Determinants of discordance in patients' and physicians' rating of rheumatoid arthritis disease activity

Objective To assess the determinants of patients' (PTGL) and physicians' (MDGL) global assessment of rheumatoid arthritis (RA) activity and factors associated with discordance among them. Methods. A total of 7,028 patients in the Quantitative Standard Monitoring of Patients with RA study had PTGL and MDGL assessed at the same clinic visit on a 0-10-cm visual analog scale (VAS). Three patient groups were defined: concordant rating group (PTGL and MDGL within >= 2 cm), higher patient rating group (PTGL exceeding MDGL by > 2 cm), and lower patient rating group (PTGL less than MDGL by > 2 cm). Multivariable regression analysis was used to identify determinants of PTGL and MDGL and their discordance. Results. The mean +/- SD VAS scores for PTGL and MDGL were 4.01 +/- 2.70 and 2.91 +/- 2.37, respectively. Pain was overwhelmingly the single most important determinant of PTGL, followed by fatigue. In contrast, MDGL was most influenced by swollen joint count (SJC), followed by erythrocyte sedimentation rate (ESR) and tender joint count (TJC). A total of 4,454 (63.4%), 2,106 (30%), and 468 (6.6%) patients were in the concordant, higher, and lower patient rating groups, respectively. Odds of higher patient rating increased with higher pain, fatigue, psychological distress, age, and morning stiffness, and decreased with higher SJC, TJC, and ESR. Lower patient rating odds increased with higher SJC, TJC, and ESR, and decreased with lower fatigue levels. Conclusion. Nearly 36% of patients had discordance in RA activity assessment from their physicians. Sensitivity to the "disease experience" of patients, particularly pain and fatigue, is warranted for effective care of RA.

Vasovagal reactions in whole blood donors at three REDS-II blood centers in Brazil

BACKGROUND: In Brazil little is known about adverse reactions during donation and the donor characteristics that may be associated with such events. Donors are offered snacks and fluids before donating and are required to consume a light meal after donation. For these reasons the frequency of reactions may be different than those observed in other countries. STUDY DESIGN AND METHODS: A cross-sectional study was conducted of eligible whole blood donors at three large blood centers located in Brazil between July 2007 and December 2009. Vasovagal reactions (VVRs) along with donor demographic and biometric data were collected. Reactions were defined as any presyncopal or syncopal event during the donation process. Multivariable logistic regression was performed to identify predictors of VVRs. RESULTS: Of 724,861 donor presentations, 16,129 (2.2%) VVRs were recorded. Rates varied substantially between the three centers: 53, 290, and 381 per 10,000 donations in Recife, Sao Paulo, and Belo Horizonte, respectively. Although the reaction rates varied, the donor characteristics associated with VVRs were similar (younger age [18-29 years], replacement donors, first-time donors, low estimated blood volume [EBV]). In multivariable analysis controlling for differences between the donor populations in each city younger age, first-time donor status, and lower EBV were the factors most associated with reactions. CONCLUSION: Factors associated with VVRs in other locations are also evident in Brazil. The difference in VVR rates between the three centers might be due to different procedures for identifying and reporting the reactions. Potential interventions to reduce the risk of reactions in Brazil should be considered.

The role of clouds in improving the regression model for hourly values of diffuse solar radiation

The study introduces a new regression model developed to estimate the hourly values of diffuse solar radiation at the surface. The model is based on the clearness index and diffuse fraction relationship, and includes the effects of cloud (cloudiness and cloud type), traditional meteorological variables (air temperature, relative humidity and atmospheric pressure observed at the surface) and air pollution (concentration of particulate matter observed at the surface). The new model is capable of predicting hourly values of diffuse solar radiation better than the previously developed ones (R-2 = 0.93 and RMSE = 0.085). A simple version with a large applicability is proposed that takes into consideration cloud effects only (cloudiness and cloud height) and shows a R-2 = 0.92. (C) 2011 Elsevier Ltd. All rights reserved.

