Lognormal and gamma mixed negative binomial regression

In regression analysis of counts, a lack of simple and efficient algorithms for posterior computation has made Bayesian approaches appear unattractive and thus underdeveloped. We propose a lognormal and gamma mixed negative binomial (NB) regression model for counts, and present efficient closed-form Bayesian inference; unlike conventional Poisson models, the proposed approach has two free parameters to include two different kinds of random effects, and allows the incorporation of prior information, such as sparsity in the regression coefficients. By placing a gamma distribution prior on the NB dispersion parameter r, and connecting a log-normal distribution prior with the logit of the NB probability parameter p, efficient Gibbs sampling and variational Bayes inference are both developed. The closed-form updates are obtained by exploiting conditional conjugacy via both a compound Poisson representation and a Polya-Gamma distribution based data augmentation approach. The proposed Bayesian inference can be implemented routinely, while being easily generalizable to more complex settings involving multivariate dependence structures. The algorithms are illustrated using real examples. Copyright 2012 by the author(s)/owner(s).

Preoperative pain level and patient expectation predict hospital length of stay after total hip arthroplasty.

The purpose of this study was to identify preoperative predictors of length of stay after primary total hip arthroplasty in a patient population reflecting current trends toward shorter hospitalization and using readily obtainable factors that do not require scoring systems. A retrospective review of 112 consecutive patients was performed. High preoperative pain level and patient expectation of discharge to extended care facilities (ECFs) were the only significant multivariable predictors of hospitalization extending beyond 2 days (P=0.001 and P<0.001 respectively). Patient expectation remained significant after adjusting for Medicare's 3-day requirement for discharge to ECFs (P<0.001). The study was adequately powered to analyze the variables in the multivariable logistic regression model, which had a concordance index of 0.857.

Patient-provider communication, self-reported medication adherence, and race in a postmyocardial infarction population.

OBJECTIVES: Our objectives were to: 1) describe patient-reported communication with their provider and explore differences in perceptions of racially diverse adherent versus nonadherent patients; and 2) examine whether the association between unanswered questions and patient-reported medication nonadherence varied as a function of patients' race. METHODS: We conducted a cross-sectional analysis of baseline in-person survey data from a trial designed to improve postmyocardial infarction management of cardiovascular disease risk factors. RESULTS: Overall, 298 patients (74%) reported never leaving their doctor's office with unanswered questions. Among those who were adherent and nonadherent with their medications, 183 (79%) and 115 (67%) patients, respectively, never left their doctor's office with unanswered questions. In multivariable logistic regression, although the simple effects of the interaction term were different for patients of nonminority race (odds ratio [OR]: 2.16; 95% confidence interval [CI]: 1.19-3.92) and those of minority race (OR: 1.19; 95% CI: 0.54-2.66), the overall interaction effect was not statistically significant (P=0.24). CONCLUSION: The quality of patient-provider communication is critical for cardiovascular disease medication adherence. In this study, however, having unanswered questions did not impact medication adherence differently as a function of patients' race. Nevertheless, there were racial differences in medication adherence that may need to be addressed to ensure optimal adherence and health outcomes. Effort should be made to provide training opportunities for both patients and their providers to ensure strong communication skills and to address potential differences in medication adherence in patients of diverse backgrounds.

Micro-Anatomical Quantitative Imaging Towards Enabling Automated Diagnosis of Thick Tissues at the Point of Care

Histopathology is the clinical standard for tissue diagnosis. However, histopathology has several limitations including that it requires tissue processing, which can take 30 minutes or more, and requires a highly trained pathologist to diagnose the tissue. Additionally, the diagnosis is qualitative, and the lack of quantitation leads to possible observer-specific diagnosis. Taken together, it is difficult to diagnose tissue at the point of care using histopathology.

Several clinical situations could benefit from more rapid and automated histological processing, which could reduce the time and the number of steps required between obtaining a fresh tissue specimen and rendering a diagnosis. For example, there is need for rapid detection of residual cancer on the surface of tumor resection specimens during excisional surgeries, which is known as intraoperative tumor margin assessment. Additionally, rapid assessment of biopsy specimens at the point-of-care could enable clinicians to confirm that a suspicious lesion is successfully sampled, thus preventing an unnecessary repeat biopsy procedure. Rapid and low cost histological processing could also be potentially useful in settings lacking the human resources and equipment necessary to perform standard histologic assessment. Lastly, automated interpretation of tissue samples could potentially reduce inter-observer error, particularly in the diagnosis of borderline lesions.

