Network-based genome-wide association study of Crohn's disease

Genome-wide association studies (GWAS) have rapidly become a standard method for disease gene discovery. Many recent GWAS indicate that for most disorders, only a few common variants are implicated and the associated SNPs explain only a small fraction of the genetic risk. The current study incorporated gene network information into gene-based analysis of GWAS data for Crohn's disease (CD). The purpose was to develop statistical models to boost the power of identifying disease-associated genes and gene subnetworks by maximizing the use of existing biological knowledge from multiple sources. The results revealed that Markov random field (MRF) based mixture model incorporating direct neighborhood information from a single gene network is not efficient in identifying CD-related genes based on the GWAS data. The incorporation of solely direct neighborhood information might lead to the low efficiency of these models. Alternative MRF models looking beyond direct neighboring information are necessary to be developed in the future for the purpose of this study.^

Improved cerebellar tissue classification on magnetic resonance images of brain.

PURPOSE: To develop and implement a method for improved cerebellar tissue classification on the MRI of brain by automatically isolating the cerebellum prior to segmentation. MATERIALS AND METHODS: Dual fast spin echo (FSE) and fluid attenuation inversion recovery (FLAIR) images were acquired on 18 normal volunteers on a 3 T Philips scanner. The cerebellum was isolated from the rest of the brain using a symmetric inverse consistent nonlinear registration of individual brain with the parcellated template. The cerebellum was then separated by masking the anatomical image with individual FLAIR images. Tissues in both the cerebellum and rest of the brain were separately classified using hidden Markov random field (HMRF), a parametric method, and then combined to obtain tissue classification of the whole brain. The proposed method for tissue classification on real MR brain images was evaluated subjectively by two experts. The segmentation results on Brainweb images with varying noise and intensity nonuniformity levels were quantitatively compared with the ground truth by computing the Dice similarity indices. RESULTS: The proposed method significantly improved the cerebellar tissue classification on all normal volunteers included in this study without compromising the classification in remaining part of the brain. The average similarity indices for gray matter (GM) and white matter (WM) in the cerebellum are 89.81 (+/-2.34) and 93.04 (+/-2.41), demonstrating excellent performance of the proposed methodology. CONCLUSION: The proposed method significantly improved tissue classification in the cerebellum. The GM was overestimated when segmentation was performed on the whole brain as a single object.

SURVIVAL PREDICTION FOR BRAIN TUMOR PATIENTS USING GENE EXPRESSION DATA

Brain tumor is one of the most aggressive types of cancer in humans, with an estimated median survival time of 12 months and only 4% of the patients surviving more than 5 years after disease diagnosis. Until recently, brain tumor prognosis has been based only on clinical information such as tumor grade and patient age, but there are reports indicating that molecular profiling of gliomas can reveal subgroups of patients with distinct survival rates. We hypothesize that coupling molecular profiling of brain tumors with clinical information might improve predictions of patient survival time and, consequently, better guide future treatment decisions. In order to evaluate this hypothesis, the general goal of this research is to build models for survival prediction of glioma patients using DNA molecular profiles (U133 Affymetrix gene expression microarrays) along with clinical information. First, a predictive Random Forest model is built for binary outcomes (i.e. short vs. long-term survival) and a small subset of genes whose expression values can be used to predict survival time is selected. Following, a new statistical methodology is developed for predicting time-to-death outcomes using Bayesian ensemble trees. Due to a large heterogeneity observed within prognostic classes obtained by the Random Forest model, prediction can be improved by relating time-to-death with gene expression profile directly. We propose a Bayesian ensemble model for survival prediction which is appropriate for high-dimensional data such as gene expression data. Our approach is based on the ensemble "sum-of-trees" model which is flexible to incorporate additive and interaction effects between genes. We specify a fully Bayesian hierarchical approach and illustrate our methodology for the CPH, Weibull, and AFT survival models. We overcome the lack of conjugacy using a latent variable formulation to model the covariate effects which decreases computation time for model fitting. Also, our proposed models provides a model-free way to select important predictive prognostic markers based on controlling false discovery rates. We compare the performance of our methods with baseline reference survival methods and apply our methodology to an unpublished data set of brain tumor survival times and gene expression data, selecting genes potentially related to the development of the disease under study. A closing discussion compares results obtained by Random Forest and Bayesian ensemble methods under the biological/clinical perspectives and highlights the statistical advantages and disadvantages of the new methodology in the context of DNA microarray data analysis.

