Journal of Surgical Oncology 2014;109:509–515

REVIEW Making the Case for Cost-Effectiveness Research DANIEL E. ABBOTT, MD,* JEFFREY M. SUTTON, MD, AND MICHAEL J. EDWARDS,

MD

Department of Surgery, University of Cincinnati, Cincinnati, Ohio

Cost‐effectiveness research is a component of clinical outcomes that addresses both cost and outcomes simultaneously, providing an understanding of what incremental costs, if any, are required for better clinical outcomes. In the current health care climate, these analyses are increasingly performed, and critical, as practitioners must optimize patient care at lower costs. This review discusses cost effectiveness research, its utilization in surgical oncology, and future opportunities provided by its methodologies.

J. Surg. Oncol. 2014;109:509–515. ß 2013 Wiley Periodicals, Inc.

KEY WORDS: comparative; effectiveness; cost effectiveness; outcomes research

INTRODUCTION Clinical outcomes research has grown exponentially over the last decade for many reasons. Firstly, the initial investment and commitment, many years ago, in collecting patient data in properly maintained databases (e.g., American College of Surgeons National Cancer Data Base) is increasingly recognized and used as datasets mature. Secondly, prospective randomized clinical trials have become more difficult to complete, for a variety of reasons: difficulties with accrual (especially in the surgical realm due to both provider and patient bias), the often‐ prohibitive financial expense required to conduct them, and the rarity of many disease processes that, despite even multi‐institutional efforts, do not provide adequate sample sizes. Thirdly, increasing bureaucracy and health care costs in our country have demanded that more immediate, effective, and less costly answers be given to our everyday clinical questions. Lastly, the economic climate has dramatically decreased funding for both basic‐science research and large‐scale clinical trials. Cost‐effectiveness, discussed here, is one of the disciplines that fall under the “outcomes” umbrella, helping to answer the questions of who should be performing interventions, where such interventions should be done, when treatments/interventions are most appropriate, and what exactly is the best intervention for a given clinical problem. Who should provide care, more pertinent to technical specialties (e.g., surgery), has led to an entire body of literature. It is clear that high volume surgeons, particularly when performing complex, less common procedures, have better outcomes than surgeons who perform such procedures only occasionally [1–3]. But determining the characteristics of that surgeon, and which procedures that requisite experience applies to, is an entire field of study [4–6]. While ensuring optimal outcomes, there cannot be discrimination against the majority of surgeons who perform well. Mal‐alignment of these realities will lead to poor patient outcomes and/or attrition of qualified surgeons. Where medical care is delivered is equally important. While not entirely aligned with expertise of practitioners (e.g., there are high‐ quality providers at low‐quality hospitals and vice‐versa), there is frequently a correlation between high‐quality practitioner and high‐ quality hospital. This distinction is important so as to (1) not penalize either provider or hospital for a difference in quality between the two, and (2) demonstrate to patients where care should be provided. Just as in the case of who should provide care, there is an entire body of work about

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where care should be provided [2,7–10]. Significant resources and expertise, in the form of the American College of Surgeons National Surgical Quality Improvement Program and the Leapfrog Group, to name a few, continue to guide our understanding of hospital quality and outcome [11–14]. Metrics that define hospital quality are continuously being recognized and refined, and this effort is resulting in transparency and quality that will continue to improve patient safety and outcomes [15–18]. Additionally, there is increased awareness that the timing of an intervention should be more carefully scrutinized [19–22]. When, for example, should urologists be performing prostatectomy, if ever, for prostate cancer—a frequently indolent malignancy?[23,24] And for particularly aggressive diseases such as pancreatic or gastric cancer, the timing of treatment becomes increasingly complex as multimodality therapy (radiation therapy and systemic chemotherapy) is incorporated into treatment planning [25–28]. Traditionally a surgery first approach for many malignancies has been advocated (largely by surgeons) but there are emerging data that—in terms of both outcome and cost—the use of pre‐operative chemotherapy and/or radiation can improve outcomes [27–30]. Lastly, what intervention, whether medicinal or operative, has long‐ been the focus of both basic science investigations and clinical trials. But the answer of what therapy to administer is becoming increasingly difficult to answer. There are ever more pharmaceutical drugs (often times marginally different than predecessors) flooding markets, frequently without objective head‐to‐head comparisons that guide management decisions in an unbiased fashion. Public funding for novel bench work and the success of applicants for these basic science dollars continues to decline, hampering efforts to develop therapies that may

The authors have no financial support or disclosures to report. *Correspondence to: Daniel Abbott, MD, 234 Goodman St, ML 0772, Cincinnati, OH 45219. Fax: þ513‐584‐0459. E‐mail: [email protected] Received 30 August 2013; Accepted 28 November 2013 DOI 10.1002/jso.23543 Published online 24 December 2013 in Wiley Online Library (wileyonlinelibrary.com).

