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Consolidated health economic evaluation reporting standards 2022 (CHEERS 2022) statement: updated reporting guidance for health economic evaluations
- Don Husereau, Michael Drummond, Federico Augustovski, Esther de Bekker-Grob, Andrew H. Briggs, Chris Carswell, Lisa Caulley, Nathorn Chaiyakunapruk, Dan Greenberg, Elizabeth Loder, Josephine Mauskopf, C. Daniel Mullins, Stavros Petrou, Raoh-Fang Pwu, Sophie Staniszewska
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- Journal:
- International Journal of Technology Assessment in Health Care / Volume 38 / Issue 1 / 2022
- Published online by Cambridge University Press:
- 11 January 2022, e13
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Health economic evaluations are comparative analyses of alternative courses of action in terms of their costs and consequences. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement, published in 2013, was created to ensure health economic evaluations are identifiable, interpretable, and useful for decision making. It was intended as guidance to help authors report accurately which health interventions were being compared and in what context, how the evaluation was undertaken, what the findings were, and other details that may aid readers and reviewers in interpretation and use of the study. The new CHEERS 2022 statement replaces previous CHEERS reporting guidance. It reflects the need for guidance that can be more easily applied to all types of health economic evaluation, new methods and developments in the field, as well as the increased role of stakeholder involvement including patients and the public. It is also broadly applicable to any form of intervention intended to improve the health of individuals or the population, whether simple or complex, and without regard to context (such as health care, public health, education, social care, etc.). This summary article presents the new CHEERS 2022 28-item checklist and recommendations for each item. The CHEERS 2022 statement is primarily intended for researchers reporting economic evaluations for peer-reviewed journals, as well as the peer reviewers and editors assessing them for publication. However, we anticipate familiarity with reporting requirements will be useful for analysts when planning studies. It may also be useful for health technology assessment bodies seeking guidance on reporting, as there is an increasing emphasis on transparency in decision making.
The estimation of health state utility values in rare diseases: overview of existing techniques
- Michela Meregaglia, Elena Nicod, Michael Drummond
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- International Journal of Technology Assessment in Health Care / Volume 36 / Issue 5 / October 2020
- Published online by Cambridge University Press:
- 25 September 2020, pp. 469-473
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There are several techniques for estimating health state utility values, each of which presents pros and cons in the context of rare diseases (RDs). Direct approaches (e.g. standard gamble and time trade-off) may be too demanding for patients with RDs, since most of them affect young children or cause cognitive impairment. The alternatives are using “vignettes” that describe hypothetical health states for the general public, which may not reflect the heterogeneous manifestations of RDs, or multi-attribute utility instruments (i.e. indirect techniques), such as EQ-5D, which may be less sensitive in capturing the specificities of RDs. The “rule of rescue” approach is a promising alternative in RDs, since it prioritizes identifiable patients with life-threatening or disabling conditions. However, it raises measurement challenges and ethical issues. Furthermore, the literature reports on relevant implications of choosing a technique over others for health technology assessment, which should be considered in relation to individual RDs.
Prevalence of suicide attempt and clinical characteristics of suicide attempters with obsessive-compulsive disorder: a report from the International College of Obsessive-Compulsive Spectrum Disorders (ICOCS)
- Bernardo Dell’Osso, Beatrice Benatti, Chiara Arici, Carlotta Palazzo, A. Carlo Altamura, Eric Hollander, Naomi Fineberg, Dan J. Stein, Humberto Nicolini, Nuria Lanzagorta, Donatella Marazziti, Stefano Pallanti, Michael van Ameringen, Christine Lochner, Oguz Karamustafalioglu, Luchezar Hranov, Martijn Figee, Lynne Drummond, Carolyn I. Rodriguez, John Grant, Damiaan Denys, Jose M. Menchon, Joseph Zohar
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- CNS Spectrums / Volume 23 / Issue 1 / February 2018
- Published online by Cambridge University Press:
- 16 March 2017, pp. 59-66
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Objective
Obsessive-compulsive disorder (OCD) is associated with variable risk of suicide and prevalence of suicide attempt (SA). The present study aimed to assess the prevalence of SA and associated sociodemographic and clinical features in a large international sample of OCD patients.