Is paromomycin an effective and safe treatment against cutaneous leishmaniasis? A meta-analysis of 14 randomized controlled trials.

BACKGROUND: High cost, poor compliance, and systemic toxicity have limited the use of pentavalent antimony compounds (SbV), the treatment of choice for cutaneous leishmaniasis (CL). Paromomycin (PR) has been developed as an alternative to SbV, but existing data are conflicting. METHODOLOGY/PRINCIPAL FINDINGS: We searched PubMed, Scopus, and Cochrane Central Register of Controlled Trials, without language restriction, through August 2007, to identify randomized controlled trials that compared the efficacy or safety between PR and placebo or SbV. Primary outcome was clinical cure, defined as complete healing, disappearance, or reepithelialization of all lesions. Data were extracted independently by two investigators, and pooled using a random-effects model. Fourteen trials including 1,221 patients were included. In placebo-controlled trials, topical PR appeared to have therapeutic activity against the old world and new world CL, with increased local reactions, when used with methylbenzethonium chloride (MBCL) compared to when used alone (risk ratio [RR] for clinical cure, 2.58 versus 1.01: RR for local reactions, 1.60 versus 1.07). In SbV-controlled trials, the efficacy of topical PR was not significantly different from that of intralesional SbV in the old world CL (RR, 0.70; 95% confidence interval, 0.26-1.89), whereas topical PR was inferior to parenteral SbV in treating the new world CL (0.67; 0.54-0.82). No significant difference in efficacy was found between parenteral PR and parenteral SbV in the new world CL (0.88; 0.56-1.38). Systemic side effects were fewer with topical or parenteral PR than parenteral SbV. CONCLUSIONS/SIGNIFICANCE: Topical PR with MBCL could be a therapeutic alternative to SbV in selected cases of the old world CL. Development of new formulations with better efficacy and tolerability remains to be an area of future research.

An examination of the relationship between maternal and neighborhood-level characteristics and intrauterine growth retardation in Harris County, Texas, 1999--2001

The persistence of low birth weight and intrauterine growth retardation (IUGR) in the United States has puzzled researchers for decades. Much of the work that has been conducted on adverse birth outcomes has focused on low birth weight in general and not on IUGR. Studies that have examined IUGR specifically thus far have focused primarily on individual-level maternal risk factors. These risk factors have only been able to explain a small portion of the variance in IUGR. Therefore, recent work has begun to focus on community-level risk factors in addition to the individual-level maternal characteristics. This study uses Social Ecology to examine the relationship of individual and community-level risk factors and IUGR. Logistic regression was used to establish an individual-level model based on 155, 856 births recorded in Harris County, TX during 1999-2001. IUGR was characterized using a fetal growth ratio method with race/ethnic and sex specific mean birth weights calculated from national vital records. The spatial distributions of 114,460 birth records spatially located within the City of Houston were examined using choropleth, probability and density maps. Census tracts with higher than expected rates of IUGR and high levels of neighborhood disadvantage were highlighted. Neighborhood disadvantage was constructed using socioeconomic variables from the 2000 U.S. Census. Factor analysis was used to create a unified single measure. Lastly, a random coefficients model was used to examine the relationship between varying levels of community disadvantage, given the set of individual-level risk factors for 152,997 birth records spatially located within Harris County, TX. Neighborhood disadvantage was measured using three different indices adapted from previous work. The findings show that pregnancy-induced hypertension, previous preterm infant, tobacco use and insufficient weight gain have the highest association with IUGR. Neighborhood disadvantage only slightly further increases the risk of IUGR (OR 1.12 to 1.23). Although community level disadvantage only helped to explain a small proportion of the variance of IUGR, it did have a significant impact. This finding suggests that community level risk factors should be included in future work with IUGR and that more work needs to be conducted. ^