Estimation and diagnostics for heteroscedastic nonlinear regression models based on scale mixtures of skew-normal distributions

An extension of some standard likelihood based procedures to heteroscedastic nonlinear regression models under scale mixtures of skew-normal (SMSN) distributions is developed. This novel class of models provides a useful generalization of the heteroscedastic symmetrical nonlinear regression models (Cysneiros et al., 2010), since the random term distributions cover both symmetric as well as asymmetric and heavy-tailed distributions such as skew-t, skew-slash, skew-contaminated normal, among others. A simple EM-type algorithm for iteratively computing maximum likelihood estimates of the parameters is presented and the observed information matrix is derived analytically. In order to examine the performance of the proposed methods, some simulation studies are presented to show the robust aspect of this flexible class against outlying and influential observations and that the maximum likelihood estimates based on the EM-type algorithm do provide good asymptotic properties. Furthermore, local influence measures and the one-step approximations of the estimates in the case-deletion model are obtained. Finally, an illustration of the methodology is given considering a data set previously analyzed under the homoscedastic skew-t nonlinear regression model. (C) 2012 Elsevier B.V. All rights reserved.

The geometric Birnbaum-Saunders regression model with cure rate

In this paper, we propose a cure rate survival model by assuming the number of competing causes of the event of interest follows the Geometric distribution and the time to event follow a Birnbaum Saunders distribution. We consider a frequentist analysis for parameter estimation of a Geometric Birnbaum Saunders model with cure rate. Finally, to analyze a data set from the medical area. (C) 2011 Elsevier B.V. All rights reserved.

THE LOG-BURR XII REGRESSION MODEL FOR GROUPED SURVIVAL DATA

The log-Burr XII regression model for grouped survival data is evaluated in the presence of many ties. The methodology for grouped survival data is based on life tables, where the times are grouped in k intervals, and we fit discrete lifetime regression models to the data. The model parameters are estimated by maximum likelihood and jackknife methods. To detect influential observations in the proposed model, diagnostic measures based on case deletion, so-called global influence, and influence measures based on small perturbations in the data or in the model, referred to as local influence, are used. In addition to these measures, the total local influence and influential estimates are also used. We conduct Monte Carlo simulation studies to assess the finite sample behavior of the maximum likelihood estimators of the proposed model for grouped survival. A real data set is analyzed using a regression model for grouped data.

Local power properties of some asymptotic tests in symmetric linear regression models

In this paper we obtain asymptotic expansions, up to order n(-1/2) and under a sequence of Pitman alternatives, for the nonnull distribution functions of the likelihood ratio, Wald, score and gradient test statistics in the class of symmetric linear regression models. This is a wide class of models which encompasses the t model and several other symmetric distributions with longer-than normal tails. The asymptotic distributions of all four statistics are obtained for testing a subset of regression parameters. Furthermore, in order to compare the finite-sample performance of these tests in this class of models, Monte Carlo simulations are presented. An empirical application to a real data set is considered for illustrative purposes. (C) 2011 Elsevier B.V. All rights reserved.

Error Autocorrelation and Linear Regression for Temperature-Based Evapotranspiration Estimates Improvement

Estimates of evapotranspiration on a local scale is important information for agricultural and hydrological practices. However, equations to estimate potential evapotranspiration based only on temperature data, which are simple to use, are usually less trustworthy than the Food and Agriculture Organization (FAO)Penman-Monteith standard method. The present work describes two correction procedures for potential evapotranspiration estimates by temperature, making the results more reliable. Initially, the standard FAO-Penman-Monteith method was evaluated with a complete climatologic data set for the period between 2002 and 2006. Then temperature-based estimates by Camargo and Jensen-Haise methods have been adjusted by error autocorrelation evaluated in biweekly and monthly periods. In a second adjustment, simple linear regression was applied. The adjusted equations have been validated with climatic data available for the Year 2001. Both proposed methodologies showed good agreement with the standard method indicating that the methodology can be used for local potential evapotranspiration estimates.

Blood transfusion utilization and recipient survival at Hospital das Clinicas in Sao Paulo, Brazil

