LSDA+U approximation-based analysis of the electronic estructure of CeFeGe3

We perform ab initio electronic structure calculations of the intermetallic compound CeFeGe3 by means of the Tight Binding Linear Muffin-Tin Orbitals-Atomic Sphere Approximation (TB-LMTO-ASA) within the Local Spin Density Approximation containing the so-called Hubbard correction term (LSDA+U^SIC), using the Sttutgart's TB (Tight Binding)-LMTO-ASA code in the framework of the Density Funcional Theory (DFT).Comment: 12 pages 8 figures, submitted to Int. J. Modern Phys.

The Adsorption of Chlorofluoromethane on Pristine, Al‐, Ga‐, P‐, and As‐doped Boron Nitride Nanotubes: A PBC‐DFT, NBO, and QTAIM Study

The feasibility of detecting a Chlorofluoromethane (CFM) gas molecule on the outer surface of a pristine single‐walled boron nitride nanotube as well as Al‐, Ga‐, P‐, and As‐doped structures. A periodic boundary condition (PBC), within a density functional theory (DFT) method, using the Perdew, Burke, and Ernzerhof exchange‐correlation (PBE0) functional, together with a 6‐311G(d) basis set was used. Subsequently, the B3LYP, CAM−B3LYP, ωB97XD, and M06‐2X functionals were also employed to consider the single point energies. Natural bond orbital (NBO) and quantum theory of atoms in molecules (QTAIM) were implemented by using the PBE0/6‐311G(d). To explore the nature of the intermolecular interactions, density of state (DOS), Wiberg bond index (WBI), natural charge, natural electron configuration, donor–acceptor natural bond orbital interactions, the second‐order perturbation energies tests, and noncovalent interaction (NCI) analysis are performed. The sensitivity of the adsorption will be increased when the gas molecule interacts with decorated nanotubes; therefore, the change of electronic properties can be used to design suitable nanosensors

Effects and moderators of exercise on quality of life and physical function in patients with cancer:An individual patient data meta-analysis of 34 RCTs

This individual patient data meta-analysis aimed to evaluate the effects of exercise on quality of life (QoL) and physical function (PF) in patients with cancer, and to identify moderator effects of demographic (age, sex, marital status, education), clinical (body mass index, cancer type, presence of metastasis), intervention-related (intervention timing, delivery mode and duration, and type of control group), and exercise-related (exercise frequency, intensity, type, time) characteristics. Relevant published and unpublished studies were identified in September 2012 via PubMed, EMBASE, PsycINFO, and CINAHL, reference checking and personal communications. Principle investigators of all 69 eligible trials were requested to share IPD from their study. IPD from 34 randomised controlled trials (n=4,519 patients) that evaluated the effects of exercise compared to a usual care, wait-list or attention control group on QoL and PF in adult patients with cancer were retrieved and pooled. Linear mixed-effect models were used to evaluate the effects of the exercise on post-intervention outcome values (z-score) adjusting for baseline values. Moderator effects were studies by testing interactions. Exercise significantly improved QoL (β=0.15, 95%CI=0.10;0.20) and PF (β=0.18,95%CI=0.13;0.23). The effects were not moderated by demographic, clinical or exercise characteristics. Effects on QoL (βdifference_in_effect=0.13, 95%CI=0.03;0.22) and PF (βdifference_in_effect=0.10, 95%CI=0.01;0.20) were significantly larger for supervised than unsupervised interventions. In conclusion, exercise, and particularly supervised exercise, effectively improves QoL and PF in patients with cancer with different demographic and clinical characteristics during and following treatment. Although effect sizes are small, there is consistent empirical evidence to support implementation of exercise as part of cancer care

Handling informative dropout in longitudinal analysis of health-related quality of life: application of three approaches to data from the esophageal cancer clinical trial PRODIGE 5/ACCORD 17

International audienceAbstract Background Health-related quality of life (HRQoL) has become a major endpoint to assess the clinical benefit of new therapeutic strategies in oncology clinical trials. Typically, HRQoL outcomes are analyzed using linear mixed models (LMMs). However, longitudinal analysis of HRQoL in the presence of missing data remains complex and unstandardized. Our objective was to compare the modeling alternatives that account for informative dropout. Methods We investigated three alternative methods—the selection model (SM), pattern-mixture model (PMM), and shared-parameters model (SPM)—in relation to the LMM. We first compared them on the basis of methodological arguments highlighting their advantages and drawbacks. Then, we applied them to data from a randomized clinical trial that included 267 patients with advanced esophageal cancer for the analysis of four HRQoL dimensions evaluated using the European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30 questionnaire. Results We highlighted differences in terms of outputs, interpretation, and underlying modeling assumptions; this methodological comparison could guide the choice of method according to the context. In the application, none of the four models detected a significant difference between the two treatment arms. The estimated effect of time on HRQoL varied according to the method: for all analyzed dimensions, the PMM estimated an effect that contrasted with those estimated by the SM and SPM; the LMM estimated effects were confirmed by the SM (on two of four HRQoL dimensions) and SPM (on three of four HRQoL dimensions). Conclusions The PMM, SM, or SPM should be used to confirm or invalidate the results of LMM analysis when informative dropout is suspected. Of these three alternative methods, the SPM appears to be the most interesting from both theoretical and practical viewpoints. Trial registration This study is registered with ClinicalTrials.gov , number NCT00861094

