425 research outputs found
Penalized Poisson model for network meta-analysis of individual patient time-to-event data
Network meta-analysis (NMA) allows the combination of direct and indirect
evidence from a set of randomized clinical trials. Performing NMA using
individual patient data (IPD) is considered as a gold standard approach as it
provides several advantages over NMA based on aggregate data. For example, it
allows to perform advanced modelling of covariates or covariate-treatment
interactions. An important issue in IPD NMA is the selection of influential
parameters among terms that account for inconsistency, covariates,
covariate-by-treatment interactions or non-proportionality of treatments effect
for time to event data. This issue has not been deeply studied in the
literature yet and in particular not for time-to-event data. A major difficulty
is to jointly account for between-trial heterogeneity which could have a major
influence on the selection process. The use of penalized generalized mixed
effect model is a solution, but existing implementations have several
shortcomings and an important computational cost that precludes their use for
complex IPD NMA. In this article, we propose a penalized Poisson regression
model to perform IPD NMA of time-to-event data. It is based only on fixed
effect parameters which improve its computational cost over the use of random
effects. It could be easily implemented using existing penalized regression
package. Computer code is shared for implementation. The methods were applied
on simulated data to illustrate the importance to take into account between
trial heterogeneity during the selection procedure. Finally, it was applied to
an IPD NMA of overall survival of chemotherapy and radiotherapy in
nasopharyngeal carcinoma
Tutorial in Joint Modeling and Prediction: A Statistical Software for Correlated Longitudinal Outcomes, Recurrent Events and a Terminal Event
Extensions in the field of joint modeling of correlated data and dynamic predictions improve the development of prognosis research. The R package frailtypack provides estimations of various joint models for longitudinal data and survival events. In particular, it fits models for recurrent events and a terminal event (frailtyPenal), models for two survival outcomes for clustered data (frailtyPenal), models for two types of recurrent events and a terminal event (multivPenal), models for a longitudinal biomarker and a terminal event (longiPenal) and models for a longitudinal biomarker, recurrent events and a terminal event (trivPenal). The estimators are obtained using a standard and penalized maximum likelihood approach, each model function allows to evaluate goodness-of-fit analyses and provides plots of baseline hazard functions. Finally, the package provides individual dynamic predictions of the terminal event and evaluation of predictive accuracy. This paper presents the theoretical models with estimation techniques, applies the methods for predictions and illustrates frailtypack functions details with examples
Art-175 is a highly efficient antibacterial against multidrug-resistant strains and persisters of Pseudomonas aeruginosa
Artilysins constitute a novel class of efficient enzyme-based antibacterials. Specifically, they covalently combine a bacteriophage-encoded endolysin, which degrades the peptidoglycan, with a targeting peptide that transports the endolysin through the outer membrane of Gram-negative bacteria. Art-085, as well as Art-175, its optimized homolog with increased thermostability, are each composed of the sheep myeloid 29-amino acid (SMAP-29) peptide fused to the KZ144 endolysin. In contrast to KZ144, Art-085 and Art-175 pass the outer membrane and kill Pseudomonas aeruginosa, including multidrug-resistant strains, in a rapid and efficient (similar to 5 log units) manner. Time-lapse microscopy confirms that Art-175 punctures the peptidoglycan layer within 1 min, inducing a bulging membrane and complete lysis. Art-175 is highly refractory to resistance development by naturally occurring mutations. In addition, the resistance mechanisms against 21 therapeutically used antibiotics do not show cross-resistance to Art-175. Since Art-175 does not require an active metabolism for its activity, it has a superior bactericidal effect against P. aeruginosa persisters (up to > 4 log units compared to that of the untreated controls). In summary, Art-175 is a novel antibacterial that is well suited for a broad range of applications in hygiene and veterinary and human medicine, with a unique potential to target persister-driven chronic infections
Statistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common approaches and challenges
International audienceBackground: In high-dimensional data (HDD) settings, the number of variables associated with each observation is very large. Prominent examples of HDD in biomedical research include omics data with a large number of variables such as many measurements across the genome, proteome, or metabolome, as well as electronic health records data that have large numbers of variables recorded for each patient. The statistical analysis of such data requires knowledge and experience, sometimes of complex methods adapted to the respective research questions. Methods: Advances in statistical methodology and machine learning methods offer new opportunities for innovative analyses of HDD, but at the same time require a deeper understanding of some fundamental statistical concepts. Topic group TG9 “High-dimensional data” of the STRATOS (STRengthening Analytical Thinking for Observational Studies) initiative provides guidance for the analysis of observational studies, addressing particular statistical challenges and opportunities for the analysis of studies involving HDD. In this overview, we discuss key aspects of HDD analysis to provide a gentle introduction for non-statisticians and for classically trained statisticians with little experience specific to HDD. Results: The paper is organized with respect to subtopics that are most relevant for the analysis of HDD, in particular initial data analysis, exploratory data analysis, multiple testing, and prediction. For each subtopic, main analytical goals in HDD settings are outlined. For each of these goals, basic explanations for some commonly used analysis methods are provided. Situations are identified where traditional statistical methods cannot, or should not, be used in the HDD setting, or where adequate analytic tools are still lacking. Many key references are provided. Conclusions: This review aims to provide a solid statistical foundation for researchers, including statisticians and non-statisticians, who are new to research with HDD or simply want to better evaluate and understand the results of HDD analyses
Validation of a new fully automated software for 2D digital mammographic breast density evaluation in predicting breast cancer risk.