BACKGROUND: The characteristics of blood recipients including diagnoses associated with transfusion and posttransfusion survival are unreported in Brazil. The goals of this analysis were: 1) to describe blood utilization according to clinical diagnoses and patient characteristics and 2) to determine the factors associated with survival of blood recipients. STUDY DESIGN AND METHODS: A retrospective cross-sectional analysis was conducted on all inpatients in 2004. Data came from three sources: The first two files consist of data about patient characteristics, clinical diagnosis, and transfusion. Analyses comparing transfused and nontransfused patients were conducted. The third file was used to determine survival recipients up to 3 years after transfusion. Logistic regression was conducted among transfused patients to examine characteristics associated with survival. RESULTS: In 2004, a total of 30,779 patients were admitted, with 3835 (12.4%) transfused. These patients had 10,479 transfusions episodes, consisting of 39,561 transfused components: 16,748 (42%) red blood cells, 15,828 (40%) platelets (PLTs), and 6190 (16%) plasma. The median number of components transfused was three (range, 1-656) per patient admission. Mortality during hospitalization was different for patients whose admissions included transfusion or not (24% vs. 4%). After 1 year, 56% of transfusion recipients were alive. The multivariable model of factors associated with mortality after transfusion showed that the most significant factors in descending order were hospital ward, increasing age, increasing number of components transfused, and type of components received. CONCLUSION: Ward and transfusion are markers of underlying medical conditions and are associated with the probability of survival. PLT transfusions are common and likely reflect the types of patients treated. This comprehensive blood utilization study, the first of its kind in Brazil, can help in developing transfusion policy analyses in South America.

The Poisson-exponential regression model under different latent activation schemes

In this paper, a new family of survival distributions is presented. It is derived by considering that the latent number of failure causes follows a Poisson distribution and the time for these causes to be activated follows an exponential distribution. Three different activation schemes are also considered. Moreover, we propose the inclusion of covariates in the model formulation in order to study their effect on the expected value of the number of causes and on the failure rate function. Inferential procedure based on the maximum likelihood method is discussed and evaluated via simulation. The developed methodology is illustrated on a real data set on ovarian cancer.

Relation of Uric Acid to Serum Levels of High-Sensitivity C-Reactive Protein, Triglycerides, and High-Density Lipoprotein Cholesterol and to Hepatic Steatosis

Increased uric acid (UA) is strongly linked to cardiovascular disease. However, the independent role of UA is still debated because it is associated with several cardiovascular risk factors including obesity and metabolic syndrome. This study assessed the association of UA with increased high-sensitivity C-reactive protein (hs-CRP), increased ratio of triglyceride to high-density lipoprotein cholesterol (TG/HDL), sonographically detected hepatic steatosis, and their clustering in the presence and absence of obesity and metabolic syndrome. We evaluated 3,518 employed subjects without clinical cardiovascular disease from November 2008 through July 2010. Prevalence of tis-CRP >= 3 mg/L was 19%, that of TG/HDL >= 3 was 44%, and that of hepatic steatosis was 43%. In multivariable logistic regression after adjusting for traditional cardiovascular risk factors and confounders, highest versus lowest UA quartile was associated with hs-CRP >= 3 mg/L (odds ratio [OR] 1.52, 95% confidence interval [CI] 1.01 to 2.28, p = 0.04), TG/HDL >= 3 (OR 3.29, 95% CI 2.36 to 4.60, p <0.001), and hepatic steatosis (OR 3.10, 95% CI 2.22 to 4.32, p <0.001) independently of obesity and metabolic syndrome. Association of UA with hs-CRP >= 3 mg/L became nonsignificant in analyses stratified by obesity. Ascending UA quartiles compared to the lowest UA quartile demonstrated a graded increase in the odds of having 2 or 3 of these risk conditions and a successive decrease in the odds of having none. In conclusion, high UA levels were associated with increased TG/HDL and hepatic steatosis independently of metabolic syndrome and obesity and with increased hs-CRP independently of metabolic syndrome. (C) 2012 Elsevier Inc. All rights reserved. (Am J Cardiol 2012;110:1787-1792)

Examination of the performance of different pointwise linear regression progression criteria to detect glaucomatous visual field change

Background: We aimed to investigate the performance of five different trend analysis criteria for the detection of glaucomatous progression and to determine the most frequently and rapidly progressing locations of the visual field. Design: Retrospective cohort. Participants or Samples: Treated glaucoma patients with =8 Swedish Interactive Thresholding Algorithm (SITA)-standard 24-2 visual field tests. Methods: Progression was determined using trend analysis. Five different criteria were used: (A) =1 significantly progressing point; (B) =2 significantly progressing points; (C) =2 progressing points located in the same hemifield; (D) at least two adjacent progressing points located in the same hemifield; (E) =2 progressing points in the same Garway-Heath map sector. Main Outcome Measures: Number of progressing eyes and false-positive results. Results: We included 587 patients. The number of eyes reaching a progression endpoint using each criterion was: A = 300 (51%); B = 212 (36%); C = 194 (33%); D = 170 (29%); and E = 186 (31%) (P = 0.03). The numbers of eyes with positive slopes were: A = 13 (4.3%); B = 3 (1.4%); C = 3 (1.5%); D = 2 (1.1%); and E = 3 (1.6%) (P = 0.06). The global slopes for progressing eyes were more negative in Groups B, C and D than in Group A (P = 0.004). The visual field locations that progressed more often were those in the nasal field adjacent to the horizontal midline. Conclusions: Pointwise linear regression criteria that take into account the retinal nerve fibre layer anatomy enhances the specificity of trend analysis for the detection glaucomatous visual field progression.

