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PubMed2005

The pleasures of uncertainty: prolonging positive moods in ways people do not anticipate.

The authors hypothesized that uncertainty following a positive event prolongs the pleasure it causes and that people are generally unaware of this effect of uncertainty. In 3 experimental settings, people experienced a positive event (e.g., received an unexpected gift of a dollar coin attached to an index card) under conditions of certainty or uncertainty (e.g., it was easy or difficult to make sense of the text on the card). As predicted, people's positive moods lasted longer in the uncertain conditions. The results were consistent with a pleasure paradox, whereby the cognitive processes used to make sense of positive events reduce the pleasure people obtain from them. Forecasters seemed unaware of this paradox; they overwhelmingly preferred to be in the certain conditions and tended to predict that they would be in better moods in these conditions.

Adolescent↗
PubMed2006

Methodological uncertainties in multi-regression analyses of middle-atmospheric data series.

Multi-regression analyses have often been used recently to detect trends, in particular in ozone or temperature data sets in the stratosphere. The confidence in detecting trends depends on a number of factors which generate uncertainties. Part of these uncertainties comes from the random variability and these are what is usually considered. They can be statistically estimated from residual deviations between the data and the fitting model. However, interferences between different sources of variability affecting the data set, such as the Quasi-Biennal Oscillation (QBO), volcanic aerosols, solar flux variability and the trend can also be a critical source of errors. This type of error has hitherto not been well quantified. In this work an artificial data series has been generated to carry out such estimates. The sources of errors considered here are: the length of the data series, the dependence on the choice of parameters used in the fitting model and the time evolution of the trend in the data series. Curves provided here, will permit future studies to test the magnitude of the methodological bias expected for a given case, as shown in several real examples. It is found that, if the data series is shorter than a decade, the uncertainties are very large, whatever factors are chosen to identify the source of the variability. However the errors can be limited when dealing with natural variability, if a sufficient number of periods (for periodic forcings) are covered by the analysed dataset. However when analysing the trend, the response to volcanic eruption induces a bias, whatever the length of the data series. The signal to noise ratio is a key factor: doubling the noise increases the period for which data is required in order to obtain an error smaller than 10%, from 1 to 3-4 decades. Moreover, if non-linear trends are superimposed on the data, and if the length of the series is longer than five years, a non-linear function has to be used to estimate trends. When applied to real data series, and when a breakpoint in the series occurs, the study reveals that data extending over 5 years are needed to detect a significant change in the slope of the ozone trends at mid-latitudes.

Aerosols↗
PubMed2003

Understanding uncertainty and minimizing families' anxiety at the time of transfer from intensive care.

When general ward registered nurses (RN) receive patients from an intensive care unit (ICU) they report that much of their time in the initial phases revolves around meeting family needs (Farvis, 2002). Families experience anxiety when leaving the security of the close monitoring seen in ICU (Leith, 1999) and their anxiety reduces their ability to play a key role in the patient's recovery (McShane, 1991; Leske, 1992) as it can impair their decision-making (Cagan, 1988; Halm et al., 1993). By reducing a family's anxiety, they may be more able to cope with the necessary transition to a general ward and support the patient's recovery. A literature search from 1990 onwards was performed within the CINAHL, Medline and Cochrane databases using the key words: intensive care, family, General System Theory, uncertainty, anxiety and transfer. Further articles were retrieved from citation references from the Web of Science or through the reference lists of retrieved literature. Library catalogues were searched using the same key words for books and book chapters. von Bertalanffy's General System Theory provides a framework for understanding the importance of family in a critical illness situation. Critical illness permits little or no time to adapt, thus reducing the family's ability to cope with the situation. Transfer out of ICU is a significant anxiety-producing event for families. Uncertainty in illness is reported in other illness situations to reduce family's adaptation to illness events, but has not been researched with an ICU cohort of families. Seven out of the top 10 needs of ICU families are information needs, highlighting the importance of communication regarding progress and future plans. Nurses require an increased awareness that transfer anxiety exists for families and to be knowledgeable about ways to reduce its occurrence. Research is required to evaluate the efficacy of interventions to reduce anxiety for families and examine the level of uncertainty in illness in this cohort.

Anxiety↗
PubMed2004

Measurement uncertainty associated with overall migration testing.

