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Chris McCabe

Publications and source records attributed to Chris McCabe.

10 recordsLinked to original sources
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↗
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

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↗
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↗
PubMed2003

Cost effectiveness of HMG-CoA reductase inhibitors in the management of coronary artery disease: the problem of under-treatment.

HMG-CoA reductase inhibitors significantly reduce the risk of coronary artery disease (CAD) events and CAD-related mortality in patients with and without established CAD. Consequently, HMG-CoA reductase inhibitors have a central role within recommendations for lipid-modifying therapy. However, despite these guidelines, only one-third to one-half of eligible patients receive lipid-lowering therapy and as few as one-third of these patients achieve recommended target serum levels of low density lipoprotein-cholesterol. The underuse of HMG-CoA reductase inhibitors in eligible patients has important implications for mortality, morbidity and cost, given the enormous economic burden associated with CAD; direct healthcare costs, estimated at US $16-53 billion (2000 values) in the US and 1.6 billion pound (1996 values) in the UK alone, are largely driven by inpatient care. Hospitalization costs are reduced by treatment with HMG-CoA reductase inhibitors, particularly in high-risk groups such as patients with CAD and diabetes mellitus in whom net cost savings may be achieved. HMG-CoA reductase inhibitors are underused because of institutional factors and clinician and patient factors. Also, the vast number of patients eligible for treatment means that the use of HMG-CoA reductase inhibitors is undoubtedly limited by budgetary considerations. Secondary prevention in CAD using HMG-CoA reductase inhibitors is certainly cost effective. Primary prevention with HMG-CoA reductase inhibitors is also cost effective in many patients, depending upon CAD risk and drug dosage. As new, more powerful, HMG-CoA reductase inhibitors come to market, and the established HMG-CoA reductase inhibitors come off patent, the identification of the most cost-effective therapy becomes increasingly complex. Research in to the relative cost effectiveness of alternative HMG-CoA reductase inhibitors, taking full account of the institutional, clinician and patient barriers to uptake should be undertaken to identify the most appropriate role for the new therapies.

Coronary Artery Disease↗
PubMed2002

Effectiveness of appropriately trained nurses in preoperative assessment: randomised controlled equivalence/non-inferiority trial.

OBJECTIVE: To determine whether preoperative assessments carried out by appropriately trained nurses are inferior in quality to those carried out by preregistration house officers. DESIGN: Randomised controlled equivalence/non-inferiority trial. SETTING: Four NHS hospitals in three trusts. Three of the four were teaching hospitals. PARTICIPANTS: All patients attending for assessment before general anaesthesia for general, vascular, urological, or breast surgery between April 1998 and March 1999. INTERVENTION: Assessment by one of three appropriately trained nurses or by one of several preregistration house officers. MAIN OUTCOME MEASURES: History taken, physical examination, and investigations ordered. Measures evaluated by a specialist registrar in anaesthetics and placed in four categories: correct, overassessment, underassessment not affecting management, and underassessment possibly affecting management (primary outcome). RESULTS: 1907 patients were randomised, and 1874 completed the study; 926 were assessed by house officers and 948 by nurses. Overall 121/948 (13%) assessments carried out by nurses were judged to have possibly affected management compared with 138/926 (15%) of those performed by house officers. Nurses were judged to be non-inferior to house officers in assessment, although there was variation among them in terms of the quality of history taking. The house officers ordered considerably more unnecessary tests than the nurses (218/926 (24%) v 129/948 (14%). CONCLUSIONS: There is no reason to inhibit the development of nurse led preoperative assessment provided that the nurses involved receive adequate training. However, house officers will continue to require experience in preoperative assessment.

Clinical Competence↗
PubMedDate not supplied

Principles of good practice for decision analytic modeling in health-care evaluation: report of the ISPOR Task Force on Good Research Practices--Modeling Studies.

OBJECTIVES: Mathematical modeling is used widely in economic evaluations of pharmaceuticals and other health-care technologies. Users of models in government and the private sector need to be able to evaluate the quality of models according to scientific criteria of good practice. This report describes the consensus of a task force convened to provide modelers with guidelines for conducting and reporting modeling studies. METHODS: The task force was appointed with the advice and consent of the Board of Directors of ISPOR. Members were experienced developers or users of models, worked in academia and industry, and came from several countries in North America and Europe. The task force met on three occasions, conducted frequent correspondence and exchanges of drafts by electronic mail, and solicited comments on three drafts from a core group of external reviewers and more broadly from the membership of ISPOR. RESULTS: Criteria for assessing the quality of models fell into three areas: model structure, data used as inputs to models, and model validation. Several major themes cut across these areas. Models and their results should be represented as aids to decision making, not as statements of scientific fact; therefore, it is inappropriate to demand that models be validated prospectively before use. However, model assumptions regarding causal structure and parameter estimates should be continually assessed against data, and models should be revised accordingly. Structural assumptions and parameter estimates should be reported clearly and explicitly, and opportunities for users to appreciate the conditional relationship between inputs and outputs should be provided through sensitivity analyses. CONCLUSIONS: Model-based evaluations are a valuable resource for health-care decision makers. It is the responsibility of model developers to conduct modeling studies according to the best practicable standards of quality and to communicate results with adequate disclosure of assumptions and with the caveat that conclusions are conditional upon the assumptions and data on which the model is built.

Benchmarking↗