Bartlett corrections in Birnbaum-Saunders nonlinear regression models

Lemonte and Cordeiro [Birnbaum-Saunders nonlinear regression models, Comput. Stat. Data Anal. 53 (2009), pp. 4441-4452] introduced a class of Birnbaum-Saunders (BS) nonlinear regression models potentially useful in lifetime data analysis. We give a general matrix Bartlett correction formula to improve the likelihood ratio (LR) tests in these models. The formula is simple enough to be used analytically to obtain several closed-form expressions in special cases. Our results generalize those in Lemonte et al. [Improved likelihood inference in Birnbaum-Saunders regressions, Comput. Stat. DataAnal. 54 (2010), pp. 1307-1316], which hold only for the BS linear regression models. We consider Monte Carlo simulations to show that the corrected tests work better than the usual LR tests.

Classification and regression tree (CART) model to predict pulmonary tuberculosis in hospitalized patients

Background: Tuberculosis (TB) remains a public health issue worldwide. The lack of specific clinical symptoms to diagnose TB makes the correct decision to admit patients to respiratory isolation a difficult task for the clinician. Isolation of patients without the disease is common and increases health costs. Decision models for the diagnosis of TB in patients attending hospitals can increase the quality of care and decrease costs, without the risk of hospital transmission. We present a predictive model for predicting pulmonary TB in hospitalized patients in a high prevalence area in order to contribute to a more rational use of isolation rooms without increasing the risk of transmission. Methods: Cross sectional study of patients admitted to CFFH from March 2003 to December 2004. A classification and regression tree (CART) model was generated and validated. The area under the ROC curve (AUC), sensitivity, specificity, positive and negative predictive values were used to evaluate the performance of model. Validation of the model was performed with a different sample of patients admitted to the same hospital from January to December 2005. Results: We studied 290 patients admitted with clinical suspicion of TB. Diagnosis was confirmed in 26.5% of them. Pulmonary TB was present in 83.7% of the patients with TB (62.3% with positive sputum smear) and HIV/AIDS was present in 56.9% of patients. The validated CART model showed sensitivity, specificity, positive predictive value and negative predictive value of 60.00%, 76.16%, 33.33%, and 90.55%, respectively. The AUC was 79.70%. Conclusions: The CART model developed for these hospitalized patients with clinical suspicion of TB had fair to good predictive performance for pulmonary TB. The most important variable for prediction of TB diagnosis was chest radiograph results. Prospective validation is still necessary, but our model offer an alternative for decision making in whether to isolate patients with clinical suspicion of TB in tertiary health facilities in countries with limited resources.

Response plateau models fitting via isotonic regression

Within the nutritional context, the supplementation of microminerals in bird food is often made in quantities exceeding those required in the attempt to ensure the proper performance of the animals. The experiments of type dosage x response are very common in the determination of levels of nutrients in optimal food balance and include the use of regression models to achieve this objective. Nevertheless, the regression analysis routine, generally, uses a priori information about a possible relationship between the response variable. The isotonic regression is a method of estimation by least squares that generates estimates which preserves data ordering. In the theory of isotonic regression this information is essential and it is expected to increase fitting efficiency. The objective of this work was to use an isotonic regression methodology, as an alternative way of analyzing data of Zn deposition in tibia of male birds of Hubbard lineage. We considered the models of plateau response of polynomial quadratic and linear exponential forms. In addition to these models, we also proposed the fitting of a logarithmic model to the data and the efficiency of the methodology was evaluated by Monte Carlo simulations, considering different scenarios for the parametric values. The isotonization of the data yielded an improvement in all the fitting quality parameters evaluated. Among the models used, the logarithmic presented estimates of the parameters more consistent with the values reported in literature.