A meta-analysis: Obesity and colorectal cancer screening

Of cancer death, colorectal cancer death ranks second in the United States. Obesity is an important risk factor for colorectal cancer (1). Early detection of colorectal cancer when it is localized can effectively reduce mortality of colorectal cancer and increase survival time of patients if they are treated. Also, previous studies showed that obese women were more likely to delay breast cancer screening and cervical cancer screening than normal weight women (2-5). However, results from prior studies demonstrating the relationship between obesity and colorectal cancer screening are not consistent. This research was done to conduct a meta-analysis of previous cross-sectional studies selected from the Medline database and to evaluate the association between obesity and colorectal cancer screening. While the odds ratio was not statistically different from one, the results from this meta-analysis under the random effects model showed that obese people are slightly less likely to have colorectal cancer screening compared to normal weight individuals (OR,0.93;95% CI 0.75-1.15). This meta-analysis was particularly sensitive to one individual study (6) and the effect of obesity on colorectal cancer screening was statistically significant (OR, 0.87; 95% CI, 0.81-0.92) after removing Heo's study. Further systematic studies focused on whether the effect of obesity on colorectal cancer screening is limited to women only are suggested. ^

Cost-effectiveness analysis of a 2-stage community-based hepatocellular carcinoma screening program in Taiwan

Hepatocellular carcinoma (HCC) has been ranked as the top cause of death due to neoplasm malignancy in Taiwan for years. The high incidence of HCC in Taiwan is primarily attributed to high prevalence of hepatitis viral infection. Screening the subjects with liver cirrhosis for HCC was widely recommended by many previous studies. The latest practice guideline for management of HCC released by the American Association for the Study of Liver Disease (AASLD) in 2005 recommended that the high risk groups, including cirrhotic patients, chronic HBV/HCV carriers, and subjects with family history of HCC and etc., should undergo surveillance.^ This study aims to investigate (1) whether the HCC screening program can prolong survival period of the high risk group, (2) what is the incremental cost-effectiveness ratio of the HCC screening program in Taiwan, as compared with a non-screening strategy from the payer perspective, (3) which high risk group has the lowest ICER for the HCC screening program from the insurer's perspective, in comparison with no screening strategy of each group, and (4) the estimated total cost of providing the HCC screening program to all high risk groups.^ The high risk subjects in the study were identified from the communities with high prevalence of hepatitis viral infection and classified into three groups (cirrhosis group, early cirrhosis group, and no cirrhosis group) at different levels of risk to HCC by status of liver disease at the time of enrollment. The repeated ultrasound screenings at an interval of 3, 6, and 12 months were applied to cirrhosis group, early cirrhosis group, and no cirrhosis group, respectively. The Markov-based decision model was constructed to simulate progression of HCC and to estimate the ICER for each group of subjects.^ The screening group had longer survival in the statistical results and the model outcomes. Owing to the low HCC incidence rate in the community-based screening program, screening services only have limited effect on survival of the screening group. The incremental cost-effectiveness ratio of the HCC screening program was $3834 per year of life saved, in comparison with the non-screening strategy. The estimated total cost of each group from the screening model over 13.5 years approximately consumes 0.13%, 1.06%, and 0.71% of total amount of adjusted National Health Expenditure from Jan 1992 to Jun 2005. ^ The subjects at high risk of developing HCC to undergo repeated ultrasound screenings had longer survival than those without screening, but screening was not the only factor to cause longer survival in the screening group. The incremental cost-effectiveness ratio of the 2-stage community-based HCC screening program in Taiwan was small. The HCC screening program was worthy of investment in Taiwan. In comparison with early cirrhosis group and no cirrhosis group, cirrhosis group has the lowest ICER when the screening period is less than 19 years. The estimated total cost of providing the HCC screening program to all high risk groups consumes approximately 1.90% of total amount of adjusted 13.5-year NHE in Taiwan.^

