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PubMed1997

The generalisability of pharmacoeconomic studies.

Authors of pharmacoeconomic analyses understandably want their findings to apply as broadly as possible. Also, decision-makers may have to interpret the results of analyses conducted in healthcare settings other than their own. The validity of transferring or generalising results from one setting to another raises important issues for health-economic evaluation. Pharmacoeconomic analyses attempt to model the costs and benefits of alternative treatments in normal clinical practice. Usually, no single clinical study directly provides all the required information, and a variety of data sources is generally included in each analysis. Different data sources present different problems in terms of their relevance to decision-makers. At one extreme, an analysis based purely on trial outcomes and resource use may be precise, but not reflect normal practice; at the other extreme, an analysis using practice data may appear relevant, but be exposed to biases and confounding. Reviews of published studies suggest that general standards have been inadequate in the past. Reapplying such analyses in different localities may simply replicate inadequate findings. The 'perfect' should not become the enemy of the merely 'good'. Models can be helpful in decision-making, provided that they accurately communicate uncertainties in modelling and data. Even so, there will be limits to the generalisability of pharmacoeconomic models, since the required analysis differs between jurisdictions, and because of variations in normal clinical practice. The transferability of research findings re-opens the issue of credibility in pharmacoeconomics. Methodological standardisation, reporting standards and researcher independence are recognised as important factors for enhancing credibility. Where possible, pharmacoeconomic analyses should reflect the findings of systematic reviews of health outcomes to avoid the risk of biased selection of the evidence. In addition, the application of findings to individual healthcare settings must be considered, since cost effectiveness may vary markedly by setting and perspective.

Decision Support Techniques↗

PubMed2003

Pharmacoeconomic analysis of osteoporosis treatment with risedronate.

Hip fracture is an important and costly problem. Therapy with the bisphosphonate risedronate effectively prevents hip and other fractures among women with established osteoporosis. Risedronate is a first-choice therapy option in the German Guidelines of the Dachverband Osteologie for Osteoporosis according to evidence-based medicine criteria for the treatment of postmenopausal osteoporosis, osteoporosis of the elderly (women aged > 75 years) and glucocorticoid-induced osteoporosis. There are few published economic evaluations of bisphosphonates in Germany. Therefore, an assessment of the cost-effectiveness of risedronate utilizing a state transition Markov model of established postmenopausal osteoporosis based on randomized clinical trial data was developed. Uncertainty underlying model parameters and outcomes was dealt with using traditional sensitivity analysis and stochastic sensitivity analysis to produce quasi-95% Cls. We focused on patients aged 70 years, since this population most closely matches the randomized controlled trial and is typical of osteoporosis patients in Germany. The baseline model was a cohort of 1,000 70-year-old women, who received risedronate for 3 years and were followed up for an overall observation period of 10 years, modelling transitions through estimated health states and evaluating outcomes. Over the 3-year treatment period and 10-year observation period, risedronate dominated the current average basic treatment in Germany. In the risedronate group 33 hip fractures were averted and 32 quality-adjusted life years (QALYs) were gained (discounted values). Risedronate treatment saves costs for German social insurance: the present net value of the associated costs from the perspective of German social insurance is [symbol: see text]10.66 million if risedronate treatment is used versus [symbol: see text]11 million if basic treatment is used. Thus, net savings of [symbol: see text]340,000 for the treatment group per 1,000 treated women were calculated. Furthermore, risedronate treatment is cost effective from the perspective of the statutory health insurance with costs per averted hip fracture in the analyzed population of [symbol: see text]33,856 and cost per QALY gained of [symbol: see text]35,690. Both results demonstrate cost-effectiveness and are far below the accepted threshold level of [symbol: see text]50,000. Based on this analysis, risedronate is a cost-effective treatment for postmenopausal osteoporosis within the German health care system, offering benefits for osteoporotic patients and for budget decision-makers.

Aged↗

PubMed2006

Improving efficiency of uncertainty analysis in complex integrated assessment models: the case of the RAINS emission module.

Ever since the Regional Acidification Information and Simulation Model (RAINS) has been constructed, the treatment of uncertainty has remained an issue of major interest. In a recent review of the model performed for the Clean Air for Europe (CAFE) programme of the European Commission, a more systematic and structured uncertainty analysis has been recommended. This paper aims at contributing to the scientific debate how this can be achieved. Because of its complex structure on the one hand and limited research resources (time, computational capacities) on the other hand a full-blown uncertainty analysis in RAINS is hardly feasible. Therefore, all types of uncertainty require more efficient ways for uncertainty analysis. With respect to parameter uncertainty, we propose to focus research efforts for uncertainty analysis on key parameters. Among different approaches to select key parameters that have been discussed in the literature screening methods seem to be particularly appropriate for complex, deterministic Integrated Assessment models such as RAINS. Surprisingly, in Integrated Assessment modelling for air pollution problems of screening design have not been taken up so far. As a case study we consider the emission module of RAINS. We show that its structure allows for a straightforward and effective screening procedure.

