Plans d'expérience pour mélanges à deux niveaux et facteurs externes

Mixture experiment is applied when the response dependent only on the proportions of the mixture components. In a mixture of mixtures formulation each major component (CP) is a sub-mixture of minor components (CS). These systems are classified in: type A: CP proportions are fixed and CS proportions are variable, type B: both CP and CS proportions are variable. In order to investigate blending properties we propose additive model for types A and B mixtures. Additive model performs well with many components because the number of parameters is quite lower than the number of parameters in multiplicative models proposed in previous works. We build designs satisfying the orthogonality condition within each group of CS and balance condition between CP couples. The uniform optimality is verified by Orthogonal-balanced (OE) designs composed of pure sub-mixtures. Identification between orthogonal arrays and the OE experiments for pure substances makes it possible to obtain small size experiments for certain configurations of mixture systems. Identification between this sub-class of OE design and the axial sub-mixtures is also established. It provide a way to obtain OE experiments where the proportions of CS and the CP are non zero. Models for the effect of both proportions and process variables are also considered through first degree polynomials and orthogonal fractions constructed from factorial design jointing CS, CP and process variables.Les plans d'expérience pour mélanges traitent des cas où les propriétés du mélange sont dépendantes uniquement des proportions de ses composants. Dans un systèmes de mélanges à deux niveaux chaque composant principal (CP) est lui-même un sous-mélange de composants secondaires (CS). Ces systèmes sont classés en: mélanges de type A: les proportions des CP sont fixées et celles des CS sont variables, mélanges de type B: les proportions des CP et des CS sont toutes variables. Afin d'analyser les deux types de mélanges on propose des modèles additifs. Ce type de modèles est bien adapté pour l'expérimentation avec de nombreux composants car le nombre de paramètres est très inférieur au nombre de paramètres des modèles multiplicatifs proposés dans la littérature. On construit des plans vérifiant deux hypothèses, l'une d'orthogonalité à l'intérieur de chaque groupe de CS et l'autre d'équilibre entre sous-mélanges de CS par couple de CP. Pour la classe des plans orthogonaux-équilibré (OE) on déduit l'optimalité uniforme des plans composés de sous-mélanges purs. L'identification entre les plans orthogonaux factoriels et les corps purs permet d'obtenir des plans de taille restreinte pour certaines configurations de mélanges. L'identification entre les sous-mélanges purs et les expériences axiales est aussi établie, ceci donne une méthode pour la construction de plans OE où les proportions des CS et celles des CP sont non nulles. La modélisation conjointe de mélanges et facteurs externes est aussi considérée en utilisant des polynômes d'ordre un et des fractions orthogonales construites à partir d'un plan factoriel composé du rassemblement de CS, CP et facteur externe

