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    Knowing how little you know

    Bas Groot Koerkamp

  • ISBN : 978-90-9024801-1 Cover design : Bas Groot Koerkamp Layout : A.W. Everaers Printed by : Print Partners Ipskamp

    The printing of this dissertation was financially supported by the Departments of Epidemiology and Radiology of the Erasmus MC, Rotterdam, the Netherlands.

    The research presented in this dissertation was financially supported by a grant (no. 904-66-091) from the Netherlands Organisation for Health Research and Development (ZonMW).

    Copyright © 2009 Bas Groot Koerkamp, Rotterdam, The Netherlands All rights reserved. No part of this dissertation may be reproduced, stored in a retrieval system or transmitted in any form or by any means, without written permission of the author, or, when appropriate, of the scientific journal in which parts of this dissertation may have been published.


    Knowing how little you know

    Onzekerheid in medische besliskunde Weten hoe weinig je weet


    ter verkrijging van de graad van doctor aan de Erasmus Universiteit Rotterdam

    op gezag van de rector magnificus

    Prof.dr. H.G. Schmidt

    en volgens besluit van het College voor Promoties.

    De openbare verdediging zal plaatsvinden op dinsdag 1 december 2009 om 13:30 uur


    Bas Groot Koerkamp geboren te Eindhoven


    Promotores: Prof.dr. M.G.M. Hunink Prof. M.C. Weinstein Prof.dr. T. Stijnen

    Overige leden: Prof.dr. E.W. Steyerberg Prof.dr. W.B.F. Brouwer Prof.dr. K. Claxton

  • Aan mijn ouders


    Chapter 1 Introduction

    Chapter 2 Cost-effectiveness analysis for surgeons

    Chapter 3 Uncertainty and patient heterogeneity in medical decision models

    Chapter 4 The combined analysis of uncertainty and patient heterogeneity in medical decision models

    Chapter 5 Limitations of acceptability curves for presenting uncertainty in cost-effectiveness analysis

    Chapter 6 Identifying key parameters in cost-effectiveness analysis using value of information: a comparison of methods

    Chapter 7 Competing imaging tests for patients with chest pain: a value of information analysis to optimize study design

    Chapter 8 Value of information analyses of economic randomized controlled trials: the treatment of intermittent claudication

    Chapter 9 Value of information analysis used to determine the necessity of additional research: MR imaging in acute knee trauma as an example










  • Chapter 10 Summary Samenvatting

    Chapter 11 Epilogue

    Chapter 12 Appendix

    Chapter 13 References

    Chapter 14 Contributing authors List of publications PhD portfolio Acknowledgements / Dankwoord About the author

    157 163




    215 219 223 227 231

  • 1 Introduction

  • chapter 1


    GENERal baCkGROuNd

    Medicine is decision making Making decisions about the care of individual patients is fundamental to health care. For each patient, many decisions have to be made. In the emergency room, for example, a doctor should decide which patient to see first, decide whether an x-ray should be made of an injured ankle, and decide how this specific ankle fracture of this specific patient should be treated. Medical training is focused on acquiring the knowledge and experience to make such decisions. Other factors that are essential for patient care, including empathy and technical abilities, also involve decision making. For example, in the outpatient clinic, a trade-off is needed when one patient needs more time and empathy, but the waiting room is packed and the physician is an hour behind schedule. In the operating room, a surgeon must decide whether to proceed with a complicated laparoscopic procedure to remove a gall bladder, to convert to an open procedure, or to ask a more experienced surgeon for help.

    Informal decision making is prone to error In daily practice, most medical decisions are based on experience and judgment. Infor- mally, an assessment is made of the probabilities and outcomes of each alternative, as well as the patient’s preference for each outcome. Unfortunately, human judgment is fallible: people (including professionals) can make severe errors in estimating probabilities and out- comes.1 Therefore, patients may benefit from a formal assessment of the probabilities and outcomes involved in a medical decision. Many decisions are nowadays resolved by such a formal assessment. For example, whether to make an x-ray of an injured ankle is resolved based on a decision rule.2 On the other hand, a formal consideration of each individual decision with which a doctor is confronted seems infeasible.

    Paradigms for formal assessment of decisions Medical decision making (MDM) and evidence-based medicine (EBM) are separate para- digms that provide tools for formal assessment of medical decisions. They were developed because of concerns about human judgment, practice variation, and the proliferation of diagnostic and treatment options.3 The mainstay of EBM is critical literature appraisal, starting with an answerable clinical question that is summarized in the mnemonic PICO: patient’s problem, intervention, compare with alternative intervention, and outcome. The results of such an appraisal still demand considerable informal judgment on the part of the clinician: for example, results may not apply very well to an individual patient or studies may have conflicting results. Moreover, patient preferences, rare events, and health care costs are typically ignored in EBM, meaning that informal judgment on the part of the cli- nician still plays a role. MDM applies decision models to guide medical decisions and has

  • 11


    a strong foundation in decision theory.4 Decision models can bring together all available evidence relevant for a decision; for example, disease incidence from population statistics, treatment effects from meta-analyses, patient preferences and rare complications from observational studies, and costs from medical claims databases. The model has no limit to the number of alternatives compared or to the length of follow-up. The aim of MDM is to perform a complete formal assessment of every aspect that is relevant for a decision. The main drawback of decision models is that building them is very time consuming. As a result, EBM has a higher acceptance in daily patient care and MDM in guideline develop- ment, health policy, and cost-effectiveness analysis.

    Decision making and health care costs Consideration of cost in addition to health benefits has more recently complicated decision making in health care. More beneficial health care interventions have become available than a health care system can afford. Priorities therefore have to be set. Most new interven- tions are beneficial but also more costly. Implementing such interventions requires increas- ing the overall health care budget or withholding other interventions. The latter seems fair only if the new intervention has a better value for money. The purpose of cost-effectiveness analysis is to provide information regarding the decision to implement new interventions by weighing the additional benefits against the additional costs. As a result, it may improve people’s health by setting the appropriate priorities. As Stinnett noted, “investing in a cost- ineffective intervention is not simply an unwise use of money in some vague sense, but a foregone opportunity to achieve greater gains in people’s health”.5 The cost-effectiveness of interventions is evaluated in clinical trials or in decision models. Trials have appeal be- cause of a high internal validity6, but only models can synthesize all available evidence.7 The National Institute for Health and Clinical Excellence (NICE) makes recommendations on the adoption of health care interventions in the United Kingdom. In doing so it is re- quired to explicitly consider cost-effectiveness.8

    Decision making and uncertainty Decision making is further complicated by uncertainty about probabilities and outcomes. For example, clinical trials often lack power to draw definitive conclusions. Even if arbi- trary levels of significance are reached (typically a p-value < 0.05), there remains a finite possibility that the supposedly optimal intervention is not the “true” optimal intervention. However, while tests of hypotheses are relevant for exploring scientific phenomena, they are less useful in decision making. A decision has to be made, regardless of the amount of evidence and the extent of uncertainty.

    Uncertainty is an even larger problem if clinical trials also consider health care costs, be- cause the variation in costs typically exceeds the variation in health outcomes, requiring

  • chapter 1


    larger sample sizes. When most uncertainty has been resolved at the decision level, uncer- tainty will remain at the patient level. For example, little doubt remains that a patient (in a good general condition) with a 7 cm aneurysm of the abdominal aorta is expected to be better off with elective repair of his aneurysm. However, it is still uncertain whether or not he will survive the intervention.

    Various methods are used to present uncertainty. For clinical trials measuring efficacy, the consensus is that confi