We compared accuracy for breast cancer (BC) risk stratification of a new fully automated system (DenSeeMammo-DSM) for breast density (BD) assessment to a non-inferiority threshold based on radiologists' visual assessment. Pooled analysis was performed on 14,267 2D mammograms collected from women aged 48-55 years who underwent BC screening within three studies: RETomo, Florence study and PROCAS. BD was expressed through clinical Breast Imaging Reporting and Data System (BI-RADS) density classification. Women in BI-RADS D category had a 2.6 (95% CI 1.5-4.4) and a 3.6 (95% CI 1.4-9.3) times higher risk of incident and interval cancer, respectively, than women in the two lowest BD categories. The ability of DSM to predict risk of incident cancer was non-inferior to radiologists' visual assessment as both point estimate and lower bound of 95% CI (AUC 0.589; 95% CI 0.580-0.597) were above the predefined visual assessment threshold (AUC 0.571). AUC for interval (AUC 0.631; 95% CI 0.623-0.639) cancers was even higher. BD assessed with new fully automated method is positively associated with BC risk and is not inferior to radiologists' visual assessment. It is an even stronger marker of interval cancer, confirming an appreciable masking effect of BD that reduces mammography sensitivity
Prognostic and predictive effect of KRAS gene copy number and mutation status in early stage non-small cell lung cancer patients
Background: In the current analysis, we characterize the prognostic significance of
Methods: Clinical and genomic data from the LACE (Lung Adjuvant Cisplatin Evaluation)-Bio consortium was utilized. CNAs were categorized as Gain (CN ≥2) or Neutral (Neut)/Loss;
Results: Of the 946 (399 adenocarcinoma) NSCLC patients, 41 [30] had MUT + Gain, 145 [99] MUT + Neut/Loss, 125 [16] WT + Gain, and 635 [254] WT + Neut/Loss. A non-significant trend towards worse lung cancer-specific survival (LCSS; HR =1.34; 95% CI, 0.83-2.17, P=0.232), DFS (HR =1.34; 95% CI, 0.86-2.09, P=0.202) and OS (HR =1.59; 95% CI, 0.99-2.54, P=0.055) was seen in
Conclusions: A small prognostic effect o
Reactive stroma and trastuzumab resistance in HER2-positive early breast cancer
We investigated the value of reactive stroma as a predictor for trastuzumab resistance in patients with early HER2-positive breast cancer receiving adjuvant therapy. The pathological reactive stroma and the mRNA gene signatures that reflect reactive stroma in 209 HER2-positive breast cancer samples from the FinHer adjuvant trial were evaluated. Levels of stromal gene signatures were determined as a continuous parameter, and pathological reactive stromal findings were defined as stromal predominant breast cancer (SPBC; >= 50% stromal) and correlated with distant disease-free survival. Gene signatures associated with reactive stroma in HER2-positive early breast cancer (N = 209) were significantly associated with trastuzumab resistance in estrogen receptor (ER)-negative tumors (hazard ratio [HR] = 1.27 p interaction = 0.014 [DCN], HR = 1.58, p interaction = 0.027 [PLAU], HR = 1.71, p interaction = 0.019 [HER2STROMA, novel HER2 stromal signature]), but not in ER-positive tumors (HR = 0.73 p interaction = 0.47 [DCN], HR = 0.71, p interaction = 0.73 [PLAU], HR = 0.84; p interaction = 0.36 [HER2STROMA]). Pathological evaluation of HER2-positive/ER-negative tumors suggested an association between SPBC and trastuzumab resistance. Reactive stroma did not correlate with tumor-infiltrating lymphocytes (TILs), and the expected benefit from trastuzumab in patients with high levels of TILs was pronounced only in tumors with low stromal reactivity (SPBCPeer reviewe
An inclusive Lifecycle Approach for IoT Devices Trust and Identity Management
ERATOSTHENES is an EC, co-funded, research project strongly considering modern security challenges in the domain of Internet of Things in mind of their huge penetration into our day to day lives. There are a series of recent challenges that recently have been converted into obstacles or risk points that could block the secure operation of IoT networks in all day to day activities, from home to office, to leisure and security. These include examples such as the highly increased number of connected devices (at all network levels) that are on top forming inhomogeneous networks and systems of systems. Different vendor characteristics further increase the attack surface that is expected to further rise in the upcoming years. Such, highly critical, characteristics, dramatically increase the needs for confidentiality access control, user and things’ privacy, devices’ trustworthiness and compliance that require lifecycle considerations. The ERATOSTHENES project orchestrates a novel distributed, automated, auditable, yet privacy-respectful, Trust and Identity Management Framework and Reference Architecture with the ultimate scope to dynamically and holistically manage IoT devices in a lifecycle approach, strengthening trust, identities, and resilience in the entire IoT ecosystem while supporting the enforcement of the NIS directive, GDPR and Cybersecurity Act. This publication describes the ERATOSTHENES technical concept and reference architecture as well as design considerations, architecture characteristics, connectivity and interoperability.submittedVersio
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