A simulation model for diarrhoea and other common recurrent infections: a tool for exploring epidemiological methods

The measurement and analysis of common recurrent conditions such as diarrhoea, respiratory infections or fever pose methodological challenges with regard to case definition, disease surveillance and statistical analysis. In this paper we describe a flexible and robust model that can generate simulated longitudinal datasets for a range of recurrent infections, reflecting the stochastic processes that underpin the data collected in the field. It can be used to evaluate and compare alternative disease definitions, surveillance strategies and statistical methods under ‘controlled conditions’. Parameters in the model include: characterizing the distributions of the individual disease incidence and the duration of disease episodes; allowing the average disease duration to depend on an individual's number of episodes (simulating a correlation between incidence and duration); making the individual risk of disease depend on the occurrence of previous episodes (simulating autocorrelation of successive episodes); finally, incorporating seasonal variation of disease

Data production models for the CDF experiment

The data production for the CDF experiment is conducted on a large Linux PC farm designed to meet the needs of data collection at a maximum rate of 40 MByte/sec. We present two data production models that exploits advances in computing and communication technology. The first production farm is a centralized system that has achieved a stable data processing rate of approximately 2 TByte per day. The recently upgraded farm is migrated to the SAM (Sequential Access to data via Metadata) data handling system. The software and hardware of the CDF production farms has been successful in providing large computing and data throughput capacity to the experiment.Comment: 8 pages, 9 figures; presented at HPC Asia2005, Beijing, China, Nov 30 - Dec 3, 200

Novel statistical approaches for non-normal censored immunological data: analysis of cytokine and gene expression data

Background: For several immune-mediated diseases, immunological analysis will become more complex in the future with datasets in which cytokine and gene expression data play a major role. These data have certain characteristics that require sophisticated statistical analysis such as strategies for non-normal distribution and censoring. Additionally, complex and multiple immunological relationships need to be adjusted for potential confounding and interaction effects. Objective: We aimed to introduce and apply different methods for statistical analysis of non-normal censored cytokine and gene expression data. Furthermore, we assessed the performance and accuracy of a novel regression approach in order to allow adjusting for covariates and potential confounding. Methods: For non-normally distributed censored data traditional means such as the Kaplan-Meier method or the generalized Wilcoxon test are described. In order to adjust for covariates the novel approach named Tobit regression on ranks was introduced. Its performance and accuracy for analysis of non-normal censored cytokine/gene expression data was evaluated by a simulation study and a statistical experiment applying permutation and bootstrapping. Results: If adjustment for covariates is not necessary traditional statistical methods are adequate for non-normal censored data. Comparable with these and appropriate if additional adjustment is required, Tobit regression on ranks is a valid method. Its power, type-I error rate and accuracy were comparable to the classical Tobit regression. Conclusion: Non-normally distributed censored immunological data require appropriate statistical methods. Tobit regression on ranks meets these requirements and can be used for adjustment for covariates and potential confounding in large and complex immunological datasets

Data processing model for the CDF experiment

The data processing model for the CDF experiment is described. Data processing reconstructs events from parallel data streams taken with different combinations of physics event triggers and further splits the events into datasets of specialized physics datasets. The design of the processing control system faces strict requirements on bookkeeping records, which trace the status of data files and event contents during processing and storage. The computing architecture was updated to meet the mass data flow of the Run II data collection, recently upgraded to a maximum rate of 40 MByte/sec. The data processing facility consists of a large cluster of Linux computers with data movement managed by the CDF data handling system to a multi-petaByte Enstore tape library. The latest processing cycle has achieved a stable speed of 35 MByte/sec (3 TByte/day). It can be readily scaled by increasing CPU and data-handling capacity as required.Comment: 12 pages, 10 figures, submitted to IEEE-TN

Applied immuno-epidemiological research: an approach for integrating existing knowledge into the statistical analysis of multiple immune markers.

BACKGROUND: Immunologists often measure several correlated immunological markers, such as concentrations of different cytokines produced by different immune cells and/or measured under different conditions, to draw insights from complex immunological mechanisms. Although there have been recent methodological efforts to improve the statistical analysis of immunological data, a framework is still needed for the simultaneous analysis of multiple, often correlated, immune markers. This framework would allow the immunologists' hypotheses about the underlying biological mechanisms to be integrated. RESULTS: We present an analytical approach for statistical analysis of correlated immune markers, such as those commonly collected in modern immuno-epidemiological studies. We demonstrate i) how to deal with interdependencies among multiple measurements of the same immune marker, ii) how to analyse association patterns among different markers, iii) how to aggregate different measures and/or markers to immunological summary scores, iv) how to model the inter-relationships among these scores, and v) how to use these scores in epidemiological association analyses. We illustrate the application of our approach to multiple cytokine measurements from 818 children enrolled in a large immuno-epidemiological study (SCAALA Salvador), which aimed to quantify the major immunological mechanisms underlying atopic diseases or asthma. We demonstrate how to aggregate systematically the information captured in multiple cytokine measurements to immunological summary scores aimed at reflecting the presumed underlying immunological mechanisms (Th1/Th2 balance and immune regulatory network). We show how these aggregated immune scores can be used as predictors in regression models with outcomes of immunological studies (e.g. specific IgE) and compare the results to those obtained by a traditional multivariate regression approach. CONCLUSION: The proposed analytical approach may be especially useful to quantify complex immune responses in immuno-epidemiological studies, where investigators examine the relationship among epidemiological patterns, immune response, and disease outcomes

