When Is Evidence Sufficient?

A Guide to an Iterative Approach to Model-Based Decision Making in Health and Medicine: An Iterative Decision-Making Framework

Decision makers frequently face decisions about optimal resource allocation. A model-based economic evaluation can be used to guide decision makers in their choices by systematically evaluating the magnitude of expected health effects and costs of decision options and by making trade-offs explicit. We provide a guide to an iterative approach to the medical decision-making process by following a coherent framework, and outline the overarching iterative steps of model-based decision making. We systematized the framework by performing three steps. First, we compiled the existing guidelines provided by the ISPOR-SMDM Modeling Good Research Practices Task Force, and the ISPOR Value of Information Task Force. Second, we identified other previous work related to frameworks and guidelines for model-based decision analyses through a literature search in PubMed. Third, we assessed the role of the evidence and iterative process in decision making and formalized key steps in a model-based decision-making framework. We provide guidance on an iterative approach to medical decision making by applying the compiled iterative model-based decision-making framework. The framework formally combines the decision problem conceptualization (Part I), the model conceptualization and development (Part II), and the process of model-based decision analysis (Part III). Following the overarching steps of the framework ensures compliance to the principles of evidence-based medicine and regular updates of the evidence, given that value of information analysis represents an essential component of model-based decision analysis in the framework. Following the provided guide and the steps outlined in the framework can help inform various health care decisions, and therefore it has the potential to improve decision making.

Methodological Note: Reporting Deterministic versus Probabilistic Results of Markov, Partitioned Survival and Other Non-Linear Models

When making decisions under uncertainty, it is reasonable to choose the path that leads to the highest expected net benefit. Therefore, to inform decision making, decision-model-based health economic evaluations should always present expected outputs (i.e. the mean costs and outcomes associated with each course of action). In non-linear models such as Markov models, a single 'run' of the model with each input at its mean (a deterministic analysis) will not generate the expected value of the outputs. In a worst-case scenario, presenting deterministic analyses as the base case can lead to misleading recommendations. Therefore, the base-case analysis of a non-linear model should always be the means from a probabilistic analysis. In this paper, I explain why this is the case and provide recommendations for reporting economic evaluations based on Markov models, noting that the same principle applies to other non-linear structures such as partitioned survival models and individual sampling models. I also provide recommendations for conducting one-way sensitivity analyses of such models. Code illustrating the examples is provided in both Microsoft Excel and R, along with a video abstract and user guides in the electronic supplementary material. Supplementary file 6 (MP4 20900 kb).

Framework for the development and evaluation of complex interventions: gap analysis, workshop and consultation-informed update

BACKGROUND

The Medical Research Council published the second edition of its framework in 2006 on developing and evaluating complex interventions. Since then, there have been considerable developments in the field of complex intervention research. The objective of this project was to update the framework in the light of these developments. The framework aims to help research teams prioritise research questions and design, and conduct research with an appropriate choice of methods, rather than to provide detailed guidance on the use of specific methods.

METHODS

There were four stages to the update: (1) gap analysis to identify developments in the methods and practice since the previous framework was published; (2) an expert workshop of 36 participants to discuss the topics identified in the gap analysis; (3) an open consultation process to seek comments on a first draft of the new framework; and (4) findings from the previous stages were used to redraft the framework, and final expert review was obtained. The process was overseen by a Scientific Advisory Group representing the range of relevant National Institute for Health Research and Medical Research Council research investments.

RESULTS

Key changes to the previous framework include (1) an updated definition of complex interventions, highlighting the dynamic relationship between the intervention and its context; (2) an emphasis on the use of diverse research perspectives: efficacy, effectiveness, theory-based and systems perspectives; (3) a focus on the usefulness of evidence as the basis for determining research perspective and questions; (4) an increased focus on interventions developed outside research teams, for example changes in policy or health services delivery; and (5) the identification of six 'core elements' that should guide all phases of complex intervention research: consider context; develop, refine and test programme theory; engage stakeholders; identify key uncertainties; refine the intervention; and economic considerations. We divide the research process into four phases: development, feasibility, evaluation and implementation. For each phase we provide a concise summary of recent developments, key points to address and signposts to further reading. We also present case studies to illustrate the points being made throughout.

LIMITATIONS

The framework aims to help research teams prioritise research questions and design and conduct research with an appropriate choice of methods, rather than to provide detailed guidance on the use of specific methods. In many of the areas of innovation that we highlight, such as the use of systems approaches, there are still only a few practical examples. We refer to more specific and detailed guidance where available and note where promising approaches require further development.

CONCLUSIONS

This new framework incorporates developments in complex intervention research published since the previous edition was written in 2006. As well as taking account of established practice and recent refinements, we draw attention to new approaches and place greater emphasis on economic considerations in complex intervention research. We have introduced a new emphasis on the importance of context and the value of understanding interventions as 'events in systems' that produce effects through interactions with features of the contexts in which they are implemented. The framework adopts a pluralist approach, encouraging researchers and research funders to adopt diverse research perspectives and to select research questions and methods pragmatically, with the aim of providing evidence that is useful to decision-makers.

FUTURE WORK

We call for further work to develop relevant methods and provide examples in practice. The use of this framework should be monitored and the move should be made to a more fluid resource in the future, for example a web-based format that can be frequently updated to incorporate new material and links to emerging resources.

FUNDING

This project was jointly funded by the Medical Research Council (MRC) and the National Institute for Health Research (Department of Health and Social Care 73514).

Evidence-Based Medicine in Otolaryngology, Part XI: Modeling and Analysis to Support Decisions

OBJECTIVE

To provide a resource to educate clinical decision makers about the analyses and models that can be employed to support data-driven choices.

DATA SOURCES

Published studies and literature regarding decision analysis, decision trees, and models used to support clinical decisions.

REVIEW METHODS

Decision models provide insights into the evidence and its implications for those who make choices about clinical care and resource allocation. Decision models are designed to further our understanding and allow exploration of the common problems that we face, with parameters derived from the best available evidence. Analysis of these models demonstrates critical insights and uncertainties surrounding key problems via a readily interpretable yet quantitative format. This 11th installment of the Evidence-Based Medicine in Otolaryngology series thus provides a step-by-step introduction to decision models, their typical framework, and favored approaches to inform data-driven practice for patient-level decisions, as well as comparative assessments of proposed health interventions for larger populations.

CONCLUSIONS

Information to support decisions may arise from tools such as decision trees, Markov models, microsimulation models, and dynamic transmission models. These data can help guide choices about competing or alternative approaches to health care.

IMPLICATIONS FOR PRACTICE

Methods have been developed to support decisions based on data. Understanding the related techniques may help promote an evidence-based approach to clinical management and policy.