MethodsA total of 425 OCD outpatients, recruited through the International College of Obsessive-Compulsive Spectrum Disorders (ICOCS) network, were assessed and categorized in groups with or without a history of SA, and their sociodemographic and clinical features compared through Pearson’s chi-squared and t tests. Logistic regression was performed to assess the impact of the collected data on the SA variable.
Results14.6% of our sample reported at least one SA during their lifetime. Patients with an SA had significantly higher rates of comorbid psychiatric disorders (60 vs. 17%, p<0.001; particularly tic disorder), medical disorders (51 vs. 15%, p<0.001), and previous hospitalizations (62 vs. 11%, p<0.001) than patients with no history of SA. With respect to geographical differences, European and South African patients showed significantly higher rates of SA history (40 and 39%, respectively) compared to North American and Middle-Eastern individuals (13 and 8%, respectively) (χ2=11.4, p<0.001). The logistic regression did not show any statistically significant predictor of SA among selected independent variables.
ConclusionsOur international study found a history of SA prevalence of ~15% in OCD patients, with higher rates of psychiatric and medical comorbidities and previous hospitalizations in patients with a previous SA. Along with potential geographical influences, the presence of the abovementioned features should recommend additional caution in the assessment of suicide risk in OCD patients.
Providers’ perceptions of barriers and facilitators to disclosure of alcohol use by women veterans
- Traci H. Abraham, Eleanor T. Lewis, Karen L. Drummond, Christine Timko, Michael A. Cucciare
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- Primary Health Care Research & Development / Volume 18 / Issue 1 / January 2017
- Published online by Cambridge University Press:
- 03 October 2016, pp. 64-72
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Aim
To better understand barriers and facilitators that hinder or help women veterans discuss their alcohol use with providers in primary care in order to better identify problematic drinking and enhance provider–patient communication about harmful drinking.
BackgroundWomen presenting to primary care may be less likely than men to disclose potentially harmful alcohol use. No studies have qualitatively examined the perspectives of primary care providers about factors that affect accurate disclosure of alcohol use by women veterans during routine clinic visits.
MethodsProviders (n=14) were recruited from primary care at two veterans Administration Women’s Health Clinics in California, United States. An open-ended interview guide was developed from domains of the consolidated framework for implementation science. Interviews elicited primary care providers’ perspectives on barriers and facilitators to women veterans’ (who may or may not be using alcohol in harmful ways) disclosure of alcohol use during routine clinic visits. Interview data were analyzed deductively using a combination of template analysis and matrix analysis.
FindingsParticipants reported six barriers and five facilitators that they perceived affect women veteran’s decision to accurately disclose alcohol use during screenings and openness to discussing harmful drinking with a primary care provider. The most commonly described barriers to disclosure were stigma, shame, and discomfort, and co-occuring mental health concerns, while building strong therapeutic relationships and using probes to ‘dig deeper’ were most often described as facilitators. Findings from this study may enhance provider–patient discussions about alcohol use and help primary care providers to better identify problematic drinking among women veterans, ultimately improving patient outcomes.
CHALLENGES FACED IN TRANSFERRING ECONOMIC EVALUATIONS TO MIDDLE INCOME COUNTRIES
- Michael Drummond, Federico Augustovski, Zoltán Kaló, Bong-Min Yang, Andres Pichon-Riviere, Eun-Young Bae, Sachin Kamal-Bahl
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- International Journal of Technology Assessment in Health Care / Volume 31 / Issue 6 / 2015
- Published online by Cambridge University Press:
- 01 February 2016, pp. 442-448
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Background: Decision makers in middle-income countries are using economic evaluations (EEs) in pricing and reimbursement decisions for pharmaceuticals. However, whilst many of these jurisdictions have local submission guidelines and local expertise, the studies themselves often use economic models developed elsewhere and elements of data from countries other than the jurisdiction concerned. The objectives of this study were to describe the current situation and to assess the challenges faced by decision makers in transferring data and analyses from other jurisdictions.
Methods: Experienced health service researchers in each region conducted an interview survey of representatives of decision making bodies from jurisdictions in Asia, Central and Eastern Europe, and Latin America that had at least 1 year's experience of using EEs.
