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Karl Claxton

Publications and source records attributed to Karl Claxton.

At least 19 recordsLinked to original sources
PubMed2006

Evidence synthesis, parameter correlation and probabilistic sensitivity analysis.

Over the last decade or so, there have been many developments in methods to handle uncertainty in cost-effectiveness studies. In decision modelling, it is widely accepted that there needs to be an assessment of how sensitive the decision is to uncertainty in parameter values. The rationale for probabilistic sensitivity analysis (PSA) is primarily based on a consideration of the needs of decision makers in assessing the consequences of decision uncertainty. In this paper, we highlight some further compelling reasons for adopting probabilistic methods for decision modelling and sensitivity analysis, and specifically for adopting simulation from a Bayesian posterior distribution. Our reasoning is as follows. Firstly, cost-effectiveness analyses need to be based on all the available evidence, not a selected subset, and the uncertainties in the data need to be propagated through the model in order to provide a correct analysis of the uncertainties in the decision. In many--perhaps most--cases the evidence structure requires a statistical analysis that inevitably induces correlations between parameters. Deterministic sensitivity analysis requires that models are run with parameters fixed at 'extreme' values, but where parameter correlation exists it is not possible to identify sets of parameter values that can be considered 'extreme' in a meaningful sense. However, a correct probabilistic analysis can be readily achieved by Monte Carlo sampling from the joint posterior distribution of parameters. In this paper, we review some evidence structures commonly occurring in decision models, where analyses that correctly reflect the uncertainty in the data induce correlations between parameters. Frequently, this is because the evidence base includes information on functions of several parameters. It follows that, if health technology assessments are to be based on a correct analysis of all available data, then probabilistic methods must be used both for sensitivity analysis and for estimation of expected costs and benefits.

Bayes Theorem↗
PubMed2006

Discounting and cost-effectiveness in NICE - stepping back to sort out a confusion.

Brouwer and colleagues [1] argue that the reasons for specifying an equal discount rate for health outcomes and costs in the recent guidance on methods of technology appraisal issued by the National Institute for Clinical Excellence (NICE) [2] is both opaque and wrong. They argue that a lower rate should apply to health outcomes like QALYs. It is also claimed that the guidance on discounting represents a step backwards, that is both inconsistent with current theoretical insights and will prejudice the outcome of cost-effectiveness studies of preventive interventions.The reasoning behind the use of equal discount rates for costs and health outcomes is indeed not well developed in the published guidance. Nor does it reflect the debate that underpinned the guidance. We therefore welcome the opportunity to explain more completely the rationale in the minds of the principal authors of the current guidance.

Academies and Institutes↗
PubMed2006

Whither trial-based economic evaluation for health care decision making?

The randomised controlled trial (RCT) has developed a central role in applied cost-effectiveness studies in health care as the vehicle for analysis. This paper considers the role of trial-based economic evaluation in this era of explicit decision making. It is argued that any framework for economic analysis can only be judged insofar as it can inform two key decisions and be consistent with the objectives of a health care system subject to its resource constraints. The two decisions are, firstly, whether to adopt a health technology given existing evidence and, secondly, an assessment of whether more evidence is required to support this decision in the future. It is argued that a framework of economic analysis is needed which can estimate costs and effects, based on all the available evidence, relating to the full range of possible alternative interventions and clinical strategies, over an appropriate time horizon and for specific patient groups. It must also enable the accumulated evidence to be synthesised in an explicit and transparent way in order to fully represent the decision uncertainty. These requirements suggest that, in most circumstances, the use of a single RCT as a vehicle for economic analysis will be an inadequate and partial basis for decision making. It is argued that RCT evidence, with or without economic content, should be viewed as simply one of the sources of evidence, which must be placed in a broader framework of evidence synthesis and decision analysis.

Cost-Benefit Analysis↗
PubMed2006

Good practice guidelines for decision-analytic modelling in health technology assessment: a review and consolidation of quality assessment.