Air Pollution↗

PubMedDate not supplied

Application of uncertainty reasoning based on cloud model in time series prediction.

Time series prediction has been successfully used in several application areas, such as meteorological forecasting, market prediction, network traffic forecasting, etc., and a number of techniques have been developed for modeling and predicting time series. In the traditional exponential smoothing method, a fixed weight is assigned to data history, and the trend changes of time series are ignored. In this paper, an uncertainty reasoning method, based on cloud model, is employed in time series prediction, which uses cloud logic controller to adjust the smoothing coefficient of the simple exponential smoothing method dynamically to fit the current trend of the time series. The validity of this solution was proved by experiments on various data sets.

Information Systems↗

PubMed1987

Optimal experiment design for nonlinear models subject to large prior uncertainties.

Classical experiment design generally yields an experiment that depends on the value of the parameters to be estimated, which are, of course, unknown. Assuming that the model parameters belong to a population with known statistics, we propose to take the a priori parameter uncertainty into account by optimizing the mathematical expectation of a functional of the Fisher information matrix. This optimization is performed with a stochastic approximation algorithm that makes robust experiment design almost as simple as classical D-optimal design. The resulting methodology is applied to the choice of measurement times for multiexponential models.

Models, Theoretical↗

PubMed2002

Measuring uncertainty in complex decision analysis models.

Prediction models used in support of clinical and health policy decision making often need to consider the course of a disease over an extended period of time, and draw evidence from a broad knowledge base, including epidemiologic cohort and case control studies, randomized clinical trials, expert opinions, and more. This paper is a brief introduction to these complex decision models, their relation to Bayesian decision theory, and the tools typically used to describe the uncertainties involved. Concepts are illustrated throughout via a simplified tutorial.

Bayes Theorem↗

PubMed2001

An exploration of the relationships between uncertainty, psychological distress and type of coping strategy among Chinese men after cardiac catheterization.

The experience of cardiac catheterization (CC) has included feelings of uncertainty, stress, fear and anxiety in many patients. However, conflicting findings from previous research have been reported. Chinese patients who undergo CC may experience psychological distress in a different way to other cultures as a result of traditional beliefs. Moreover, little research examining the impact of CC among Hong Kong Chinese has been carried out. Therefore, the aim of the study was to explore relationships between uncertainty, psychological distress and coping strategy in Chinese men after CC, using Mishel's model of uncertainty in illness as a framework. A convenience sample of 27 men hospitalized for cardiac catheterization participated in this study using a descriptive, correlational research design. Ethical approval was obtained from the Ethics Committee of the Medical Faculty at the Chinese University of Hong Kong and from the Executive Committee of the hospital. Participation was on a voluntary basis with patient confidentiality assured. Self-report questionnaires included Chinese versions of Mishel's Uncertainty in Illness Scale (MUIS), the Profile of Mood States (POMS), the State-Trait Anxiety Inventory (STAI) and the Chinese Coping Scale (CCS) for data collection. Data were analysed using the Statistical Package for Social Sciences. High mean scores for uncertainty (mean=101.4, SD=11.49) and variables measuring psychological distress (mood disturbance mean=36.6, SD=33.6, state-anxiety mean=39.1, SD=8.95, trait-anxiety mean=43.7, SD=8.1) among these participants suggest that Hong Kong Chinese men experience uncertainty and psychological distress when undergoing cardiac catheterization. Strong relationships between uncertainty and mood disturbance (r=0.57, P=0.01), trait-anxiety and mood disturbance (r=0.65, P=0.01) and state-anxiety and external coping strategies (r=0.50, P=0.05) were found. These findings suggest that relationships between uncertainty, psychological distress and external coping strategies exist in Chinese men hospitalized for cardiac catheterization. Moreover, these findings may help nurses' design culturally specific interventions for their patients.

Adaptation, Psychological↗

PubMed2004

A bayesian approach to parameter estimation for a crayfish (Procambarus spp.) bioaccumulation model.