The results from a single laboratory that took part in a series of check-sample exercises for overall migration were used to calculate the measurement uncertainty for the overall migration methodology. The results span 10 years of proficiency testing and cover a range of plastic materials tested using a variety of time and temperature test conditions. Twelve sets of results for overall migration into olive oil and 10 sets of results for overall migration into volatile simulants were used. The measurement uncertainty associated with the determination of overall migration from plastics into olive oil was estimated as +/- 2.6 mg dm-2 for results between 1.2 and 15.4 mg dm-2. The measurement uncertainty associated with the determination of overall migration into volatile simulants was estimated as +/- 1.4 mg dm-2 for results between 2.1 and 13 mg dm-2. These estimates are within the analytical tolerances set in European Union regulations, of 3 and 2 mg dm-2, respectively. The performance of all laboratories participating in these 22 check sample exercises was also evaluated. A very large majority (93%) of the participants used official test methods, sometimes with minor modifications as stated. For the simulant olive oil, an average of 81% of laboratories (13 rounds with an average of 21 participants in each) using official methods was within the tolerance specified in regulations. For the volatile food simulants, an average of 87% of laboratories (11 rounds with an average of 38 participants in each) using official methods was within the tolerance specified. It is concluded from this evaluation of within- and between-laboratory data that the official European Standard methods for overall migration testing are suitable for the analysis of routine samples.

Dietary Fats, Unsaturated↗
PubMed2006

Comparative performance of Bayesian and AIC-based measures of phylogenetic model uncertainty.

Reversible-jump Markov chain Monte Carlo (RJ-MCMC) is a technique for simultaneously evaluating multiple related (but not necessarily nested) statistical models that has recently been applied to the problem of phylogenetic model selection. Here we use a simulation approach to assess the performance of this method and compare it to Akaike weights, a measure of model uncertainty that is based on the Akaike information criterion. Under conditions where the assumptions of the candidate models matched the generating conditions, both Bayesian and AIC-based methods perform well. The 95% credible interval contained the generating model close to 95% of the time. However, the size of the credible interval differed with the Bayesian credible set containing approximately 25% to 50% fewer models than an AIC-based credible interval. The posterior probability was a better indicator of the correct model than the Akaike weight when all assumptions were met but both measures performed similarly when some model assumptions were violated. Models in the Bayesian posterior distribution were also more similar to the generating model in their number of parameters and were less biased in their complexity. In contrast, Akaike-weighted models were more distant from the generating model and biased towards slightly greater complexity. The AIC-based credible interval appeared to be more robust to the violation of the rate homogeneity assumption. Both AIC and Bayesian approaches suggest that substantial uncertainty can accompany the choice of model for phylogenetic analyses, suggesting that alternative candidate models should be examined in analysis of phylogenetic data. [AIC; Akaike weights; Bayesian phylogenetics; model averaging; model selection; model uncertainty; posterior probability; reversible jump.].

Bayes Theorem↗
PubMed2006

Relating two techniques for handling uncertainties in IMRT optimization.

Radiotherapy treatment planning is associated with uncertainties. Examples are uncertainties in the tumour location due to organ movement or the inter/intra observer variability in target definition. Different approaches to incorporate uncertainties into IMRT optimization have been proposed. In this note, we point out a relation between two previously published methods: the coverage probability approach and the concept of optimizing the expectation value of an objective function that depends on a set of random variables. Both concepts are generally different, but turn out to be equivalent in special cases.

Biophysical Phenomena↗
PubMed2026

Quantifying uncertainty of predictions from cancer progression models.

MOTIVATION: Cancer progresses through the accumulation of genomic events. Cancer progression models such as Mutual Hazard Networks (MHNs) describe this dynamic, enabling prediction of temporal event positions and patient-specific risks of acquiring mutations. However, current MHN analyses rely on single most likely models and do not quantify the uncertainty inherent to parameter estimation. Assessing forecast stability is essential before using them to anticipate treatment-relevant mutations, adapt targeted therapies, or prioritize monitoring of patients at elevated progression risk. RESULTS: We address a key prerequisite for the responsible clinical use of cancer progression models by making MHN-derived predictions uncertainty-aware. We present a Bayesian framework for MHN that uses Markov Chain Monte Carlo to sample from the posterior distributions of model parameters and derived predictions. For practical use we implemented the Random-Walk Metropolis, Metropolis-Adjusted Langevin Algorithm (MALA), and simplified manifold MALA samplers as part of the existing mhn Python package. Only MALA and smMALA were successful in sampling from MHN posteriors, with MALA performing best. While most MHN parameters and predictions showed low posterior variance, a small subset displayed greater variability across the posterior distribution. This differentiation cannot be obtained from a single most likely model, emphasizing the need for uncertainty quantification, especially in clinical contexts. As an illustrative example, posterior sampling identified a subgroup of STK11$-$, KRAS$+$ lung adenocarcinoma patients with a high predicted short-term risk-with low variance across posterior samples-to develop an STK11 mutation. This subgroup exhibited poorer survival under immunotherapy, resembling patterns observed in STK11+ patients. AVAILABILITY AND IMPLEMENTATION: Our implementation is part of version 1.2.0 of the mhn package (https://github.com/spang-lab/LearnMHN). All analyses including the code to produce all figures in this article can be found under https://github.com/huy29433/MCMC-sampling-for-MHN (https://doi.org/10.5281/zenodo.21160219).

