Analysis of variables that are not directly observable: influence on decision-making during the research process

The sample dimension, types of variables, format used for measurement, and construction of instruments to collect valid and reliable data must be considered during the research process. In the social and health sciences, and more specifically in nursing, data-collection instruments are usually composed of latent variables or variables that cannot be directly observed. Such facts emphasize the importance of deciding how to measure study variables (using an ordinal scale or a Likert or Likert-type scale). Psychometric scales are examples of instruments that are affected by the type of variables that comprise them, which could cause problems with measurement and statistical analysis (parametric tests versus non-parametric tests). Hence, investigators using these variables must rely on suppositions based on simulation studies or recommendations based on scientific evidence in order to make the best decisions.

Adherence to nutritional recommendations and sociodemographic variables in patients with diabetes mellitus

OBJECTIVETo determine if there is a relationship between adherence to nutritional recommendations and sociodemographic variables in Brazilian patients with type 2 diabetes mellitus.METHODSCross-sectional observational study using a stratified random sample of 423 individuals. The Food Frequency Questionnaire (FFQ) was used, and the Fisher's exact test was applied with 95% confidence interval (p<0.05).RESULTSOf the 423 subjects, 66.7% were women, mean age of 62.4 years (SD = 11.8), 4.3 years of schooling on average (SD = 3.6) and family income of less than two minimum wages. There was association between the female gender and adherence to diet with adequate cholesterol content (OR: 2.03; CI: 1.23; 3.34), between four and more years of education and adherence to fractionation of meals (OR: 1 92 CI: 1.19; 3.10), and income of less than two minimum wages and adherence to diet with adequate cholesterol content (OR: 1.74; CI: 1.03, 2.95).CONCLUSIONAdherence to nutritional recommendations was associated with the female gender, more than four years of education and family income of less than two minimum wages.

Clustering of cardiovascular risk factors mimicking the human metabolic syndrome X in eNOS null mice.

AIMS/HYPOTHESIS: The metabolic syndrome comprises a clustering of cardiovascular risk factors but the underlying mechanism is not known. Mice with targeted disruption of endothelial nitric oxide synthase (eNOS) are hypertensive and insulin resistant. We wondered, whether eNOS deficiency in mice is associated with a phenotype mimicking the human metabolic syndrome. METHODS AND RESULTS: In addition to arterial pressure and insulin sensitivity (euglycaemic hyperinsulinaemic clamp), we measured the plasma concentration of leptin, insulin, cholesterol, triglycerides, free fatty acids, fibrinogen and uric acid in 10 to 12 week old eNOS-/- and wild type mice. We also assessed glucose tolerance under basal conditions and following a metabolic stress with a high fat diet. As expected eNOS-/- mice were hypertensive and insulin resistant, as evidenced by fasting hyperinsulinaemia and a roughly 30 percent lower steady state glucose infusion rate during the clamp. eNOS-/- mice had a 1.5 to 2-fold elevation of the cholesterol, triglyceride and free fatty acid plasma concentration. Even though body weight was comparable, the leptin plasma level was 30% higher in eNOS-/- than in wild type mice. Finally, uric acid and fibrinogen were elevated in the eNOS-/- mice. Whereas under basal conditions, glucose tolerance was comparable in knock out and control mice, on a high fat diet, knock out mice became significantly more glucose intolerant than control mice. CONCLUSIONS: A single gene defect, eNOS deficiency, causes a clustering of cardiovascular risk factors in young mice. We speculate that defective nitric oxide synthesis could trigger many of the abnormalities making up the metabolic syndrome in humans.

Fusion of data sets in multivariate linear regression with errors-in-variables

We consider the application of normal theory methods to the estimation and testing of a general type of multivariate regressionmodels with errors--in--variables, in the case where various data setsare merged into a single analysis and the observable variables deviatepossibly from normality. The various samples to be merged can differ on the set of observable variables available. We show that there is a convenient way to parameterize the model so that, despite the possiblenon--normality of the data, normal--theory methods yield correct inferencesfor the parameters of interest and for the goodness--of--fit test. Thetheory described encompasses both the functional and structural modelcases, and can be implemented using standard software for structuralequations models, such as LISREL, EQS, LISCOMP, among others. An illustration with Monte Carlo data is presented.