Bioaccumulation models are used to describe chemical uptake and clearances by organisms. Averaged input parameter values are traditionally used and yield point estimates of model outputs. Hence, the uncertainty and variability of model predictions are ignored. Probabilistic modeling approaches, such as Monte Carlo simulation and the Bayesian method, have been recommended by the U.S. Environmental Protection Agency to provide a quantitative description of the degree of uncertainty and/or variability in risk estimates in ecological hazards and human health effects. In this study, a Bayesian analysis was conducted to account for the combined uncertainty and variability of model parameters in a crayfish bioaccumulation model. After a 5-d exposure in the LaBranche Wetlands (LA, USA), crayfish were analyzed for polycyclic aromatic hydrocarbon concentrations and lipid fractions. The posterior distribution of model parameters were derived from the joint posterior parameter distributions using a Markov chain Monte Carlo approach and the experimental data. The results were then used to predict the distribution of chrysene concentration versus time in the crayfish to compare the predicted ranges at the different study sites.

Animals↗

PubMed2001

Models for combining random and systematic errors. assumptions and consequences for different models.

A series of models for handling and combining systematic and random variations/errors are investigated in order to characterize the different models according to their purpose, their application, and discuss their flaws with regard to their assumptions. The following models are considered 1. linear model, where the random and systematic elements are combined according to a linear concept (TE = absolute value(bias) + z x sigma), where TE is total error, bias is the systematic error component, sigma is the random error component (standard deviation or coefficient of variation) and z is the probability factor; 2. squared model with two sub-models of which one is the classical statistical variance model and the other is the GUM (Guide to Uncertainty in Measurements) model for estimating uncertainty of a measurement; 3. combined model developed for the estimation of analytical quality specifications according to the clinical consequences (clinical outcome) of errors. The consequences of these models are investigated by calculation of the functions of transformation of bias into imprecision according to the assumptions and model calculations. As expected, the functions turn out to be rather different with considerable consequences for these types of transformations. It is concluded that there are at least three models for combining systematic and random variation/errors, each created for its own specific purpose, with its own assumptions and resulting in considerably different results. These models should be used according to their purposes.

Algorithms↗

PubMed2004

Current issues and uncertainties in the measurement and modelling of air-vegetation exchange and within-plant processing of POPs.

Air-vegetation exchange of POPs is an important process controlling the entry of POPs into terrestrial food chains, and may also have a significant effect on the global movement of these compounds. Many factors affect the air-vegetation transfer including: the physicochemical properties of the compounds of interest; environmental factors such as temperature, wind speed, humidity and light conditions; and plant characteristics such as functional type, leaf surface area, cuticular structure, and leaf longevity. The purpose of this review is to quantify the effects these differences might have on air/plant exchange of POPs, and to point out the major gaps in the knowledge of this subject that require further research. Uptake mechanisms are complicated, with the role of each factor in controlling partitioning, fate and behaviour process still not fully understood. Consequently, current models of air-vegetation exchange do not incorporate variability in these factors, with the exception of temperature. These models instead rely on using average values for a number of environmental factors (e.g. plant lipid content, surface area), ignoring the large variations in these values. The available models suggest that boundary layer conductance is of key importance in the uptake of POPs, although large uncertainties in the cuticular pathway prevents confirmation of this with any degree of certainty, and experimental data seems to show plant-side resistance to be important. Models are usually based on the assumption that POP uptake occurs through the lipophilic cuticle which covers aerial surfaces of plants. However, some authors have recently attached greater importance to the stomatal route of entry into the leaf for gas phase compounds. There is a need for greater mechanistic understanding of air-plant exchange and the 'scaling' of factors affecting it. The review also suggests a number of key variables that researchers should measure in their experiments to allow comparisons to be made between studies in order to improve our understanding of what causes any differences in measured data between sites.

Air Movements↗

PubMed2002

The cost utility of bisphosphonate treatment in established osteoporosis.

BACKGROUND: Hip fracture is an important and costly problem. Bisphosphonate therapy prevents hip and other fractures among women with established osteoporosis, but there are few published economic evaluations of this treatment. AIM: To assess the cost-effectiveness of risedronate, a recently launched bisphosphonate for the prevention of fractures among women with established osteoporosis. METHODS: A state transition Markov model of established post-menopausal osteoporosis based upon randomized clinical trial data was developed. Uncertainty underlying model parameters and outcomes was dealt with using traditional sensitivity analysis and stochastic sensitivity analysis to produce quasi-95%CIs. We focussed on patients aged approximately 75 years, since this population most closely matches the randomized controlled trial, and is typical of osteoporosis patients in the UK. RESULTS: The baseline model of treating a cohort of 1000 75-year-old women for 3 years with risedronate and then modelling the costs and benefits over their expected lifetimes, produced net savings of pound sterling 786 000 for the treatment group per 1000 treated women, (95%CI pound sterling 1.55m savings to pound sterling 47000 extra costs). Restricting the horizon of the analysis to only three years led to a small net cost of pound sterling 138 000 per 1000 treated women (95%CI pound sterling 196 000 savings to pound sterling 477 000 extra costs) with a net increment in Quality Adjusted Life years (QALYs) of 16 per 1000 treated women. This resulted in a cost per QALY of pound sterling 8625 per treated woman. CONCLUSIONS: In this example, the use of risedronate therapy in 75-year-old women at high risk of hip fracture leads to an improvement in quality of life with possible cost savings. Restricting the analysis to a time horizon of only three years leads to a QALY gain at a modest net cost.