Socioeconomic Factors and Caries in People between 19 and 60 Years of Age: An Update of a Systematic Review and Meta-Analysis of Observational Studies

This study is aimed to perform an update of a systematic review and meta-regression to evaluate the effect modification of the socioeconomic indicators on caries in adults. We included studies that associated social determinants with caries, with no restriction of year and language. The Newcastle-Ottawa Scale was used to evaluate the risk of bias. With regard to the meta-analysis, statistical heterogeneity was evaluated by I², and the random effect model was used when it was high. A subgroup analysis was conducted for socioeconomic indicators, and a meta-regression was performed. Publication bias was assessed through Egger's test. Sixty-one studies were included in the systematic review and 25 were included in the meta-analysis. All of the studies were published between 1975 and 2016. The most frequent socioeconomic indicators were schooling, income, and socioeconomic status (SES). In the quantitative analysis, the DMFT (decayed, missing, filled teeth) variation was attributed to the studies' heterogeneity. The increase of 10.35 units in the proportion of people with lower SES was associated with an increase of one unit in DMFT, p = 0.050. The findings provide evidence that populations with the highest proportions of people with low SES are associated with a greater severity of caries. The results suggest the need for actions to reduce the inequalities in oral health (PROSPERO [CRD42017074434]).

How good are GPs at adhering to a pragmatic trial protocol in primary care? Results from the ADDITION-Cambridge cluster-randomised pragmatic trial

OBJECTIVE

To assess the fidelity of general practitioners' (GPs) adherence to a long-term pragmatic trial protocol.

DESIGN

Retrospective analyses of electronic primary care records of participants in the pragmatic cluster-randomised ADDITION (Anglo-Danish-Dutch Study of Intensive Treatment In People with Screen Detected Diabetes in Primary Care)-Cambridge trial, comparing intensive multifactorial treatment (IT) versus routine care (RC). Data were collected from the date of diagnosis until December 2010.

SETTING

Primary care surgeries in the East of England.

STUDY SAMPLE/PARTICIPANTS

A subsample (n=189, RC arm: n=99, IT arm: n=90) of patients from the ADDITION-Cambridge cohort (867 patients), consisting of patients 40-69 years old with screen-detected diabetes mellitus.

INTERVENTIONS

In the RC arm treatment was delivered according to concurrent treatment guidelines. Surgeries in the IT arm received funding for additional contacts between GPs/nurses and patients, and GPs were advised to follow more intensive treatment algorithms for the management of glucose, lipids and blood pressure and aspirin therapy than in the RC arm.

OUTCOME MEASURES

The number of annual contacts between patients and GPs/nurses, the proportion of patients receiving prescriptions for cardiometabolic medication in years 1-5 after diabetes diagnosis and the adherence to prescription algorithms.

RESULTS

The difference in the number of annual GP contacts (β=0.65) and nurse contacts (β=-0.15) between the study arms was small and insignificant. Patients in the IT arm were more likely to receive glucose-lowering (OR=3.27), ACE-inhibiting (OR=2.03) and lipid-lowering drugs (OR=2.42, all p values <0.01) than patients in the RC arm. The prescription adherence varied between medication classes, but improved in both trial arms over the 5-year follow-up.

CONCLUSIONS

The adherence of GPs to different aspects of the trial protocol was mixed. Background changes in healthcare policy need to be considered as they have the potential to dilute differences in treatment intensity and hence incremental effects.

TRIAL REGISTRATION NUMBER

ISRCTN86769081.

A Gaussian Approximation Approach for Value of Information Analysis

Most decisions are associated with uncertainty. Value of information (VOI) analysis quantifies the opportunity loss associated with choosing a suboptimal intervention based on current imperfect information. VOI can inform the value of collecting additional information, resource allocation, research prioritization, and future research designs. However, in practice, VOI remains underused due to many conceptual and computational challenges associated with its application. Expected value of sample information (EVSI) is rooted in Bayesian statistical decision theory and measures the value of information from a finite sample. The past few years have witnessed a dramatic growth in computationally efficient methods to calculate EVSI, including metamodeling. However, little research has been done to simplify the experimental data collection step inherent to all EVSI computations, especially for correlated model parameters. This article proposes a general Gaussian approximation (GA) of the traditional Bayesian updating approach based on the original work by Raiffa and Schlaifer to compute EVSI. The proposed approach uses a single probabilistic sensitivity analysis (PSA) data set and involves 2 steps: 1) a linear metamodel step to compute the EVSI on the preposterior distributions and 2) a GA step to compute the preposterior distribution of the parameters of interest. The proposed approach is efficient and can be applied for a wide range of data collection designs involving multiple non-Gaussian parameters and unbalanced study designs. Our approach is particularly useful when the parameters of an economic evaluation are correlated or interact.

The role of imperfect surrogate endpoint information in drug approval and reimbursement decisions

Approval of new drugs is increasingly reliant on "surrogate endpoints," which correlate with but imperfectly predict clinical benefits. Proponents argue surrogate endpoints allow for faster approval, but critics charge they provide inadequate evidence. We develop an economic framework that addresses the value of improvement in the predictive power, or "quality," of surrogate endpoints, and clarifies how quality can influence decisions by regulators, payers, and manufacturers. For example, the framework shows how lower-quality surrogates lead to greater misalignment of incentives between payers and regulators, resulting in more drugs that are approved for use but not covered by payers. Efficient price-negotiation in the marketplace can help align payer incentives for granting access based on surrogates. Higher-quality surrogates increase manufacturer profits and social surplus from early access to new drugs. Since the return on better quality is shared between manufacturers and payers, private incentives to invest in higher-quality surrogates are inefficiently low.

The Decision to Conduct a Head-to-Head Comparative Trial: A Game-Theoretic Analysis

Recent Medicare legislation calls on the Agency for Healthcare Research and Quality to conduct research related to the comparative effectiveness of health care items and services, including prescription drugs. This reinforces earlier calls for head-to-head comparative trials of clinically relevant treatment alternatives. Using a game-theoretic model, the authors explore the decision of pharmaceutical companies to conduct such trials. The model suggests that an important factor affecting this decision is the potential loss in market share and profits following a result of inferiority or comparability. This hidden cost is higher for the market leader than the market follower, making it less likely that the leader will choose to conduct a trial. The model also suggests that in a full-information environment, it will never be the case that both firms choose to conduct such a trial. Furthermore, if market shares and the probability of proving superiority are similar for both firms, it is quite possible that neither firm will choose to conduct a trial. Finally, the results indicate that incentives that offset the direct cost of a trial can prevent a no-trial equilibrium, even when both firms face the possibility of an inferior outcome.

A Comprehensive Algorithm for Approval of Health Technologies With, Without, or Only in Research: The Key Principles for Informing Coverage Decisions

BACKGROUND

The value of evidence about the performance of a technology and the value of access to a technology are central to policy decisions regarding coverage with, without, or only in research and managed entry (or risk-sharing) agreements.

OBJECTIVES

We aim to outline the key principles of what assessments are needed to inform "only in research" (OIR) or "approval with research" (AWR) recommendations, in addition to approval or rejection.