Local-scale spatial variation in diversity of social wasps in an Amazonian rain forest in Caxiuanã, Pará, Brazil (Hymenoptera, Vespidae, Polistinae)

Polistine wasps are important in Neotropical ecosystems due to their ubiquity and diversity. Inventories have not adequately considered spatial attributes of collected specimens. Spatial data on biodiversity are important for study and mitigation of anthropogenic impacts over natural ecosystems and for protecting species. We described and analyzed local-scale spatial patterns of collecting records of wasp species, as well as spatial variation of diversity descriptors in a 2500-hectare area of an Amazon forest in Brazil. Rare species comprised the largest fraction of the fauna. Close range spatial effects were detected for most of the more common species, with clustering of presence-data at short distances. Larger spatial lag effects could also be identified in some species, constituting probably cases of exogenous autocorrelation and candidates for explanations based on environmental factors. In a few cases, significant or near significant correlations were found between five species (of Agelaia, Angiopolybia, and Mischocyttarus) and three studied environmental variables: distance to nearest stream, terrain altitude, and the type of forest canopy. However, association between these factors and biodiversity variables were generally low. When used as predictors of polistine richness in a linear multiple regression, only the coefficient for the forest canopy variable resulted significant. Some level of prediction of wasp diversity variables can be attained based on environmental variables, especially vegetation structure. Large-scale landscape and regional studies should be scheduled to address this issue.

Essays in general equilibrium asset pricing

ABSTRACT : Research in empirical asset pricing has pointed out several anomalies both in the cross section and time series of asset prices, as well as in investors' portfolio choice. This dissertation aims to discover the forces driving some of these "puzzling" asset pricing dynamics and portfolio decisions observed in the financial market. Through the dissertation I construct and study dynamic general equilibrium models of heterogeneous investors in the presence of frictions and evaluate quantitatively their implications for financial-market asset prices and portfolio choice. I also explore the potential roots of puzzles in international finance. Chapter 1 shows that, by introducing jointly endogenous no-default type of borrowing constraints and heterogeneous beliefs in a dynamic general-equilibrium economy, many empirical features of stock return volatility can be reproduced. While most of the research on stock return volatility is empirical, this paper provides a theoretical framework that is able to reproduce simultaneously the cross section and time series stylized facts concerning stock returns and their volatility. In contrast to the existing theoretical literature related to stock return volatility, I don't impose persistence or regimes in any of the exogenous state variables or in preferences. Volatility clustering, asymmetry in the stock return-volatility relationship, and pricing of multi-factor volatility components in the cross section all arise endogenously as a consequence of the feedback between the binding of no-default constraints and heterogeneous beliefs. Chapters 2 and 3 explore the implications of differences of opinion across investors in different countries for international asset pricing anomalies. Chapter 2 demonstrates that several international finance "puzzles" can be reproduced by a single risk factor which captures heterogeneous beliefs across international investors. These puzzles include: (i) home equity preference; (ii) the dependence of firm returns on local and foreign factors; (iii) the co-movement of returns and international capital flows; and (iv) abnormal returns around foreign firm cross-listing events in the local market. These are reproduced in a setup with symmetric information and in a perfectly integrated world with multiple countries and independent processes producing the same good. Chapter 3 shows that by extending this framework to multiple goods and correlated production processes; the "forward premium puzzle" arises naturally as a compensation for the heterogeneous expectations about the depreciation of the exchange rate held by international investors. Chapters 2 and 3 propose differences of opinion across international investors as the potential resolution of several international finance `puzzles'. In a globalized world where both capital and information flow freely across countries, this explanation seems more appealing than existing asymmetric information or segmented markets theories aiming to explain international finance puzzles.

