Model-based clustering for populations of networks

Until recently obtaining data on populations of networks was typically rare. However, with the advancement of automatic monitoring devices and the growing social and scientific interest in networks, such data has become more widely available. From sociological experiments involving cognitive social structures to fMRI scans revealing large-scale brain networks of groups of patients, there is a growing awareness that we urgently need tools to analyse populations of networks and particularly to model the variation between networks due to covariates. We propose a model-based clustering method based on mixtures of generalized linear (mixed) models that can be employed to describe the joint distribution of a populations of networks in a parsimonious manner and to identify subpopulations of networks that share certain topological properties of interest (degree distribution, community structure, effect of covariates on the presence of an edge, etc.). Maximum likelihood estimation for the proposed model can be efficiently carried out with an implementation of the EM algorithm. We assess the performance of this method on simulated data and conclude with an example application on advice networks in a small business.Comment: The final (published) version of the article can be downloaded for free (Open Access) from the editor's website (click on the DOI link below

SUrvival Control Chart EStimation Software in R: the success package

Monitoring the quality of statistical processes has been of great importance, mostly in industrial applications. Control charts are widely used for this purpose, but often lack the possibility to monitor survival outcomes. Recently, inspecting survival outcomes has become of interest, especially in medical settings where outcomes often depend on risk factors of patients. For this reason many new survival control charts have been devised and existing ones have been extended to incorporate survival outcomes. The R package success allows users to construct risk-adjusted control charts for survival data. Functions to determine control chart parameters are included, which can be used even without expert knowledge on the subject of control charts. The package allows to create static as well as interactive charts, which are built using ggplot2 (Wickham 2016) and plotly (Sievert 2020).Comment: 29 pages, 10 figures, guide for the R package success, see https://cran.r-project.org/package=succes

Penalized regression calibration: a method for the prediction of survival outcomes using complex longitudinal and high-dimensional data

Longitudinal and high-dimensional measurements have become increasingly common in biomedical research. However, methods to predict survival outcomes using covariates that are both longitudinal and high-dimensional are currently missing. In this article, we propose penalized regression calibration (PRC), a method that can be employed to predict survival in such situations. PRC comprises three modeling steps: First, the trajectories described by the longitudinal predictors are flexibly modeled through the specification of multivariate mixed effects models. Second, subject-specific summaries of the longitudinal trajectories are derived from the fitted mixed models. Third, the time to event outcome is predicted using the subject-specific summaries as covariates in a penalized Cox model. To ensure a proper internal validation of the fitted PRC models, we furthermore develop a cluster bootstrap optimism correction procedure that allows to correct for the optimistic bias of apparent measures of predictiveness. PRC and the CBOCP are implemented in the R package pencal, available from CRAN. After studying the behavior of PRC via simulations, we conclude by illustrating an application of PRC to data from an observational study that involved patients affected by Duchenne muscular dystrophy, where the goal is predict time to loss of ambulation using longitudinal blood biomarkers.Comment: The article is now published in Statistics in Medicine (with Open Access

Inspecting the quality of care:a comparison of CUSUM methods for inter hospital performance

During the past 14 years, a clinical audit has been used in the Netherlands to provide hospitals with data on their performance in colorectal cancer care. Continuous feedback on the quality of care provided at each hospital is essential to improve patient outcomes. It is unclear which methods should be used to generate most informative output for the identification of potential quality issues. Our aim is to compare the commonly employed funnel plot with existing cumulative sum (CUSUM) methodology for the evaluation of postoperative survival and hospital stay outcomes of patients who underwent colorectal surgery in the Netherlands. Data from the Dutch ColoRectal Audit on 25367 patients in the Netherlands who underwent surgical resection for colorectal cancer in 71 hospitals between 2019 and 2021 is used to compare four methods for the detection of deviations in the quality of care. Two methods based on binary outcomes (funnel plot, binary CUSUM) and two CUSUM charts based on survival outcomes (BK-CUSUM and CGR-CUSUM) are considered. A novel approach for determining hospital specific control limits for CUSUM charts is proposed. The ability to detect deviations as well as the time until detection are compared for the four methods. Charts were constructed for the inspection of both postoperative survival and hospital stay. Methods using survival outcomes always yielded faster detection times compared to approaches employing binary outcomes. Detections between methods mostly coincided for postoperative survival. For hospital stay detections varied strongly, with methods based on survival outcomes signalling over half the hospitals. Further pros and cons as well as pitfalls of all methods under consideration are discussed. Methodology for the continuous inspection of the quality of care should be tailored to the specific outcome. Properly understanding how the mechanism of a control chart functions is crucial for the correct interpretation of results. This is particularly true for CUSUM charts, which require the choice of a parameter that greatly influences the results. When applying CUSUM charts, consideration of these issues is strongly recommended.</p

LKB1 Down-Modulation by miR-17 Identifies Patients With NSCLC Having Worse Prognosis Eligible for Energy-Stress–Based Treatments

Abstract Introduction Preclinical models recently unveiled the vulnerability of LKB1/KRAS comutated NSCLC to metabolic stress-based treatments. Because miR-17 is a potential epigenetic regulator of LKB1, we hypothesized that wild-type LKB1 (LKB1WT) NSCLC with high miR-17 expression may be sensitive to an energetic stress condition, and eligible for metabolic frailties-based therapeutic intervention. Methods We took advantage of NSCLC cell lines with different combinations of KRAS mutation and LKB1 deletion and of patient-derived xenografts (PDXs) with high (LKB1WT/miR-17 high) or low (LKB1WT/miR-17 low) miR-17 expression. We evaluated LKB1 pathway impairment and apoptotic response to metformin. We retrospectively evaluated LKB1 and miR-17 expression levels in tissue specimens of patients with NSCLC and PDXs. In addition, a lung cancer series from The Cancer Genome Atlas data set was analyzed for miR-17 expression and potential correlation with clinical features. Results We identified miR-17 as an epigenetic regulator of LKB1 in NSCLC and confirmed targeting of miR-17 to LKB1 3′ untranslated region by luciferase reporter assay. We found that miR-17 overexpression functionally impairs the LKB1/AMPK pathway. Metformin treatment prompted apoptosis on miR-17 overexpression only in LKB1WT cell lines, and in LKB1WT/miR-17 high PDXs. A retrospective analysis in patients with NSCLC revealed an inverse correlation between miR-17 and LKB1 expression and highlighted a prognostic role of miR-17 expression in LKB1WT patients, which was further confirmed by The Cancer Genome Atlas data analysis. Conclusions We identified miR-17 as a mediator of LKB1 expression in NSCLC tumors. This study proposes a miR-17 expression score potentially exploitable to discriminate LKB1WT patients with NSCLC with impaired LKB1 expression and poor outcome, eligible for energy-stress-based treatments

Metformin +/- cyclic fasting mimicking diet in combination with platinum-pemetrexed chemotherapy for advanced LKB1 inactive lung adenocarcinoma: The FAME trial

n/