Plans d expérience pour mélanges à deux niveaux et facteurs externes

Les plans d expérience pour mélanges traitent de cas où les propriétés du mélange sont dépendantes uniquement des proportions de ses composants. Dans un système de mélanges à deux niveaux chaque composant principal (CP) est lui-même un sous mélange de composés secondaires (CS). Ces systèmes sont classés. Mélange de type A : les proportions des CP sont fixées et celles des CS sont variables. Mélanges d type B : les proportions des CP et CS sont toutes variables. Afin d analyser les deux types de mélanges on propose des modèles additifs. Ce type de modèles est bien adapté pour l expérimentation avec de nombreux composants car le nombre de paramètres est bien inférieur au nombre de paramètres des modèles multiplicatifs proposés par la littérature. On construit des plans vérifiant deux hypothèses, l une d orthogonalité à l intérieur de chaque groupe de CS et l autre d équilibre entre sous-mélanges de CS par couple de CP. Pour la classe des plans orthogonaux-équilibré (OE) on déduit l optimalité uniforme des plans composés de sous-mélanges purs. L identification entre les plans orthogonaux factoriels et les corps purs permet d obtenir des plans de taille restreinte pour certaines configurations de mélanges. L identification entre les sous-mélanges purs et les expériences axiales est ainsi établie, ceci donne une méthode pour la construction de plans OE où les proportions des CS et celle des CP sont non nulles. La modélisation conjointe de mélanges et facteurs externes est aussi considérée en utilisant des polynômes d ordre un et des fractions orthogonales construites à partir d un plan factoriel composé du rassemblement de CS, CP et facteur externe.Mixture experiment is applied where the response dependent only on the proportion of the mixture components. In a mixture of mixtures formulation each major component (CP) is a sub-mixture of minor components (CS). These systems are classified. Type A : CP proportions are fixed and CS proportions are variable. Type B : both CP and CS proportions are variables. In order to investigate blending properties we propose additive model for type A and B mixtures. Additive model performs well at the experimentation with many components because the number of parameter is quite lower than the number of parameters in multiplicative models proposed in previous works. We build designs verifying the orthogonality condition within each group of CS and balance condition between CP couples. The uniform optimality is verified by Orthogonal-balanced (OE) designs composed of pure sub-mixtures. Identification between orthogonal arrays and the OE experiments for pure substances makes it possible to obtain small size experiments for certain configurations of mixture systems. Identification between sub-class of OE design and the axial sub-mixtures is also established. It provides a way to obtain OE experiments where the proportions of CS and the CP are non null. Modes for the effect of both proportions and process variables are also considered through first degree polynomials and orthogonal fractions constructed from factorial jointing CS, CP and process variables.PAU-BU Sciences (644452103) / SudocSudocFranceF

Global variation in postoperative mortality and complications after cancer surgery: a multicentre, prospective cohort study in 82 countries

© 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 licenseBackground: 80% of individuals with cancer will require a surgical procedure, yet little comparative data exist on early outcomes in low-income and middle-income countries (LMICs). We compared postoperative outcomes in breast, colorectal, and gastric cancer surgery in hospitals worldwide, focusing on the effect of disease stage and complications on postoperative mortality. Methods: This was a multicentre, international prospective cohort study of consecutive adult patients undergoing surgery for primary breast, colorectal, or gastric cancer requiring a skin incision done under general or neuraxial anaesthesia. The primary outcome was death or major complication within 30 days of surgery. Multilevel logistic regression determined relationships within three-level nested models of patients within hospitals and countries. Hospital-level infrastructure effects were explored with three-way mediation analyses. This study was registered with ClinicalTrials.gov, NCT03471494. Findings: Between April 1, 2018, and Jan 31, 2019, we enrolled 15 958 patients from 428 hospitals in 82 countries (high income 9106 patients, 31 countries; upper-middle income 2721 patients, 23 countries; or lower-middle income 4131 patients, 28 countries). Patients in LMICs presented with more advanced disease compared with patients in high-income countries. 30-day mortality was higher for gastric cancer in low-income or lower-middle-income countries (adjusted odds ratio 3·72, 95% CI 1·70–8·16) and for colorectal cancer in low-income or lower-middle-income countries (4·59, 2·39–8·80) and upper-middle-income countries (2·06, 1·11–3·83). No difference in 30-day mortality was seen in breast cancer. The proportion of patients who died after a major complication was greatest in low-income or lower-middle-income countries (6·15, 3·26–11·59) and upper-middle-income countries (3·89, 2·08–7·29). Postoperative death after complications was partly explained by patient factors (60%) and partly by hospital or country (40%). The absence of consistently available postoperative care facilities was associated with seven to 10 more deaths per 100 major complications in LMICs. Cancer stage alone explained little of the early variation in mortality or postoperative complications. Interpretation: Higher levels of mortality after cancer surgery in LMICs was not fully explained by later presentation of disease. The capacity to rescue patients from surgical complications is a tangible opportunity for meaningful intervention. Early death after cancer surgery might be reduced by policies focusing on strengthening perioperative care systems to detect and intervene in common complications. Funding: National Institute for Health Research Global Health Research Unit

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

© 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licenseBackground: Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide. Methods: A multimethods analysis was performed as part of the GlobalSurg 3 study—a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital. Findings: Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3·85 [95% CI 2·58–5·75]; p<0·0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63·0% vs 82·7%; OR 0·35 [0·23–0·53]; p<0·0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer. Interpretation: Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised. Funding: National Institute for Health and Care Research