Gas-sensing properties of sprayed films of (CdO)x(ZnO)1-x mixed oxide

A novel NO2 sensor based on (CdO)x(ZnO)1-x mixed-oxide thin films deposited by the spray pyrolysis technique is developed. The sensor response to 3-ppm NO2 is studied in the range 50°C-350°C for three different film compositions. The device is also tested for other harmful gases, such as CO (300 ppm) and CH4 (3000 ppm). The sensor response to these reducing gases is different at different temperatures varying from the response typical for the p-type semiconductor to that typical for the n-type semiconductor. Satisfactory response to NO2 and dynamic behavior at 230°C, as well as low resistivity, are observed for the mixed-oxide film with 30% Cd. The response to interfering gas is poor at working temperature (230°C). On the basis of this study, a possible sensing mechanism is proposed.

BIO-INSPIRED COMPUTATIONAL TECHNIQUES APPLIED TO THE CLUSTERING AND VISUALIZATION OF SPATIO-TEMPORAL GEOSPATIAL DATA

The coverage and volume of geo-referenced datasets are extensive and incessantly¦growing. The systematic capture of geo-referenced information generates large volumes¦of spatio-temporal data to be analyzed. Clustering and visualization play a key¦role in the exploratory data analysis and the extraction of knowledge embedded in¦these data. However, new challenges in visualization and clustering are posed when¦dealing with the special characteristics of this data. For instance, its complex structures,¦large quantity of samples, variables involved in a temporal context, high dimensionality¦and large variability in cluster shapes.¦The central aim of my thesis is to propose new algorithms and methodologies for¦clustering and visualization, in order to assist the knowledge extraction from spatiotemporal¦geo-referenced data, thus improving making decision processes.¦I present two original algorithms, one for clustering: the Fuzzy Growing Hierarchical¦Self-Organizing Networks (FGHSON), and the second for exploratory visual data analysis:¦the Tree-structured Self-organizing Maps Component Planes. In addition, I present¦methodologies that combined with FGHSON and the Tree-structured SOM Component¦Planes allow the integration of space and time seamlessly and simultaneously in¦order to extract knowledge embedded in a temporal context.¦The originality of the FGHSON lies in its capability to reflect the underlying structure¦of a dataset in a hierarchical fuzzy way. A hierarchical fuzzy representation of¦clusters is crucial when data include complex structures with large variability of cluster¦shapes, variances, densities and number of clusters. The most important characteristics¦of the FGHSON include: (1) It does not require an a-priori setup of the number¦of clusters. (2) The algorithm executes several self-organizing processes in parallel.¦Hence, when dealing with large datasets the processes can be distributed reducing the¦computational cost. (3) Only three parameters are necessary to set up the algorithm.¦In the case of the Tree-structured SOM Component Planes, the novelty of this algorithm¦lies in its ability to create a structure that allows the visual exploratory data analysis¦of large high-dimensional datasets. This algorithm creates a hierarchical structure¦of Self-Organizing Map Component Planes, arranging similar variables' projections in¦the same branches of the tree. Hence, similarities on variables' behavior can be easily¦detected (e.g. local correlations, maximal and minimal values and outliers).¦Both FGHSON and the Tree-structured SOM Component Planes were applied in¦several agroecological problems proving to be very efficient in the exploratory analysis¦and clustering of spatio-temporal datasets.¦In this thesis I also tested three soft competitive learning algorithms. Two of them¦well-known non supervised soft competitive algorithms, namely the Self-Organizing¦Maps (SOMs) and the Growing Hierarchical Self-Organizing Maps (GHSOMs); and the¦third was our original contribution, the FGHSON. Although the algorithms presented¦here have been used in several areas, to my knowledge there is not any work applying¦and comparing the performance of those techniques when dealing with spatiotemporal¦geospatial data, as it is presented in this thesis.¦I propose original methodologies to explore spatio-temporal geo-referenced datasets¦through time. Our approach uses time windows to capture temporal similarities and¦variations by using the FGHSON clustering algorithm. The developed methodologies¦are used in two case studies. In the first, the objective was to find similar agroecozones¦through time and in the second one it was to find similar environmental patterns¦shifted in time.¦Several results presented in this thesis have led to new contributions to agroecological¦knowledge, for instance, in sugar cane, and blackberry production.¦Finally, in the framework of this thesis we developed several software tools: (1)¦a Matlab toolbox that implements the FGHSON algorithm, and (2) a program called¦BIS (Bio-inspired Identification of Similar agroecozones) an interactive graphical user¦interface tool which integrates the FGHSON algorithm with Google Earth in order to¦show zones with similar agroecological characteristics.

