Fit Of Logistic Regression Model Research Articles

Risk Factors for Postpartum Hemorrhage Following Nulliparous Induction of Labor [3C

INTRODUCTION: Postpartum hemorrhage is the leading cause of maternal mortality worldwide. There are multiple known risk factors, including induction of labor. METHODS: A retrospective cohort study was completed with review of nulliparous women undergoing labor induction at term, from July 2006 to May 2011. Baseline maternal variables and risk factors for postpartum hemorrhage were assessed. All combinations of methods of induction were sequential rather than concurrent. The need for additional uterotonics (beyond routine oxytocin) was used as a surrogate for postpartum hemorrhage. Variables were evaluated for an association with additional uterotonic use based on fitting logistic regression models. RESULTS: A total of 785 women were included in the study. Additional uterotonics were required for 84/785 (11%) of women. Based on univariate analysis, only birth weight and presence of meconium were significantly (p < 0.05) associated with additional uterotonic use, while method of induction approached statistical significance (p=0.06). The mean (SD) birth weight was 3668 (584) vs. 3463 (500) grams in those with and without additional uterotonic use. Multivariate analysis showed an increased risk of additional uterotonics with higher birth weight (adjusted odds ratio (aOR) 1.44 per 500 gram increase in birth weight; 95% CI 1.15, 1.82) and use of Foley plus oxytocin (aOR 4.40; 95% CI 1.36, 14.23). CONCLUSION: In our model, higher birth weight and induction method of Foley followed by oxytocin were associated with increased need for additional uterotonics. This data adds detail to previous studies that have broadly identified induction of labor as a risk factor for postpartum hemorrhage.

Impact of communities, health, and emotional-related factors on smoking use: comparison of joint modeling of mean and dispersion and Bayes\u2019 hierarchical models on add health survey

BackgroundThe analysis of correlated binary data is commonly addressed through the use of conditional models with random effects included in the systematic component as opposed to generalized estimating equations (GEE) models that addressed the random component. Since the joint distribution of the observations is usually unknown, the conditional distribution is a natural approach. Our objective was to compare the fit of different binary models for correlated data in Tabaco use. We advocate that the joint modeling of the mean and dispersion may be at times just as adequate. We assessed the ability of these models to account for the intraclass correlation. In so doing, we concentrated on fitting logistic regression models to address smoking behaviors.MethodsFrequentist and Bayes’ hierarchical models were used to predict conditional probabilities, and the joint modeling (GLM and GAM) models were used to predict marginal probabilities. These models were fitted to National Longitudinal Study of Adolescent to Adult Health (Add Health) data for Tabaco use.ResultsWe found that people were less likely to smoke if they had higher income, high school or higher education and religious. Individuals were more likely to smoke if they had abused drug or alcohol, spent more time on TV and video games, and been arrested. Moreover, individuals who drank alcohol early in life were more likely to be a regular smoker. Children who experienced mistreatment from their parents were more likely to use Tabaco regularly.ConclusionsThe joint modeling of the mean and dispersion models offered a flexible and meaningful method of addressing the intraclass correlation. They do not require one to identify random effects nor distinguish from one level of the hierarchy to the other. Moreover, once one can identify the significant random effects, one can obtain similar results to the random coefficient models. We found that the set of marginal models accounting for extravariation through the additional dispersion submodel produced similar results with regards to inferences and predictions. Moreover, both marginal and conditional models demonstrated similar predictive power.

Is diabetes prevalence higher among HIV-infected individuals compared with the general population? Evidence from MMP and NHANES 2009–2010

BackgroundNationally representative estimates of diabetes mellitus (DM) prevalence among HIV-infected adults in the USA are lacking, and whether HIV-infected adults are at increased risk of DM compared with the general...