METHODS

We developed a comprehensive algorithm to inform the sequence of assessments and judgments that lead to different types of guidance: OIR, AWR, Approve, or Reject. This algorithm identifies the order in which assessments might be made, how similar guidance might be arrived at through different combinations of considerations, and when guidance might change.

RESULTS

The key principles are whether the technology is expected to be cost-effective; whether the technology has significant irrecoverable costs; whether additional research is needed; whether research is possible with approval and whether there are opportunity costs that once committed by approval cannot be recovered; and whether there are effective price reductions. Determining expected cost-effectiveness is only a first step. In addition to AWR for technologies expected to be cost-effective and OIR for those not expected to be cost-effective, there are other important circumstances when OIR should be considered.

CONCLUSIONS

These principles demonstrate that cost-effectiveness is a necessary but not sufficient condition for approval. Even when research is possible with approval, OIR may be appropriate when a technology is expected to be cost-effective due to significant irrecoverable costs.

Some Health States Are Better Than Others: Using Health State Rank Order to Improve Probabilistic Analyses

BACKGROUND

Probabilistic sensitivity analyses (PSA) may lead policy makers to take nonoptimal actions due to misestimates of decision uncertainty caused by ignoring correlations. We developed a method to establish joint uncertainty distributions of quality-of-life (QoL) weights exploiting ordinal preferences over health states.

METHODS

Our method takes as inputs independent, univariate marginal distributions for each QoL weight and a preference ordering. It establishes a correlation matrix between QoL weights intended to preserve the ordering. It samples QoL weight values from their distributions, ordering them with the correlation matrix. It calculates the proportion of samples violating the ordering, iteratively adjusting the correlation matrix until this proportion is below an arbitrarily small threshold. We compare our method with the uncorrelated method and other methods for preserving rank ordering in terms of violation proportions and fidelity to the specified marginal distributions along with PSA and expected value of partial perfect information (EVPPI) estimates, using 2 models: 1) a decision tree with 2 decision alternatives and 2) a chronic hepatitis C virus (HCV) Markov model with 3 alternatives.

RESULTS

All methods make tradeoffs between violating preference orderings and altering marginal distributions. For both models, our method simultaneously performed best, with largest performance advantages when distributions reflected wider uncertainty. For PSA, larger changes to the marginal distributions induced by existing methods resulted in differing conclusions about which strategy was most likely optimal. For EVPPI, both preference order violations and altered marginal distributions caused existing methods to misestimate the maximum value of seeking additional information, sometimes concluding that there was no value.

CONCLUSIONS

Analysts can characterize the joint uncertainty in QoL weights to improve PSA and value-of-information estimates using Open Source implementations of our method.

Unifying Research and Reimbursement Decisions: Case Studies Demonstrating the Sequence of Assessment and Judgments Required

BACKGROUND

The key principles regarding what assessments lead to different types of guidance about the use of health technologies (Only in Research, Approval with Research, Approve, or Reject) provide an explicit and transparent framework for technology appraisal.

OBJECTIVE

We aim to demonstrate how these principles and assessments can be applied in practice through the use of a seven-point checklist of assessment.

METHODS

The value of access to a technology and the value of additional evidence are explored through the application of the checklist to the case studies of enhanced external counterpulsation for chronic stable angina and clopidogrel for the management of patients with non-ST-segment elevation acute coronary syndromes.

RESULTS

The case studies demonstrate the importance of considering 1) the expected cost-effectiveness and population net health effects; 2) the need for evidence and whether the type of research required can be conducted once a technology is approved for widespread use; 3) whether there are sources of uncertainty that cannot be resolved by research but only over time; and 4) whether there are significant (opportunity) costs that once committed by approval cannot be recovered.

CONCLUSIONS

The checklist demonstrates that cost-effectiveness is a necessary but not sufficient condition for approval. Only in Research may be appropriate when a technology is expected to be cost-effective due to significant irrecoverable costs. It is only approval that can be ruled out if a technology is not expected to be cost-effective. Lack of cost-effectiveness is not a necessary or sufficient condition for rejection.

Computing Expected Value of Partial Sample Information from Probabilistic Sensitivity Analysis Using Linear Regression Metamodeling

Decision makers often desire both guidance on the most cost-effective interventions given current knowledge and also the value of collecting additional information to improve the decisions made (i.e., from value of information [VOI] analysis). Unfortunately, VOI analysis remains underused due to the conceptual, mathematical, and computational challenges of implementing Bayesian decision-theoretic approaches in models of sufficient complexity for real-world decision making. In this study, we propose a novel practical approach for conducting VOI analysis using a combination of probabilistic sensitivity analysis, linear regression metamodeling, and unit normal loss integral function--a parametric approach to VOI analysis. We adopt a linear approximation and leverage a fundamental assumption of VOI analysis, which requires that all sources of prior uncertainties be accurately specified. We provide examples of the approach and show that the assumptions we make do not induce substantial bias but greatly reduce the computational time needed to perform VOI analysis. Our approach avoids the need to analytically solve or approximate joint Bayesian updating, requires only one set of probabilistic sensitivity analysis simulations, and can be applied in models with correlated input parameters.

Stump closure reinforcement with absorbable fibrin collagen sealant sponge (TachoSil) does not prevent pancreatic fistula after distal pancreatectomy: the FIABLE multicenter controlled randomized study

BACKGROUND

The aim of this study is to evaluate the effectiveness of TachoSil sponge on distal pancreatectomy remnant stump in reducing the rate and severity of postoperative pancreatic fistula (POPF).

METHODS

All consecutive patients requiring distal pancreatectomy were randomized in 45 centers. The principal end point was onset of "clinically relevant" POPF. Univariate and multivariate analyses were searched for predictive factors.

RESULTS

Of the 270 patients randomized (134 with TachoSil; 136 without), 150 (55.6%) patients sustained a POPF [74 clinically relevant and 76 clinically silent (27.4% and 28.1%), respectively]: no statistically significant difference was found between patients sustaining clinically relevant POPF [41 (30.6%) with vs 33 (24.3%) without TachoSil (P = .276)], or overall POPF [73 (54.5%) with vs 77 (56.6%) without TachoSil, (P = .807)], but there were more clinically relevant POPF after hand-sewn (32.3%) versus mechanical closure (19.8%) (P = .025) and, in case of splenic preservation, after splenic vessel ligation (15/32, 46.9%) versus vascular preservation (17/72, 23.6%) (P = .024). Hand-sewn pancreatic remnant closure (P = .023) and splenic vessel ligation in splenic preservation (P = .035) were independent predictive factors for the onset of clinically relevant POPF.

CONCLUSION

TachoSil sponge reinforcement of the proximal remnant after distal pancreatectomy reduced neither the rate nor the severity of POPF.