To address these needs, high quality microscopic images of the tissue must be obtained in rapid timeframes, in order for a pathologic assessment to be useful for guiding the intervention. Optical microscopy is a powerful technique to obtain high-resolution images of tissue morphology in real-time at the point of care, without the need for tissue processing. In particular, a number of groups have combined fluorescence microscopy with vital fluorescent stains to visualize micro-anatomical features of thick (i.e. unsectioned or unprocessed) tissue. However, robust methods for segmentation and quantitative analysis of heterogeneous images are essential to enable automated diagnosis. Thus, the goal of this work was to obtain high resolution imaging of tissue morphology through employing fluorescence microscopy and vital fluorescent stains and to develop a quantitative strategy to segment and quantify tissue features in heterogeneous images, such as nuclei and the surrounding stroma, which will enable automated diagnosis of thick tissues.

To achieve these goals, three specific aims were proposed. The first aim was to develop an image processing method that can differentiate nuclei from background tissue heterogeneity and enable automated diagnosis of thick tissue at the point of care. A computational technique called sparse component analysis (SCA) was adapted to isolate features of interest, such as nuclei, from the background. SCA has been used previously in the image processing community for image compression, enhancement, and restoration, but has never been applied to separate distinct tissue types in a heterogeneous image. In combination with a high resolution fluorescence microendoscope (HRME) and a contrast agent acriflavine, the utility of this technique was demonstrated through imaging preclinical sarcoma tumor margins. Acriflavine localizes to the nuclei of cells where it reversibly associates with RNA and DNA. Additionally, acriflavine shows some affinity for collagen and muscle. SCA was adapted to isolate acriflavine positive features or APFs (which correspond to RNA and DNA) from background tissue heterogeneity. The circle transform (CT) was applied to the SCA output to quantify the size and density of overlapping APFs. The sensitivity of the SCA+CT approach to variations in APF size, density and background heterogeneity was demonstrated through simulations. Specifically, SCA+CT achieved the lowest errors for higher contrast ratios and larger APF sizes. When applied to tissue images of excised sarcoma margins, SCA+CT correctly isolated APFs and showed consistently increased density in tumor and tumor + muscle images compared to images containing muscle. Next, variables were quantified from images of resected primary sarcomas and used to optimize a multivariate model. The sensitivity and specificity for differentiating positive from negative ex vivo resected tumor margins was 82% and 75%. The utility of this approach was further tested by imaging the in vivo tumor cavities from 34 mice after resection of a sarcoma with local recurrence as a bench mark. When applied prospectively to images from the tumor cavity, the sensitivity and specificity for differentiating local recurrence was 78% and 82%. The results indicate that SCA+CT can accurately delineate APFs in heterogeneous tissue, which is essential to enable automated and rapid surveillance of tissue pathology.

Two primary challenges were identified in the work in aim 1. First, while SCA can be used to isolate features, such as APFs, from heterogeneous images, its performance is limited by the contrast between APFs and the background. Second, while it is feasible to create mosaics by scanning a sarcoma tumor bed in a mouse, which is on the order of 3-7 mm in any one dimension, it is not feasible to evaluate an entire human surgical margin. Thus, improvements to the microscopic imaging system were made to (1) improve image contrast through rejecting out-of-focus background fluorescence and to (2) increase the field of view (FOV) while maintaining the sub-cellular resolution needed for delineation of nuclei. To address these challenges, a technique called structured illumination microscopy (SIM) was employed in which the entire FOV is illuminated with a defined spatial pattern rather than scanning a focal spot, such as in confocal microscopy.