A detection probability model for aerial surveys of mule deer

Population estimates derived from aerial surveys of ungulates are biased by imperfect detection, where probability of sighting groups is influenced by variables specific to terrain features and vegetation communities. Therefore, methods for bias-correction must be validated for the region to which they will be applied. Our objectives were to quantify factors affecting detection probability of mule deer (Odocoileus hemionus) during helicopter surveys in Texas, USA, rangelands, and develop a detection probability model to reduce bias in deer population estimates. We placed global positioning system (GPS) collars on 215 deer on 6 sites representative of mule deer range in the southern Great Plains and the Chihuahuan Desert during 2008–2010. We collected data during aerial surveys in January–March and fit logistic regression models to predict detection probability of mule deer based on ecological and behavioral covariates. We evaluated the model using independent estimates of population size derived from a mark-resight procedure. Detection of mule deer was negatively related to distance from the transect, increasing brush cover, sunlight, and increasing terrain ruggedness (P < 0.01). Probability of detection in brush cover was greater if deer were active (P = 0.02). Population estimates corrected for visibility bias using our detection probability model or mark-resight models averaged 2.1 ± 0.49 (SD; n = 50) and 2.2 ± 0.62 times larger, respectively, than uncorrected counts. Estimates of population size derived from the detection probability model averaged 101 ± 26% of mark-resight estimates. However, the detection probability model did not improve precision of population estimates, probably because of unmodeled variation in availability of deer during surveys. Our detection probability model is a simple and effective means to reduce bias in estimates of mule deer population size in southwestern rangelands. Availability bias may be a persistent issue for surveys of mule deer in the Southwest, and appears to be a primary influence of variance of population estimates. © 2016 The Wildlife Society.

Factors and conditions promoting academic resilience: a TIMSS-based analysis of five Asian education systems

It is well documented that academic achievement of students from families of low socioeconomic status (SES) tends to be below their more socially advantaged peers. Several studies have identified factors and conditions that facilitate academic success for disadvantaged students (i.e., promote academic resilience). However, one of the main criticisms of this body of research is in the set of variables that explain academic success for low-SES students and which is not very different from the variables that would explain academic success for all students. The objectives of this article are dual: firstly, to identify factors and conditions associated with academic success, regardless of student SES, and secondly, to identify factors and conditions associated with academic resilience, that is, exclusively for low-SES students. To this end, we used data from Singapore, South Korea, Hong Kong, Chinese Taipei, and Japan in the Trends in International Mathematics and Science Study (TIMSS) 2011. The study sample covered 23,354 students in 720 schools in the five countries. The strategy for analysis was driven by fit of logistic regression models, first predicting the probability of academic success and then subsequent identification of variables significant as predictors for success within the pool of low-SES students. Results indicated that variables, such as positive student attitude to mathematics, teacher confidence in student performance and the test language being spoken at home, were associated with greater chances of academic success. High academic expectations and time spent on mathematics at home demonstrated a differential effect between disadvantaged and non-disadvantaged students in Singapore. In Korea, being male (gender) and in Taipei, low levels of bullying at school, increased the likelihood of resilience. Results suggested that interventions impacting behavior reflected in differentially associated variables could help disadvantaged students to become academically resilient.

The Effect of Initiatives to Overcome Language Barriers and Improve Attendance: A Cross-Sectional Analysis of Adherence in an Inner City Chronic Pain Clinic.

We evaluated the benefit of comprehensive initiatives to overcome language barriers to improve patient adherence with initial scheduled chronic pain clinic appointments. After implementation of our initiative, we performed a retrospective cross-sectional analysis and fit logistic regression models to investigate the association between demographic factors and adherence. We collected retrospective data from an observational cohort with a scheduled appointment at the adult inner-city underserved outpatient Pain Center at Montefiore Medical Center, Bronx, New York. Between March 2012 and March 2014, 14,459 appointments were scheduled; 3,035 of these appointments represented initial first visits; patients had a mean age of 53 years; 15% were predominantly Spanish-speaking, 65% were female. Our initiative to overcome language barriers in our pain clinic included appointment reminders in the patient’s preferred language, Spanish-speaking staff, and unified locations with equitable access. Our dependent variable was adherence with a first scheduled pain clinic appointment. We found that after implementation of our initiative, speaking Spanish was now statistically significantly associated with higher rates of adherence with appointments (Odds Ratio 1.32, 95% confidence interval [1.06–1.64]). We infer from our results that coordinated initiatives to overcome language barriers can be beneficial in improving appointment adherence and access to care by enhancing rapport and communication between pain physicians and their patients. The results of this retrospective cross-sectional analysis of patients’ adherence with scheduled appointments in an inner-city chronic pain clinic suggests that targeted initiatives including a pre-clinic reminder phone call in the patient’s own language may help to overcome language barriers and improve access to care.