Results: Representatives of the relevant organizations in twelve countries were interviewed. All twelve jurisdictions had developed official guidelines for the conduct of EEs. All but one of the organizations evaluated studies submitted to them, but 9 also conducted studies and 7 commissioned them. Nine of the organizations stated that, in evaluating EEs submitted to them, they had consulted a study performed in a different jurisdiction. Data on relevant treatment effect was generally considered more transferable than those on prices/unit costs. Views on the transferability of epidemiological data, data on resource use and health state preference values were more mixed. Eight of the respondents stated that analyses submitted to them had used models developed in other jurisdictions. Four of the organizations had a policy requiring models to be adapted to reflect local circumstances. The main obstacles to transferring EEs were the different patterns of care or wealth of the developed countries from which most economic evaluations originate.
Conclusions: In middle-income countries it is commonplace to deal with the issue of transferring analyses or data from other jurisdictions. Decision makers in these countries face several challenges, mainly due to differences in current standard of care, practice patterns, or gross domestic product between the developed countries where the majority of the studies are conducted and their own jurisdiction
IMPLICATIONS OF GLOBAL PRICING POLICIES ON ACCESS TO INNOVATIVE DRUGS: THE CASE OF TRASTUZUMAB IN SEVEN LATIN AMERICAN COUNTRIES
- Andres Pichon-Riviere, Osvaldo Ulises Garay, Federico Augustovski, Carlos Vallejos, Leandro Huayanay, Maria del Pilar Navia Bueno, Alarico Rodriguez, Carlos José Coelho de Andrade, Jefferson Antonio Buendía, Michael Drummond
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- International Journal of Technology Assessment in Health Care / Volume 31 / Issue 1-2 / 2015
- Published online by Cambridge University Press:
- 20 May 2015, pp. 2-11
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Objectives: Differential pricing, based on countries’ purchasing power, is recommended by the World Health Organization to secure affordable medicines. However, in developing countries innovative drugs often have similar or even higher prices than in high-income countries. We evaluated the potential implications of trastuzumab global pricing policies in terms of cost-effectiveness (CE), coverage, and accessibility for patients with breast cancer in Latin America (LA).
Methods: A Markov model was designed to estimate life-years (LYs), quality-adjusted life-years (QALYs), and costs from a healthcare perspective. To better fit local cancer prognosis, a base case scenario using transition probabilities from clinical trials was complemented with two alternative scenarios with transition probabilities adjusted to reflect breast cancer epidemiology in each country.
Results: Incremental discounted benefits ranged from 0.87 to 1.00 LY and 0.51 to 0.60 QALY and incremental CE ratios from USD 42,104 to USD 110,283 per QALY (2012 U.S. dollars), equivalent to 3.6 gross domestic product per capita (GDPPC) per QALY in Uruguay and to 35.5 GDPPC in Bolivia. Probabilistic sensitivity analysis showed 0 percent probability that trastuzumab is CE if the willingness-to-pay threshold is one GDPPC per QALY, and remained so at three GDPPC threshold except for Chile and Uruguay (4.3 percent and 26.6 percent, respectively). Trastuzumab price would need to decrease between 69.6 percent to 94.9 percent to became CE in LA.
Conclusions: Although CE in other settings, trastuzumab was not CE in LA. The use of health technology assessment to prioritize resource allocation and support price negotiations is critical to making innovative drugs available and affordable in developing countries.
GPI-1046 Increases Presenilin-1 Expression and Restores NMDA Channel Activity
- Joseph P. Steiner, Kathryn B. Payne, Christopher Drummond Main, Sabrina D'Alfonso, Kirsten X. Jacobsen, T. Philip Hicks, William A. Staines, Michael O. Poulter
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- Canadian Journal of Neurological Sciences / Volume 37 / Issue 4 / July 2010
- Published online by Cambridge University Press:
- 02 December 2014, pp. 457-467
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Background:
Previously we showed that 6-hydroxydopamine lesions of the substantia nigra eliminate corticostriatal LTP and that the neuroimmunolophilin ligand (NIL), GPI-1046, restores LTP.
Methods:We used cDNA microarrays to determine what mRNAs may be over- or under-expressed in response to lesioning and/or GPI-1046 treatment. Patch clamp recordings were performed to investigate changes in NMDA channel function before and after treatments.