Thus, the second aim was to improve image contrast and increase the FOV through employing wide-field, non-contact structured illumination microscopy and optimize the segmentation algorithm for new imaging modality. Both image contrast and FOV were increased through the development of a wide-field fluorescence SIM system. Clear improvement in image contrast was seen in structured illumination images compared to uniform illumination images. Additionally, the FOV is over 13X larger than the fluorescence microendoscope used in aim 1. Initial segmentation results of SIM images revealed that SCA is unable to segment large numbers of APFs in the tumor images. Because the FOV of the SIM system is over 13X larger than the FOV of the fluorescence microendoscope, dense collections of APFs commonly seen in tumor images could no longer be sparsely represented, and the fundamental sparsity assumption associated with SCA was no longer met. Thus, an algorithm called maximally stable extremal regions (MSER) was investigated as an alternative approach for APF segmentation in SIM images. MSER was able to accurately segment large numbers of APFs in SIM images of tumor tissue. In addition to optimizing MSER for SIM image segmentation, an optimal frequency of the illumination pattern used in SIM was carefully selected because the image signal to noise ratio (SNR) is dependent on the grid frequency. A grid frequency of 31.7 mm-1 led to the highest SNR and lowest percent error associated with MSER segmentation.

Once MSER was optimized for SIM image segmentation and the optimal grid frequency was selected, a quantitative model was developed to diagnose mouse sarcoma tumor margins that were imaged ex vivo with SIM. Tumor margins were stained with acridine orange (AO) in aim 2 because AO was found to stain the sarcoma tissue more brightly than acriflavine. Both acriflavine and AO are intravital dyes, which have been shown to stain nuclei, skeletal muscle, and collagenous stroma. A tissue-type classification model was developed to differentiate localized regions (75x75 µm) of tumor from skeletal muscle and adipose tissue based on the MSER segmentation output. Specifically, a logistic regression model was used to classify each localized region. The logistic regression model yielded an output in terms of probability (0-100%) that tumor was located within each 75x75 µm region. The model performance was tested using a receiver operator characteristic (ROC) curve analysis that revealed 77% sensitivity and 81% specificity. For margin classification, the whole margin image was divided into localized regions and this tissue-type classification model was applied. In a subset of 6 margins (3 negative, 3 positive), it was shown that with a tumor probability threshold of 50%, 8% of all regions from negative margins exceeded this threshold, while over 17% of all regions exceeded the threshold in the positive margins. Thus, 8% of regions in negative margins were considered false positives. These false positive regions are likely due to the high density of APFs present in normal tissues, which clearly demonstrates a challenge in implementing this automatic algorithm based on AO staining alone.

Thus, the third aim was to improve the specificity of the diagnostic model through leveraging other sources of contrast. Modifications were made to the SIM system to enable fluorescence imaging at a variety of wavelengths. Specifically, the SIM system was modified to enabling imaging of red fluorescent protein (RFP) expressing sarcomas, which were used to delineate the location of tumor cells within each image. Initial analysis of AO stained panels confirmed that there was room for improvement in tumor detection, particularly in regards to false positive regions that were negative for RFP. One approach for improving the specificity of the diagnostic model was to investigate using a fluorophore that was more specific to staining tumor. Specifically, tetracycline was selected because it appeared to specifically stain freshly excised tumor tissue in a matter of minutes, and was non-toxic and stable in solution. Results indicated that tetracycline staining has promise for increasing the specificity of tumor detection in SIM images of a preclinical sarcoma model and further investigation is warranted.

In conclusion, this work presents the development of a combination of tools that is capable of automated segmentation and quantification of micro-anatomical images of thick tissue. When compared to the fluorescence microendoscope, wide-field multispectral fluorescence SIM imaging provided improved image contrast, a larger FOV with comparable resolution, and the ability to image a variety of fluorophores. MSER was an appropriate and rapid approach to segment dense collections of APFs from wide-field SIM images. Variables that reflect the morphology of the tissue, such as the density, size, and shape of nuclei and nucleoli, can be used to automatically diagnose SIM images. The clinical utility of SIM imaging and MSER segmentation to detect microscopic residual disease has been demonstrated by imaging excised preclinical sarcoma margins. Ultimately, this work demonstrates that fluorescence imaging of tissue micro-anatomy combined with a specialized algorithm for delineation and quantification of features is a means for rapid, non-destructive and automated detection of microscopic disease, which could improve cancer management in a variety of clinical scenarios.

Genetic Variants of the MDM2 Gene Are Predictive of Treatment-Related Toxicities and Overall Survival in Patients With Advanced NSCLC.