Religious Socialization and Interracial Dating

This article examines the impact of religious socialization on Americans’ propensity to engage in interracial dating or romance. Drawing on national survey data, I fit logistic regression models to estimate the net effects of four measures of religious socialization on respondents’ likelihood of engaging in interracial romance. Findings reveal that strong bivariate associations between respondents’ level of religious salience and practice at age 12 and interracial romantic engagement become nonsignificant in the face of sociodemographic controls. However, respondents with Jewish fathers and mothers remain significantly more likely to have interracially dated than those with Protestant fathers and mothers. Moreover, region moderates the effect of childhood religious service attendance such that respondents who more frequently attended religious services at age 12 and reside in non-Western regions, and particularly within the South, are less likely to have interracially dated. Findings ultimately suggest that parents’ religious tradition and childhood religious practice, within certain regional contexts, affect Americans’ likelihood of dating interracially.

Census-derived migration data as a tool for informing malaria elimination policy.

BackgroundNumerous countries around the world are approaching malaria elimination. Until global eradication is achieved, countries that successfully eliminate the disease will contend with parasite reintroduction through international movement of infected people. Human-mediated parasite mobility is also important within countries near elimination, as it drives parasite flows that affect disease transmission on a subnational scale.MethodsMovement patterns exhibited in census-based migration data are compared with patterns exhibited in a mobile phone data set from Haiti to quantify how well migration data predict short-term movement patterns. Because short-term movement data were unavailable for Mesoamerica, a logistic regression model fit to migration data from three countries in Mesoamerica is used to predict flows of infected people between subnational administrative units throughout the region.ResultsPopulation flows predicted using census-based migration data correlated strongly with mobile phone-derived movements when used as a measure of relative connectivity. Relative population flows are therefore predicted using census data across Mesoamerica, informing the areas that are likely exporters and importers of infected people. Relative population flows are used to identify community structure, useful for coordinating interventions and elimination efforts to minimize importation risk. Finally, the ability of census microdata inform future intervention planning is discussed in a country-specific setting using Costa Rica as an example.ConclusionsThese results show long-term migration data can effectively predict the relative flows of infected people to direct malaria elimination policy, a particularly relevant result because migration data are generally easier to obtain than short-term movement data such as mobile phone records. Further, predicted relative flows highlight policy-relevant population dynamics, such as major exporters across the region, and Nicaragua and Costa Rica’s strong connection by movement of infected people, suggesting close coordination of their elimination efforts. Country-specific applications are discussed as well, such as predicting areas at relatively high risk of importation, which could inform surveillance and treatment strategies.Electronic supplementary materialThe online version of this article (doi:10.1186/s12936-016-1315-5) contains supplementary material, which is available to authorized users.

Comparison of Boosted Gabor Feature based Local Descriptor

In the domain of computer vision boosting has become a very powerful tool. The method is used to form a strong classifier applied in several applications of pattern recognition and machine vision. Boosting is a sequential algorithm which separates the instances by the selection of weak classifiers and adds them to a final classifier, thereafter modifies the weights of different training data samples and applies the same classification algorithm in iterative way. The final decision is made by the so called final classifier, the responses of which are applied in a weighted voting decision.In this approach we start from a part-based object detection system described in previous articles [14.15]. The developed patch descriptor is based on two-dimensional Gabor wavelets. The Gabor filters describe the neighborhood of a given image pixel in two-dimensional space. From these local descriptors we created different weak classifiers which are used in the training phase of boosting. We have chosen the boosting algorithm for classification because, in the last decade, the best classification results have been obtained by this algorithm in the domain.In this paper we compare three classification methods based on the boosting approach. The first is Discrete AdaBoost, which considers discrete outputs (+1 for objects,-1 or 0 for non-objects). The second, GentleBoost, this algorithm minimizes the exponential loss and returns real values as classification responses. The third, LogitBoost is the fitting of an additive symmetric logistic regression model by log-likelihood to the training set and solves this optimization with Newton numerical method. Finally, we make a comparison of these classifiers in order to draw conclusions regarding the detection rate, false detection rate and other classification measures.