Results:We found that 51 gene products were differentially expressed. Among these we found that GPI-1046 treatment up-regulated presenilin-1 (PS-1) mRNA abundance. This finding was confirmed using QPCR. PS-1 protein was also shown to be over-expressed in the striatum of lesioned/GPI-1046-treated rats. As PS-1 has been implicated in controlling NMDA-receptor function and LTP is reduced by lesioning we assayed NMDA mediated synaptic activity in striatal brain slices. The lesion-induced reduction of dopaminergic innervation was accompanied by the near complete loss of NDMA receptor-mediated synaptic transmission between the cortex and striatum. GPI-1046 treatment of the lesioned rats restored NMDA-mediated synaptic transmission but not the dopaminergic innervation. Restoration of NDMA channel function was apparently specific as the sodium channel current density was also reduced due to lesioning but GPI-1046 did not reverse this effect. We also found that restoration of NMDA receptor function was also not associated with either an increase in NMDA receptor mRNA or protein expression.
Conclusion:As it has been previously shown that PS-1 is critical for normal NMDA receptor function, our data suggest that the improvement of excitatory neurotransmission occurs through the GPI-1046-induced up-regulation of PS-1.
The role of hospital payments in the adoption of new medical technologies: an international survey of current practice
- Corinna Sorenson, Michael Drummond, Aleksandra Torbica, Giuditta Callea, Ceu Mateus
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- Health Economics, Policy and Law / Volume 10 / Issue 2 / April 2015
- Published online by Cambridge University Press:
- 17 October 2014, pp. 133-159
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This study examined the role of prospective payment systems in the adoption of new medical technologies across different countries. A literature review was conducted to provide background for the study and guide development of a survey instrument. The survey was disseminated to hospital payment systems experts in 15 jurisdictions. Fifty-one surveys were disseminated, with 34 returned. The surveys returned covered 14 of the 15 jurisdictions invited to participate. The majority (71%) of countries update the patient classification system and/or payment tariffs on an annual basis to try to account for new technologies. Use of short-term separate or supplementary payments for new technologies occurs in 79% of countries to ensure adequate funding and facilitate adoption. A minority (43%) of countries use evidence of therapeutic benefit and/or costs to determine or update payment tariffs, although it is somewhat more common in establishing short-term payments. The main barrier to using evidence is uncertain or unavailable clinical evidence. Almost three-fourths of respondents believed diagnosis-related group systems incentivize or deter technology adoption, depending on the particular circumstances. Improvements are needed, such as enhanced strategies for evidence generation and linking evidence of value to payments, national and international collaboration and training to improve existing practice, and flexible timelines for short-term payments. Importantly, additional research is needed to understand how different payment policies impact technology uptake as well as quality of care and costs.
About the authors
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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- 05 October 2014
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- 16 October 2014, pp xviii-xxii
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7 - Multiple test results
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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- 05 October 2014
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- 16 October 2014, pp 165-208
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Summary
Even though the diagnostic radiologist examines black-and-white images, the information that is derived from the images is hardly ever black-and-white.
M.G. Myriam HuninkIntroduction
In the previous chapters we focused on dichotomous test results, e.g., fecal occult blood is either present or absent. Test results can conveniently be dichotomized, and thinking in terms of dichotomous test results is generally helpful. Distinguishing patients with and without the target disease is useful for the purpose of subsequent decision making because most medical actions are dichotomous. In reality, however, most test results have more than two possible outcomes. Test results can be categorical, ordinal, or continuous. For example, categories of a diagnostic imaging test may be defined by key findings on the images. These categories may be ordered (intuitively) according to the observer’s confidence in the diagnosis, based on the findings. As an example, abnormalities seen on mammography are commonly reported as definitely malignant, probably malignant, possibly malignant, probably benign, or definitely benign. As we shall see later in this chapter, it makes sense to order the categories (explicitly) according to increasing likelihood ratio (LR). Some test results are inherently ordinal, e.g., the five categories of a Papanicolaou smear (test for cervical cancer) are ordinal. Results of biochemical tests are usually given on a continuous scale, which may be reduced to an ordinal scale by grouping the test results. Thus, a test result on a continuous scale can be considered a result on an ordinal scale with an infinite number of very narrow categories. Scores from prediction models are on an ordinal scale if there are a finite number of possible scores, and on a continuous scale if there are an infinite number of scores. When test results are categorical, ordinal, or continuous, we have to consider many test results Ri, where i can be any value from two (the case we have considered in Chapter 5 and Chapter 6, T+ and T−) up to any number of categories. Interpretation of a test result on an ordinal scale can be considered a generalization of the situation of dichotomous test results.