The use of decision-analytic modelling for the purpose of health technology assessment (HTA) has increased dramatically in recent years. Several guidelines for best practice have emerged in the literature; however, there is no agreed standard for what constitutes a 'good model' or how models should be formally assessed. The objective of this paper is to identify, review and consolidate existing guidelines on the use of decision-analytic modelling for the purpose of HTA and to develop a consistent framework against which the quality of models may be assessed. The review and resultant framework are summarised under the three key themes of Structure, Data and Consistency. 'Structural' aspects relate to the scope and mathematical structure of the model including the strategies under evaluation. Issues covered under the general heading of 'Data' include data identification methods and how uncertainty should be addressed. 'Consistency' relates to the overall quality of the model. The review of existing guidelines showed that although authors may provide a consistent message regarding some aspects of modelling, such as the need for transparency, they are contradictory in other areas. Particular areas of disagreement are how data should be incorporated into models and how uncertainty should be assessed. For the purpose of evaluation, the resultant framework is applied to a decision-analytic model developed as part of an appraisal for the National Institute for Health and Clinical Excellence (NICE) in the UK. As a further assessment, the review based on the framework is compared with an assessment provided by an independent experienced modeller not using the framework. It is hoped that the framework developed here may form part of the appraisals process for assessment bodies such as NICE and decision models submitted to peer review journals. However, given the speed with which decision-modelling methodology advances, there is a need for its continual update.

Cost-Benefit Analysis↗
PubMed2006

Comprehensive decision-analytic model and Bayesian value-of-information analysis: pentoxifylline in the treatment of chronic venous leg ulcers.

OBJECTIVE: To conduct a Bayesian value-of-information analysis of the cost effectiveness of pentoxifylline (vs placebo) as an adjunct to compression for venous leg ulcers. METHODS: A probabilistic Markov model was developed to estimate mean clinical benefits and costs associated with oral pentoxifylline (400mg three times daily) and placebo. Clinical data were obtained from a systematic review and synthesised using Bayesian methods. The decision uncertainty associated with the adoption of pentoxifylline as well as the maximum value associated with further research were estimated before and after the completion of the largest 'definitive' treatment trial. Resource use was obtained from a UK national audit and unit costs applied (pounds, 2004 values). RESULTS: The prior and posterior analyses suggest that pentoxifylline is a dominant therapy versus placebo. In the prior analysis, patients in the pentoxifylline group healed an average of 8.28 weeks quicker than patients in the placebo group (95% credibility interval [CI] 1.89, 14.56), had a 0.02 gain in QALYs (95% CI -0.12, 0.17) and an average reduction in cost of 153.4 pounds (95% CI -53.11, 354.9). Estimates of the uncertainty surrounding the cost effectiveness of pentoxifylline and the value of perfect information in both analyses did not suggest further research was justified. In the prior analysis, for willingness-to-pay values of 0 pounds, 100 pounds and 500 pounds per QALY gained, the estimated values of perfect information were 128,200 pounds, 127,100 pounds and 126,700 pounds, respectively. Incorporation of the information from the largest randomised controlled trial on pentoxifylline did improve the estimate of the clinical effect associated with this drug; however, the variation was not large enough to reverse either the decision regarding the dominance of pentoxifylline or the maximum value associated with further research. CONCLUSION: Bayesian value-of-information analysis represents a valuable tool for healthcare decision making. Had the results from this analysis been available before the largest trial was funded, a more efficient allocation of research and development resources could have been made.

Administration, Oral↗
PubMed2005

Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra.

Recently the National Institute for Clinical Excellence (NICE) updated its methods guidance for technology assessment. One aspect of the new guidance is to require the use of probabilistic sensitivity analysis with all cost-effectiveness models submitted to the Institute. The purpose of this paper is to place the NICE guidance on dealing with uncertainty into a broader context of the requirements for decision making; to explain the general approach that was taken in its development; and to address each of the issues which have been raised in the debate about the role of probabilistic sensitivity analysis in general. The most appropriate starting point for developing guidance is to establish what is required for decision making. On the basis of these requirements, the methods and framework of analysis which can best meet these needs can then be identified. It will be argued that the guidance on dealing with uncertainty and, in particular, the requirement for probabilistic sensitivity analysis, is justified by the requirements of the type of decisions that NICE is asked to make. Given this foundation, the main issues and criticisms raised during and after the consultation process are reviewed. Finally, some of the methodological challenges posed by the need fully to characterise decision uncertainty and to inform the research agenda will be identified and discussed.