Fixation of stem in revision of total knee arthroplasty: cemented versus cementless—a meta-analysis

Revision of total knee arthroplasty (TKA) is growing rapidly all over the world. The introduction of intramedullary stems for additional stability in revision is well accepted by most of the surgeons, while the philosophy of stem fixation is still under controversy. A meta-analysis was performed to compare the survivorship of revised implants with regard to a cemented or cementless stem fixation. Publications with patients who underwent revision TKA with minimum 24-month follow-up were systematically reviewed. Type of intramedullary stem fixation, failure rate for any reason, incidence of aseptic loosening and infection were extracted with follow-up interval specified. Random-effects meta-analysis was used to aggregate incidence data, which was compared between different fixation groups by fitting of logistic regression model. Seventeen observational studies were included in this meta-analysis. There was a similar likelihood of failure for any reason (risk ratio, RR 0.97), general reoperation (RR 1.02), aseptic loosening (RR 1.0) and infection (RR 1.0) in cemented stem fixation group compared to cementless stem fixation group with follow-up <60months. When follow-up period extend to more than 60months, the same likelihood was observed as 0.98, 0.96, 0.97 and 0.98, respectively. There was no significant difference in any of these comparisons of survival-related indices. There was no significant difference in failure for any reason, reoperation, aseptic loosening and infection between revision TKA with cemented or cementless stem fixation. Based on the available literature, no superiority of any type of stem fixation was found. If follow-up period was neglected, aseptic loosening would be the leading reason for pain and dysfunction of patient undertaken revision TKA. Systematic review of Level IV, Therapeutic studies, Level IV.

Contraceptive use before first pregnancy by women in India (2005-2006): determinants and differentials.

BackgroundThere exist ample of research literature investigating the various facet of contraceptive use behaviors in India but the use of contraception by married Indian women, prior to having their first pregnancy has been neglected so far. This study attempts to identify the socio demographic determinants and differentials of contraceptive use or non use by a woman in India, before she proceeds to have her first child. The analysis was done using data from the third National Family Health Survey (2005–2006), India.MethodsThis study utilized information from 54,918 women who ever have been married and whose current age at the time of NFHS-3 survey was 15–34 years. To identify the crucial socio-demographic determinants governing this pioneering behavior, logistic regression technique has been used. Hosmer Lemeshow test and ROC curve analysis was also performed in order to check the fitting of logistic regression model to the data under consideration.ResultsOf all the considered explanatory variables religion, caste, education, current age, age at marriage, media exposure and zonal classifications were found to be significantly affecting the study behavior. Place of residence i.e. urban - rural locality came to be insignificant in multivariable logistic regression.ConclusionsIn the light of sufficient evidences confirming the presence of early marriages and child bearing practices in India, conjunct efforts are required to address the socio demographic differentials in contraceptive use by the young married women prior to their first pregnancy. Encouraging women to opt for higher education, ensuring marriages only after legal minimum age at marriage and promoting the family planning programs via print and electronic media may address the existing socio economic barriers. Also, the family planning programs should be oriented to take care of the geographical variations in the study behavior.

Abstract 17098: Evaluation of a Non-laboratory-based Cardiovascular Disease Risk Score in Predicting Cardiovascular Mortality in the Southern Community Cohort Study

Introduction: Cardiovascular disease (CVD) risk prediction guides clinical recommendations regarding primary prevention strategies. Recently, a non-laboratory-based CVD risk score was derived and validated in several populations. We evaluated the performance of the non-laboratory CVD risk score among individuals of low socioeconomic status in the southeastern US, with high CVD incidence rates. Methods: The Southern Community Cohort Study (SCCS) is a prospective cohort study of black and white adults enrolled primarily from community health centers in the southeastern US between 2002-2009. We calculated c-statistics and 95% confidence intervals (CI) to assess the performance of the non-laboratory risk score in discriminating 5- year risk of CVD death (ICD-10 I00-I99) in 14,243 participants (median enrollment age 51 yrs; 74% black, 71% female, 61% income &lt; $15,000 yr) with no history of MI/CABG or stroke at baseline and with complete data on all input variables (age, sex, smoking, diabetes mellitus, reported hypertension (HTN), measured SBP, and BMI). Reported HTN was substituted for anti-HTN therapy, used in the original risk score, because the latter was available only for a subset of the SCCS population. However, among this subset, 82% of individuals who reported HTN also reported use of anti-HTN drugs. Results: CVD risk factors were common: HTN (66%), smoking (59%), diabetes (29%), median SBP 130 mmHg, and median BMI 31 kg/m2. CVD death occurred in 263 participants (1.8%). The non-laboratory CVD risk score demonstrated poor discrimination for CVD mortality, with c-statistics of 0.65 (95% CI: 0.61-0.68) overall and 0.64 (0.59-0.69), 0.65 (0.6-0.7), 0.67 (0.63-0.71) and 0.58 (0.51-0.65) for males, females, blacks and whites, respectively. In an additional logistic regression model fit to the SCCS data, the inclusion of education and income did not substantially improve model performance; overall c-statistic 0.70 (0.67-0.73). Conclusion: In this population of predominantly low-income blacks and whites, a non-laboratory CVD risk score did not perform well in predicting CVD death in any of the race and gender groups. Future studies are warranted to develop algorithms for identifying individuals at increased CVD risk in low resource populations.