Regulation of 11β-hydroxysteroid dehydrogenase type 2 by microRNA.

The enzyme 11β-hydroxysteroid dehydrogenase type 2 (11β-HSD2) is selectively expressed in aldosterone target tissues, conferring aldosterone selectivity for the mineralocorticoid receptor. A diminished activity causes salt-sensitive hypertension. The mechanism of the variable and distinct 11β-hydroxysteroid dehydrogenase type 2 gene (HSD11B2) expression in the cortical collecting duct is poorly understood. Here, we analyzed for the first time whether the 11β-HSD2 expression is modulated by microRNAs (miRNAs). In silico analysis revealed 53 and 27 miRNAs with potential binding sites on human or rat HSD11B2 3'-untranslated region. A reporter assay demonstrated 3'-untranslated region-dependent regulation of human and rodent HSD11B2. miRNAs were profiled from cortical collecting ducts and proximal convoluted tubules. Bioinformatic analyses showed a distinct clustering for cortical collecting ducts and proximal convoluted tubules with 53 of 375 miRNAs, where 13 were predicted to bind to the rat HSD11B2 3'-untranslated region. To gain insight into potentially relevant miRNAs in vivo, we investigated 2 models with differential 11β-HSD2 activity linked with salt-sensitive hypertension. (1) Comparing Sprague-Dawley with low and Wistar rats with high 11β-HSD2 activity revealed rno-miR-20a-5p, rno-miR-19b-3p, and rno-miR-190a-5p to be differentially expressed. (2) Uninephrectomy lowered 11β-HSD2 activity in the residual kidney with differentially expressed rno-miR-19b-3p, rno-miR-29b-3p, and rno-miR-26-5p. In conclusion, miRNA-dependent mechanisms seem to modulate 11β-HSD2 dosage in health and disease states.

Community Policing in Switzerland's Major Urban Areas: An Observational Study of the Implementation and Impact Using Geospatial Data Mining

