18,796 research outputs found
Model parameter estimation and uncertainty analysis: a report of the ISPOR-SMDM modeling good research practices task force working group - 6
A model’s purpose is to inform medical decisions and health care resource allocation. Modelers employ quantitative methods to structure the clinical, epidemiological, and economic evidence base and gain qualitative insight to assist decision makers in making better decisions. From a policy perspective, the value of a model-based analysis lies not simply in its ability to generate a precise point estimate for a specific outcome but also in the systematic examination and responsible reporting of uncertainty surrounding this outcome and the ultimate decision being addressed. Different concepts relating to uncertainty in decision modeling are explored. Stochastic (first-order) uncertainty is distinguished from both parameter (second-order) uncertainty and from heterogeneity, with structural uncertainty relating to the model itself forming another level of uncertainty to consider. The article argues that the estimation of point estimates and uncertainty in parameters is part of a single process and explores the link between parameter uncertainty through to decision uncertainty and the relationship to value-of-information analysis. The article also makes extensive recommendations around the reporting of uncertainty, both in terms of deterministic sensitivity analysis techniques and probabilistic methods. Expected value of perfect information is argued to be the most appropriate presentational technique, alongside cost-effectiveness acceptability curves, for representing decision uncertainty from probabilistic analysis
Prediction of survival probabilities with Bayesian Decision Trees
Practitioners use Trauma and Injury Severity Score (TRISS) models for predicting the survival probability of an injured patient. The accuracy of TRISS predictions is acceptable for patients with up to three typical injuries, but unacceptable for patients with a larger number of injuries or with atypical injuries. Based on a regression model, the TRISS methodology does not provide the predictive density required for accurate assessment of risk. Moreover, the regression model is difficult to interpret. We therefore consider Bayesian inference for estimating the predictive distribution of survival. The inference is based on decision tree models which recursively split data along explanatory variables, and so practitioners can understand these models. We propose the Bayesian method for estimating the predictive density and show that it outperforms the TRISS method in terms of both goodness-of-fit and classification accuracy. The developed method has been made available for evaluation purposes as a stand-alone application
Discussion of "Calibrated Bayes, for Statistics in General, and Missing Data in Particular" by R. J. A. Little
Discussion of "Calibrated Bayes, for Statistics in General, and Missing Data
in Particular" by R. Little [arXiv:1108.1917]Comment: Published in at http://dx.doi.org/10.1214/10-STS318B the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Global estimation of child mortality using a Bayesian B-spline Bias-reduction model
Estimates of the under-five mortality rate (U5MR) are used to track progress
in reducing child mortality and to evaluate countries' performance related to
Millennium Development Goal 4. However, for the great majority of developing
countries without well-functioning vital registration systems, estimating the
U5MR is challenging due to limited data availability and data quality issues.
We describe a Bayesian penalized B-spline regression model for assessing levels
and trends in the U5MR for all countries in the world, whereby biases in data
series are estimated through the inclusion of a multilevel model to improve
upon the limitations of current methods. B-spline smoothing parameters are also
estimated through a multilevel model. Improved spline extrapolations are
obtained through logarithmic pooling of the posterior predictive distribution
of country-specific changes in spline coefficients with observed changes on the
global level. The proposed model is able to flexibly capture changes in U5MR
over time, gives point estimates and credible intervals reflecting potential
biases in data series and performs reasonably well in out-of-sample validation
exercises. It has been accepted by the United Nations Inter-agency Group for
Child Mortality Estimation to generate estimates for all member countries.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS768 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors
Meaningful quantification of data and structural uncertainties in conceptual rainfall-runoff modeling is a major scientific and engineering challenge. This paper focuses on the total predictive uncertainty and its decomposition into input and structural components under different inference scenarios. Several Bayesian inference schemes are investigated, differing in the treatment of rainfall and structural uncertainties, and in the precision of the priors describing rainfall uncertainty. Compared with traditional lumped additive error approaches, the quantification of the total predictive uncertainty in the runoff is improved when rainfall and/or structural errors are characterized explicitly. However, the decomposition of the total uncertainty into individual sources is more challenging. In particular, poor identifiability may arise when the inference scheme represents rainfall and structural errors using separate probabilistic models. The inference becomes ill‐posed unless sufficiently precise prior knowledge of data uncertainty is supplied; this ill‐posedness can often be detected from the behavior of the Monte Carlo sampling algorithm. Moreover, the priors on the data quality must also be sufficiently accurate if the inference is to be reliable and support meaningful uncertainty decomposition. Our findings highlight the inherent limitations of inferring inaccurate hydrologic models using rainfall‐runoff data with large unknown errors. Bayesian total error analysis can overcome these problems using independent prior information. The need for deriving independent descriptions of the uncertainties in the input and output data is clearly demonstrated.Benjamin Renard, Dmitri Kavetski, George Kuczera, Mark Thyer, and Stewart W. Frank
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