Incorporating Confidence Intervals on the Decision Threshold in Logistic Regression

This paper discusses an application of confidence intervals to the threshold decision value used in logistic regres]sion and discusses its effect on changing the quantification of false positive and false negative errors. In doing this a grey area, in which observations are not classified as success (1) or failure (0), but rather ‘uncertain’ is developed. The size of this grey area is related to the level of confidence chosen to create the interval around the threshold as well as the quality of logistic regression model fit. This method shows that potential errors may be mitigated. Monte Carlo simulation and an experimental design approach are used to study the relationship between a number of responses relating to classification of observations and the following factors: threshold level, confidence level, noise in the data, and number of observations collected. Copyright © 2015 John Wiley & Sons, Ltd.

A new test and graphical tool to assess the goodness of fit of logistic regression models.

A prognostic model is well calibrated when it accurately predicts event rates. This is first determined by testing for goodness of fit with the development dataset. All existing tests and graphic tools designed for the purpose suffer several drawbacks, related mainly to the subgrouping of observations or to heavy dependence on arbitrary parameters. We propose a statistical test and a graphical method to assess the goodness of fit of logistic regression models, obtained through an extension of similar techniques developed for external validation. We analytically computed and numerically verified the distribution of the underlying statistic. Simulations on a set of realistic scenarios show that this test and the well-known Hosmer-Lemeshow approach have similar type I error rates. The main advantage of this new approach is that the relationship between model predictions and outcome rates across the range of probabilities can be represented in the calibration belt plot, together with its statistical confidence. By readily spotting any deviations from the perfect fit, this new graphical tool is designed to identify, during the process of model development, poorly modeled variables that call for further investigation. This is illustrated through an example based on real data.

Observed-to-expected ratio for adherence to treatment guidelines as a quality of care indicator for ovarian cancer

ObjectiveTo develop an observed-to-expected ratio (O/E) for adherence to National Comprehensive Cancer Network (NCCN) ovarian cancer treatment guidelines as a risk-adjusted hospital measure of quality care correlated with disease-specific survival. MethodsConsecutive patients with stages I–IV epithelial ovarian cancer were identified from the California Cancer Registry (1/1/96–12/31/06). Using a fit logistic regression model, O/E for guideline adherence was calculated for each hospital and distributed into quartiles stratified by hospital annual case volume: lowest O/E quartile or annual hospital case volume <5, middle two O/E quartiles and volume ≥5, and highest O/E quartile and volume ≥5. A multivariable logistic regression model was used to characterize the independent effect of hospital O/E on ovarian cancer-specific survival. ResultsOverall, 18,491 patients were treated at 405 hospitals; 37.3% received guideline adherent care. Lowest O/E hospitals (n=285) treated 4661 patients (25.2%), mean O/E=0.77±0.55 and median survival 38.9months (95%CI=36.2–42.0months). Intermediate O/E hospitals (n=85) treated 8715 patients (47.1%), mean O/E=0.87±0.17 and median survival of 50.5months (95% CI=48.4–52.8months). Highest O/E hospitals (n=35) treated 5115 patients (27.7%), mean O/E=1.34±0.14 and median survival of 53.8months (95% CI=50.2–58.2months). After controlling for other variables, treatment at highest O/E hospitals was associated with independent and statistically significant improvement in ovarian cancer-specific survival compared to intermediate O/E (HR=1.06, 95% CI=1.01–1.11) and lowest O/E (1.16, 95% CI=1.10–1.23) hospitals. ConclusionsCalculation of hospital-specific O/E for NCCN treatment guideline adherence, combined with minimum case volume criterion, as a measure of ovarian cancer quality of care is feasible and is an independent predictor of survival.