La présente étude est à la fois une évaluation du processus de la mise en oeuvre et des impacts de la police de proximité dans les cinq plus grandes zones urbaines de Suisse - Bâle, Berne, Genève, Lausanne et Zurich. La police de proximité (community policing) est à la fois une philosophie et une stratégie organisationnelle qui favorise un partenariat renouvelé entre la police et les communautés locales dans le but de résoudre les problèmes relatifs à la sécurité et à l'ordre public. L'évaluation de processus a analysé des données relatives aux réformes internes de la police qui ont été obtenues par l'intermédiaire d'entretiens semi-structurés avec des administrateurs clés des cinq départements de police, ainsi que dans des documents écrits de la police et d'autres sources publiques. L'évaluation des impacts, quant à elle, s'est basée sur des variables contextuelles telles que des statistiques policières et des données de recensement, ainsi que sur des indicateurs d'impacts construit à partir des données du Swiss Crime Survey (SCS) relatives au sentiment d'insécurité, à la perception du désordre public et à la satisfaction de la population à l'égard de la police. Le SCS est un sondage régulier qui a permis d'interroger des habitants des cinq grandes zones urbaines à plusieurs reprises depuis le milieu des années 1980. L'évaluation de processus a abouti à un « Calendrier des activités » visant à créer des données de panel permettant de mesurer les progrès réalisés dans la mise en oeuvre de la police de proximité à l'aide d'une grille d'évaluation à six dimensions à des intervalles de cinq ans entre 1990 et 2010. L'évaluation des impacts, effectuée ex post facto, a utilisé un concept de recherche non-expérimental (observational design) dans le but d'analyser les impacts de différents modèles de police de proximité dans des zones comparables à travers les cinq villes étudiées. Les quartiers urbains, délimités par zone de code postal, ont ainsi été regroupés par l'intermédiaire d'une typologie réalisée à l'aide d'algorithmes d'apprentissage automatique (machine learning). Des algorithmes supervisés et non supervisés ont été utilisés sur les données à haute dimensionnalité relatives à la criminalité, à la structure socio-économique et démographique et au cadre bâti dans le but de regrouper les quartiers urbains les plus similaires dans des clusters. D'abord, les cartes auto-organisatrices (self-organizing maps) ont été utilisées dans le but de réduire la variance intra-cluster des variables contextuelles et de maximiser simultanément la variance inter-cluster des réponses au sondage. Ensuite, l'algorithme des forêts d'arbres décisionnels (random forests) a permis à la fois d'évaluer la pertinence de la typologie de quartier élaborée et de sélectionner les variables contextuelles clés afin de construire un modèle parcimonieux faisant un minimum d'erreurs de classification. Enfin, pour l'analyse des impacts, la méthode des appariements des coefficients de propension (propensity score matching) a été utilisée pour équilibrer les échantillons prétest-posttest en termes d'âge, de sexe et de niveau d'éducation des répondants au sein de chaque type de quartier ainsi identifié dans chacune des villes, avant d'effectuer un test statistique de la différence observée dans les indicateurs d'impacts. De plus, tous les résultats statistiquement significatifs ont été soumis à une analyse de sensibilité (sensitivity analysis) afin d'évaluer leur robustesse face à un biais potentiel dû à des covariables non observées. L'étude relève qu'au cours des quinze dernières années, les cinq services de police ont entamé des réformes majeures de leur organisation ainsi que de leurs stratégies opérationnelles et qu'ils ont noué des partenariats stratégiques afin de mettre en oeuvre la police de proximité. La typologie de quartier développée a abouti à une réduction de la variance intra-cluster des variables contextuelles et permet d'expliquer une partie significative de la variance inter-cluster des indicateurs d'impacts avant la mise en oeuvre du traitement. Ceci semble suggérer que les méthodes de géocomputation aident à équilibrer les covariables observées et donc à réduire les menaces relatives à la validité interne d'un concept de recherche non-expérimental. Enfin, l'analyse des impacts a révélé que le sentiment d'insécurité a diminué de manière significative pendant la période 2000-2005 dans les quartiers se trouvant à l'intérieur et autour des centres-villes de Berne et de Zurich. Ces améliorations sont assez robustes face à des biais dus à des covariables inobservées et covarient dans le temps et l'espace avec la mise en oeuvre de la police de proximité. L'hypothèse alternative envisageant que les diminutions observées dans le sentiment d'insécurité soient, partiellement, un résultat des interventions policières de proximité semble donc être aussi plausible que l'hypothèse nulle considérant l'absence absolue d'effet. Ceci, même si le concept de recherche non-expérimental mis en oeuvre ne peut pas complètement exclure la sélection et la régression à la moyenne comme explications alternatives. The current research project is both a process and impact evaluation of community policing in Switzerland's five major urban areas - Basel, Bern, Geneva, Lausanne, and Zurich. Community policing is both a philosophy and an organizational strategy that promotes a renewed partnership between the police and the community to solve problems of crime and disorder. The process evaluation data on police internal reforms were obtained through semi-structured interviews with key administrators from the five police departments as well as from police internal documents and additional public sources. The impact evaluation uses official crime records and census statistics as contextual variables as well as Swiss Crime Survey (SCS) data on fear of crime, perceptions of disorder, and public attitudes towards the police as outcome measures. The SCS is a standing survey instrument that has polled residents of the five urban areas repeatedly since the mid-1980s. The process evaluation produced a "Calendar of Action" to create panel data to measure community policing implementation progress over six evaluative dimensions in intervals of five years between 1990 and 2010. The impact evaluation, carried out ex post facto, uses an observational design that analyzes the impact of the different community policing models between matched comparison areas across the five cities. Using ZIP code districts as proxies for urban neighborhoods, geospatial data mining algorithms serve to develop a neighborhood typology in order to match the comparison areas. To this end, both unsupervised and supervised algorithms are used to analyze high-dimensional data on crime, the socio-economic and demographic structure, and the built environment in order to classify urban neighborhoods into clusters of similar type. In a first step, self-organizing maps serve as tools to develop a clustering algorithm that reduces the within-cluster variance in the contextual variables and simultaneously maximizes the between-cluster variance in survey responses. The random forests algorithm then serves to assess the appropriateness of the resulting neighborhood typology and to select the key contextual variables in order to build a parsimonious model that makes a minimum of classification errors. Finally, for the impact analysis, propensity score matching methods are used to match the survey respondents of the pretest and posttest samples on age, gender, and their level of education for each neighborhood type identified within each city, before conducting a statistical test of the observed difference in the outcome measures. Moreover, all significant results were subjected to a sensitivity analysis to assess the robustness of these findings in the face of potential bias due to some unobserved covariates. The study finds that over the last fifteen years, all five police departments have undertaken major reforms of their internal organization and operating strategies and forged strategic partnerships in order to implement community policing. The resulting neighborhood typology reduced the within-cluster variance of the contextual variables and accounted for a significant share of the between-cluster variance in the outcome measures prior to treatment, suggesting that geocomputational methods help to balance the observed covariates and hence to reduce threats to the internal validity of an observational design. Finally, the impact analysis revealed that fear of crime dropped significantly over the 2000-2005 period in the neighborhoods in and around the urban centers of Bern and Zurich. These improvements are fairly robust in the face of bias due to some unobserved covariate and covary temporally and spatially with the implementation of community policing. The alternative hypothesis that the observed reductions in fear of crime were at least in part a result of community policing interventions thus appears at least as plausible as the null hypothesis of absolutely no effect, even if the observational design cannot completely rule out selection and regression to the mean as alternative explanations.