INTRODUCTION: Platinum agents can cause the formation of DNA adducts and induce apoptosis to eliminate tumor cells. The aim of the present study was to investigate the influence of genetic variants of MDM2 on chemotherapy-related toxicities and clinical outcomes in patients with advanced non-small-cell lung cancer (NSCLC). MATERIALS AND METHODS: We recruited 663 patients with advanced NSCLC who had been treated with first-line platinum-based chemotherapy. Five tagging single nucleotide polymorphisms (SNPs) in MDM2 were genotyped in these patients. The associations of these SNPs with clinical toxicities and outcomes were evaluated using logistic regression and Cox regression analyses. RESULTS: Two SNPs (rs1470383 and rs1690924) showed significant associations with chemotherapy-related toxicities (ie, overall, hematologic, and gastrointestinal toxicity). Compared with the wild genotype AA carriers, patients with the GG genotype of rs1470383 had an increased risk of overall toxicity (odds ratio [OR], 3.28; 95% confidence interval [CI], 1.34-8.02; P = .009) and hematologic toxicity (OR, 4.10; 95% CI, 1.73-9.71; P = .001). Likewise, patients with the AG genotype of rs1690924 showed more sensitivity to gastrointestinal toxicity than did those with the wild-type homozygote GG (OR, 2.32; 95% CI, 1.30-4.14; P = .004). Stratified survival analysis revealed significant associations between rs1470383 genotypes and overall survival in patients without overall or hematologic toxicity (P = .007 and P = .0009, respectively). CONCLUSION: The results of our study suggest that SNPs in MDM2 might be used to predict the toxicities of platinum-based chemotherapy and overall survival in patients with advanced NSCLC. Additional validations of the association are warranted.

Colchicine effectiveness in symptom and inflammation modification in knee osteoarthritis (COLKOA): study protocol for a randomized controlled trial.

BACKGROUND: Despite the high prevalence and global impact of knee osteoarthritis (KOA), current treatments are palliative. No disease modifying anti-osteoarthritic drug (DMOAD) has been approved. We recently demonstrated significant involvement of uric acid and activation of the innate immune response in osteoarthritis (OA) pathology and progression, suggesting that traditional gout therapy may be beneficial for OA. We therefore assess colchicine, an existing commercially available agent for gout, for a new therapeutic application in KOA. METHODS/DESIGN: COLKOA is a double-blind, placebo-controlled, randomized trial comparing a 16-week treatment with standard daily dose oral colchicine to placebo for KOA. A total of 120 participants with symptomatic KOA will be recruited from a single center in Singapore. The primary end point is 30% improvement in total Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score at week 16. Secondary end points include improvement in pain, physical function, and quality of life and change in serum, urine and synovial fluid biomarkers of cartilage metabolism and inflammation. A magnetic resonance imaging (MRI) substudy will be conducted in 20 participants to evaluate change in synovitis. Logistic regression will be used to compare changes between groups in an intention-to-treat analysis. DISCUSSION: The COLKOA trial is designed to evaluate whether commercially available colchicine is effective for improving signs and symptoms of KOA, and reducing synovial fluid, serum and urine inflammatory and biochemical joint degradation biomarkers. These biomarkers should provide insights into the underlying mechanism of therapeutic response. This trial will potentially provide data to support a new treatment option for KOA. TRIAL REGISTRATION: The trial has been registered at clinicaltrials.gov as NCT02176460 . Date of registration: 26 June 2014.

Factores explicativos de las actitudes hacia el castigo juvenil. Evidencias de un diseño factorial

El rol desempeñado por la opinión pública en el desarrollo de la política criminal actual justifica el incremento de investigaciones destinadas a evaluar las actitudes de los ciudadanos hacia el castigo. No obstante, los avances en este ámbito han sido limitados debido a la utilización de rudimentarios instrumentos de medida. Por ello, el presente trabajo tiene como propósito explorar el efecto que generan en la opinión ciudadana ciertas variables referidas al hecho delictivo y al infractor, precisando su contribución relativa y la interacción existente entre ellas. Para satisfacer este objetivo se recurrió a un diseño factorial de la encuesta, creando una población de 256 casos-escenario fruto de la combinación de cuatro factores: la edad del joven, su historial delictivo, el grado de implicación en el hecho y el tipo de delito cometido. Los mismos fueron distribuidos en grupos de ocho casos ordenados aleatoriamente y fueron suministrados a 32 sujetos. Posteriormente se aplicaron análisis de regresión logística binaria. Los resultados obtenidos revelan que la naturaleza violenta de los hechos, la implicación activa de los jóvenes y el historial delictivo son predictores importantes de las condenas punitivas. Sin embargo la edad, una variable fundamental en la configuración de la justicia juvenil, no resulta significativa. De este modo, el trabajo muestra el potencial explicativo de este conjunto de factores y debate sus implicaciones teóricas y metodológicas para la investigación futura en este terreno.