Humans↗
PubMed2006

The use of a Monte Carlo method for uncertainty calculation, with an application to the measurement of neutron ambient dose equivalent rate.

This paper is concerned with the use of a Monte Carlo method for uncertainty calculation as an implementation of the propagation of distributions. It reviews the basic principles of the propagation of distributions and numerical aspects of a Monte Carlo implementation. It also discusses the possible advantages in some circumstances of the propagation of distributions over the GUM uncertainty framework, and how the results obtained in any particular instance can be compared with those provided by that framework. To illustrate these various aspects, an application to the measurement of neutron dose equivalent rate is given. A key consideration in this application is the manner in which the dominant source of uncertainty, namely that associated with the field-specific correction factor, is treated. The information available concerning this factor constitutes the correction factors for a set of fields of the same type as that in which a measurement is being made. This information is encoded as a probability density function (PDF) for the correction factor. This PDF constitutes an input to both methods of evaluation.

Anisotropy↗
PubMed2006

Uncertainties in doses from intakes of radionuclides assessed from monitoring measurements.

The evaluation of uncertainties in doses from intakes of radionuclides is one of the most difficult problems in internal dosimetry. In this paper, the process of assessing internal doses from monitoring measurements is reviewed and the major sources of uncertainty are discussed. Methods developed independently at HPA and at IRSN for the determination of uncertainties in internal doses assessed from monitoring measurements are described. Both use a Monte Carlo simulation approach. Results are described for three illustrative examples. An alternative method developed at the Los Alamos National Laboratory that uses Bayesian statistical methods is also described briefly.

Animals↗
PubMed2006

Uncertainties in personal dosimetry for external radiation: a Monte Carlo approach.

This paper explores the possibilities of numerical methods for uncertainty analysis of personal dosimetry systems. Using a numerical method based on Monte Carlo sampling the probability density function (PDF) of the dose measured using a personal dosemeter can be calculated using type-test measurements. From this PDF the combined standard uncertainty in the measurements with the dosemeter and the confidence interval can be calculated. The method calculates the output PDF directly from the PDFs of the inputs of the system such as the spectral distribution of the radiation and distributions of detector parameters like sensitivity and zero signal. The method can be used not only in its own right but also for validating other methods because it is not limited by restrictions that apply to using the Law of Propagation of Uncertainty and the Central Limit Theorem. The use of the method is demonstrated using the type-test data of the NRG-TLD.

Computer Simulation↗
PubMedDate not supplied

Uncertainty in illness. An integrative review.

The purpose of this article is to report an integrative review of uncertainty in the illness experience, describe the uncertainty in illness theory, and to discuss the importance of assessing the appraisal of uncertainty experienced in illness with individuals and families faced with illness.

Adaptation, Psychological↗
PubMed2004

Statistical approaches to handling uncertainty in health economic evaluation.

Not so long ago, uncertainty in economic evaluation was handled almost exclusively using simple one-way sensitivity analysis methods whereby individual parameters of an analysis were varied one-by-one over a range of (often ad-hoc) values. With the increasing use of the clinical trial as a vehicle for economic evaluation, there has been increasing interest in the use of statistical methods for handling uncertainty in patient-level data on both costs and effects. However, even for studies based on secondary data, the use of statistical methods for specifying parameter distributions in so-called probabilistic analyses is becoming increasingly common and is now the method recommended by a number of good-practice guides and regulatory agencies. The aim of this paper is to review these statistical approaches to the handling of uncertainty, both for primary studies where patient level cost and effect information is available and in secondary studies that typically use decision analytical methods to synthesize summary information from the published literature. The approach throughout the paper will be to emphasize the intuition behind the methods rather than the technical details. Although the methodology is generally applicable, examples will be drawn from both primary and secondary economic evaluations in the area of gastroenterology. The first example relates to a primary care trial of dyspepsia management which sought to estimate the cost-effectiveness of managing patients through early endoscopy compared to usual care. The second example is a secondary study of the management of gastro-oesophageal reflux disease symptoms which considered six potential strategies simultaneously.

