1,014 research outputs found
False discovery rates in somatic mutation studies of cancer
The purpose of cancer genome sequencing studies is to determine the nature
and types of alterations present in a typical cancer and to discover genes
mutated at high frequencies. In this article we discuss statistical methods for
the analysis of somatic mutation frequency data generated in these studies. We
place special emphasis on a two-stage study design introduced by Sj\"{o}blom et
al. [Science 314 (2006) 268--274]. In this context, we describe and compare
statistical methods for constructing scores that can be used to prioritize
candidate genes for further investigation and to assess the statistical
significance of the candidates thus identified. Controversy has surrounded the
reliability of the false discovery rates estimates provided by the
approximations used in early cancer genome studies. To address these, we
develop a semiparametric Bayesian model that provides an accurate fit to the
data. We use this model to generate a large collection of realistic scenarios,
and evaluate alternative approaches on this collection. Our assessment is
impartial in that the model used for generating data is not used by any of the
approaches compared. And is objective, in that the scenarios are generated by a
model that fits data. Our results quantify the conservative control of the
false discovery rate with the Benjamini and Hockberg method compared to the
empirical Bayes approach and the multiple testing method proposed in Storey [J.
R. Stat. Soc. Ser. B Stat. Methodol. 64 (2002) 479--498]. Simulation results
also show a negligible departure from the target false discovery rate for the
methodology used in Sj\"{o}blom et al. [Science 314 (2006) 268--274].Comment: Published in at http://dx.doi.org/10.1214/10-AOAS438 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Mixed Effect Modeling of Dose and Linear Energy Transfer Correlations With Brain Image Changes After Intensity Modulated Proton Therapy for Skull Base Head and Neck Cancer
Purpose
Intensity modulated proton therapy (IMPT) could yield high linear energy transfer (LET) in critical structures and increased biological effect. For head and neck cancers at the skull base this could potentially result in radiation-associated brain image change (RAIC). The purpose of the current study was to investigate voxel-wise dose and LET correlations with RAIC after IMPT.
Methods and Materials
For 15 patients with RAIC after IMPT, contrast enhancement observed on T1-weighted magnetic resonance imaging was contoured and coregistered to the planning computed tomography. Monte Carlo calculated dose and dose-averaged LET (LETd) distributions were extracted at voxel level and associations with RAIC were modelled using uni- and multivariate mixed effect logistic regression. Model performance was evaluated using the area under the receiver operating characteristic curve and precision-recall curve.
Results
An overall statistically significant RAIC association with dose and LETd was found in both the uni- and multivariate analysis. Patient heterogeneity was considerable, with standard deviation of the random effects of 1.81 (1.30-2.72) for dose and 2.68 (1.93-4.93) for LETd, respectively. Area under the receiver operating characteristic curve was 0.93 and 0.95 for the univariate dose-response model and multivariate model, respectively. Analysis of the LETd effect demonstrated increased risk of RAIC with increasing LETd for the majority of patients. Estimated probability of RAIC with LETd = 1 keV/µm was 4% (95% confidence interval, 0%, 0.44%) and 29% (95% confidence interval, 0.01%, 0.92%) for 60 and 70 Gy, respectively. The TD15 were estimated to be 63.6 and 50.1 Gy with LETd equal to 2 and 5 keV/µm, respectively.
Conclusions
Our results suggest that the LETd effect could be of clinical significance for some patients; LETd assessment in clinical treatment plans should therefore be taken into consideration.publishedVersio
Rescaled bootstrap confidence intervals for the population variance in the presence of outliers or spikes in the distribution of a variable of interest
Confidence intervals for the population variance in the presence of outliers or spikes in the distribution of a variable of interest are topics that have not been investigated in depth previously. Results derived from a first Monte Carlo simulation study reveal the limitations of the customary confidence interval for the population variance when the underlying assumptions are violated, and the use of alternative confidence intervals is thus justified. We suggest confidence intervals based on the rescaled bootstrap method for many reasons. First, this is a simple technique that can be easily applied in practice. Second, it is free of probabilistic distributions. Finally, it can be easily applied to the cases of finite populations and samples
selected from complex sampling designs. Results derived from a second Monte Carlo simulation study indicate that the suggested confidence intervals have desirable coverage rates with smaller average widths.
