Pranab Kumar Sen: Life and works

In this article, we describe briefly the highlights and various accomplishments in the personal as well as the academic life of Professor Pranab Kumar Sen.Comment: Published in at http://dx.doi.org/10.1214/193940307000000013 the IMS Collections (http://www.imstat.org/publications/imscollections.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

Simple and objective prediction of survival in patients with lung cancer: staging the host systemic inflammatory response

Background. Prediction of survival in patients diagnosed with lung cancer remains problematical. The aim of the present study was to examine the clinical utility of an established objective marker of the systemic inflammatory response, the Glasgow Prognostic Score, as the basis of risk stratification in patients with lung cancer. Methods. Between 2005 and 2008 all newly diagnosed lung cancer patients coming through the multidisciplinary meetings (MDTs) of four Scottish centres were included in the study. The details of 882 patients with a confirmed new diagnosis of any subtype or stage of lung cancer were collected prospectively. Results. The median survival was 5.6 months (IQR 4.8–6.5). Survival analysis was undertaken in three separate groups based on mGPS score. In the mGPS 0 group the most highly predictive factors were performance status, weight loss, stage of NSCLC, and palliative treatment offered. In the mGPS 1 group performance status, stage of NSCLC, and radical treatment offered were significant. In the mGPS 2 group only performance status and weight loss were statistically significant. Discussion. This present study confirms previous work supporting the use of mGPS in predicting cancer survival; however, it goes further by showing how it might be used to provide more objective risk stratification in patients diagnosed with lung cancer

Clinical Features and Outcomes Differ between Skeletal and Extraskeletal Osteosarcoma.

Background. Extraskeletal osteosarcoma (ESOS) is a rare subtype of osteosarcoma. We investigated patient characteristics, overall survival, and prognostic factors in ESOS. Methods. We identified cases of high-grade osteosarcoma with known tissue of origin in the Surveillance, Epidemiology, and End Results database from 1973 to 2009. Demographics were compared using univariate tests. Overall survival was compared with log-rank tests and multivariate analysis using Cox proportional hazards methods. Results. 256/4,173 (6%) patients with high-grade osteosarcoma had ESOS. Patients with ESOS were older, were more likely to have an axial tumor and regional lymph node involvement, and were female. Multivariate analysis showed ESOS to be favorable after controlling for stage, age, tumor site, gender, and year of diagnosis [hazard ratio 0.75 (95% CI 0.62 to 0.90); p = 0.002]. There was an interaction between age and tissue of origin such that older patients with ESOS had superior outcomes compared to older patients with skeletal osteosarcoma. Adverse prognostic factors in ESOS included metastatic disease, larger tumor size, older age, and axial tumor site. Conclusion. Patients with ESOS have distinct clinical features but similar prognostic factors compared to skeletal osteosarcoma. Older patients with ESOS have superior outcomes compared to older patients with skeletal osteosarcoma

Recent advances in directional statistics

Mainstream statistical methodology is generally applicable to data observed in Euclidean space. There are, however, numerous contexts of considerable scientific interest in which the natural supports for the data under consideration are Riemannian manifolds like the unit circle, torus, sphere and their extensions. Typically, such data can be represented using one or more directions, and directional statistics is the branch of statistics that deals with their analysis. In this paper we provide a review of the many recent developments in the field since the publication of Mardia and Jupp (1999), still the most comprehensive text on directional statistics. Many of those developments have been stimulated by interesting applications in fields as diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics, image analysis, text mining, environmetrics, and machine learning. We begin by considering developments for the exploratory analysis of directional data before progressing to distributional models, general approaches to inference, hypothesis testing, regression, nonparametric curve estimation, methods for dimension reduction, classification and clustering, and the modelling of time series, spatial and spatio-temporal data. An overview of currently available software for analysing directional data is also provided, and potential future developments discussed.Comment: 61 page

High Prevalence of Tuberculosis among Adults with Fever Admitted at a Tertiary Hospital in North-western Tanzania