Cost-Benefit Analysis↗
PubMed2005

Incorporation of uncertainty in health economic modelling studies.

In a recent leading article in PharmacoEconomics, Nuijten described some methods for incorporating uncertainty into health economic models and for utilising the information on uncertainty regarding the cost effectiveness of a therapy in resource allocation decision-making. His proposals are found to suffer from serious flaws in statistical and health economic reasoning.Nuijten's suggestions for incorporating uncertainty: (a) wrongly interpret the p-value as the probability that the null hypothesis is true; (b) represent this probability wrongly by truncating the input distribution; and (c) in the specific example of an antiparkinsonian drug uses a completely inappropriate p-value of 0.05 when the null hypothesis would, in reality, be emphatically disproved by the data.His suggestions regarding minimum important differences in cost effectiveness: (a) introduce areas of indifference that suggest inappropriate reliance on cost minimisation while failing to recognise that decisions should be based on expected costs versus benefits; and (b) offer no guidance on how the probabilities associated with these areas could be used in decision-making. Furthermore, Nuijten's model for Parkinson's disease is over-simplified to the point of providing a bad example of modelling practice, which may mislead the readers of PharmacoEconomics. The rationale for this paper is to ensure that readers do not apply inappropriate analyses as a result of following the proposals contained in Nuijten's paper. In addition to a detailed critique of Nuijten's proposals, we provide brief summaries of the currently accepted best practice in cost-effectiveness decision-making under uncertainty.

Antiparkinson Agents↗
PubMed2005

Using value of information analysis to inform publicly funded research priorities.

INTRODUCTION: The purpose of this article is to demonstrate the application and feasibility of using value of information analysis to help set priorities for research as part of the UK National Health Service (NHS) Health Technology Assessment Programme. Probabilistic decision analysis and value of information methods were applied to a research topic under consideration by the National Coordinating Centre for Health Technology Assessment (NCCHTA), in the UK. The case study presented considers whether long-term, low-dose antibacterial treatment of recurrent urinary tract infections (UTIs) in children is effective and cost effective compared with short-term antibacterial therapy. METHODS: A probabilistic decision-analytic model was developed, within which evidence from published sources was synthesised. Eight subgroups were considered and defined in terms of sex and presence of vesico-ureteral reflux (VUR). Costs were assessed from an NHS perspective, and benefits were expressed as quality-adjusted life-years (QALYs). Simulation methods were used to determine the probability that alternative therapies would be cost effective at a range of threshold values that the NHS may attach to an additional QALY. Value of information analysis was used to quantify the cost of uncertainty associated with the decision about which therapy to adopt, which indicates the maximum value of future research. The feasibility and practicality of using value of information methods to help inform research prioritization was evaluated. RESULTS: At a threshold value for an additional QALY of 30,000 pound , long-term antibacterial treatment may be regarded as cost effective for all eight patient groups. There was, however, substantial uncertainty surrounding the choice of antibacterial. DISCUSSION/CONCLUSION: The use of value of information methods was feasible and could inform research prioritization for the NHS. In the context of this specific decision faced by the NHS, the results show that long-term low-dose antibacterials for preventing recurrent UTIs may be cost effective, based on current evidence. However, the analysis suggests that further primary research with longer follow-up may be worthwhile, particularly for girls with no VUR.

Anti-Bacterial Agents↗
PubMed2005

Should patients have a greater role in valuing health states?

Currently, health state values are usually obtained from members of the general public trying to imagine what the state would be like rather than by patients who are actually in the various states of health. Valuations of a health state by patients tend to vary from those of the general population, and this seems to be due to a range of factors including errors in the descriptive system, adaptation to the state and changes in internal standards. The question of whose values are used in cost-effectiveness analysis is ultimately a normative one, but the decision should be informed by evidence on the reasons for the differences. There is a case for obtaining better informed general population preferences by providing more information on what it is like for patients (including the process of adaptation).

Adaptation, Psychological↗
PubMed2004

The heterogeneity of schizophrenia in disease states.

