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PubMed1992

Resources for medical decision-making in situations of high uncertainty.

The high standards of medical practice rest on the assumption that a physician's competence equips him or her to act on a patient's behalf. The conundrum created by medical uncertainty regarding the patient's diagnosis or the appropriate course of treatment seldom receives attention in the medical literature. This article highlights how the age-old medical ideal of beneficence, the modern concept of probabilities, the added dimension of treatment modification based on patients' preferences, and a compassionate disposition operate as competence-supporting tools in high uncertainty medical situations.

Aged↗

PubMedDate not supplied

Amnesic shellfish poisoning toxins in shellfish: estimation of uncertainty of measurement for a liquid chromatography/tandem mass spectrometry method.

A liquid chromatography/mass spectrometry (LC/MS) method for amnesic shellfish poisoning toxins in shellfish was developed and validated. Tissue homogenate (4 g) was extracted with 16 mL methanol-water (1 + 1, v/v). Dilution into acetonitrile-water (1 + 9, v/v) was followed by C18 solid-phase extraction cleanup. Domoic acid (DA) and epi-domoic acid were determined by LC/MS/MS with electrospray ionization and multiple reaction monitoring. External calibration was performed with dilutions of a certified reference standard. Advantages of this method include speed, lower detection limits, and a very high degree of specificity. The LC/MS response was highly linear, and there were no significant interferences to the determination of DA. Formal method validation was performed on 4 shellfish species. Fortification studies gave recoveries (mean +/- SD; n = 24) of 93 +/- 14% at 1 mg/kg, and 93.3 +/- 7.6% at 20 mg/kg over all the species. Analysis of a mussel certified reference material showed the bias as < 5%. The limits of detection and quantitation were 0.15 and 0.5 mg/kg, respectively. Routine application of the method over 4 months gave a recovery for the QC sample (1 mg/kg fortified blank mussel homogenate) run with each batch of 88.9 +/- 5.5% (mean +/- SD; n = 37). The total uncertainty of measurement results were estimated as 0.12 (12%) at 0.25-5 mg/kg and 0.079 (7.9%) at 5-50 mg/kg. The major contribution to the uncertainty was the repeatability of the LC/MS determination, probably arising from subtle matrix effects.

Amnesia↗

PubMed2002

Environmental uncertainty and collaboration among California nonprofit hospitals.

Meeting the health needs of a community involves developing relationships with a wide range of partners. The purpose of this study was to determine the effect of environmental uncertainty on the level of interorganizational relations among nonprofit hospitals with the intent of providing community benefit. Findings indicate that nonprofit hospitals in California are collaborating to provide community benefit. Significant relationships were found between select hospital characteristics, perceived environmental uncertainty, and components of interorganizational relations.

California↗

PubMed2004

The Precautionary Principle and statistical approaches to uncertainty.

The central challenge from the Precautionary Principle to statistical methodology is to help delineate (preferably quantitatively) the possibility that some exposure is hazardous, even in cases where this is not established beyond reasonable doubt. The classical approach to hypothesis testing is unhelpful, because lack of significance can be due either to uninformative data or to genuine lack of effect (the Type II error problem). Its inversion, bioequivalence testing, might sometimes be a model for the Precautionary Principle in its ability to "prove the null hypothesis". Current procedures for setting safe exposure levels are essentially derived from these classical statistical ideas, and we outline how uncertainties in the exposure and response measurements affect the no observed adverse effect level, the Benchmark approach and the "Hockey Stick" model. A particular problem concerns model uncertainty: usually these procedures assume that the class of models describing dose/response is known with certainty; this assumption is, however, often violated, perhaps particularly often when epidemiological data form the source of the risk assessment, and regulatory authorities have occasionally resorted to some average based on competing models. The recent methodology of the Bayesian model averaging might be a systematic version of this, but is this an arena for the Precautionary Principle to come into play?

Bayes Theorem↗

PubMed2005

Effect of uncertainties in agricultural working schedules and Monte-Carlo evaluation of the model input in basin-scale runoff model analysis of herbicides.