Confidence Intervals↗
PubMed2003

Quantitative risk assessment for the induction of allergic contact dermatitis: uncertainty factors for mucosal exposures.

The quantitative risk assessment (QRA) paradigm has been extended to evaluating the risk of induction of allergic contact dermatitis from consumer products. Sensitization QRA compares product-related, topical exposures to a safe benchmark, the sensitization reference dose. The latter is based on an experimentally or clinically determined 'no observable adverse effect level' (NOAEL) and further refined by incorporating 'sensitization uncertainty factors' (SUFs) that address variables not adequately reflected in the data from which the threshold NOAEL was derived. A critical area of uncertainty for the risk assessment of oral care or feminine hygiene products is the extrapolation from skin to mucosal exposures. Most sensitization data are derived from skin contact, but the permeability of vulvovaginal and oral mucosae is greater than that of keratinized skin. Consequently, the QRA for some personal products that are exposed to mucosal tissue may require the use of more conservative SUFs. This article reviews the scientific basis for SUFs applied to topical exposure to vulvovaginal and oral mucosae. We propose a 20-fold range in the default uncertainty factor used in the contact sensitization QRA when extrapolating from data derived from the skin to situations involving exposure to non-keratinized mucosal tissue.

Allergens↗
PubMed2004

Elicitation of expert judgments of uncertainty in the risk assessment of herbicide-tolerant oilseed crops.

One of the lay public's concerns about genetically modified (GM) organisms (GMO) and related emerging technologies is that not all the important risks are evaluated or even identified yet--and that ignorance of the unanticipated risks could lead to severe environmental or public health consequences. To some degree, even the scientists who participated in the analysis of the risks from GMOs (arguably the people most qualified to critique these analyses) share some of this concern. To formally explore the uncertainty in the risk assessment of a GM crop, we conducted detailed interviews of seven leading experts on GM oilseed crops to obtain qualitative and quantitative information on their understanding of the uncertainties associated with the risks to agriculture from GM oilseed crops (canola or rapeseed). The results of these elicitations revealed three issues of potential concern that are currently left outside the scope of risk assessments. These are (1) the potential loss of the agronomic and environmental benefits of glyphosate (a herbicide widely used in no-till agriculture) due to the combined problems of glyphosate-tolerant canola and wheat volunteer plants, (2) the growing problem of seed lot contamination, and (3) the potential market impacts. The elicitations also identified two areas where knowledge is insufficient. These are: the occurrence of hybridization between canola and wild relatives and the ability of the hybrids to perpetuate themselves in nature, and the fate of the herbicide-tolerance genes in soil and their interaction with soil microfauna and -flora. The methodological contribution of this work is a formal approach to analyzing the uncertainty surrounding complex problems.

Agriculture↗
PubMed2006

Balancing uncertainty and acceptance: understanding Chinese women's responses to an abnormal cervical smear result.

