting in combination with stability selection is able to identify a small subset of informative predictors from a much larger set of non-informative ones while controlling the per-family error rate. In an application to discover biomarkers for breast cancer patients based on gene expression data, stability selection yielded sparser models and the resulting discriminatory power was higher than with lasso penalized Cox regression models. Conclusion The combination of stability selection and C-index boosting can be used to select small numbers of informative biomarkers and to derive new prediction rules that are optimal with respect to their discriminatory power. Stability selection controls the per-family error rate which makes the new approach also appealing from an inferential point of view, as it provides an alternative to classical hypothesis tests for single predictor effects. Due to the shrinkage and variable selection properties of statistical boosting algorithms, the latter tests are typically unfeasible for prediction models fitted by boosting

Cytochrome P450 2B6 (CYP2B6) and constitutive androstane receptor (CAR) polymorphisms are associated with early discontinuation of efavirenz-containing regimens

Objectives Cytochrome P450 2B6 (CYP2B6) is responsible for the metabolic clearance of efavirenz and single nucleotide polymorphisms (SNPs) in the CYP2B6 gene are associated with efavirenz pharmacokinetics. Since the constitutive androstane receptor (CAR) and the pregnane X receptor (PXR) correlate with CYP2B6 in liver, and a CAR polymorphism (rs2307424) and smoking correlate with efavirenz plasma concentrations, we investigated their association with early (<3 months) discontinuation of efavirenz therapy. Methods Three hundred and seventy-three patients initiating therapy with an efavirenz-based regimen were included (278 white patients and 95 black patients; 293 male). DNA was extracted from whole blood and genotyping for CYP2B6 (516G → T, rs3745274), CAR (540C → T, rs2307424) and PXR (44477T → C, rs1523130; 63396C → T, rs2472677; and 69789A → G, rs763645) was conducted. Binary logistic regression using the backwards method was employed to assess the influence of SNPs and demographics on early discontinuation. Results Of the 373 patients, 131 withdrew from therapy within the first 3 months. Black ethnicity [odds ratio (OR) = 0.27; P = 0.0001], CYP2B6 516TT (OR = 2.81; P = 0.006), CAR rs2307424 CC (OR = 1.92; P = 0.007) and smoking status (OR = 0.45; P = 0.002) were associated with discontinuation within 3 months. Conclusions These data indicate that genetic variability in CYP2B6 and CAR contributes to early treatment discontinuation for efavirenz-based antiretroviral regimens. Further studies are now required to define the clinical utility of these association