A reference case for economic evaluations in osteoarthritis: an expert consensus article from the European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis (ESCEO)

BACKGROUND

General recommendations for a reference case for economic studies in rheumatic diseases were published in 2002 in an initiative to improve the comparability of cost-effectiveness studies in the field. Since then, economic evaluations in osteoarthritis (OA) continue to show considerable heterogeneity in methodological approach.

OBJECTIVES

To develop a reference case specific for economic studies in OA, including the standard optimal care, with which to judge new pharmacologic and non-pharmacologic interventions.

METHODS

Four subgroups of an ESCEO expert working group on economic assessments (13 experts representing diverse aspects of clinical research and/or economic evaluations) were charged with producing lists of recommendations that would potentially improve the comparability of economic analyses in OA: outcome measures, comparators, costs and methodology. These proposals were discussed and refined during a face-to-face meeting in 2013. They are presented here in the format of the recommendations of the recently published Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement, so that an initiative on economic analysis methodology might be consolidated with an initiative on reporting standards.

RESULTS

Overall, three distinct reference cases are proposed, one for each hand, knee and hip OA; with diagnostic variations in the first two, giving rise to different treatment options: interphalangeal or thumb-based disease for hand OA and the presence or absence of joint malalignment for knee OA. A set of management strategies is proposed, which should be further evaluated to help establish a consensus on the "standard optimal care" in each proposed reference case. The recommendations on outcome measures, cost itemisation and methodological approaches are also provided.

CONCLUSIONS

The ESCEO group proposes a set of disease-specific recommendations on the conduct and reporting of economic evaluations in OA that could help the standardisation and comparability of studies that evaluate therapeutic strategies of OA in terms of costs and effectiveness.

Value of research and value of development in early assessments of new medical technologies

OBJECTIVES

In early stages of development of new medical technologies, there are conceptually separate but related societal decisions to be made concerning adoption, further development (i.e., technical improvement), and research (i.e., clinical trials) of new technologies. This article presents a framework to simultaneously support these three decisions from a societal perspective. The framework is applied to the 70-gene signature, a gene-expression profile for breast cancer, deciding which patients should receive adjuvant systemic therapy after surgery. The "original" signature performed on fresh frozen tissue (70G-FFT) could be further developed to a paraffin-based signature (70G-PAR) to reduce test failures.

METHODS

A Markov decision model comparing the "current" guideline Adjuvant Online (AO), 70G-FFT, and 70G-PAR was used to simulate 20-year costs and outcomes in a hypothetical cohort in The Netherlands. The 70G-PAR strategy was based on projected data from a comparable technology. Incremental net monetary benefits were calculated to support the adoption decision. Expected net benefit of development for the population and expected net benefit of sampling were calculated to support the development and research decision.

RESULTS

The 70G-PAR had the highest net monetary benefit, followed by the 70G-FFT. The population expected net benefit of development amounted to €91 million over 20 years (assuming €250 development costs per patient receiving the test). The expected net benefit of sampling amounted to €61 million for the optimal trial (n = 4000).

CONCLUSIONS

We presented a framework to simultaneously support adoption, development, and research decisions in early stages of medical technology development. In this case, the results indicate that there is value in both further development of 70G-FFT into 70G-PAR and further research.

A systematic and critical review of the evolving methods and applications of value of information in academia and practice

OBJECTIVE

This article provides a systematic and critical review of the evolving methods and applications of value of information (VOI) in academia and practice and discusses where future research needs to be directed.

METHODS

Published VOI studies were identified by conducting a computerized search on Scopus and ISI Web of Science from 1980 until December 2011 using pre-specified search terms. Only full-text papers that outlined and discussed VOI methods for medical decision making, and studies that applied VOI and explicitly discussed the results with a view to informing healthcare decision makers, were included. The included papers were divided into methodological and applied papers, based on the aim of the study.

RESULTS

A total of 118 papers were included of which 50 % (n = 59) are methodological. A rapidly accumulating literature base on VOI from 1999 onwards for methodological papers and from 2005 onwards for applied papers is observed. Expected value of sample information (EVSI) is the preferred method of VOI to inform decision making regarding specific future studies, but real-life applications of EVSI remain scarce. Methodological challenges to VOI are numerous and include the high computational demands, dealing with non-linear models and interdependency between parameters, estimations of effective time horizons and patient populations, and structural uncertainties.

CONCLUSION

VOI analysis receives increasing attention in both the methodological and the applied literature bases, but challenges to applying VOI in real-life decision making remain. For many technical and methodological challenges to VOI analytic solutions have been proposed in the literature, including leaner methods for VOI. Further research should also focus on the needs of decision makers regarding VOI.

Private manufacturers' thresholds to invest in comparative effectiveness trials

The recent rush of enthusiasm for public investment in comparative effectiveness research (CER) in the US has focussed attention on these public investments. However, little attention has been given to how changing public investment in CER may affect private manufacturers' incentives for CER, which has long been a major source of CER. In this work, based on a simple revenue maximizing economic framework, we generate predictions on thresholds to invest in CER for a private manufacturer that compares its own product to a competitor's product in head-to-head trials. Our analysis shows that private incentives to invest in CER are determined by how the results of CER may affect the price and quantity of the product sold and the duration over which resulting changes in revenue would accrue, given the time required to complete CER and the time from the completion of CER to the time of patent expiration. We highlight the result that private incentives may often be less than public incentives to invest in CER and may even be negative if the likelihood of adverse findings is sufficient. We find that these incentives imply a number of predictions about patterns of CER and how they will be affected by changes in public financing of CER and CER methods. For example, these incentives imply that incumbent patent holders may be less likely to invest in CER than entrants and that public investments in CER may crowd out similar private investments. In contrast, newer designs and methods for CER, such as Bayesian adaptive trials, which can reduce ex post risk of unfavourable results and shorten the time for the production of CER, may increase the expected benefits of CER and may tend to increase private investment in CER as long as the costs of such innovative designs are not excessive. Bayesian approaches to design also naturally highlight the dynamic aspects of CER, allowing less expensive initial studies to guide decisions about future investments and thereby encouraging greater initial investments in CER. However, whether the potential effects we highlight of public funding of CER and of Bayesian approaches to trial design actually produce changes in private investment in CER remains an empirical question.

Methods to assess cost-effectiveness and value of further research when data are sparse: negative-pressure wound therapy for severe pressure ulcers

Health care resources are scarce, and decisions have to be made about how to allocate funds. Often, these decisions are based on sparse or imperfect evidence. One such example is negative-pressure wound therapy (NPWT), which is a widely used treatment for severe pressure ulcers; however, there is currently no robust evidence that it is effective or cost-effective. This work considers the decision to adopt NPWT given a range of alternative treatments, using a decision analytic modeling approach. Literature searches were conducted to identify existing evidence on model parameters. Given the limited evidence base, a second source of evidence, beliefs elicited from experts, was used. Judgments from experts on relevant (uncertain) quantities were obtained through a formal elicitation exercise. Additionally, data derived from a pilot trial were also used to inform the model. The 3 sources of evidence were collated, and the impact of each on cost-effectiveness was evaluated. An analysis of the value of further information indicated that a randomized controlled trial may be worthwhile in reducing decision uncertainty, where from a set of alternative designs, a 3-arm trial with longer follow-up was estimated to be the most efficient. The analyses presented demonstrate how allocation decisions about medical technologies can be explicitly informed when data are sparse and how this kind of analyses can be used to guide future research prioritization, not only indicating whether further research is worthwhile but what type of research is needed and how it should be designed.