Aged↗

PubMedDate not supplied

A multiscale investigation of ground water flow at Clear Lake, Iowa.

Ground water flow was investigated at Clear Lake, a 1468-ha glacial lake in north-central Iowa, as part of a comprehensive water quality study. A multiscale approach, consisting of seepage meters (and a potentiomanometer), Darcy's law, and an analytic element (AE) model, was used to estimate ground water inflow to and outflow from the lake. Estimates from the three methods disagreed. Seepage meters recorded a median-specific discharge of 0.25 mum/s, which produced a lake inflow rate between 90,750 and 138,200 m3/d, but no detectable outflow. A wave-induced Bernoulli effect probably compromised both inflow and outflow measurements. Darcy's law was applied to 11 zones around the lake, producing inflow and outflow values of 10,500 and 5000 m3/d, respectively. The AE model, GFLOW, coupled with the parameter estimation model, UCODE, simulated ground water flow in a 700-km2 region using 31 hydraulic head and base flow measurements as calibration targets. The model produced ground water inflow and outflow rates of 14,300 and 9200 m3/d, respectively. Although not a substitute for field data, the model's ability to simulate ground water flow to the lake and the region, estimate uncertainty for model parameters, and calculate a lake stage and associated lake water balance makes it a powerful tool for water quality management and an attractive alternative to the traditional methods of ground water/lake investigation.

Calibration↗

PubMed2003

Pooled fecal culture sampling for Mycobacterium avium subsp. paratuberculosis at different herd sizes and prevalence.

A stochastic spreadsheet model was developed to obtain estimates of the costs of whole herd testing on dairy farms for Mycobacterium avium subsp. paratuberculosis (Map) with pooled fecal samples. The optimal pool size was investigated for 2 scenarios, prevalence (a low-prevalence herd [< or = 5%] and a high-prevalence herd [> 5%]) and for different herd sizes (100-, 250-, 500- and 1,000-cow herds). All adult animals in the herd were sampled, and the samples of the individuals were divided into equal sized pools. When a pool tested positive, the manure samples of the animals in the pool were tested individually. The individual samples from a negative pool were assumed negative and not tested individually. Distributions were used to model the uncertainty about the sensitivity of the fecal culture at farm level and Map prevalence. The model randomly allocated a disease status to the cows (not shedding, low Map shedder, moderate Map shedder, and heavy Map shedder) on the basis of the expected prevalence in the herd. Pooling was not efficient in 100-cow and 250-cow herds with low prevalence because the probability to detect a map infection in these herds became poor (53% and 88%) when samples were pooled. When samples were pooled in larger herds, the probability to detect at least 1 (moderate to heavy) shedder was > 90%. The cost reduction as a result of pooling varied from 43% in a 100-cow herd with a high prevalence to 71% in a 1,000-cow herd with a low prevalence. The optimal pool size increased with increasing herd size and varied from 3 for a 500-cow herd with a low prevalence to 5 for a 1,000-cow herd with a high prevalence.

Animals↗

PubMed1999

PROBES: a framework for probability elicitation from experts.

A decision analytic model represents uncertainties as probability distributions. These distributions are hard to assess especially for large and dynamic models. We propose an integrated framework that facilitates elicitation of the relevant probability distributions for dynamic decision models from the domain experts. The experts usually use some judgmental heuristics to aid probability assessments; the resulting distributions may be proned to cognitive biases. Our framework aims to minimize the effects of these biases and to improve the quality of decisions made. We have implemented a prototype system of the framework and evaluated its effectiveness via a case study in the follow-up management of colorectal cancer patients after curative surgery. Preliminary results demonstrate the practical promise of the framework.

Colorectal Neoplasms↗

PubMedDate not supplied

Finding meaning: antecedents of uncertainty in illness.