Odor Emotional Quality Predicts Odor Identification.

It is commonly agreed upon a strong link between emotion and olfaction. Odor-evoked memories are experienced as more emotional compared with verbal, visual, and tactile stimuli. Moreover, the emotional quality of odor cues increases memory performance, but contrary to this, odors are poor retrieval cues for verbal labels. To examine the relation between the emotional quality of an odor and its likelihood of identification, this study evaluates how normative emotion ratings based on the 3-dimensional affective space model (that includes valence, arousal, and dominance), using the Self-Assessment Manikin by Bradley and Lang (Bradley MM, Lang PJ. 1994. Measuring emotion: the Self-Assessment Manikin and the Semantic Differential. J Behav Ther Exp Psychiatry. 25(1):49-59.) and the Positive and Negative Affect Schedule (Watson D, Clark LA, Tellegen A. 1988. Development and validation of brief measures of positive and negative affect: the PANAS scales. J Pers Soc Psychol. 54(6):1063-1070.) predict the identification of odors in a multiple choice condition. The best fitting logistic regression model includes squared valence and dominance and thus, points to a significant role of specific emotional features of odors as a main clue for odor identification.

MP60-11 SHOULD A NORMAL MULTIPARAMETRIC MRI PRECLUDE PROSTATE BIOPSY?

You have accessJournal of UrologyProstate Cancer: Detection and Screening II1 Apr 2015MP60-11 SHOULD A NORMAL MULTIPARAMETRIC MRI PRECLUDE PROSTATE BIOPSY? Christopher Filson, Daniel Margolis, Jiaoti Huang, Shyam Natarajan, Patricia Lieu, Frederick Dorey, and Leonard Marks Christopher FilsonChristopher Filson More articles by this author , Daniel MargolisDaniel Margolis More articles by this author , Jiaoti HuangJiaoti Huang More articles by this author , Shyam NatarajanShyam Natarajan More articles by this author , Patricia LieuPatricia Lieu More articles by this author , Frederick DoreyFrederick Dorey More articles by this author , and Leonard MarksLeonard Marks More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2015.02.2213AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Multi-parametric magnetic resonance imaging (mpMRI) may identify regions of the prostate suspicious for cancer (CaP), allowing targeted biopsy. However, the negative predictive value (NPV) of mpMRI for prostate cancer detection is not yet known. To determine the NPV of mpMRI, we determined the frequency of CaP detection by systematic mapping biopsy in men whose mpMRI showed no suspicious regions. METHODS In a prospective, IRB-approved protocol, 1044 men underwent mpMRI and Artemis-biopsy (2009 – 2014). Of these, we report on 244 (mean age = 64±7.6 yrs, median PSA = 4.9 ng/ml, mean prostate volume = 54±30 cc) whose mpMRI showed no suspicious regions, and who underwent 12-core template-based mapping biopsy. Among this cohort, 74 were undergoing first biopsy, 54 had ≥ 1 prior negative biopsies, and 116 had a prior positive biopsy. The mpMRI used a 3T trans-abdominal coil, was performed within 3 months of fusion biopsy, and deemed negative by an expert reader (DM). Biopsy sites were identified and sampled by an single operator (LM) using the Artemis system for guidance. Cores were submitted separately and read by a single uropathologist (JH). We performed chi-square testing and fit logistic regression models to evaluate the relationship between biopsy findings and clinical characteristics. RESULTS Overall, 113 patients with no region of interest on mpMRI (47%) were found to have GS ≥ 6 CaP upon mapping biopsy (Figure), which was more likely among patients with smaller prostates and prior positive biopsies (all p<0.05). Furthermore, 38 patients (16%) had GS ≥ 7 CaP; in these men PSA was directly related to finding of CaP (p=0.02). Age, race, and CaP location within the organ were not related in either group. The NPV of mpMRI for GS ≥ 6 and GS ≥ 7 CaP was 53% and 84%, respectively. CONCLUSIONS 16% of men with GS ≥ 7 CaP would have gone undiagnosed, if a normal mpMRI precluded biopsy. The present data suggest that, if otherwise clinically indicated, prostate biopsy should be performed even if the mpMRI shows no target. © 2015 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 193Issue 4SApril 2015Page: e742 Advertisement Copyright & Permissions© 2015 by American Urological Association Education and Research, Inc.MetricsAuthor Information Christopher Filson More articles by this author Daniel Margolis More articles by this author Jiaoti Huang More articles by this author Shyam Natarajan More articles by this author Patricia Lieu More articles by this author Frederick Dorey More articles by this author Leonard Marks More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Impact of sleep duration on seizure frequency in adults with epilepsy: A sleep diary study

