ASSOCIATON TESTS THAT ACCOMMODATE GENOTYPING ERRORS

High-throughput SNP arrays provide estimates of genotypes for up to one million loci, often used in genome-wide association studies. While these estimates are typically very accurate, genotyping errors do occur, which can influence in particular the most extreme test statistics and p-values. Estimates for the genotype uncertainties are also available, although typically ignored. In this manuscript, we develop a framework to incorporate these genotype uncertainties in case-control studies for any genetic model. We verify that using the assumption of a “local alternative” in the score test is very reasonable for effect sizes typically seen in SNP association studies, and show that the power of the score test is simply a function of the correlation of the genotype probabilities with the true genotypes. We demonstrate that the power to detect a true association can be substantially increased for difficult to call genotypes, resulting in improved inference in association studies.

Cholesky Residuals for Assessing Normal Errors in a Linear Model with Correlated Outcomes: Technical Report

Despite the widespread popularity of linear models for correlated outcomes (e.g. linear mixed models and time series models), distribution diagnostic methodology remains relatively underdeveloped in this context. In this paper we present an easy-to-implement approach that lends itself to graphical displays of model fit. Our approach involves multiplying the estimated margional residual vector by the Cholesky decomposition of the inverse of the estimated margional variance matrix. The resulting "rotated" residuals are used to construct an empirical cumulative distribution function and pointwise standard errors. The theoretical framework, including conditions and asymptotic properties, involves technical details that are motivated by Lange and Ryan (1989), Pierce (1982), and Randles (1982). Our method appears to work well in a variety of circumstances, including models having independent units of sampling (clustered data) and models for which all observations are correlated (e.g., a single time series). Our methods can produce satisfactory results even for models that do not satisfy all of the technical conditions stated in our theory.

Underestimation of Standard Errors in Multi-Site Time Series Studies

Multi-site time series studies of air pollution and mortality and morbidity have figured prominently in the literature as comprehensive approaches for estimating acute effects of air pollution on health. Hierarchical models are generally used to combine site-specific information and estimate pooled air pollution effects taking into account both within-site statistical uncertainty, and across-site heterogeneity. Within a site, characteristics of time series data of air pollution and health (small pollution effects, missing data, highly correlated predictors, non linear confounding etc.) make modelling all sources of uncertainty challenging. One potential consequence is underestimation of the statistical variance of the site-specific effects to be combined. In this paper we investigate the impact of variance underestimation on the pooled relative rate estimate. We focus on two-stage normal-normal hierarchical models and on under- estimation of the statistical variance at the first stage. By mathematical considerations and simulation studies, we found that variance underestimation does not affect the pooled estimate substantially. However, some sensitivity of the pooled estimate to variance underestimation is observed when the number of sites is small and underestimation is severe. These simulation results are applicable to any two-stage normal-normal hierarchical model for combining information of site-specific results, and they can be easily extended to more general hierarchical formulations. We also examined the impact of variance underestimation on the national average relative rate estimate from the National Morbidity Mortality Air Pollution Study and we found that variance underestimation as much as 40% has little effect on the national average.

LEARNING FROM NEAR MISSES IN MEDICATION ERRORS: A BAYESIAN APPROACH

Medical errors originating in health care facilities are a significant source of preventable morbidity, mortality, and healthcare costs. Voluntary error report systems that collect information on the causes and contributing factors of medi- cal errors regardless of the resulting harm may be useful for developing effective harm prevention strategies. Some patient safety experts question the utility of data from errors that did not lead to harm to the patient, also called near misses. A near miss (a.k.a. close call) is an unplanned event that did not result in injury to the patient. Only a fortunate break in the chain of events prevented injury. We use data from a large voluntary reporting system of 836,174 medication errors from 1999 to 2005 to provide evidence that the causes and contributing factors of errors that result in harm are similar to the causes and contributing factors of near misses. We develop Bayesian hierarchical models for estimating the log odds of selecting a given cause (or contributing factor) of error given harm has occurred and the log odds of selecting the same cause given that harm did not occur. The posterior distribution of the correlation between these two vectors of log-odds is used as a measure of the evidence supporting the use of data from near misses and their causes and contributing factors to prevent medical errors. In addition, we identify the causes and contributing factors that have the highest or lowest log-odds ratio of harm versus no harm. These causes and contributing factors should also be a focus in the design of prevention strategies. This paper provides important evidence on the utility of data from near misses, which constitute the vast majority of errors in our data.