PREVIOUS PRESENTATION: Some of the contents of this paper have been previously presented at the 16th Annual Meeting of the International Society for Technology Assessment in Health Care June 20, 2000 in the Hague, Netherlands and at the 21st Annual Meeting of the Society for Medical Decision Making as a poster on October 3, 1999 in Reno, NV. BACKGROUND: Studies of schizophrenia treatment often oversimplify the array of health outcomes among patients. Our objective was to derive a set of disease states for schizophrenia using the Positive and Negative Symptom Assessment Scale (PANSS) that captured the heterogeneity of symptom responses. METHODS: Using data from a 1-year clinical trial that collected PANSS scores and costs on schizophrenic patients (N=663), we conducted a k-means cluster analyses on PANSS scores for items in five factor domains. Results of the cluster analysis were compared with a conceptual framework of disease states developed by an expert panel. Final disease states were defined by combining our conceptual framework with the empirical results. We tested its utility by examining the influence of disease state on treatment costs and prognosis. RESULTS: Analyses led to an eight-state framework with varying levels of positive, negative, and cognitive impairment. The extent of hostile/aggressive symptoms and mood disorders correlated with severity of disease states. Direct treatment costs for schizophrenia vary significantly across disease states (F=27.47, df=7, p<0.0001), and disease state at baseline was among the most important predictors of treatment outcomes. CONCLUSION: The disease states we describe offer a useful paradigm for understanding the links between symptom profiles and outcomes.

Adolescent↗
PubMed2003

Modelling the cost effectiveness of interferon beta and glatiramer acetate in the management of multiple sclerosis. Commentary: evaluating disease modifying treatments in multiple sclerosis.

OBJECTIVE: To evaluate the cost effectiveness of four disease modifying treatments (interferon betas and glatiramer acetate) for relapsing remitting and secondary progressive multiple sclerosis in the United Kingdom. DESIGN: Modelling cost effectiveness. SETTING: UK NHS. PARTICIPANTS: Patients with relapsing remitting multiple sclerosis and secondary progressive multiple sclerosis. MAIN OUTCOME MEASURES: Cost per quality adjusted life year gained. RESULTS: The base case cost per quality adjusted life year gained by using any of the four treatments ranged from pound 42,000 (66,469 dollars; 61,630 euro) to pound 98,000 based on efficacy information in the public domain. Uncertainty analysis suggests that the probability of any of these treatments having a cost effectiveness better than pound 20,000 at 20 years is below 20%. The key determinants of cost effectiveness were the time horizon, the progression of patients after stopping treatment, differential discount rates, and the price of the treatments. CONCLUSIONS: Cost effectiveness varied markedly between the interventions. Uncertainty around point estimates was substantial. This uncertainty could be reduced by conducting research on the true magnitude of the effect of these drugs, the progression of patients after stopping treatment, the costs of care, and the quality of life of the patients. Price was the key modifiable determinant of the cost effectiveness of these treatments.

Cost-Benefit Analysis↗
PubMed2002

A rational framework for decision making by the National Institute For Clinical Excellence (NICE).

Regulatory and reimbursement authorities face uncertain choices when considering the adoption of health-care technologies. In this Viewpoint, we present an analytic framework that separates the issue of whether a technology should be adopted on the basis of existing evidence from whether more research should be demanded to support future decisions. We show the application of this framework to the assessment of heath-care technologies using a published analysis of a new drug treatment for Alzheimer's disease. The results of the analysis show that the amount and type of evidence required to support the adoption of a health technology will differ substantially between technologies with different characteristics. Additionally, the analysis can be used to aid the efficient design of research. We discuss the implications of adoption of this new framework for regulatory and reimbursement decisions.

Alzheimer Disease↗
PubMed2002

Pre-operative optimisation employing dopexamine or adrenaline for patients undergoing major elective surgery: a cost-effectiveness analysis.