Accordingly, an advantage of the suggested confidence intervals is that they offer a good compromise between simplicity and desirable properties. The various simulation studies are based on different scenarios that may arise in practice, such as the presence of outliers or spikes, and the fact that the underlying assumptions of the customary confidence interval are violated
Continuous testing for Poisson process intensities: A new perspective on scanning statistics
We propose a novel continuous testing framework to test the intensities of
Poisson Processes. This framework allows a rigorous definition of the complete
testing procedure, from an infinite number of hypothesis to joint error rates.
Our work extends traditional procedures based on scanning windows, by
controlling the family-wise error rate and the false discovery rate in a
non-asymptotic manner and in a continuous way. The decision rule is based on a
\pvalue process that can be estimated by a Monte-Carlo procedure. We also
propose new test statistics based on kernels. Our method is applied in
Neurosciences and Genomics through the standard test of homogeneity, and the
two-sample test
Algorithmic Analysis Techniques for Molecular Imaging
This study addresses image processing techniques for two medical imaging
modalities: Positron Emission Tomography (PET) and Magnetic Resonance
Imaging (MRI), which can be used in studies of human body functions and
anatomy in a non-invasive manner.
In PET, the so-called Partial Volume Effect (PVE) is caused by low
spatial resolution of the modality. The efficiency of a set of PVE-correction
methods is evaluated in the present study. These methods use information
about tissue borders which have been acquired with the MRI technique. As
another technique, a novel method is proposed for MRI brain image segmen-
tation. A standard way of brain MRI is to use spatial prior information
in image segmentation. While this works for adults and healthy neonates,
the large variations in premature infants preclude its direct application.
The proposed technique can be applied to both healthy and non-healthy
premature infant brain MR images. Diffusion Weighted Imaging (DWI) is
a MRI-based technique that can be used to create images for measuring
physiological properties of cells on the structural level. We optimise the
scanning parameters of DWI so that the required acquisition time can be
reduced while still maintaining good image quality.
In the present work, PVE correction methods, and physiological DWI
models are evaluated in terms of repeatabilityof the results. This gives in-
formation on the reliability of the measures given by the methods. The
evaluations are done using physical phantom objects, correlation measure-
ments against expert segmentations, computer simulations with realistic
noise modelling, and with repeated measurements conducted on real pa-
tients. In PET, the applicability and selection of a suitable partial volume
correction method was found to depend on the target application. For MRI,
the data-driven segmentation offers an alternative when using spatial prior is
not feasible. For DWI, the distribution of b-values turns out to be a central
factor affecting the time-quality ratio of the DWI acquisition. An optimal
b-value distribution was determined. This helps to shorten the imaging time
without hampering the diagnostic accuracy.Siirretty Doriast
Ownership and Financial Performance in the German Hospital Sector
This paper considers the role of ownership form for the financial performance of German acute care hospitals and its development over time.We measure financial performance by a hospital-specific yearly probability of default (PD). Using a panel of hospital data, our models allow for state dependence in the PD as well as unobserved individual heterogeneity. We find that private ownership is more likely to be associated with sound levels in financial performance than public ownership. Moreover, state dependence in the PD is substantial, albeit not ownership-specific.Finally, our evidence suggests that overall efficiency may be enhanced most by closing down some loss-making public hospitals rather than by their restructuring, especially because the German hospital market has substantial excess capacities.Hospitals ownership, financial performance, state dependence
UNCERTAINTY IN MACHINE LEARNING A SAFETY PERSPECTIVE ON BIOMEDICAL APPLICATIONS
Uncertainty is an inevitable and essential aspect of the worldwe live in and a fundamental
aspect of human decision-making. It is no different in the realm of machine learning. Just
as humans seek out additional information and perspectives when faced with uncertainty,
machine learning models must also be able to account for and quantify the uncertainty
in their predictions. However, the uncertainty quantification in machine learning models
is often neglected. By acknowledging and incorporating uncertainty quantification into
machine learning models, we can build more reliable and trustworthy systems that are
better equipped to handle the complexity of the world and support clinical decisionmaking.