2 - Managing uncertainty
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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- 05 October 2014
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- 16 October 2014, pp 29-52
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Summary
Much of medical training consists of learning to cope with pervasive uncertainty and with the limits of medical knowledge. Making serious clinical decisions on the basis of conflicting, incomplete, and untimely data is routine.
J.D. McCueIntroduction
Much of clinical medicine and health care involves uncertainties: some reducible, but some irreducible despite our best efforts and tests. Better decisions will be made if we are open and honest about these uncertainties, and develop skills in estimating, communicating, and working with such uncertainties. What types of uncertainty exist? Consider the following example.
Needlestick injury:
It has been a hard week. It is time to go home when you are called to yet another heroin overdose: a young woman has been found unconscious outside your clinic. After giving intravenous (IV) naloxone (which reverses the effects of heroin), you are accidentally jabbed by the needle. After her recovery, despite your reassurances, the young woman flees for fear of the police. As the mêlée settles, the dread of human immunodeficiency virus (HIV) infection begins to develop. You talk to the senior doctor about what you should do. She is very sympathetic, and begins to tell you about the risks and management. The good news is that, even if the patient was HIV-positive, a needlestick injury rarely leads to HIV infection (about 3 per 1000). And if she was HIV-positive then a basic two-drug regime of antivirals such as zidovudine (AZT) plus lamivudine are likely to be able to prevent most infections (perhaps 80%).
Unfortunately, the HIV status of the young woman who had overdosed is unknown. Since she was not a patient of your clinic, you are uncertain about whether she is infected, but think that it is possible since she is an IV drug user. The Centers for Disease Control and Prevention (CDC) guidelines (1) suggest: ‘If the exposure source is unknown, use of post-exposure prophylaxis should be decided on a case-by-case basis. Consider the severity of exposure and the epidemiologic likelihood of HIV.’ What do you do?
Dedication
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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- 05 October 2014
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- 16 October 2014, pp v-vi
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11 - Estimation, calibration, and validation
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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- 05 October 2014
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- 16 October 2014, pp 334-355
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Summary
Essentially, all models are wrong, but some are useful.
George E. P. BoxIntroduction
As discussed in Chapter 8, ‘good decision analyses depend on both the veracity of the decision model and the validity of the individual data elements.’ The validity of each individual data element relies on the comprehensiveness of the literature search for the best and most appropriate study or studies, criteria for selecting the source studies, the design of the study or studies, and methods for synthesizing the data from multiple sources. Nonetheless, Sir Michael David Rawlins avers that ‘Decision makers have to incorporate judgements, as part of their appraisal of the evidence, in reaching their conclusions. Such judgements relate to the extent to which each of the components of the evidence base is “fit for purpose.” Is it reliable?’(1) Because the integration of a multitude of these ‘best available’ data elements forms the basis for model results, some individuals refer to decision analyses as black boxes, so this last question applies particularly to the overall model predictions. Consequently, assessing model validity becomes paramount. However, prior to assessing model validity, model construction requires attention to parameter estimation and model calibration. This chapter focuses on parameter estimation, calibration, and validation in the context of Markov and, more generally, state-transition models (Chapter 10) in which recurrent events may occur over an extended period of time. The process of parameter estimation, calibration, and validation is iterative: it involves both adjustment of the data to fit the model and adjustment of the model to fit the data.
Parameter estimation
Survival analysis involves determining the probability that an event such as death or disease progression will occur over time. The events modeled in survival analysis are called ‘failure’ events, because once they occur, they cannot occur again. ‘Survival’ is the absence of the failure event. The failure event may be death, or it may be death combined with a non-fatal outcome such as developing cancer or having a heart attack, in which case the absence of the event is referred to as event-free survival. Commonly used methods for survival analysis include life-table analysis, Kaplan–Meier product limit estimates, and Cox proportional hazards models. A survival curve plots the probability of being alive over time (Figure 11.1).
3 - Choosing the best treatment
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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- 05 October 2014
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- 16 October 2014, pp 53-77
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Summary
Firstly, do no (net) harm.