OBJECTIVE: To compare the cost and cost-effectiveness of a policy of pre-operative optimisation of oxygen delivery (using either adrenaline or dopexamine) to reduce the risk associated with major elective surgery, in high-risk patients. METHODS: A cost-effectiveness analysis using data from a randomised controlled trial (RCT). In the RCT 138 patients undergoing major elective surgery were allocated to receive pre-operative optimisation employing either adrenaline or dopexamine (assigned randomly), or to receive routine peri-operative care. Differential health service costs were based on trial data on the number and cause of hospital in-patient days and the utilisation of health care resources. These were costed using unit costs from a UK hospital. The cost-effectiveness analysis related differential costs to differential life-years during a 2 year trial follow-up. RESULTS: The mean number of in-patient days was 16 in the pre-optimised groups (19 adrenaline; 13 dopexamine) and 22 in the standard care group. The number (%) of deaths, over a 2 year follow-up, was 24 (26%) in the pre-optimised groups and 15 (33%) in the standard care group. The mean total costs were EUR 11,310 in the pre-optimised groups and EUR 16,965 in the standard care group. Life-years were 1.68 in the pre-optimised groups and 1.46 in the standard care group. The probability that pre-operative optimisation is less costly than standard care is 98%. The probability that it dominates standard care is 93%. CONCLUSIONS: Based on resource use and effectiveness data collected in the trial, pre-operative optimisation of high-risk surgical patients undergoing major elective surgery is cost-effective compared with standard treatment.

Bayes Theorem↗
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↗
PubMedDate not supplied

Probabilistic analysis and computationally expensive models: Necessary and required?

OBJECTIVE: To assess the importance of considering decision uncertainty, the appropriateness of probabilistic sensitivity analysis (PSA), and the use of patient-level simulation (PLS) in appraisals for the National Institute for Health and Clinical Excellence (NICE). METHODS: Decision-makers require estimates of decision uncertainty alongside expected net benefits (NB) of interventions. This requirement may be difficult in computationally expensive models, for example, those employing PLS. NICE appraisals published up until January 2005 were reviewed to identify those where the assessment group utilized a PLS model structure to estimate NB. After identifying PLS models, all appraisals published in the same year were reviewed. RESULTS: Among models using PLS, one out of six conducted PSA, compared with 16 out of 24 cohort models. Justification for omitting PSA was absent in most cases. Reasons for choosing PLS included treatment switching, sampling patient characteristics and dependence on patient history. Alternative modeling approaches exist to handle these, including semi-Markov models and emulators that eliminate the need for two-level simulation. Stochastic treatment switching and sampling baseline characteristics do not inform adoption decisions. Modeling patient history does not necessitate PLS, and can depend on the software used. PLS addresses nonlinear relationships between patient variability and model outputs, but other options exist. Increased computing power, emulators or closed-form approximations can facilitate PSA in computationally expensive models. CONCLUSIONS: In developing models analysts should consider the dual requirement of estimating expected NB and characterizing decision uncertainty. It is possible to develop models that meet these requirements within the constraints set by decision-makers.

Computer Simulation↗
PubMedDate not supplied

An iterative Bayesian approach to health technology assessment: application to a policy of preoperative optimization for patients undergoing major elective surgery.

PURPOSE: This article presents an iterative framework for managing the dynamic process of health technology assessment. The framework uses Bayesian statistical decision theory and value of information (VOI) analysis to inform decision making regarding appropriate patient management and to direct future research effort over the lifetime of a technology. Within the article, the framework is applied to a policy decision regarding preoperative patient management before major elective surgery, for which trial data are available. METHOD: The evidence available prior to the trial is used to determine the appropriate method of patient management and to ascertain whether, at the time of commissioning, the trial was potentially worthwhile. The prior information is then updated with the trial data via a Bayesian analysis using informative priors. This post trial information set is then used to reassess the appropriate method for patient management and to determine whether there is a requirement for any further research. RESULTS: Prior to the trial, preoperative optimization with dopexamine is identified as the appropriate method of patient management. The results of the VOI analysis suggest that a short-term trial was potentially worthwhile (population expected value of perfect information [EVPI] = 48 million pounds sterling). Following the trial, the uncertainty surrounding the choice of appropriate patient management and the potential worth of further research had increased (population EVPI = 67 million pounds sterling). CONCLUSIONS: The article demonstrates the value and practicality of applying the iterative framework to the dynamic process of health technology assessment. It is only by formally incorporating all of the information available to decision makers, through informed priors, that the appropriate decisions can be made.

Aged↗