This thesis addresses the broad issue of uncertainty quantification in machine learning,
covering the development and adaptation of uncertainty quantification methods,
their integration in the machine learning development pipeline, and their practical application
in clinical decision-making.
Original contributions include the development of methods to support practitioners
in developing more robust and interpretable models, which account for different sources
of uncertainty across the core components of the machine learning pipeline, encompassing
data, the machine learning model, and its outputs. Moreover, these machine learning
models are designed with abstaining capabilities, enabling them to accept or reject predictions
based on the level of uncertainty present. This emphasizes the importance of using
classification with rejection option in clinical decision support systems. The effectiveness
of the proposed methods was evaluated across databases with physiological signals from
medical diagnosis and human activity recognition. The results support that uncertainty
quantification was important for more reliable and robust model predictions.
By addressing these topics, this thesis aims to improve the reliability and trustworthiness
of machine learning models and contribute to fostering the adoption of machineassisted
clinical decision-making. The ultimate goal is to enhance the trust and accuracy
of models’ predictions and increase transparency and interpretability, ultimately leading
to better decision-making across a range of applications.A incerteza é um aspeto inevitável e essencial do mundo em que vivemos e um aspeto
fundamental na tomada de decisão humana. Não é diferente no âmbito da aprendizagem
automática. Assim como os seres humanos, quando confrontados com um determinado
nível de incerteza exploram novas abordagens ou procuram recolher mais informação,
também os modelos de aprendizagem automática devem ter a capacidade de ter em conta
e quantificar o grau de incerteza nas suas previsões. No entanto, a quantificação da incerteza
nos modelos de aprendizagem automática é frequentemente negligenciada. O
reconhecimento e incorporação da quantificação de incerteza nos modelos de aprendizagem
automática, irá permitir construir sistemas mais fiáveis, melhor preparados para
apoiar a tomada de decisão clinica em situações complexas e com maior nível de confiança.
Esta tese aborda a ampla questão da quantificação de incerteza na aprendizagem
automática, incluindo o desenvolvimento e adaptação de métodos de quantificação de
incerteza, a sua integração no pipeline de desenvolvimento de modelos de aprendizagem
automática e a sua aplicação prática na tomada de decisão clínica.
Nos contributos originais, inclui-se o desenvolvimento de métodos para apoiar os
profissionais de desenvolvimento na criação de modelos mais robustos e interpretáveis,
que tenham em consideração as diferentes fontes de incerteza nos diversos componenteschave
do pipeline de aprendizagem automática: os dados, o modelo de aprendizagem
automática e os seus resultados. Adicionalmente, os modelos de aprendizagem automática
são construídos com a capacidade de se abster, o que permite aceitar ou rejeitar uma
previsão com base no nível de incerteza presente, o que realça a importância da utilização
de modelos de classificação com a opção de rejeição em sistemas de apoio à decisão
clínica. A eficácia dos métodos propostos foi avaliada em bases de dados contendo sinais
fisiológicos provenientes de diagnósticos médicos e reconhecimento de atividades humanas.
As conclusões sustentam a importância da quantificação da incerteza nos modelos
de aprendizagem automática para obter previsões mais fiáveis e robustas.
Desenvolvendo estes tópicos, esta tese pretende aumentar a fiabilidade e credibilidade
dos modelos de aprendizagem automática, promovendo a utilização e desenvolvimento dos sistemas de apoio à decisão clínica. O objetivo final é aumentar o grau de confiança e a
fiabilidade das previsões dos modelos, bem como, aumentar a transparência e interpretabilidade,
proporcionando uma melhor tomada de decisão numa variedade de aplicações
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