Exploring Uncertainty in Cost-Effectiveness Analysis

This paper describes the key principles of why an assessment of uncertainty and its consequences are critical for the types of decisions that a body such as the UK National Institute for Health and Clinical Excellence (NICE) has to make. In doing so, it poses the question of whether formal methods may be useful to NICE and its advisory committees in making such assessments. Broadly, these include the following: (i) should probabilistic sensitivity analysis continue to be recommended as a means to characterize parameter uncertainty; (ii) which methods should be used to represent other sources of uncertainty; (iii) when can computationally expensive models be justified and is computation expense a sufficient justification for failing to express uncertainty; (iv) which summary measures of uncertainty should be used to present the results to decision makers; and (v) should formal methods be recommended to inform the assessment of the need for evidence and the consequences of an uncertain decision for the UK NHS?

[1]  Andrew Briggs,et al.  An Introduction to Markov Modelling for Economic Evaluation , 1998, PharmacoEconomics.

[2]  M. Sculpher,et al.  Representing uncertainty: the role of cost-effectiveness acceptability curves. , 2001, Health economics.

[3]  A A Stinnett,et al.  Net Health Benefits , 1998, Medical decision making : an international journal of the Society for Medical Decision Making.

[4]  C E Phelps,et al.  On the (Near) Equivalence of Cost-Effectiveness and Cost-Benefit Analyses , 1991, International Journal of Technology Assessment in Health Care.

[5]  John D. Graham,et al.  Going beyond the single number: Using probabilistic risk assessment to improve risk management , 1996 .

[6]  M D Stevenson,et al.  Gaussian Process Modeling in Conjunction with Individual Patient Simulation Modeling: A Case Study Describing the Calculation of Cost-Effectiveness Ratios for the Treatment of Established Osteoporosis , 2004, Medical decision making : an international journal of the Society for Medical Decision Making.

[7]  D J Nokes,et al.  Evaluating the cost-effectiveness of vaccination programmes: a dynamic perspective. , 1999, Statistics in medicine.

[8]  A E Ades,et al.  Evidence synthesis, parameter correlation and probabilistic sensitivity analysis. , 2006, Health economics.

[9]  Mark Sculpher,et al.  Subgroups and Heterogeneity in Cost-Effectiveness Analysis , 2012, PharmacoEconomics.

[10]  Karl Claxton,et al.  Searching for a threshold, not setting one: the role of the National Institute for Health and Clinical Excellence , 2007, Journal of health services research & policy.

[11]  John C. Hershey,et al.  Carrier Screening for Cystic Fibrosis , 1998, Medical decision making : an international journal of the Society for Medical Decision Making.

[12]  W. H. Pun,et al.  Statistical Decision Theory , 2014 .

[13]  I. White,et al.  Eliciting and using expert opinions about dropout bias in randomized controlled trials , 2007, Clinical trials.

[14]  Mark J Sculpher,et al.  Dangerous omissions: the consequences of ignoring decision uncertainty. , 2011, Health economics.

[15]  Stephen Palmer,et al.  The Half-Life of Truth: What Are Appropriate Time Horizons for Research Decisions? , 2008, Medical decision making : an international journal of the Society for Medical Decision Making.

[16]  Gordon B. Hazen,et al.  Sensitivity Analysis and the Expected Value of Perfect Information , 1998, Medical decision making : an international journal of the Society for Medical Decision Making.

[17]  Mark Sculpher NICE’s 2008 Methods Guide , 2012, PharmacoEconomics.

[18]  Peter Littlejohns,et al.  Making a decision to wait for more evidence: when the National Institute for Health and Clinical Excellence recommends a technology only in the context of research. , 2007, Journal of the Royal Society of Medicine.

[19]  Karl Claxton,et al.  Probabilistic analysis and computationally expensive models: Necessary and required? , 2006, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[20]  Nick Black,et al.  The Cooksey review of UK health research funding , 2006, BMJ : British Medical Journal.

[21]  Sarah Wordsworth,et al.  Eliciting expert opinion for economic models: an applied example. , 2007, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[22]  James O. Berger Statistical Decision Theory , 1980 .

[23]  Andrew Briggs,et al.  Value based pricing for NHS drugs: an opportunity not to be missed? , 2008, BMJ : British Medical Journal.

[24]  Andrew Briggs,et al.  Cost-effectiveness acceptability curves--facts, fallacies and frequently asked questions. , 2004, Health economics.

[25]  M J Al,et al.  Costs, effects and C/E-ratios alongside a clinical trial. , 1994, Health economics.

[26]  Andrew R Willan,et al.  Expected value of information and decision making in HTA. , 2007, Health economics.

[27]  A E Ades,et al.  Expected Value of Sample Information Calculations in Medical Decision Modeling , 2004, Medical decision making : an international journal of the Society for Medical Decision Making.

[28]  Alex J. Sutton,et al.  Multiparameter evidence synthesis in epidemiology and medical decision‐making: current approaches , 2006 .

[29]  Karl Claxton,et al.  Dimensions of Design Space: A Decision-Theoretic Approach to Optimal Research Design , 2009, Medical decision making : an international journal of the Society for Medical Decision Making.

[30]  Julien Taieb,et al.  Truth Survival in Clinical Research: An Evidence-Based Requiem? , 2002, Annals of Internal Medicine.

[31]  Karl Claxton,et al.  Defining and characterising structural uncertainty in decision analytic models , 2006 .

[32]  Nicky J Welton,et al.  Preventive strategies for group B streptococcal and other bacterial infections in early infancy: cost effectiveness and value of information analyses , 2007, BMJ : British Medical Journal.

[33]  David Draper,et al.  Assessment and Propagation of Model Uncertainty , 2011 .

[34]  Keith Abrams,et al.  Use of Indirect and Mixed Treatment Comparisons for Technology Assessment , 2012, PharmacoEconomics.

[35]  K Claxton,et al.  The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies. , 1999, Journal of health economics.

[36]  Anthony O'Hagan,et al.  Calculating Partial Expected Value of Perfect Information via Monte Carlo Sampling Algorithms , 2007, Medical decision making : an international journal of the Society for Medical Decision Making.

[37]  Jeremy E. Oakley,et al.  Uncertain Judgements: Eliciting Experts' Probabilities , 2006 .

[38]  Fumie Yokota,et al.  Value of Information Literature Analysis: A Review of Applications in Health Risk Management , 2004, Medical decision making : an international journal of the Society for Medical Decision Making.

[39]  C. Chatfield Model uncertainty, data mining and statistical inference , 1995 .

[40]  Zaid Chalabi,et al.  Budgetary policies and available actions: a generalisation of decision rules for allocation and research decisions. , 2010, Journal of health economics.

[41]  Matt Stevenson,et al.  Monte Carlo probabilistic sensitivity analysis for patient level simulation models: efficient estimation of mean and variance using ANOVA. , 2007, Health economics.

[42]  S. Palmer,et al.  Incorporating option values into the economic evaluation of health care technologies. , 2000, Journal of health economics.

[43]  Alan Brennan,et al.  Expected value of sample information for Weibull survival data. , 2007, Health economics.

[44]  M. Sculpher,et al.  Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra. , 2005, Health economics.

[45]  Simon Dixon,et al.  Testing the Validity of Cost-Effectiveness Models , 2000, PharmacoEconomics.