The determinants of improvements in health outcomes and of cost reduction in hospital inpatient care

This study aims to address two research questions. First, ‘Can we identify factors that are determinants both of improved health outcomes and of reduced costs for hospitalized patients with one of six common diagnoses?’ Second, ‘Can we identify other factors that are determinants of improved health outcomes for such hospitalized patients but which are not associated with costs?’ The Healthcare Cost and Utilization Project (HCUP) Nationwide Inpatient Sample (NIS) database from 2003 to 2006 was employed in this study. The total study sample consisted of hospitals which had at least 30 patients each year for the given diagnosis: 954 hospitals for acute myocardial infarction (AMI), 1552 hospitals for congestive heart failure (CHF), 1120 hospitals for stroke (STR), 1283 hospitals for gastrointestinal hemorrhage (GIH), 979 hospitals for hip fracture (HIP), and 1716 hospitals for pneumonia (PNE). This study used simultaneous equations models to investigate the determinants of improvement in health outcomes and of cost reduction in hospital inpatient care for these six common diagnoses. In addition, the study used instrumental variables and two-stage least squares random effect model for unbalanced panel data estimation. The study concluded that a few factors were determinants of high quality and low cost. Specifically, high specialty was the determinant of high quality and low costs for CHF patients; small hospital size was the determinant of high quality and low costs for AMI patients. Furthermore, CHF patients who were treated in Midwest, South, and West region hospitals had better health outcomes and lower hospital costs than patients who were treated in Northeast region hospitals. Gastrointestinal hemorrhage and pneumonia patients who were treated in South region hospitals also had better health outcomes and lower hospital costs than patients who were treated in Northeast region hospitals. This study found that six non-cost factors were related to health outcomes for a few diagnoses: hospital volume, percentage emergency room admissions for a given diagnosis, hospital competition, specialty, bed size, and hospital region.^

A systematic review of clostridium difficile infection in patients with iatrogenic immunesuppression

Background: Incidence of C. difficile infection (CDI) has increased dramatically in the past decade and is the most frequent cause of nosocomial infectious diarrhea. The outcome of infection may range from mild diarrhea to life-threatening pseudomembranous colitis depending on the immunological response of the host, which is highly compromised in this special population that includes bone marrow transplant (BMT), solid organ transplant (SOT) and cancer patients on cytotoxic chemotherapy. ^ Objectives: We conducted a meta-analysis to assess the incidence rates of CDI and the time to onset of infection in patients with iatrogenic immune suppression. ^ Methods: Original studies were identified through an extensive search of electronic databases including PubMed, Ovid Medline (R), RefWorks and Biological Abstracts and their references. The overall incidence rate of CDI in the immune suppressed population was calculated using random effects model and their 95% confidence interval was derived. Differences in the incidence of CDI and time to onset of infection were calculated between the groups and within the groups. Publication bias was assessed using a funnel plot. Results: Twenty nine published articles involving 7,424 patients met the eligibility requirements. The overall incidence of CDI in the immune suppressed population is 11.1% (95% Confidence Interval (CI): 9.2–13.4%). The incidence of CDI was higher in SOT patients (14.2%, 95% CI: 6.8–21.5%); (p-value-0.022) and in cancer patients on cytotoxic chemotherapy (11.4%, 95% CI: 8.4–15.4%); (p = 0.042) than in BMT patients (10.5%, 95% CI: 7.9–13.1%). In a subgroup analysis of BMT population, the incidence of CDI is significantly higher in patients who received allogeneic BMT (15.1%, 95% CI: 11.2–20.0%; p value <0.0001). Similarly, in the SOT population, the incidence of CDI was higher in patients who underwent liver transplantation (11.0%, 95% CI: 5.6–20.3%); (p= 0.0672). The median time to onset of infection was shorter in BMT patients (p=0.0025). ^ Conclusions: It is evident from the combined analysis of these 29 published studies that the incidence of CDI in the immune suppressed population is higher. However, early diagnosis and treatment of CDI will help reduce the morbidity and mortality due to CDI in this special population.^