In the prediction of time-series concentrations of herbicides in river water with diffuse-pollution hydrological models, farming schedules (the dates of herbicide application and drainage of irrigation water from rice paddies) greatly affect the runoff behavior of the herbicides. For large catchments, obtaining precise data on farming schedules is impractical, and so the model input inevitably includes substantial uncertainty. This paper evaluates the effectiveness of using the Monte-Carlo method to generate sets of estimated farming schedules to use as input to a GIS-based basin-scale runoff model to predict the concentrations of paddy-farming herbicides in river water. The effects of using the Monte-Carlo method to compensate for uncertainty in the evaluated parameters for herbicide decomposition and sorption were also evaluated.

Acetanilides↗

PubMed2005

Stormwater quality modelling in combined sewers: calibration and uncertainty analysis.

Estimating the level of uncertainty in urban stormwater quality models is vital for their utilization. This paper presents the results of application of a Monte Carlo Markov Chain method based on the Bayesian theory for the calibration and uncertainty analysis of a storm water quality model commonly used in available software. The tested model uses a hydrologic/hydrodynamic scheme to estimate the accumulation, the erosion and the transport of pollutants on surfaces and in sewers. It was calibrated for four different initial conditions of in-sewer deposits. Calibration results showed large variability in the model's responses in function of the initial conditions. They demonstrated that the model's predictive capacity is very low.

Bayes Theorem↗

PubMed2005

Reflections on uncertainty in risk assessment and risk management by the Society of Environmental Toxicology and Chemistry (SETAC) precautionary principle workgroup.

Quantitative uncertainty assessments and the distribution of risk are under scrutiny and significant criticism has been made of null hypothesis testing when careful consideration of Type I (false positive) and II (false negative) error rates have not been taken into account. An alternative method, equivalence testing, is discussed yielding more transparency and potentially more precaution in the quantifiable uncertainty assessments. With thousands of chemicals needing regulation in the near future and low public trust in the regulatory process, decision models are required with transparency and learning processes to manage this task. Adaptive, iterative, and learning decision making tools and processes can help decision makers evaluate the significance of Type I or Type II errors on decision alternatives and can reduce the risk of committing Type III errors (accurate answers to the wrong questions). Simplistic cost-benefit based decision-making tools do not incorporate the complex interconnectedness characterizing environmental risks, nor do they enhance learning, participation, or include social values and ambiguity. Hence, better decision-making tools are required, and MIRA is an attempt to include some of the critical aspects.

Decision Support Techniques↗

PubMed2005

Management of environmental uncertainty in maintenance dredging of polluted harbours in Norway.

High levels of toxic chemicals such as tributyltin (TBT) have been found in the bottom sediments of many Norwegian harbours. Norwegian environmental authorities consider maintenance dredging of such harbours to be potentially harmful to the environment due to the suspension and spread of polluted sediments during the dredging operation. Accordingly, permission to dredge may be denied, or special environmental measures may be required on a case-to-case basis. In this paper, we describe the prevailing uncertainty in the assessment of the environmental effects. Employing Jerome Ravetz' distinction between practical and technical problems in the analysis of a case study from Northern Norway, we show the disadvantages with an exclusive focus on technical solutions. Finally, we discuss informal and simple means to strengthen the presence of the practical problem in the decision process and thereby improve the management of uncertainty.

Environment↗

PubMed2005

A guidance for assessing and communicating uncertainties.

In the daily practice of science for policy, as experienced by governmental agencies which inform the policy and the public on the state and outlook of the environment, there is a pressing need for guidance in assessing and communicating uncertainties. This need extends beyond the quantitative assessment of uncertainties in model results, and focuses on the entire process of environmental assessment, running from problem framing towards reporting the results of the study. Using the Netherlands Environmental Assessment Agency (RIVM/MNP) as a case, the development, structure and content of such a guidance system is highlighted. Conditions for a successful implementation of the guidance system are discussed, and some prospects for future work are outlined.

Communication↗

PubMed2006

[Markov Chain Monte Carlo scheme for parameter uncertainty analysis in water quality model].

Parameter identification plays an important role in environmental model application. Markov Chain Monte Carlo method was introduced to estimate parameter uncertainty, since usual Bayes discrete methods were not applicable to produce posterior distribution of complicated environmental model due to the limit of computation. In order to study the performance and efficiency of MCMC, two case studies were used. Results indicate that, either sampling performance or sampling efficiency, MCMC method both has its special advantages in producing posterior distribution. Moreover, results of Gelman convergence diagnostics indicate that sampling sequence can converge to a stationary distribution. A key finding was that the MCMC scheme presented herein provided a powerful means of parameter identification and uncertainty analysis.