Variation in dermatologist beliefs about the safety and effectiveness of treatments for moderate to severe psoriasis

BACKGROUND

Multiple systemic treatments are available for moderate to severe psoriasis, but dermatologists' perceptions of these treatments are unknown. Physician perceptions can influence prescribing patterns and patient outcomes, and may help to explain variations in clinical practice.

OBJECTIVE

We sought to describe the variation in dermatologist's beliefs about the safety and effectiveness of psoriasis treatments and evaluate how these relate to dermatologist characteristics and treatment preferences.

METHODS

We conducted a cross-sectional mail survey of a random sample of 500 National Psoriasis Foundation (NPF) members and 500 American Academy of Dermatology (AAD) members who treat psoriasis.

RESULTS

Of 989 clinicians who could be contacted, 246 NPF members and 141 AAD members returned the survey (39% response rate). Respondents perceived infliximab, ustekinumab, cyclosporine, and adalimumab to have the highest likelihood of skin clearance in 3 months (67%-75%). Etanercept, adalimumab, ultraviolet B, and ustekinumab had the lowest perceived likelihood of side effects requiring treatment discontinuation (9%-11%). Up to 49% of respondents "didn't know" the effectiveness or likelihood of side effects; calculated coefficients of variation were higher for perceived likelihood of side effects than perceived effectiveness. There were few significant associations between safety and effectiveness perceptions and respondent characteristics, and treatment preferences were not consistently predictive of perceptions.

LIMITATIONS

Only dermatologists with interest in treating psoriasis were surveyed and general perceptions were elicited via survey format. Perceptions may differ between survey respondents and nonrespondents.

CONCLUSIONS

Psoriasis providers demonstrate wide variation in their perception of the effectiveness and especially safety of systemic treatments.

Developing clinical practice guidelines: types of evidence and outcomes; values and economics, synthesis, grading, and presentation and deriving recommendations

Clinical practice guidelines are one of the foundations of efforts to improve healthcare. In 1999, we authored a paper about methods to develop guidelines. Since it was published, the methods of guideline development have progressed both in terms of methods and necessary procedures and the context for guideline development has changed with the emergence of guideline clearinghouses and large scale guideline production organisations (such as the UK National Institute for Health and Clinical Excellence). It therefore seems timely to, in a series of three articles, update and extend our earlier paper. In this second paper, we discuss issues of identifying and synthesizing evidence: deciding what type of evidence and outcomes to include in guidelines; integrating values into a guideline; incorporating economic considerations; synthesis, grading, and presentation of evidence; and moving from evidence to recommendations.

Expected net present value of sample information: from burden to investment

The Expected Value of Information Framework has been proposed as a method for identifying when health care technologies should be immediately reimbursed and when any reimbursement should be withheld while awaiting more evidence. This framework assesses the value of obtaining additional evidence to inform a current reimbursement decision. This represents the burden of not having the additional evidence at the time of the decision. However, when deciding whether to reimburse now or await more evidence, decision makers need to know the value of investing in more research to inform a future decision. Assessing this value requires consideration of research costs, research time, and what happens to patients while the research is undertaken and after completion. The investigators describe a development of the calculation of the expected value of sample information that assesses the value of investing in further research, including an only-in-research strategy and an only-with-research strategy.

Comparative analysis of cost of endoscopic endonasal minimally invasive and sublabial-transseptal approaches to the pituitary

BACKGROUND

Two surgical approaches to the pituitary are commonly used: the sublabial-transseptal (SLTS) approach using microscopy and the endonasal endoscopic minimally invasive (MIPS) approach. Although outcomes are similar for both procedures, MIPS has become increasingly prevalent over the last 15 years. Limited cost analysis data comparing the 2 alternatives are available.

METHODS

A retrospective analysis of cost and volume data was performed using data from the published literature and University of North Carolina at Chapel Hill (UNC) Hospitals. A sensitivity analysis of the parameters was used to evaluate the uncertainty in parameter estimates.

RESULTS

The total cost in real dollars ranges from $11,438 to $12,513 and $18,095 to $21,005 per patient per procedure for MIPS and SLTS, respectively, with a cost difference ranging between $5582 and $9567 per patient per procedure. The sensitivity analysis indicates that the total cost for MIPS is most sensitive to: (1) average length of stay, (2) nursing costs, and (3) number of total complications, whereas the total cost for SLTS is most sensitive to: (1) average length of stay, (2) nursing cost, and (3) operating time. MIPS is less costly than SLTS between 94% and 98% of the time.

CONCLUSION

The results indicate that MIPS is less costly than SLTS at a large academic center. Future research should compare the outcomes and quality of life (QoL) associated with the 2 surgeries to improve the data used to determine the cost-effectiveness of MIPS compared to SLTS.

Long term costs and effects of reducing the number of twin pregnancies in IVF by single embryo transfer: the TwinSing study

Background

Pregnancies induced by in vitro fertilisation (IVF) often result in twin gestations, which are associated with both maternal and perinatal complications. An effective way to reduce the number of IVF twin pregnancies is to decrease the number of embryos transferred from two to one. The interpretation of current studies is limited because they used live birth as outcome measure and because they applied limited time horizons. So far, research on long-term outcomes of IVF twins and singletons is scarce and inconclusive. The objective of this study is to investigate the short (1-year) and long-term (5 and 18-year) costs and health outcomes of IVF singleton and twin children and to consider these in estimating the cost-effectiveness of single embryo transfer compared with double embryo transfer, from a societal and a healthcare perspective.

Methods/Design

A multi-centre cohort study will be performed, in which IVF singletons and IVF twin children born between 2003 and 2005 of whom parents received IVF treatment in one of the five participating Dutch IVF centres, will be compared. Data collection will focus on children at risk of health problems and children in whom health problems actually occurred. First year of life data will be collected in approximately 1,278 children (619 singletons and 659 twin children). Data up to the fifth year of life will be collected in approximately 488 children (200 singletons and 288 twin children). Outcome measures are health status, health-related quality of life and costs. Data will be obtained from hospital information systems, a parent questionnaire and existing registries. Furthermore, a prognostic model will be developed that reflects the short and long-term costs and health outcomes of IVF singleton and twin children. This model will be linked to a Markov model of the short-term cost-effectiveness of single embryo transfer strategies versus double embryo transfer strategies to enable the calculation of the long-term cost-effectiveness.

Discussion

This is, to our knowledge, the first study that investigates the long-term costs and health outcomes of IVF singleton and twin children and the long-term cost-effectiveness of single embryo transfer strategies versus double embryo transfer strategies.

