380,716 research outputs found
Approximate Models and Robust Decisions
Decisions based partly or solely on predictions from probabilistic models may
be sensitive to model misspecification. Statisticians are taught from an early
stage that "all models are wrong", but little formal guidance exists on how to
assess the impact of model approximation on decision making, or how to proceed
when optimal actions appear sensitive to model fidelity. This article presents
an overview of recent developments across different disciplines to address
this. We review diagnostic techniques, including graphical approaches and
summary statistics, to help highlight decisions made through minimised expected
loss that are sensitive to model misspecification. We then consider formal
methods for decision making under model misspecification by quantifying
stability of optimal actions to perturbations to the model within a
neighbourhood of model space. This neighbourhood is defined in either one of
two ways. Firstly, in a strong sense via an information (Kullback-Leibler)
divergence around the approximating model. Or using a nonparametric model
extension, again centred at the approximating model, in order to `average out'
over possible misspecifications. This is presented in the context of recent
work in the robust control, macroeconomics and financial mathematics
literature. We adopt a Bayesian approach throughout although the methods are
agnostic to this position
Infant self-regulation and body mass index in early childhood
BACKGROUND: Poor self-regulation during preschool and early school age years is associated with rapid weight gain. However, the association between self-regulatory capacities in infancy and weight status in early childhood has not been well studied.
Objective: Examine prospective associations between infant self-regulation and body mass index (BMI) in early childhood. We hypothesized that infants exhibiting less optimal self-regulation would be at greater risk of obesity at 3–5 years of life.
METHODS: We used data from 5750 children in the Early Childhood Longitudinal Study-Birth Cohort (ECLS-B), excluding premature infants and infants small or large for gestational age. Our primary predictor was infant self-regulation measured at age 9 months by parent completion of the Infant Toddler Symptom Checklist (ITSC). We defined child obesity at preschool and kindergarten age (approximately 4 years and 5–6 years respectively) as a body mass index (BMI) ≥ 95th percentile for age and sex by US Centers for Disease Control growth charts. We created logistic regression models comparing risk of obesity at preschool and kindergarten age in infants with ITSC scores ≥ 6 to infants with scores < 6, controlling for covariates.
RESULTS: Twenty-one percent of children with ITSC scores ≥ 6 at 9 months were obese at preschool age compared to 16% of children with lower ITSC scores. At kindergarten age this difference decreased to 18% vs. 16% respectively. After adjusting for covariates, infants with ITSC scores ≥ 6 had 32% increased odds of being obese at preschool age (aOR 1.32; 95% CI: 1.03, 1.70) though this association decreased at kindergarten age (aOR 1.07; 95% CI: 0.79, 1.45).
CONCLUSIONS: Poor infant self-regulation at 9 months is associated with an increased risk of obesity at preschool entry but not at kindergarten entry. Helping parents manage and respond to children’s self-regulation difficulties prior to preschool age may serve as a focal point for future interventions.2016-12-01T00:00:00
Minimizing Metastatic Risk in Radiotherapy Fractionation Schedules
Metastasis is the process by which cells from a primary tumor disperse and
form new tumors at distant anatomical locations. The treatment and prevention
of metastatic cancer remains an extremely challenging problem. This work
introduces a novel biologically motivated objective function to the radiation
optimization community that takes into account metastatic risk instead of the
status of the primary tumor. In this work, we consider the problem of
developing fractionated irradiation schedules that minimize production of
metastatic cancer cells while keeping normal tissue damage below an acceptable
level. A dynamic programming framework is utilized to determine the optimal
fractionation scheme. We evaluated our approach on a breast cancer case using
the heart and the lung as organs-at-risk (OAR). For small tumor
values, hypo-fractionated schedules were optimal, which is consistent with
standard models. However, for relatively larger values, we found
the type of schedule depended on various parameters such as the time when
metastatic risk was evaluated, the values of the OARs, and the
normal tissue sparing factors. Interestingly, in contrast to standard models,
hypo-fractionated and semi-hypo-fractionated schedules (large initial doses
with doses tapering off with time) were suggested even with large tumor
/ values. Numerical results indicate potential for significant
reduction in metastatic risk.Comment: 12 pages, 3 figures, 2 table
Development of PancRISK, a urine biomarker-based risk score for stratified screening of pancreatic cancer patients
© The Author(s) 2019. Published by Springer Nature on behalf of Cancer Research UK.BACKGROUND: An accurate and simple risk prediction model that would facilitate earlier detection of pancreatic adenocarcinoma (PDAC) is not available at present. In this study, we compare different algorithms of risk prediction in order to select the best one for constructing a biomarker-based risk score, PancRISK. METHODS: Three hundred and seventy-nine patients with available measurements of three urine biomarkers, (LYVE1, REG1B and TFF1) using retrospectively collected samples, as well as creatinine and age, were randomly split into training and validation sets, following stratification into cases (PDAC) and controls (healthy patients). Several machine learning algorithms were used, and their performance characteristics were compared. The latter included AUC (area under ROC curve) and sensitivity at clinically relevant specificity. RESULTS: None of the algorithms significantly outperformed all others. A logistic regression model, the easiest to interpret, was incorporated into a PancRISK score and subsequently evaluated on the whole data set. The PancRISK performance could be even further improved when CA19-9, commonly used PDAC biomarker, is added to the model. CONCLUSION: PancRISK score enables easy interpretation of the biomarker panel data and is currently being tested to confirm that it can be used for stratification of patients at risk of developing pancreatic cancer completely non-invasively, using urine samples.Peer reviewe
A Comparative Analysis of Influenza Vaccination Programs
The threat of avian influenza and the 2004-2005 influenza vaccine supply
shortage in the United States has sparked a debate about optimal vaccination
strategies to reduce the burden of morbidity and mortality caused by the
influenza virus. We present a comparative analysis of two classes of suggested
vaccination strategies: mortality-based strategies that target high risk
populations and morbidity-based that target high prevalence populations.
Applying the methods of contact network epidemiology to a model of disease
transmission in a large urban population, we evaluate the efficacy of these
strategies across a wide range of viral transmission rates and for two
different age-specific mortality distributions. We find that the optimal
strategy depends critically on the viral transmission level (reproductive rate)
of the virus: morbidity-based strategies outperform mortality-based strategies
for moderately transmissible strains, while the reverse is true for highly
transmissible strains. These results hold for a range of mortality rates
reported for prior influenza epidemics and pandemics. Furthermore, we show that
vaccination delays and multiple introductions of disease into the community
have a more detrimental impact on morbidity-based strategies than
mortality-based strategies. If public health officials have reasonable
estimates of the viral transmission rate and the frequency of new introductions
into the community prior to an outbreak, then these methods can guide the
design of optimal vaccination priorities. When such information is unreliable
or not available, as is often the case, this study recommends mortality-based
vaccination priorities
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