La incidencia de los valores culturales en el voluntariado: el caso de Europa

La realidad del voluntariado es sumamente compleja hasta el punto de que resulta complicado definir y caracterizar el trabajo voluntario, dada la gran variedad de interpretaciones, motivaciones, variables sociodemográficas y aspectos culturales que configuran el perfil de los voluntarios. El objetivo de este trabajo es analizar la influencia conjunta de algunas variables sociodemográficas, así como de los valores culturales de índole secular o tradicional, sobre el perfil de los voluntarios en Europa. Además, se investiga qué variables orientan a los voluntarios hacia un determinado tipo de voluntariado u otro. Para ello se ha aplicado principalmente una metodología de regresión logística a partir de la información disponible en la European Value Study. Los resultados obtenidos ayudan a establecer una caracterización del voluntariado en Europa, y confirman la influencia de los valores culturales, en primer lugar, en la realización o no de trabajos de voluntariado, y en segundo lugar, en la elección que hacen estas personas del tipo de actividad con la que están comprometidos. Al analizar dos tipos de voluntariado de motivación supuestamente muy diferente, se concluye que existe un grupo de valores que influyen en ambos, aunque el sentido y la intensidad en la que lo hacen sea diferente; por otra parte, algunos valores tienen influencia o no en la realización de trabajos de voluntariado, dependiendo del tipo específico al que nos refiramos.

Factores determinantes del uso de Internet en la comercialización del aceite de oliva. Análisis del sector en Cataluña

Las cooperativas son entidades con una gran presencia económica y social en España, y tienen una gran influencia en la economía rural de las zonas donde están ubicadas. El principal objetivo del presente trabajo es el análisis del uso de las nuevas tecnologías por parte de las cooperativas agroalimentarias, centrándose en las productoras de aceite de oliva para determinar los principales factores que condicionan su comportamiento en la Red. En el presente estudio se analizan sus sitios web y se determina qué tipo de información aporta, tanto datos generales como datos de comercialización. A partir de los resultados obtenidos, se busca la relación que pueda existir entre el tamaño de la cooperativa, su actividad exportadora o la actividad de comercio electrónico con la presencia online, mediante una regresión logística. De esta manera podremos conocer si realmente la implantación de nuevas tecnologías en las cooperativas permite desarrollar una óptima actividad económica.

The validity of a latent class typology of adolescent drinking patterns

Objectives: This study examined the validity of a latent class typology of adolescent drinking based on four alcohol dimensions; frequency of drinking, quantity consumed, frequency of binge drinking and the number of alcohol related problems encountered. Method: Data used were from the 1970 British Cohort Study sixteen-year-old follow-up. Partial or complete responses to the selected alcohol measures were provided by 6,516 cohort members. The data were collected via a series of postal questionnaires. Results: A five class LCA typology was constructed. Around 12% of the sample were classified as �hazardous drinkers� reporting frequent drinking, high levels of alcohol consumed, frequent binge drinking and multiple alcohol related problems. Multinomial logistic regression, with multiple imputation for missing data, was used to assess the covariates of adolescent drinking patterns. Hazardous drinking was associated with being white, being male, having heavy drinking parents (in particular fathers), smoking, illicit drug use, and minor and violent offending behaviour. Non-significant associations were found between drinking patterns and general mental health and attention deficient disorder. Conclusion: The latent class typology exhibited concurrent validity in terms of its ability to distinguish respondents across a number of alcohol and non-alcohol indicators. Notwithstanding a number of limitations, latent class analysis offers an alternative data reduction method for the construction of drinking typologies that addresses known weaknesses inherent in more tradition classification methods.