(adapted from) HippocratesIntroduction
Some treatment decisions are straightforward. For example, what should be done for an elderly patient with a fractured hip? Inserting a metal pin has dramatically altered the management: instead of lying in bed for weeks or months waiting for the fracture to heal while blood clots and pneumonia threatened, the patient is now ambulatory within days. The risks of morbidity and mortality are both greatly reduced. However, many treatment decisions are complex. They involve uncertainties and trade-offs that need to be carefully weighed before choosing. Tragic outcomes may occur no matter which choice is made, and the best that can be done is to minimize the overall risks. Such decisions can be difficult and uncomfortable to make. For example, consider the following historical dilemma.
Benjamin Franklin and smallpox
Benjamin Franklin argued implicitly in favor of the application to individual patients of probabilities based on previous experience with similar groups of patients. Before Edward Jenner’s discovery in 1796 of cowpox vaccination for smallpox, it was known that immunity from smallpox could be achieved by a live smallpox inoculation, but the procedure entailed a risk of death. When a smallpox epidemic broke out in Boston in 1721, the physician Zabdiel Boylston consented, at the urging of the clergyman Cotton Mather, to inoculate several hundred citizens. Mather and Boylston reported their results (1):
Out of about ten thousand Bostonians, five thousand seven hundred fifty-nine took smallpox the natural way. Of these, eight hundred eighty-five died, or one in seven. Two hundred eighty-six took smallpox by inoculation. Of these, six died, or one in forty-seven.
5 - Interpreting diagnostic information
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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- 05 October 2014
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- 16 October 2014, pp 118-144
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Summary
The interpretation of new information depends on what was already known about the patient.
Harold SoxDiagnostic information and probability revision
Physicians have at their disposal an enormous variety of diagnostic information to guide them in decision making. Diagnostic information comes from talking to the patient (symptoms, such as pain, nausea, and breathlessness), examining the patient (signs, such as abdominal tenderness, fever, and blood pressure), and from diagnostic tests (such as blood tests, X-rays, and electrocardiograms (ECGs)) and screening tests (such as Papanicolaou smears for cervical cancer or cholesterol measurements).
Physicians are not the only ones that have to interpret diagnostic information. Public policy makers in health care are equally concerned with understanding the performance of diagnostic tests. If, for example, a policy maker is considering a screening program for lung cancer, he/she will need to understand the performance of the diagnostic tests that can detect lung cancer in an early phase of the disease. In public policy making, other types of ‘diagnostic tests’ may also be relevant. For example, a survey with a questionnaire in a population sample can be considered analogous to a diagnostic test. And performing a trial to determine the efficacy of a treatment is in fact a ‘test’ with the goal of getting more information about that treatment.
list of Abbreviations
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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- 05 October 2014
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- 16 October 2014, pp xvi-xvii
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6 - Deciding when to test
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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- 05 October 2014
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- 16 October 2014, pp 145-164
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Summary
Before ordering a test ask: What will you do if the test is positive? What will you do if the test is negative? If the answers are the same, then don’t do the test.
Poster in an Emergency DepartmentIntroduction
In the previous chapter we looked at how to interpret diagnostic information such as symptoms, signs, and diagnostic tests. Now we need to consider when such information is helpful in decision making. Even if they reduce uncertainty, tests are not always helpful. If used inappropriately to guide a decision, a test may mislead more than it leads. In general, performing a test to gain additional information is worthwhile only if two conditions hold: (1) at least one decision would change given some test result, and (2) the risk to the patient associated with the test is less than the expected benefit that would be gained from the subsequent change in decision. These conditions are most likely to be fulfilled when we are confronted with intermediate probabilities of the target disease, that is, when we are in a diagnostic ‘gray zone.’ Tests are least likely to be helpful either when we are so certain a patient has the target disease that the negative result of an imperfect test would not dissuade us from treating, or, conversely, when we are so certain that the patient does not have the target disease that a positive result of an imperfect test would not persuade us to treat. These concepts are illustrated in Figure 6.1, which divides the probability of a disease into three ranges:
do not treat (for the target disease) and do not test, because even a positive test would not persuade us to treat;
test, because the test will help with treatment decisions or with follow-up; and
treat and do not test, because even a negative test would not dissuade us from treating.