Tuberculosis is a leading cause of death in developing countries where HIV is endemic. This hospital based study was done to estimate the magnitude of pulmonary and extra-pulmonary tuberculosis and to determine predictors of tuberculosis among febrile adults admitted at Bugando Medical Centre (BMC), Mwanza, Tanzania. A total of 346 adults febrile patients admitted in medical wards were studied. Sputum for AFB microscopy and chest X-rays was used to diagnose tuberculosis. Clinical features were collected using standardized data collection tool. HIV testing and CD4 counts were determined. Data were analyzed using STATA version 11 software. Of 346 febrile adults patients 116 (33.5%) were diagnosed to have tuberculosis; of which 79 (68.1%) and 37 (31.9%) had pulmonary tuberculosis (PTB) and extra-pulmonary tuberculosis, respectively. Smear negative PTB were more common in HIV positive than in HIV negative patients (50% vs. 18.5%, p=0.007). Extra-pulmonary tuberculosis was more common in HIV positive patients than pulmonary tuberculosis (86.4% vs. 13.6%), p=0.0001). On multivariate logistic regression analysis the predictors of tuberculosis were; age above 35 years (OR =2.38, p=0.007), cardinal symptoms (OR=37, p<0.0001), pleural effusion (OR=24, p=0.0001), and HIV status (OR =3.2, p=0.0001). Of 79 patients with PTB, 48 (60.7%) were AFB smear positive and 31(39.3%) were AFB smear negative. HIV patients with smear negative tuberculosis had significantly lower CD4 count than HIV patients with smear positive tuberculosis (63.5 cells/μl versus 111.5 cells/μl) [Mann- Whitney test p=0.0431]]. No different in mortality was observed between patients with TB and those without TB admitted in BMC medical wards (28.5% vs. 23.0%, p= 0.1318). Tuberculosis is the commonest cause of fever among adults patients admitted at BMC and is predicted by age above 35 years, positive HIV status, cardinal PTB symptoms, and pleural effusion. Routinely TB screening is highly recommended among adults with fever, cough, night sweating and wasting in countries where HIV is endemic.\u

Prevalence of HIV Infection among Trauma Patients Admitted to\ud Bugando Medical Centre, Mwanza, Tanzania and its\ud Influence on Outcome

HIV infection, a major health problem worldwide, has been reported to be prevalent in trauma patients, thus presents an occupational hazard to health care workers who care for these patients. The purpose of this study was to establish the prevalence of HIV among trauma patients in our setting and to compare the outcome of these patients who are HIV positive with those who are HIV negative. This was a descriptive cross sectional study involving trauma patients aged 11 years and above, admitted to the surgical wards of Bugando Medical Centre in Mwanza, Tanzania over a six-month period from October 2008 to March 2009. All eligible patients were consecutively enrolled in the study. Data were collected using a pre-tested, coded questionnaire and analyzed using SPSS computer software. A total of 250 trauma patients were recruited and studied. The mean age of the study population was 36.37±15.35. Males accounted for the majority (N=202; 80.8%) of the study population. The prevalence of HIV among trauma patients was 11.6%. Among the HIV positive patients, 26 (89.7%) were males and majority aged 31-40 years. Seventy two percent of HIV positive patients had CD4+ count of ≥ 200 cells/μl. Overall length of hospital stays (LOS) ranged from 1 - 90 days with mean of 19.11 ± 15.84 days. Using multivariate logistic regression, injury severity score (ISS) (P=0.0026), revised trauma scores (RTS) (P= 0.002,), HIV seropositivity (P= 0.0012) and CD4+ count (P= 0.001) were significantly found to be associated with increased LOS. Mortality rate was 10.8% and was significantly associated with; the body region injured (P < 0.05), ISS (P = 0.026), RTS (P = 0.001), PTS (P= 0.01), HIV positivity (P= 0.0001) and CD4+ count (P= 0.035). HIV is prevalent among trauma patients in our setting. A substantial risk of exposure to HIV exists in health workers who care for these patients. Thus, all trauma health care workers in this region need to practice universal barrier precautions in order to reduce the risk of exposure to HIV infection. HIV positive patients with CD4+ count ≥200cells/μl have similar prognosis as HIV negative patients and therefore should be treated the same way

Maximally Divergent Intervals for Anomaly Detection

We present new methods for batch anomaly detection in multivariate time series. Our methods are based on maximizing the Kullback-Leibler divergence between the data distribution within and outside an interval of the time series. An empirical analysis shows the benefits of our algorithms compared to methods that treat each time step independently from each other without optimizing with respect to all possible intervals.Comment: ICML Workshop on Anomaly Detectio

Personalized Risk Assessment in Never, Light, and Heavy Smokers in a prospective cohort in Taiwan.

The objective of this study was to develop markedly improved risk prediction models for lung cancer using a prospective cohort of 395,875 participants in Taiwan. Discriminatory accuracy was measured by generation of receiver operator curves and estimation of area under the curve (AUC). In multivariate Cox regression analysis, age, gender, smoking pack-years, family history of lung cancer, personal cancer history, BMI, lung function test, and serum biomarkers such as carcinoembryonic antigen (CEA), bilirubin, alpha fetoprotein (AFP), and c-reactive protein (CRP) were identified and included in an integrative risk prediction model. The AUC in overall population was 0.851 (95% CI = 0.840-0.862), with never smokers 0.806 (95% CI = 0.790-0.819), light smokers 0.847 (95% CI = 0.824-0.871), and heavy smokers 0.732 (95% CI = 0.708-0.752). By integrating risk factors such as family history of lung cancer, CEA and AFP for light smokers, and lung function test (Maximum Mid-Expiratory Flow, MMEF25-75%), AFP and CEA for never smokers, light and never smokers with cancer risks as high as those within heavy smokers could be identified. The risk model for heavy smokers can allow us to stratify heavy smokers into subgroups with distinct risks, which, if applied to low-dose computed tomography (LDCT) screening, may greatly reduce false positives