Models, Theoretical↗

PubMed2000

Uncertainty of AIDS incubation time and its effects on back-calculation estimates.

Incubation time is the period from the onset of HIV infection to AIDS. The distribution of the incubation time is one of the main parameters of the back-calculation method for the estimation of incidence of HIV infection. Because of the long and variable incubation time, the assessment of its distribution is uncertain and this uncertainty spreads through the back-calculation method and affects the estimation of the precision of incidence of HIV infection. We propose a method to investigate the sensitivity of the estimates to variations of the incubation times, with particular regard to the covariate AGE in the modelling of the incubation period, making use of the parametric bootstrap. An application to the HIV epidemic in Italy is presented. The amplification of the uncertainty of the HIV incidence estimates resulting from the implementation of our proposed method tends to concentrate around the earlier periods of the epidemic, corresponding to the right tail of the incubation time distribution, which is very sensitive to small perturbations.

Acquired Immunodeficiency Syndrome↗

PubMed1997

Variability and uncertainty in spatial, temporal and spatiotemporal crop-yield and related data.

Application of the theories of precision agriculture to the practicalities of broad-acre farming relies on successful handling of the ramifications of uncertainty in information, i.e. information pertaining to the spatial and temporal variation of those factors which determine yield components and/or environmental losses. This paper discusses the uncertainty of yield and related variables as measured by their spatial and temporal variance. The magnitude of these two components gives a suggestion as to the appropriate scale of management. Simultaneous reporting on spatial and temporal variation is rare and the theory of these types of process is still in its infancy. Some brief theory is presented, followed by several examples from the Rothamsted classic experiments, yield-monitoring experiments in Australia, a long-term barley trial in Denmark, and a soil moisture monitoring network. It is clear that annual temporal variation is much larger than the spatial variation within single fields. This leads to the conclusion that if precision agriculture is to have a sound scientific basis and ultimately a practical outcome then the null hypothesis that still remains to be seriously researched is: 'given the large temporal variation in yields relative to the scale of a single field, then the optimal risk aversion strategy is uniform management.'

Crops, Agricultural↗

PubMed2005

Accounting for haplotype uncertainty in matched association studies: a comparison of simple and flexible techniques.

Population-based case-control studies measuring associations between haplotypes of single nucleotide polymorphisms (SNPs) are increasingly popular, in part because haplotypes of a few "tagging" SNPs may serve as surrogates for variation in relatively large sections of the genome. Due to current technological limitations, haplotypes in cases and controls must be inferred from unphased genotypic data. Using individual-specific inferred haplotypes as covariates in standard epidemiologic analyses (e.g., conditional logistic regression) is an attractive analysis strategy, as it allows adjustment for nongenetic covariates, provides omnibus and haplotype-specific tests of association, and can estimate haplotype and haplotype x environment interaction effects. In principle, some adjustment for the uncertainty in inferred haplotypes should be made. Via simulation, we compare the performance (bias and mean squared error of haplotype and haplotype x environment interaction effect estimates) of several analytic strategies using inferred haplotypes in the context of matched case-control data. These strategies include using only the most likely haplotype assignment, the expectation substitution approach described by Stram et al. ([2003b] Hum. Hered. 55:179-190) and others, and an improper version of multiple imputation. For relatively uncomplicated haplotype structures and moderate haplotype relative risks (</=2), all methods performed comparably well (small bias with appropriately-sized confidence intervals). For larger relative risks, the most likely haplotype and multiple imputation strategies showed noticeable bias towards the null; the expectation substitution strategy still performed well. When there was more uncertainty in the inferred haplotypes, the most likely and multiple imputation strategies showed even more bias towards the null, while the expectation substitution method had slightly smaller than nominal confidence intervals for larger relative risks (>/=5). An application to progesterone-receptor haplotypes and endometrial cancer further illustrates that the performance of all these methods depends on how well the observed haplotypes "tag" the unobserved causal variant.

Algorithms↗

PubMed2001

Representing uncertainty: the role of cost-effectiveness acceptability curves.