Dosimetric impact of geometric errors due to respiratory motion prediction on dynamic multileaf collimator-based four-dimensional radiation delivery

The synchronization of dynamic multileaf collimator (DMLC) response with respiratory motion is critical to ensure the accuracy of DMLC-based four dimensional (4D) radiation delivery. In practice, however, a finite time delay (response time) between the acquisition of tumor position and multileaf collimator response necessitates predictive models of respiratory tumor motion to synchronize radiation delivery. Predicting a complex process such as respiratory motion introduces geometric errors, which have been reported in several publications. However, the dosimetric effect of such errors on 4D radiation delivery has not yet been investigated. Thus, our aim in this work was to quantify the dosimetric effects of geometric error due to prediction under several different conditions. Conformal and intensity modulated radiation therapy (IMRT) plans for a lung patient were generated for anterior-posterior/posterior-anterior (AP/PA) beam arrangements at 6 and 18 MV energies to provide planned dose distributions. Respiratory motion data was obtained from 60 diaphragm-motion fluoroscopy recordings from five patients. A linear adaptive filter was employed to predict the tumor position. The geometric error of prediction was defined as the absolute difference between predicted and actual positions at each diaphragm position. Distributions of geometric error of prediction were obtained for all of the respiratory motion data. Planned dose distributions were then convolved with distributions for the geometric error of prediction to obtain convolved dose distributions. The dosimetric effect of such geometric errors was determined as a function of several variables: response time (0-0.6 s), beam energy (6/18 MV), treatment delivery (3D/4D), treatment type (conformal/IMRT), beam direction (AP/PA), and breathing training type (free breathing/audio instruction/visual feedback). Dose difference and distance-to-agreement analysis was employed to quantify results. Based on our data, the dosimetric impact of prediction (a) increased with response time, (b) was larger for 3D radiation therapy as compared with 4D radiation therapy, (c) was relatively insensitive to change in beam energy and beam direction, (d) was greater for IMRT distributions as compared with conformal distributions, (e) was smaller than the dosimetric impact of latency, and (f) was greatest for respiration motion with audio instructions, followed by visual feedback and free breathing. Geometric errors of prediction that occur during 4D radiation delivery introduce dosimetric errors that are dependent on several factors, such as response time, treatment-delivery type, and beam energy. Even for relatively small response times of 0.6 s into the future, dosimetric errors due to prediction could approach delivery errors when respiratory motion is not accounted for at all. To reduce the dosimetric impact, better predictive models and/or shorter response times are required.

Introduction of a novel dose saving acquisition mode for the PortalVision aS500 EPID to facilitate on-line patient setup verification

In external beam radiotherapy, electronic portal imaging becomes more and more an indispensable tool for the verification of the patient setup. For the safe clinical introduction of high dose conformal radiotherapy like intensity modulated radiation therapy, on-line patient setup verification is a prerequisite to ensure that the planned dosimetric coverage of the tumor volume is actually realized in the patient. Since the direction of setup fields often deviates from the direction of the treatment beams, extra dose is delivered to the patient during the acquisition of these portal images which may reach clinical relevance. The aim of this work was to develop a new acquisition mode for the PortalVision aS500 electronic portal imaging device from Varian Medical Systems that allows one to take portal images with reduced dose while keeping good image quality. The new acquisition mode, called RadMode, selectively enables and disables beam pulses during image acquisition allowing one to stop wasting valuable dose during the initial acquisition of "reset frames." Images of excellent quality can be taken with 1 MU only. This low dose per image facilitates daily setup verification with considerably reduced extra dose.

Direct combination of nanoparticle fabrication and exposure to lung cell cultures in a closed setup as a method to simulate accidental nanoparticle exposure of humans

The tremendous application potential of nanosized materials stays in sharp contrast to a growing number of critical reports of their potential toxicity. Applications of in vitro methods to assess nanoparticles are severely limited through difficulties in exposing cells of the respiratory tract directly to airborne engineered nanoparticles. We present a completely new approach to expose lung cells to particles generated in situ by flame spray synthesis. Cerium oxide nanoparticles from a single run were produced and simultaneously exposed to the surface of cultured lung cells inside a glovebox. Separately collected samples were used to measure hydrodynamic particle size distribution, shape, and agglomerate morphology. Cell viability was not impaired by the conditions of the glovebox exposure. The tightness of the lung cell monolayer, the mean total lamellar body volume, and the generation of oxidative DNA damage revealed a dose-dependent cellular response to the airborne engineered nanoparticles. The direct combination of production and exposure allows studying particle toxicity in a simple and reproducible way under environmental conditions.