Treat implies patient management as if disease is present and may imply initiating medical therapy, performing a therapeutic procedure, advising a lifestyle or other adjuvant intervention, or a combination of these. Do not treat implies patient management as if disease is absent and usually means risk factor management, lifestyle advice, self-care and/or watchful waiting.
1 - Elements of decision making in health care
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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- 05 October 2014
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- 16 October 2014, pp 1-28
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Summary
And take the case of a man who is ill. I call two physicians: they differ in opinion. I am not to lie down and die between them: I must do something.
Samuel JohnsonIntroduction
How are decisions made in practice, and can we improve the process? Decisions in health care can be particularly awkward, involving a complex web of diagnostic and therapeutic uncertainties, patient preferences and values, and costs. It is not surprising that there is often considerable disagreement about the best course of action. One of the authors of this book tells the following story (1):
Being a cardiovascular radiologist, I regularly attend the vascular rounds at the University Hospital. It’s an interesting conference: the Professor of Vascular Surgery really loves academic discussions and each case gets a lot of attention. The conference goes on for hours. The clinical fellows complain, of course, and it sure keeps me from my regular work. But it’s one of the few conferences that I attend where there is a real discussion of the risks, benefits, and costs of the management options. Even patient preferences are sometimes (albeit rarely) considered.
And yet, I find there is something disturbing about the conference. The discussions always seem to go along the same lines. Doctor R. advocates treatment X because he recently read a paper that reported wonderful results; Doctor S. counters that treatment X has a substantial risk associated with it, as was shown in another paper published last year in the world’s highest-ranking journal in the field; and Doctor T. says that given the current limited health-care budget maybe we should consider a less expensive alternative or no treatment at all. They talk around in circles for ten to 15 minutes, each doctor reiterating his or her opinion. The professor, realizing that his fellows are getting irritated, finally stops the discussion. Practical chores are waiting; there are patients to be cared for. And so the professor concludes: ‘All right. We will offer the patient treatment X.’ About 30% of those involved in the decision-making process nod their heads in agreement; another 30% start bringing up objections which get stifled quickly by the fellows who really do not want an encore, and the remaining 40% are either too tired or too flabbergasted to respond, or are more concerned about another objective, namely their job security.
Contents
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Book:
- Decision Making in Health and Medicine
- Published online:
- 05 October 2014
- Print publication:
- 16 October 2014, pp vii-vii
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8 - Finding and summarizing the evidence
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Book:
- Decision Making in Health and Medicine
- Published online:
- 05 October 2014
- Print publication:
- 16 October 2014, pp 209-236
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- Export citation
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Summary
It is surely a great criticism of our profession that we have not organized a critical summary, by specialty or subspecialty, adapted periodically, of all relevant randomized controlled trials.
Archie CochraneIntroduction
Good decision analyses depend on both the veracity of the decision model and on the validity of the individual data elements. These elements may include probabilities (such as the pre-test probabilities, the sensitivity and specificity of diagnostic tests, the probability of an adverse event, and so on), estimates of effectiveness of interventions (such as the relative risk reduction), and the valuation of outcomes (such as quality of life, utilities, and costs). Often we lack the information needed for a confident assessment of these elements. Decision analysis, by structuring a decision problem, makes these gaps in knowledge apparent. Sensitivity analysis on these ‘soft’ numbers will also give us insight into which of these knowledge gaps is most likely to affect our decisions. These same gaps exist in less systematic decision making as well, but there is no convenient way to determine how our decisions should be affected. In this chapter we shall cover the basic methods for finding the best estimate for each of the different elements that may be included in a formal decision analysis or in less systematic decision making.
Sometimes, but not as often as one would like, the estimates one is looking for can be inferred from a published study or from a series of cases that someone has reported in the literature or recorded in a data bank. This is generally considered the most satisfactory way of assessing a probability, because it involves the use of quantitative evidence. Often we will have a choice of data sources, so it is useful to have some ‘rules’ to guide the choice of possible estimates. One helpful concept is the ‘hierarchy of evidence’ (see www.cebm.net) which explicitly ranks the available evidence; ‘perfect’ data will rarely be available, but we need to know how to choose the best from the available imperfect data. This choice will also need to be tempered by the practicalities and purpose of each decision analysis: what is feasible will differ with a range from the urgent individual patient decision to a national policy decision to fund an expensive new procedure.