Decision-making in health care is inevitably undertaken in a context of uncertainty concerning the effectiveness and costs of health care interventions and programmes. One method that has been suggested to represent this uncertainty is the cost-effectiveness acceptability curve. This technique, which directly addresses the decision-making problem, has advantages over confidence interval estimation for incremental cost-effectiveness ratios. However, despite these advantages, cost-effectiveness acceptability curves have yet to be widely adopted within the field of economic evaluation of health care technologies. In this paper we consider the relationship between cost-effectiveness acceptability curves and decision-making in health care, suggest the introduction of a new concept more relevant to decision-making, that of the cost-effectiveness frontier, and clarify the use of these techniques when considering decisions involving multiple interventions. We hope that as a result we can encourage the greater use of these techniques.

Confidence Intervals↗

PubMed2002

Opportunity costs and uncertainty in the economic evaluation of health care interventions.

Considerable methodological research has been conducted on handling uncertainty in cost-effectiveness analysis. The current literature suggests the concepts of net health benefits and cost-effectiveness acceptability curves to circumvent the technical shortcomings of cost-effectiveness ratio statistics. However, these approaches do not provide a solution for the inherent problem that the threshold cost-effectiveness ratio itself is unknown. The authors suggest analysing uncertainty in cost-effectiveness analysis by directly addressing the concept of opportunity costs using the decision rule described by Birch and Gafni (1992) and introduce a new graphical framework (the "decision making plane") for communicating with policy makers.

Budgets↗

PubMed2002

Bayesian estimation, simulation and uncertainty analysis: the cost-effectiveness of ganciclovir prophylaxis in liver transplantation.

This paper demonstrates the usefulness of combining simulation with Bayesian estimation methods in analysis of cost-effectiveness data collected alongside a clinical trial. Specifically, we use Markov Chain Monte Carlo (MCMC) to estimate a system of generalized linear models relating costs and outcomes to a disease process affected by treatment under alternative therapies. The MCMC draws are used as parameters in simulations which yield inference about the relative cost-effectiveness of the novel therapy under a variety of scenarios. Total parametric uncertainty is assessed directly by examining the joint distribution of simulated average incremental cost and effectiveness. The approach allows flexibility in assessing treatment in various counterfactual premises and quantifies the global effect of parametric uncertainty on a decision-maker's confidence in adopting one therapy over the other.

Antiviral Agents↗

PubMed1998

Case ascertainment uncertainties in prevalence surveys of Parkinson's disease.

Using unpublished data from five completed prevalence surveys of Parkinson's disease (PD), we investigated case ascertainment uncertainties that potentially have a direct effect on prevalence. These uncertainties arise from the choice of diagnostic criteria, the choice of screening method, and the amount of information lost because of nonresponse. The surveys were conducted in Argentina, India, China, Italy, and the Netherlands. Our analyses consisted of simple comparisons of prevalence results, positive predictive values (a screening measure), and nonresponse percentages. We found that (a) prevalence comparisons between surveys have diminished value if the surveys used different diagnostic criteria for PD; (b) screening performance may be affected adversely if symptom questions are answered by one family member for the entire family living together rather than by each family member individually; and (c) nonresponse from refusal or unavailability does not necessarily lead to bias, but special caution may be appropriate with prevalence results pertaining to elderly women.

Aged↗

PubMed2005

Interspecies metabolism of heterocyclic aromatic amines and the uncertainties in extrapolation of animal toxicity data for human risk assessment.

Heterocyclic aromatic amines (HAAs) are potent bacterial mutagens that are formed in cooked meats, tobacco smokes condensate, and diesel exhaust. Many HAAs are carcinogenic in experimental animal models. Because of their wide-spread occurrence in the diet and environment, HAAs may contribute to some common types of human cancers. The extrapolation of animal toxicity data on HAAs to asses human health risk has many uncertainties, which can lead to tenuous risk assessment estimates. Perhaps the most critical and variable parameters in interspecies extrapolation are the effects of dose, species differences in catalytic activities of xenobiotic metabolism enzymes (XMEs), human XME polymorphisms that lead to interindividual differences in carcinogen metabolism and dietary constituents that may either augment or diminish the carcinogenic potency of these genotoxins. The impact of these parameters on the metabolism and toxicological properties of HAAS and uncertainties in extrapolation of animal toxicity data for human risk assessment are presented in this article.

Acetyltransferases↗