%TTD and %TUDD: New SAS macro programs to calculate the survival data of the time to deterioration for patient-reported outcomes data in oncology

International audienceBackground and objective: Longitudinal analysis of patient-reported outcome (PRO) data remains challenging, as no standardization of statistical methods has been proposed, making comparison of PRO results between clinical trials difficult. In this context, the time to deterioration approach has recently been proposed and is regularly used as a modality of longitudinal PRO analysis in oncology.Methods: Two new SAS macro programs were developed, %TTD and %TUDD, which implement longitudinal analysis of PRO data according to the time to deterioration approach. These programs implement the recommended deterioration definitions. We described the programs with their different functionalities.Results: The %TTD macro calculates the time to first or transient deterioration, and the %TUDD macro calculates the time until definitive deterioration. These macros allow to obtain the survival variables from the time to deterioration approach. We illustrate our programs by presenting different applications on the randomized phase II AFUGEM GERCOR clinical trial.Conclusion: The implementation of the deterioration definitions in SAS software allows the dissemination of this approach, in order to move toward the goal of standardization of longitudinal PRO analysis in oncology clinical trials

Time to deterioration in cancer randomized clinical trials for patient-reported outcomes data: a systematic review

International audienc

Qual Life Res

PURPOSE: Health-related quality of life (HRQoL) is assessed by self-administered questionnaires throughout the care process. Classically, two longitudinal statistical approaches were mainly used to study HRQoL: linear mixed models (LMM) or time-to-event models for time to deterioration/time until definitive deterioration (TTD/TUDD). Recently, an alternative strategy based on generalized linear mixed models for categorical data has also been proposed: the longitudinal partial credit model (LPCM). The objective of this article is to evaluate these methods and to propose recommendations to standardize longitudinal analysis of HRQoL data in cancer clinical trials. METHODS: The three methods are first described and compared through statistical, methodological, and practical arguments, then applied on real HRQoL data from clinical cancer trials or published prospective databases. In total, seven French studies from a collaborating group were selected with longitudinal collection of QLQ-C30. Longitudinal analyses were performed with the three approaches using SAS, Stata and R software. RESULTS: We observed concordant results between LMM and LPCM. However, discordant results were observed when we considered the TTD/TUDD approach compared to the two previous methods. According to methodological and practical arguments discussed, the approaches seem to provide additional information and complementary interpretations. LMM and LPCM are the most powerful methods on simulated data, while the TTD/TUDD approach gives more clinically understandable results. Finally, for single-item scales, LPCM is more appropriate. CONCLUSION: These results pledge for the recommendation to use of both the LMM and TTD/TUDD longitudinal methods, except for single-item scales, establishing them as the consensual methods for publications reporting HRQoL

PRODIG (Prevention of new onset diabetes after transplantation by a short term treatment of Vildagliptin in the early renal post-transplant period) study: study protocol for a randomized controlled study

International audienceBACKGROUND: Post-transplant diabetes is a frequent and serious complication of kidney transplantation. There is currently no treatment to prevent or delay the disease. Nevertheless, identification of risk factors make it possible to target a population at risk of developing de novo diabetes. We hypothesized that a short-term treatment with vildagliptin may prevent new onset diabetes after transplantation (NODAT) in high-risk patients.METHODS/DESIGN: This is a multicenter, double-blind, placebo-controlled randomized clinical trial. Patients undergoing first kidney transplantation will be included from ten French transplant centers. Included patients will be randomized (1:1) to receive either vildagliptin 100 or 50 mg/day (depending on glomerular filtration rate) during 2 months (the first dose being administered before entering the operating theatres) or placebo. Additional antidiabetic therapy could be administered according to glycemic control. The primary outcome is the proportion of diabetic patients 1 year after transplantation, defined as patients receiving a diabetic treatment, or having a fasting glucose above 7 mmol/l, and/or with an abnormal oral glucose tolerance test. Secondary outcomes include glycated hemoglobin, the occurrence of acute rejection, infection, graft loss and patient death at 3 months, 6 months, and 12 months after transplantation. Outcomes will be correlated to clinical and general characteristics of the patient, cardiovascular history, nephropathy, dialysis history, transplantation data, biological data, health-related quality of life, and the cost-effectiveness of prevention of diabetes with vildagliptin.DISCUSSION: We have scarce data on the pharmacological prevention of post-transplant diabetes. If our hypothesis is verified, our results will have a direct application in clinical practice and could limit diabetes-associated morbidity, reduce cardiovascular complications, increase quality of life of renal transplant patients, and consequently promote graft and patient survival. Our results may possibly serve for non-transplant patients carrying a high-risk of diabetes associated with other co-morbidities