Recent diarrhoeal illness and risk of lower respiratory infections in children under the age of 5 years

Background Children in low-income settings suffering from frequent diarrhoea episodes are also at a high risk of acute lower respiratory infections (ALRI). We explored whether this is due to common risk factors for both conditions or whether diarrhoea can increase the risk of ALRI directly

Sampling strategies to measure the prevalence of common recurrent infections in longitudinal studies

<p>Abstract</p> <p>Background</p> <p>Measuring recurrent infections such as diarrhoea or respiratory infections in epidemiological studies is a methodological challenge. Problems in measuring the incidence of recurrent infections include the episode definition, recall error, and the logistics of close follow up. Longitudinal prevalence (LP), the proportion-of-time-ill estimated by repeated prevalence measurements, is an alternative measure to incidence of recurrent infections. In contrast to incidence which usually requires continuous sampling, LP can be measured at intervals. This study explored how many more participants are needed for infrequent sampling to achieve the same study power as frequent sampling.</p> <p>Methods</p> <p>We developed a set of four empirical simulation models representing low and high risk settings with short or long episode durations. The model was used to evaluate different sampling strategies with different assumptions on recall period and recall error.</p> <p>Results</p> <p>The model identified three major factors that influence sampling strategies: (1) the clustering of episodes in individuals; (2) the duration of episodes; (3) the positive correlation between an individual's disease incidence and episode duration. Intermittent sampling (e.g. 12 times per year) often requires only a slightly larger sample size compared to continuous sampling, especially in cluster-randomized trials. The collection of period prevalence data can lead to highly biased effect estimates if the exposure variable is associated with episode duration. To maximize study power, recall periods of 3 to 7 days may be preferable over shorter periods, even if this leads to inaccuracy in the prevalence estimates.</p> <p>Conclusion</p> <p>Choosing the optimal approach to measure recurrent infections in epidemiological studies depends on the setting, the study objectives, study design and budget constraints. Sampling at intervals can contribute to making epidemiological studies and trials more efficient, valid and cost-effective.</p

A Quasi-Model-Independent Search for New Physics at Large Transverse Momentum

We apply a quasi-model-independent strategy ("Sleuth") to search for new high p_T physics in approximately 100 pb^-1 of ppbar collisions at sqrt(s) = 1.8 TeV collected by the DZero experiment during 1992-1996 at the Fermilab Tevatron. Over thirty-two e mu X, W+jets-like, Z+jets-like, and 3(lepton/photon)X exclusive final states are systematically analyzed for hints of physics beyond the standard model. Simultaneous sensitivity to a variety of models predicting new phenomena at the electroweak scale is demonstrated by testing the method on a particular signature in each set of final states. No evidence of new high p_T physics is observed in the course of this search, and we find that 89% of an ensemble of hypothetical similar experimental runs would have produced a final state with a candidate signal more interesting than the most interesting observed in these data.Comment: 28 pages, 17 figures. Submitted to Physical Review

A measurement of the W boson mass using large rapidity electrons

We present a measurement of the W boson mass using data collected by the D0 experiment at the Fermilab Tevatron during 1994--1995. We identify W bosons by their decays to e-nu final states where the electron is detected in a forward calorimeter. We extract the W boson mass, Mw, by fitting the transverse mass and transverse electron and neutrino momentum spectra from a sample of 11,089 W -> e nu decay candidates. We use a sample of 1,687 dielectron events, mostly due to Z -> ee decays, to constrain our model of the detector response. Using the forward calorimeter data, we measure Mw = 80.691 +- 0.227 GeV. Combining the forward calorimeter measurements with our previously published central calorimeter results, we obtain Mw = 80.482 +- 0.091 GeV

Spin Correlation in tt-bar Production from pp-bar Collisions at sqrt(s)=1.8 TeV

The D0 collaboration has performed a study of spin correlation in tt-bar production for the process tt-bar to bb-bar W^+W^-, where the W bosons decay to e-nu or mu-nu. A sample of six events was collected during an exposure of the D0 detector to an integrated luminosity of approximately 125 pb^-1 of sqrt{s}=1.8 TeV pp-bar collisions. The standard model (SM) predicts that the short lifetime of the top quark ensures the transmission of any spin information at production to the tt-bar decay products. The degree of spin correlation is characterized by a correlation coefficient k. We find that k>-0.25 at the 68% confidence level, in agreement with the SM prediction of k=0.88.Comment: Submitted to PRL, Added references, minor changes to tex