A method for addressing research gaps in HTA, developed whilst evaluating robotic-assisted surgery: a proposal

Background

When evaluating health technologies with insufficient scientific evidence, only innovative potentials can be assessed. A Regional policy initiative linking the governance of health innovations to the development of clinical research has been launched by the Region of Emilia Romagna Healthcare Authority. This program, aimed at enhancing the research capacity of health organizations, encourages the development of adoption plans that combine use in clinical practice along with experimental use producing better knowledge. Following the launch of this program we developed and propose a method that, by evaluating and ranking scientific uncertainty, identifies the moment (during the stages of the technology's development) where it would be sensible to invest in research resources and capacity to further its evaluation. The method was developed and tested during a research project evaluating robotic surgery.

Methods

A multidisciplinary panel carried out a 5-step evaluation process: 1) definition of the technology's evidence profile and of all relevant clinical outcomes; 2) systematic review of scientific literature and outline of the uncertainty profile differentiating research results into steady, plausible, uncertain and unknown results; 3) definition of the acceptable level of uncertainty for investing research resources; 4) analysis of local context; 5) identification of clinical indications with promising clinical return.

Results

Outputs for each step of the evaluation process are: 1) evidence profile of the technology and systematic review; 2) uncertainty profile for each clinical indication; 3) exclusion of clinical indications not fulfilling the criteria of maximum acceptable risk; 4) mapping of local context; 5) recommendations for research.

Outputs of the evaluation process for robotic surgery are described in the paper.

Conclusions

This method attempts to rank levels of uncertainty in order to distinguish promising from hazardous clinical use and to outline a research course of action. Decision makers wishing to tie coverage policies to the development of scientific evidence could find this method a useful aid to the governance of innovations.

An evidence-based public health approach to injury priorities and prevention recommendations for the u.s. Military

Injuries are the leading cause of morbidity and mortality confronting U.S. military forces in peacetime or combat operations. Not only are injuries the biggest health problem of the military services, they are also a complex problem. The leading causes of deaths are different from those that result in hospitalization, which are different from those that result in outpatient care. As a consequence, it is not possible to focus on just one level of injury severity if the impact of injuries on military personnel is to be reduced. To effectively reduce the impact of a problem as big and complex as injuries requires a systematic approach. The purpose of this paper is to: (1) review the steps of the public health process for injury prevention; (2) review literature on evaluation of the scientific quality and consistency of information needed to make decisions about prevention policies, programs, and interventions; and (3) summarize criteria for setting objective injury prevention priorities. The review of these topics will serve as a foundation for making recommendations to enhance the effectiveness of injury prevention efforts in the military and similarly large communities. This paper also serves as an introduction to the other articles in this supplement to the American Journal of Preventive Medicine that illustrate the recommended systematic approach.

The economics of comparative effectiveness studies: societal and private perspectives and their implications for prioritizing public investments in comparative effectiveness research

Comparative effectiveness research (CER) can provide valuable information for patients, providers and payers. These stakeholders differ in their incentives to invest in CER. To maximize benefits from public investments in CER, it is important to understand the value of CER from the perspectives of these stakeholders and how that affects their incentives to invest in CER. This article provides a conceptual framework for valuing CER, and illustrates the potential benefits of such studies from a number of perspectives using several case studies. We examine cases in which CER provides value by identifying when one treatment is consistently better than others, when different treatments are preferred for different subgroups, and when differences are small enough that decisions can be made based on price. We illustrate these findings using value-of-information techniques to assess the value of research, and by examining changes in pharmaceutical prices following publication of a comparative effectiveness study. Our results suggest that CER may have high societal value but limited private return to providers or payers. This suggests the importance of public efforts to promote the production of CER. We also conclude that value-of-information tools may help inform policy decisions about how much public funds to invest in CER and how to prioritize the use of available public funds for CER, in particular targeting public CER spending to areas where private incentives are low relative to social benefits.

Dimensions of design space: a decision-theoretic approach to optimal research design

Bayesian decision theory can be used not only to establish the optimal sample size and its allocation in a single clinical study but also to identify an optimal portfolio of research combining different types of study design. Within a single study, the highest societal payoff to proposed research is achieved when its sample sizes and allocation between available treatment options are chosen to maximize the expected net benefit of sampling (ENBS). Where a number of different types of study informing different parameters in the decision problem could be conducted, the simultaneous estimation of ENBS across all dimensions of the design space is required to identify the optimal sample sizes and allocations within such a research portfolio. This is illustrated through a simple example of a decision model of zanamivir for the treatment of influenza. The possible study designs include: 1) a single trial of all the parameters, 2) a clinical trial providing evidence only on clinical endpoints, 3) an epidemiological study of natural history of disease, and 4) a survey of quality of life. The possible combinations, samples sizes, and allocation between trial arms are evaluated over a range of cost-effectiveness thresholds. The computational challenges are addressed by implementing optimization algorithms to search the ENBS surface more efficiently over such large dimensions.

Decision analysis to complete diagnostic research by closing the gap between test characteristics and cost-effectiveness

OBJECTIVE

The lack of a standard methodology in diagnostic research impedes adequate evaluation before implementation of constantly developing diagnostic techniques. We discuss the methodology of diagnostic research and underscore the relevance of decision analysis in the process of evaluation of diagnostic tests.

STUDY DESIGN AND SETTING

Overview and conceptual discussion.

RESULTS

Diagnostic research requires a stepwise approach comprising assessment of test characteristics followed by evaluation of added value, clinical outcome, and cost-effectiveness. These multiple goals are generally incompatible with a randomized design. Decision-analytic models provide an important alternative through integration of the best available evidence. Thus, critical assessment of clinical value and efficient use of resources can be achieved.

CONCLUSION

Decision-analytic models should be considered part of the standard methodology in diagnostic research. They can serve as a valid alternative to diagnostic randomized clinical trials (RCTs).

Market and patient access to new oncology products in Europe: a current, multidisciplinary perspective

To air challenging issues related to patient and market access to new anticancer agents, the Biotherapy Development Association--an international group focused on developing targeted cancer therapies using biological agents--convened a meeting on 29 November 2007 in Brussels, Belgium. The meeting provided a forum for representatives of pharmaceutical companies and academia to interact with European regulatory and postregulatory agencies. The goal was to increase all parties' understanding of their counterparts' roles in the development, licensure, and appraisal of new agents for cancer treatment, events guided by an understanding that cancer patients should have rapid and equitable access to life-prolonging treatments. Among the outcomes of the meeting were a greater understanding of the barriers facing drug developers in an evolving postregulatory world, clarity about what regulatory and postregulatory bodies expect to see in dossiers of new anticancer agents as they contemplate licensure and reimbursement, and several sets of recommendations to optimize patients' access to innovative, safe, effective, and fairly priced cancer treatments.