Use of gold markers for setup in image-guided fractionated high-dose-rate brachytherapy as a monotherapy for prostate cancer

BACKGROUND AND PURPOSE: In order to use a single implant with one treatment plan in fractionated high-dose-rate brachytherapy (HDR-B), applicator position shifts must be corrected prior to each fraction. The authors investigated the use of gold markers for X-ray-based setup and position control between the single fractions. PATIENTS AND METHODS: Caudad-cephalad movement of the applicators prior to each HDR-B fraction was determined on radiographs using two to three gold markers, which had been inserted into the prostate as intraprostatic reference, and one to two radiopaque-labeled reference applicators. 35 prostate cancer patients, treated by HDR-B as a monotherapy between 10/2003 and 06/2006 with four fractions of 9.5 Gy each, were analyzed. Toxicity was scored according to the CTCAE Score, version 3.0. Median follow-up was 3 years. RESULTS: The mean change of applicators positions compared to baseline varied substantially between HDR-B fractions, being 1.4 mm before fraction 1 (range, -4 to 2 mm), -13.1 mm before fraction 2 (range, -36 to 0 mm), -4.1 mm before fraction 3 (range, -21 to 9 mm), and -2.6 mm at fraction 4 (range, -16 to 9 mm). The original position of the applicators could be readjusted easily prior to each fraction in every patient. In 18 patients (51%), the applicators were at least once readjusted > 10 mm, however, acute or late grade > or = 2 genitourinary toxicity was not increased (p = 1.0) in these patients. CONCLUSION: Caudad position shifts up to 36 mm were observed. Gold markers represent a valuable tool to ensure setup accuracy and precise dose delivery in fractionated HDR-B monotherapy of prostate cancer.

How the Entrepreneurial Top Management Team Setup Influences Firm Performance and the Ability to Raise Capital: A Literature Review

This paper reviews research findings on entrepreneurial top management teams within the last 20 years. It concentrates on team-based management factors and their influence on a new venture’s growth and ability to raise capital. This paper integrates recent findings and provides an overview of the current state of research. Moreover, it contributes to the overall topic by proposing five clusters of major team-specific influences, derives determinants of success and failure, and reveals recommendations for further research.

VuLF – Entwicklung eines Versuchsstandes zur Fehlerminimierung durch Fahrzeuganalyse

Fahrerlose Transportsysteme (FTS) kommen in der Intralogistik immer mehr zum Einsatz. Dabei wer-den neue Anforderungen an die FTS gestellt, die bisherige Aufgaben weit übersteigen. Der Transport von schweren Lasten und die Fahrt mehrere FTS in einer Formation sind dabei nur der Anfang einer Menge zukünftiger Einsatzgebiete. Große Probleme stellen dabei Schlupf und Verschleiß dar, die bisher noch kaum erforscht sind, welche die Performance der Fahrzeuge jedoch stark beeinträchtigen. Um diese Fehlerquellen besser verstehen zu können, wurde ein Versuchsstand entwickelt, mit dessen Hilfe Einzelfahrten aber auch Formationsfahrten in Bezug auf Schlupf und Verschleiß besser untersucht werden können. Dabei kann ein sehr breites Spektrum an Fahrzeugarten abgedeckt werden, da Parameter wie Lenksysteme oder Bereifung variabel gewählt werden können.

Climate spectrum estimation in the presence of timescale errors

We introduce an algorithm (called REDFITmc2) for spectrum estimation in the presence of timescale errors. It is based on the Lomb-Scargle periodogram for unevenly spaced time series, in combination with the Welch's Overlapped Segment Averaging procedure, bootstrap bias correction and persistence estimation. The timescale errors are modelled parametrically and included in the simulations for determining (1) the upper levels of the spectrum of the red-noise AR(1) alternative and (2) the uncertainty of the frequency of a spectral peak. Application of REDFITmc2 to ice core and stalagmite records of palaeoclimate allowed a more realistic evaluation of spectral peaks than when ignoring this source of uncertainty. The results support qualitatively the intuition that stronger effects on the spectrum estimate (decreased detectability and increased frequency uncertainty) occur for higher frequencies. The surplus information brought by algorithm REDFITmc2 is that those effects are quantified. Regarding timescale construction, not only the fixpoints, dating errors and the functional form of the age-depth model play a role. Also the joint distribution of all time points (serial correlation, stratigraphic order) determines spectrum estimation.