AIMS AND OBJECTIVES: The aims of this study were to examine the responses of Hong Kong Chinese women receiving an abnormal smear result and to compare any differences in responses depending on the diagnosis of the abnormal smear. BACKGROUND: The implementation of cervical screening programmes has resulted in an increasing number of women receiving an abnormal smear result requiring cytological surveillance or referral for colposcopy. Evidence suggests that women frequently misunderstood such results, believing that they already have cancer. However, little is known about the responses of Chinese women in this situation. DESIGN: An exploratory qualitative study. METHODS: Face-to-face semi-structured tape-recorded interviews were undertaken with 66 women sampled opportunistically from an urban centre of a major non-governmental service provider. Amongst this sample, 22 women required cytological surveillance, 20 required treatment for vaginitis and 24 were referred for colposcopy. Thematic analysis was undertaken of the translated interviews within and across groups to identify categories and themes illustrating women's responses to an abnormal smear result. FINDINGS: An important difference in the comparison of the data sets was that of women's understanding of the cause of the abnormal result. Women with vaginitis understood the cause of their abnormality, whereas those in the other groups remained unclear about their abnormality, generating feelings of fear and uncertainty. Trust in practitioners influenced women's acceptance of the result. CONCLUSIONS: Although responses of Chinese women are similar to those in other population groups, with those referred for colposcopy experiencing greater anxiety than those undergoing cytological surveillance, balancing feelings of uncertainty and acceptance influenced Chinese women's responses to their abnormal results and allowed them to make sense of their result in their everyday life. Trust in the practitioner was essential to the acceptance of their result. RELEVANCE TO CLINICAL PRACTICE: Such findings highlight implications for nursing practice, in particular developing information-giving strategies and targeted information to help women manage feelings of uncertainty. The importance of trusting professional relationships in determining women's acceptance of an abnormal result is also highlighted. The findings suggest nurses working with Chinese women should consider the underlying influence of culture on women's beliefs and actions.

Adaptation, Psychological↗
PubMed2005

Assessing the case for mandatory folate fortification: policy-making in the face of scientific uncertainties.

This paper presents the view that policy-makers face scientific uncertainties in assessing the case for mandatory folate fortification as a policy response to epidemiological evidence of the relationship between folate and neural tube defects. Moreover, the resolution of these uncertainties is confounded by the under-resourced state of nutrition information systems in Australia and New Zealand. The uncertainties relate to potential risks and benefits associated with the intervention for the target group and the population in general. These risks and benefits reflect the mismatch between evidence and policy that arises when addressing a presumed genetic abnormality in at-risk individuals with an intervention that is population-wide in its scope. There is an urgent need to conduct ongoing national nutrition surveys and monitor and evaluate policy interventions to strengthen the capacity of nutrition information systems to inform decision-making for this current, and future, public health nutrition policy.

Australia↗
PubMed1994

Treatment limitation decisions under uncertainty: the value of subsequent euthanasia.

This paper examines how decisions to limit treatment to critically ill patients under uncertainty can be made rationally. Expected utility theory offers one way of making rational decisions under uncertainty. One problem with using this approach is that we may not know the value of each option. One rational course open is to treat until further information becomes available. However, treatment can limit the range of options open. With treatment, a patient may recover such that he no longer requires life-supporting treatment. However, his life may be not worth living. If active euthanasia of 'non-terminal' conditions is prohibited, the option of dying will no longer be available. Taking a rational 'wait and see' course may result in being trapped within an unbearable life. On the other hand, sometimes present practice 'lets nature take its course'. Critically ill patients are allowed to die because it is believed that their lives will be not worth living. It is likely that some patients are allowed to die when there is some objective chance of worthwhile future life. This paper argues that a policy of treating critically ill patients until the nature of future options can be better evaluated, in company with an offer of subsequent euthanasia where appropriate, allows a more rational and humane approach to treatment limitation decisions under uncertainty.

Computers↗
PubMedDate not supplied

Establishing the cost-effectiveness of new pharmaceuticals under conditions of uncertainty--when is there sufficient evidence?

Decisions about which health-care interventions represent adequate value to collectively funded health-care systems are as widespread as they are unavoidable. In the case of new pharmaceuticals, many countries now require formal cost-effectiveness analysis to inform this decision-making process. This requires evidence on parameters associated with health-related utilities, treatment effects, resource use, and costs, for which data from available regulatory trials are invariably absent or highly uncertain. This uncertainty results from a number of factors including the predominance of intermediate end points in the clinical evidence-base and the limited period of follow-up of patients in clinical studies. Despite these imperfections in the evidence base, decisions about whether new pharmaceuticals are sufficiently cost-effective for reimbursement cannot be side-stepped. Data limitations do, however, require the use of rigorous analytical methods to support decision making. Probabilistic decision models and value of information analysis offer a means of structuring decision problems, synthesizing all available data, characterizing the uncertainty in the decision, quantifying the cost of uncertainty, and establishing the expected value of perfect information. This analytical framework is important because it addresses two fundamental questions about new pharmaceuticals. First, is the product expected to be cost-effective on the basis of existing evidence? Second, is additional research concerning the product itself cost-effective? In addressing these questions, the analytical framework can establish when sufficient evidence exists to sustain a claim for a new pharmaceutical to be cost-effective.

Cost-Benefit Analysis↗