In this study a portion of the uncertainty in illness model was tested. Antecedents to uncertainty tested were the stimuli frame variables of symptom pattern and event familiarity and the structure provider variables of education, social support, and credible authority. Data were collected on a convenience sample of 61 women with gynecological cancer at the time of major treatment effect. Findings supported the proposed model with an empirically generated revised model presenting the influence of antecedents on specific areas of uncertainty. Divergent paths for reducing uncertainty were found. Social support, credible authority, and event familiarity had the greatest influence on lowering the level of uncertainty. Event familiarity and credible authority were primarily effective in reducing the complexity surrounding treatment and the system of care. Social support functioned to decrease the level of ambiguity concerning the state of the illness. Findings generally support the proposed explantation for uncertainty arousal and have substantive significance in identifying the sources of stimuli leading to uncertainty arousal and modification.

Adult↗

PubMed2002

Scenario-model-parameter: a new method of cumulative risk uncertainty analysis.

The recently developed concepts of aggregate risk and cumulative risk rectify two limitations associated with the classical risk assessment paradigm established in the early 1980s. Aggregate exposure denotes the amount of one pollutant available at the biological exchange boundaries from multiple routes of exposure. Cumulative risk assessment is defined as an assessment of risk from the accumulation of a common toxic effect from all routes of exposure to multiple chemicals sharing a common mechanism of toxicity. Thus, cumulative risk constitutes an improvement over the classical risk paradigm, which treats exposures from multiple routes as independent events associated with each specific route. Risk assessors formulate complex models and identify many realistic scenarios of exposure that enable them to estimate risks from exposures to multiple pollutants and multiple routes. The increase in complexity of the risk assessment process is likely to increase risk uncertainty. Despite evidence that scenario and model uncertainty contribute to the overall uncertainty of cumulative risk estimates, present uncertainty analysis of risk estimates accounts only for parameter uncertainty and excludes model and scenario uncertainties. This paper provides a synopsis of the risk assessment evolution and associated uncertainty analysis methods. This evolution leads to the concept of the scenario-model-parameter (SW) cumulative risk uncertainty analysis method. The SMP uncertainty analysis is a multiple step procedure that assesses uncertainty associated with the use of judiciously selected scenarios and models of exposure and risk. Ultimately, the SMP uncertainty analysis method compares risk uncertainty estimates determined using all three sources of uncertainty with conventional risk uncertainty estimates obtained using only the parameter source. An example of applying the SMP uncertainty analysis to cumulative risk estimates from exposures to two pesticides indicates that inclusion of scenario and model sources.

Environmental Pollutants↗

PubMed2004

A genetic algorithm approach to detecting lineage-specific variation in selection pressure.

The ratio of nonsynonymous (dN) to synonymous (dS) substitution rates, omega, provides a measure of selection at the protein level. Models have been developed that allow omega to vary among lineages. However, these models require the lineages in which differential selection has acted to be specified a priori. We propose a genetic algorithm approach to assign lineages in a phylogeny to a fixed number of different classes of omega, thus allowing variable selection pressure without a priori specification of particular lineages. This approach can identify models with a better fit than a single-ratio model, and with fits that are better than (in an information theoretic sense) a fully local model, in which all lineages are assumed to evolve under different values of omega, but with far fewer parameters. By averaging over models which explain the data reasonably well, we can assess the robustness of our conclusions to uncertainty in model estimation. Our approach can also be used to compare results from models in which branch classes are specified a priori with a wide range of credible models. We illustrate our methods on primate lysozyme sequences and compare them with previous methods applied to the same data sets.

Algorithms↗

PubMed2006

Extended kalman filtering for the modeling and analysis of ICG pharmacokinetics in cancerous tumors using NIR optical methods.

Compartmental modeling of indocyanine green (ICG) pharmacokinetics, as measured by near infrared (NIR) techniques, has the potential to provide diagnostic information for tumor differentiation. In this paper, we present three different compartmental models to model the pharmacokinetics of ICG in cancerous tumors. We introduce a systematic and robust approach to model and analyze ICG pharmacokinetics based on the extended Kalman filtering (EKF) framework. The proposed EKF framework effectively models multiple-compartment and multiple-measurement systems in the presence of measurement noise and uncertainties in model dynamics. It provides simultaneous estimation of pharmacokinetic parameters and ICG concentrations in each compartment. Moreover, the recursive nature of the Kalman filter estimator potentially allows real-time monitoring of time varying pharmacokinetic rates and concentration changes in different compartments. Additionally, we introduce an information theoretic criteria for the best compartmental model order selection, and residual analysis for the statistical validation of the estimates. We tested our approach using the ICG concentration data acquired from four Fischer rats carrying adenocarcinoma tumor cells. Our study indicates that, in addition to the pharmacokinetic rates, the EKF model may provide parameters that may be useful for tumor differentiation.

Adenocarcinoma↗