Optimal cost-effectiveness decisions: the role of the cost-effectiveness acceptability curve (CEAC), the cost-effectiveness acceptability frontier (CEAF), and the expected value of perfection information (EVPI)

OBJECTIVE

To demonstrate how the optimal decision and level of uncertainty associated with that decision, can be presented when assessing the cost-effectiveness of multiple options. To explore and explain potentially counterintuitive results that can arise when analyzing multiple options.

METHODS

A template was created, based on the assumption of multivariate normality, in order to replicate a previous analysis that compared the cost-effectiveness of multiple options. We used this template to explain some of the different shapes that the cost-effectiveness acceptability curve (CEAC), cost-effectiveness acceptability frontier (CEAF), and expected value of perfection information (EVPI) may take, with changing correlation structure and variance between the multiple options.

RESULTS

We show that it is possible for 1) an option that is subject to extended dominance to have the highest probability of being cost-effective for some values of the cost-effectiveness threshold; 2) the most cost-effective (optimal) option to never have the highest probability of being cost-effective; and 3) the EVPI to increase when the probability of making the wrong decision decreases. Changing the correlation structure between multiple options did not change the presentation of results on the cost-effectiveness plane.

CONCLUSION

The cost-effectiveness plane has limited use in representing the uncertainty surrounding multiple options as it cannot represent correlation between the options. CEACs can represent decision uncertainty, but should not be used to determine the optimal decision. Instead, the CEAF shows the decision uncertainty surrounding the optimal choice and this can be augmented by the EVPI to show the potential gains to further research.

Cost-effectiveness of live attenuated influenza vaccine versus inactivated influenza vaccine among children aged 24-59 months in the United States

BACKGROUND

The US Advisory Committee on Immunization Practices (ACIP) recently expanded the influenza vaccine recommendation to include children 24-59 months of age. In a large head-to-head randomized controlled trial, live attenuated influenza vaccine, trivalent (LAIV) demonstrated a 54% relative reduction in culture-confirmed influenza illness compared with trivalent inactivated influenza vaccine (TIV) among children aged 24-59 months.

OBJECTIVE

To evaluate the relative cost and benefit between two influenza vaccines (LAIV and TIV) for healthy children 24-59 months of age.

METHODS

Using patient-level data from the clinical trial supplemented with cost data from published literature, we modeled the cost-effectiveness of these two vaccines. Effectiveness was measured in quality-adjusted life years (QALY) and cases of influenza avoided. The analysis used the societal perspective.

RESULTS

Due to its higher acquisition cost, LAIV increased vaccination costs by USD7.72 per child compared with TIV. However, compared with TIV, LAIV reduced the number of influenza illness cases and lowered the subsequent healthcare use of children and productivity losses of parents. The estimated offsets in direct and indirect costs saved USD15.80 and USD37.72 per vaccinated child, respectively. LAIV had a net total cost savings of USD45.80 per child relative to TIV. One-way and probabilistic sensitivity analyses indicated that the model was robust across a wide range of relative vaccine efficacy and cost estimates.

CONCLUSIONS

Due to its increased relative vaccine efficacy over TIV, LAIV reduced the burden of influenza and lowered both direct health care and societal costs among children 24-59 months of age.

Gaussian process metamodeling in Bayesian value of information analysis: a case of the complex health economic model for breast cancer screening

OBJECTIVES

To determine whether allocation of resources into further research of breast cancer screening is warranted; also, to identify the parameters, for which the information would be most valuable, to prioritize the further research if deemed justifiable.

METHODS

The Bayesian value of information analysis was conducted to calculate the overall expected value of perfect information (EVPI) and the partial EVPI for the six groups of parameters. Computational expense of the partial EVPI calculation was challenged with the use of Multiple Linear Regression and Gaussian Process metamodels to significantly cut down the computing time.

RESULTS

Of the two metamodeling techniques, the Gaussian Process was proven to perform superiorly and was therefore chosen for the partial EVPI calculation. The results indicate a considerable range in the population EVPI estimates, between euro100 and euro500 millions at the willingness-to-pay values between euro10,000 and euro40,000 per quality-adjusted life-year. The partial EVPI for the groups of parameters indicated that future research would be most valuable if directed toward obtaining more precise estimates of the cancer sojourn times. With the use of the Gaussian process metamodels, the computing time was reduced from 44 years to 47 days.

CONCLUSIONS

Although the large values of EVPI suggest collection of further information before choosing the screening policy, it is argued that delaying the decision would result in significantly higher opportunity loss. Therefore, the best option would be to implement the most cost-effective policy given the existing information (screening women aged 40-80 years, at 3-year intervals) and simultaneously conduct observational studies alongside the implemented policy. The decision analytic model could be in this manner periodically updated with additional information as it became available and the most cost-effective policy chosen iteratively.

Cost effectiveness of ibandronate for the prevention of fractures in inflammatory bowel disease-related osteoporosis: cost-utility analysis using a Markov model

BACKGROUND

Osteoporosis is a frequent complication in patients with inflammatory bowel disease. Recent studies have shown bisphosphonates to considerably reduce fracture risk in patients with osteoporosis, and preventing fractures with bisphosphonates has been reported to be cost effective in older populations. However, no studies of the cost effectiveness of these agents in preventing fractures in patients with inflammatory bowel disease are available.

OBJECTIVE

To investigate the cost effectiveness of the bisphosphonate ibandronate combined with calcium/colecalciferol ('ibandronate') in patients with osteopenia or osteoporosis due to inflammatory bowel disease in Germany. Treatment strategies used for comparison were sodium fluoride combined with calcium/colecalciferol ('fluoride') and calcium/colecalciferol ('calcium') alone.

STUDY DESIGN AND METHODS

A cost-utility analysis was conducted using data from a randomized controlled trial (RCT). Changes in bone mineral density (BMD) were adjusted and predicted for a standardized population receiving each respective treatment. A Markov model was developed, with probabilities of transition to fracture states consisting of BMD-dependent and -independent components. The BMD-dependent component was assessed using predicted change in BMD from the RCT. The independent component captured differences in bone quality and micro-architecture resulting from prevalent fractures or treatment with anti-resorptive drugs. The analysis was conducted for a population with a mean age of the RCT patients (women aged 36 years, men aged 38 years) with osteopenia (T-score about -2.0 at baseline), a population of the same age with osteoporosis (T-score of -3.0 at baseline) and for an older population (both sexes aged 65 years) with osteoporosis (T-score of -3.0). Outcomes were measured as costs per QALY gained from a societal perspective. The treatment duration in the RCT was 42 months. A 5-year period was assumed to follow, during which the treatment effects linearly declined to 0. The simulation time was 10 years. Prices for medication and treatment were presented as year 2004 values; costs and effects were discounted at 5%. To test the robustness of the results, univariate and probabilistic sensitivity analyses (Monte Carlo simulation) were conducted.