BackgroundProlonged sleep deprivation activates epileptiform EEG abnormalities and seizures in people with epilepsy. Few studies have addressed the effect of chronic partial sleep deprivation on seizure occurrence in populations with epilepsy. We tested the primary hypothesis that partial sleep deprivation over 24- and 72-hour periods increases seizure occurrence in adults with epilepsy. MethodsForty-four subjects completed a series of self-reported instruments, as well as 1-month sleep and seizure diaries, to characterize their sleep and quality of life. Diaries were used to determine the relationship between seizure occurrence and total sleep time 24 and 72h before seizure occurrence using random effects models and a logistic regression model fit by generalized estimating equations. ResultsA total of 237 seizures were recorded during 1295 diary days, representing 5.5±7.0 (mean±SD) seizures per month. Random effects models for 24- and 72-hour total sleep times showed no clinically or statistically significant differences in the total sleep time between preseizure periods and seizure-free periods. The average 24-hour total sleep time during preseizure 24-hour periods was 8min shorter than that during seizure-free periods (p=0.51). The average 72-hour total sleep time during preseizure periods was 20min longer than that during seizure-free periods (p=0.86). The presence of triggers was a significant predictor of seizure occurrence, with stress/anxiety noted most often as a trigger. Mean total sleep time was 9h, and subjects took an average of 12±10 naps per month, having a mean duration of 1.9±1.2h. Daytime sleepiness, fatigue, and insomnia symptoms were commonly reported. ConclusionsSmall degrees of sleep loss were not associated with seizure occurrence in our sample of adults with epilepsy. Our results also include valuable observations of the altered sleep times and frequent napping habits of adults with refractory epilepsy and the potential contribution of these habits to quality of life and seizure control.

Predictors of missing data when asking parents about their children's diet based on "Oral Health Situation of Iranian Children" Survey.

The predictors of missing data when parents fill out questionnaire about their children's diet are not defined. The aim of this study was to evaluate predictors which affect unresponsiveness to diet questions based on " Oral Health Situation of Iranian Children" Survey carried out in 1998. A dummy variable was created as dependent variable according to responding eight questions relating to diet. Then predictors of missing data were defined using multivariable logistic regression and classification tree method. To evaluate goodness of fit of logistic regression model, sensitivity and specificity were assessed. Classification tree analysis was done by Quest growing method. Significance level was set at 0.05 in logistic regression analysis. "Missing data" variable was marked as missing in 616 (7.2%) of questionnaires. In logistic regression model revealed that, gender, mother's education level and father's education level didn't affect "missing data" variable (P > 0.05). But, dmf index (OR = 0.94), Area of living (OR = 0.49), number of children in the family (OR = 1.19), sibling order (OR = 0.85), brushing (OR = 0.84) and visiting dentist (OR = 0.59) had statistically significant association with dependent variable (P < 0.05). Classification tree analysis showed that questionnaires related to children in urban area whose dmf index is under 5 and haven't visited dentist are predicted to have missing data more than other groups. Area of living, dmf index, number of children in the family, sibling order, brushing and visiting dentist are significant variables for predicting the risk of missing data when asking parents about their children's diet.

Parameter Estimation in Regression for Long-Term Survival Rate from Left-Truncated and Right-Censored Data

For right-censored data, Subramanian (2001) proposed an estimate for estimating regression parameter of “long-term” survival rate. In this article, we demonstrate that Subramanian’s estimator can be extended to left-truncated and right-censored data. When the covariate is discrete, we propose an alternative estimate and show that under logistic regression model, is asymptotically more efficient than . Based on , the chi-squared test statistic can be used for testing goodness of fit of logistic regression model. Furthermore, for a one-dimensional continuous covariate, we propose an estimator by using a generalized product-limit estimator of conditional survival function (Iglesias-Prez and Gonzlez-Manteiga, 1999). A simulation study is conducted to investigate the performance of the proposed estimators. The proposed method is applied to the Vetran’s Administration lung cancer data reported by Prentice (1973).