A systematic review of personalized therapy in patients with advanced cancer

Background: An increased understanding of the pathogenesis of cancer at the molecular level has led to the development of personalized cancer therapy based on the mutation status of the tumor. Tailoring treatments to genetic signatures has improved treatment outcomes in patients with advanced cancer. We conducted a meta-analysis to provide a quantitative summary of the response to treatment on a phase I clinical trial matched to molecular aberration in patients with advanced solid tumors. ^ Methods: Original studies that reported the results of phase I clinical trials in patients with advanced cancer treated with matched anti-cancer therapies between January 2006 and November 2011 were identified through an extensive search of Medline, Embase, Web of Science and Cochrane Library databases. Odds Ratio (OR) with 95% confidence interval (CI) was estimated for each study to assess the strength of an association between objective response rate (ORR) and mutation status. Random effects model was used to estimate the pooled OR and their 95% CI was derived. Funnel plot was used to assess publication bias. ^ Results: Thirteen studies published between January 2006 and November 2011that reported on responses to matched phase I clinical trials in patients with advanced cancer were included in the meta-analysis. Nine studies reported on the responses seen in 538 of the 835 patients with driver mutations responsive to therapy and seven studies on the responses observed in 234 of the 306 patients with mutation predictive for negative response. Random effects model was used to estimate pooled OR, which was 7.767(95% CI = 4.199 − 14.366; p-value=0.000) in patients with activating mutations that were responsive to therapy and 0.287 (95% CI = 0.119 − 0.694; p-value=0.009) in patients with mutation predictive of negative response. ^ Conclusion: It is evident from the meta-analysis that somatic mutations present in tumor tissue of patients are predictive of responses to therapy in patients with advanced cancer in phase I setting. Plethora of research and growing evidence base indicate that selection of patients based on mutation analysis of the tumor and personalizing therapy is a step forward in the war against cancer.^

Simulation study of joint transition and Weibull survival model with shared parameters

This study investigates a theoretical model where a longitudinal process, that is a stationary Markov-Chain, and a Weibull survival process share a bivariate random effect. Furthermore, a Quality-of-Life adjusted survival is calculated as the weighted sum of survival time. Theoretical values of population mean adjusted survival of the described model are computed numerically. The parameters of the bivariate random effect do significantly affect theoretical values of population mean. Maximum-Likelihood and Bayesian methods are applied on simulated data to estimate the model parameters. Based on the parameter estimates, predicated population mean adjusted survival can then be calculated numerically and compared with the theoretical values. Bayesian method and Maximum-Likelihood method provide parameter estimations and population mean prediction with comparable accuracy; however Bayesian method suffers from poor convergence due to autocorrelation and inter-variable correlation. ^

A Bayesian approach to estimating the regression coefficients of a multinomial logit model

A Bayesian approach to estimation of the regression coefficients of a multinominal logit model with ordinal scale response categories is presented. A Monte Carlo method is used to construct the posterior distribution of the link function. The link function is treated as an arbitrary scalar function. Then the Gauss-Markov theorem is used to determine a function of the link which produces a random vector of coefficients. The posterior distribution of the random vector of coefficients is used to estimate the regression coefficients. The method described is referred to as a Bayesian generalized least square (BGLS) analysis. Two cases involving multinominal logit models are described. Case I involves a cumulative logit model and Case II involves a proportional-odds model. All inferences about the coefficients for both cases are described in terms of the posterior distribution of the regression coefficients. The results from the BGLS method are compared to maximum likelihood estimates of the regression coefficients. The BGLS method avoids the nonlinear problems encountered when estimating the regression coefficients of a generalized linear model. The method is not complex or computationally intensive. The BGLS method offers several advantages over Bayesian approaches. ^