Data-driven clustering : new methods and applications

Abstract : This work is concerned with the development and application of novel unsupervised learning methods, having in mind two target applications: the analysis of forensic case data and the classification of remote sensing images. First, a method based on a symbolic optimization of the inter-sample distance measure is proposed to improve the flexibility of spectral clustering algorithms, and applied to the problem of forensic case data. This distance is optimized using a loss function related to the preservation of neighborhood structure between the input space and the space of principal components, and solutions are found using genetic programming. Results are compared to a variety of state-of--the-art clustering algorithms. Subsequently, a new large-scale clustering method based on a joint optimization of feature extraction and classification is proposed and applied to various databases, including two hyperspectral remote sensing images. The algorithm makes uses of a functional model (e.g., a neural network) for clustering which is trained by stochastic gradient descent. Results indicate that such a technique can easily scale to huge databases, can avoid the so-called out-of-sample problem, and can compete with or even outperform existing clustering algorithms on both artificial data and real remote sensing images. This is verified on small databases as well as very large problems. Résumé : Ce travail de recherche porte sur le développement et l'application de méthodes d'apprentissage dites non supervisées. Les applications visées par ces méthodes sont l'analyse de données forensiques et la classification d'images hyperspectrales en télédétection. Dans un premier temps, une méthodologie de classification non supervisée fondée sur l'optimisation symbolique d'une mesure de distance inter-échantillons est proposée. Cette mesure est obtenue en optimisant une fonction de coût reliée à la préservation de la structure de voisinage d'un point entre l'espace des variables initiales et l'espace des composantes principales. Cette méthode est appliquée à l'analyse de données forensiques et comparée à un éventail de méthodes déjà existantes. En second lieu, une méthode fondée sur une optimisation conjointe des tâches de sélection de variables et de classification est implémentée dans un réseau de neurones et appliquée à diverses bases de données, dont deux images hyperspectrales. Le réseau de neurones est entraîné à l'aide d'un algorithme de gradient stochastique, ce qui rend cette technique applicable à des images de très haute résolution. Les résultats de l'application de cette dernière montrent que l'utilisation d'une telle technique permet de classifier de très grandes bases de données sans difficulté et donne des résultats avantageusement comparables aux méthodes existantes.