RESULTS

The calcium strategy dominated the fluoride strategy. When the ibandronate strategy was compared with the calcium strategy, the base-case cost-effectiveness ratios (costs per QALY gained) were between euro 407 375 for an older female population with osteoporosis and euro 6 516 345 for a younger female population with osteopenia. Univariate sensitivity analyses resulted in variations between 4% of base-case results and dominance of calcium. In Monte Carlo simulations, conducted for the various populations, the probability of an ICER of ibandronate below euro 50 000 per QALY was never greater than 20.2%.

CONCLUSION

The ibandronate strategy is unlikely to be considered cost effective by decision makers in men or women with characteristics of those in the target population of the RCT, or in older populations with osteoporosis.

Using real-world data for coverage and payment decisions: the ISPOR Real-World Data Task Force report

OBJECTIVES

Health decision-makers involved with coverage and payment policies are increasingly developing policies that seek information on "real-world" (RW) outcomes. Motivated by these initiatives, the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) created a Task Force on Real-World Data to develop a framework to assist health-care decision-makers in dealing with RW data, especially related to coverage and payment decisions.

METHODS

Task Force cochairs were selected by the ISPOR Board of Directors. Cochairs selected chairs for four working groups on: clinical outcomes, economic outcomes, patient-reported outcomes, and evidence hierarchies. Task Force members included representatives from academia, the pharmaceutical industry, and health insurers. The Task Force met on several occasions, conducted frequent correspondence and exchanges of drafts, and solicited comments on three drafts from a core group of external reviewers and from the ISPOR membership.

RESULTS

We defined RW data as data used for decision-making that are not collected in conventional randomized controlled trials (RCTs). We considered several characterizations: by type of outcome (clinical, economic, and patient-reported), by hierarchies of evidence (which rank evidence according to the strength of research design), and by type of data source (supplementary data collection alongside RCTs, large simple trials, patient registries, administrative claims database, surveys, and medical records). Our report discusses eight key issues: 1) the importance of RW data; 2) limitations of RW data; 3) the fact that the level of evidence required depends on the circumstance; 4) the need for good research practices for collecting and reporting RW data; 5) the need for good process in using RW data in coverage and reimbursement decisions; 6) the need to consider costs and benefits of data collection; 7) the ongoing need for modeling; and 8) the need for continued stakeholder dialogue on these topics.

CONCLUSIONS

Real-world data are essential for sound coverage and reimbursement decisions. The types and applications of such data are varied, and context matters greatly in determining the value of a particular type in any circumstance. It is critical that policymakers recognize the benefits, limitations, and methodological challenges in using RW data, and the need to consider carefully the costs and benefits of different forms of data collection in different situations.

What’s More Dangerous, Your Aspirin Or Your Car? Thinking Rationally About Drug Risks (And Benefits)

We compare mortality risks of several common drugs with risks related to work, transportation, and recreation. Comparing risks can provide a more intuitive sense of the magnitude of drug risks than stand-alone estimates can, to help inform policy discussions. The drug risks we quantify generally exceed the magnitude of risks for other domains (although aspirin and cars are similarly "risky" under the definition of risk used here). Nonetheless, these comparisons underscore a crucial point: that risks should not be evaluated without considering attendant benefits. We discuss the need for the Food and Drug Administration to compare risks and benefits quantitatively, consistently, and explicitly.

Trends in cost effectiveness analyses in orthopaedic surgery

Worldwide, programs dealing with musculoskeletal health are required to set priorities and allocate resources within the constraint of limited funding. There is increasing pressure for medical technology assessment, which traditionally has involved evaluating safety and effectiveness, to also include consideration of cost effectiveness. We updated our database of orthopaedic cost-effectiveness studies, critically reviewed their methods, and examined trends over time. Current analyses have numerous shortcomings, such as the inclusion of relatively few studies, inconsistent methodologic approaches, and lack of transparency. The wide variation in cost-effectiveness ratios observed among current interventions suggests efficiency can be improved. Despite reimbursement authorities in many other countries formally considering cost-effectiveness when determining coverage of new technologies, Medicare has been resistant to considering costs of treatments. Regardless of this policy deficiency, conducting cost-effectiveness analyses represents a prudent step forward in illuminating the tradeoffs involved in difficult resource allocation decisions, and there is an urgent need to consider economic impact in future studies using standardized and transparent methods.

Comparing the clinical and economic effects of clinical examination, pulse oximetry, and echocardiography in newborn screening for congenital heart defects: A probabilistic cost-effectiveness model and value of information analysis

Objectives: Congenital heart defects (CHD) are an important cause of death and morbidity in early childhood, but the effectiveness of alternative newborn screening strategies in preventing the collapse or death—before diagnosis—of infants with treatable but life-threatening defects is uncertain. We assessed their effectiveness and efficiency to inform policy and research priorities.

Methods: We compared the effectiveness of clinical examination alone and clinical examination with either pulse oximetry or screening echocardiography in making a timely diagnosis of life-threatening CHD or in diagnosing clinically significant CHD. We contrasted their cost-effectiveness, using a decision-analytic model based on 100,000 live births, and assessed future research priorities using value of information analysis.

Results: Clinical examination alone, pulse oximetry, and screening echocardiography achieved 34.0, 70.6, and 71.3 timely diagnoses per 100,000 live births, respectively. This finding represents an additional cost per additional timely diagnosis of £4,894 and £4,496,666 for pulse oximetry and for screening echocardiography. The equivalent costs for clinically significant CHD are £1,489 and £36,013, respectively. Key determinants of cost-effectiveness are detection rates and screening test costs. The false-positive rate is very high with screening echocardiography (5.4 percent), but lower with pulse oximetry (1.3 percent) or clinical examination alone (.5 percent).

Conclusions: Adding pulse oximetry to clinical examination is likely to be a cost-effective newborn screening strategy for CHD, but further research is required before this policy can be recommended. Screening echocardiography is unlikely to be cost-effective, unless the detection of all clinically significant CHD is considered beneficial and a 5 percent false-positive rate acceptable.

A typology of health marketing research methods--combining public relations methods with organizational concern

Research plays a critical role throughout virtually every conduit of the health services industry. The key terms of research, public relations, and organizational interests are discussed. Combining public relations as a strategic methodology with the organizational concern as a factor, a typology of four different research methods emerges. These four health marketing research methods are: investigative, strategic, informative, and verification. The implications of these distinct and contrasting research methods are examined.

Challenges Ahead For Federal Technology Assessment

Sean Tunis and colleagues provide an excellent critique of current federal activities to assess new medical technology. These efforts generally do not involve primary data collection but rather reflect attempts to better synthesize existing information, to make conditional coverage decisions based on the data, and to increase coordination among government agencies. Many challenges remain on analytical, logistical, legal, and political fronts. Researchers and analysts should be more precise about what "rapid learning" means and strive to measure performance. Efforts are also needed to prioritize research, to communicate it to decision-makers, to involve stakeholders in the process, and to include cost-effectiveness information.