Stability and robustness of blood variables in an antidoping context.

Introduction:  With the setting up of the newly Athlete's Biological Passport antidoping programme, novel guidelines have been introduced to guarantee results beyond reproach. We investigated in this context, the effect of storage time on the variables commonly measured for the haematological passport. We also wanted to assess for these variables, the within and between analyzer variations. Methods:  Blood samples were obtained from top level male professional cyclists (27 samples for the first part of the study and 102 for the second part) taking part to major stage races. After collection, they were transported under refrigerated conditions (2 °C < T < 12 °C), delivered to the antidoping laboratory, analysed and then stored at approximately 4 °C to conduct analysis at different time points up to 72 h after delivery. A mixed-model procedure was used to determine the stability of the different variables. Results:  As expected haemoglobin concentration was not affected by storage and showed stability for at least 72 h. Under the conditions of our investigation, the reticulocytes percentage showed a much better stability than previous published data (> 48 h) and the technical comparison of the haematology analyzer demonstrated excellent results. Conclusion:  In conclusion, our data clearly demonstrate that as long as the World Anti-Doping Agency's guidelines are followed rigorously, all blood results reach the quality level required in the antidoping context.

Cutaneous benign mixed tumor (chondroid syringoma) of the eyelid: clinical presentation and management.

PURPOSE: To describe the clinical presentation of cutaneous benign mixed tumor of the eyelid and its management options. METHODS: Periocular cases of cutaneous benign mixed tumor were gathered from members of an oculoplastics specialty Internet discussion group. A total of 9 patients are described in this retrospective, interventional case series. The clinical presentation, histopathology, and management of these lesions is reviewed. RESULTS: Patients were typically asymptomatic, presenting with a slowly enlarging, nontender nodule of 2 to 8 years' duration. The lesions ranged from 4 mm to 17 mm in greatest dimension. Four of the lesions were on the eyelid margin, three in the sub-brow area of the upper eyelid, and two in the central lids. All six cases not involving the brow were fixed to the tarsus; one brow lesion was believed to be adherent to the skin. None of the lesions was associated with significant changes of the overlying epidermis, although one lesion showed overlying pigmentation. All patients underwent excisional biopsy for diagnostic or cosmetic reasons. On histopathologic examination, the tumors were biphasic, with an epithelial component exhibiting apocrine or hair follicle differentiation and a myxoid, adipocytic, chondroid, and/or fibrous stroma. The pathologic diagnoses were all consistent with cutaneous benign mixed tumor (chondroid syringoma, pleomorphic adenoma). Follow-up ranged from 2 weeks to 12 months, although several patients failed to keep scheduled follow-up appointments. No clinical recurrences were identified. CONCLUSIONS: Cutaneous benign mixed tumor may occur in the eyelid, and, although uncommon, should be included in the differential diagnosis of firm, nodular eyelid tumors. The histopathologic features are similar to those seen in this tumor type arising in other areas of the body. Preoperative consideration of this diagnostic possibility may allow the surgeon to plan for complete excision, thereby reducing the possibility of recurrence or malignant transformation.