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dramatically alter disease processes [31]. And finally, the costs of any intervention—medical or surgical—continues to rise [32–35]. Ultimately, robust, high quality, and resource‐efficient clinical research pursuits need to be increasingly prominent in addressing these clinical questions. But there are significant hurdles in discovery and implementation, as society has become accustomed to having access to any intervention—no matter the cost. And, unfortunately, generally speaking, patients and the public are not ready to meaningfully address this issue [36,37] (Abbott, unpublished data). Thus, there is a tremendous opportunity, and need, to demonstrate—in a scientific manner—how to optimally deliver quality and cost‐effective health care.

COMPARATIVE AND COST EFFECTIVENESS RESEARCH Comparative and cost‐effectiveness research focuses on evaluating two (or more) treatment strategies or interventions to establish an optimal course of action. These disciplines rigorously evaluate existing data with the goal of providing guidance for clinical scenarios in which the literature has not provided a clear answer. Furthermore, these types of investigations fill the void of infeasible clinical trials and address fiscal realities, in that the financial resources required for these studies are generally small. In comparative‐effectiveness research, various interventions are evaluated and differences in a single outcome are quantitated. This single outcome can take many forms, depending on the genre of medicine. A cardiologist may perform a study comparing two different statins, using low‐density lipoprotein (LDL) levels as the final metric of effectiveness [38,39]. A medical oncologist may study various chemotherapeutic interventions for breast cancer—based on previously published randomized clinical trials—and use survival as the primary endpoint [40]. Finally, a surgical oncologist may perform a comparative‐effectiveness study investigating the use of various treatment modalities—for example rectal resection with and without the use of radiation therapy—and incorporate quality of life adjustment into the survival analysis. Fortunately, there is growing national enthusiasm for comparative effectiveness, reflected in recent increases in federal funding (Affordable Care Act) for this exact kind or research [41–43]. A modification of comparative‐effectiveness research introduces cost into these analyses—hence the term cost‐effectiveness research. These studies may be more or less sophisticated than purely comparative studies, but include a cost per outcome for any specific intervention. Increased interest in cost‐effectiveness research is borne from the recognition that health care costs are continuing to rise at an unsustainable rate, and that difficult choices need to be made about health care delivery [44–46].

Why Cost‐Effectiveness Research is Important The impacts of escalating health care costs affect every American, regardless of age, gender, health state, or political affiliation. The realities of these costs can be generally described in three ways—the impact on public health care payers within federal and state governments (e.g., Medicare and Medicaid), the impact on private insurers and those who pay into them, and individuals who may or may not have health insurance. Health care expansion at its current rate will become essentially 100% of GDP by the end of the century (as estimated by the Congressional Budget Office) [47], and this spending is incurred by every taxpayer. Furthermore, private insurer premiums continue to rise such that both employers and employees must spend an increasingly disproportionate percentage of their resources on health insurance. And finally, the impacts of these increases are felt profoundly by individuals; medical bills are by far the most common reason for personal Journal of Surgical Oncology

bankruptcy. Interestingly, those individuals bankrupted by medical care are more likely to be college graduates, have private insurance, be homeowners, and be employed; these are not just indigent or poverty‐ stricken members of our populace who are affected [48–50]. But despite general agreement among many practitioners and policy makers that health care spending needs to be meaningfully addressed, there are significant barriers to implementation of cost‐cutting strategies. First, and perhaps most significantly, the American public is overwhelmingly not willing to accept limitations for health care spending. We have recently surveyed over 400 patients and family members/friends at The University of Texas MD Anderson Cancer Center about their opinions on how much should be spent in various scenarios. Strikingly, over 1/2 of respondents believed that there should be no limit on health care spending for 1 year of their life, even in an incurable situation (Abbott, unpublished data). This attitude is clearly not compatible with sustainable health care delivery, and represents the moral hazard of unrealistic spending on “self” that cannot be applied to “others.” Secondly, billions of dollars, and the fiscal health of those in the health care industry—insurers, hospitals, pharmaceutical companies, device makers, etc.—have a vested (and powerful) interest in increasingly “more” healthcare. While there are substantial data to refute the argument that “more” is better, and in fact can lead to worse outcomes, the deep pockets and tremendous influence of these participants at the policy level make meaningful health care reform increasingly difficult [51–54]. Unfortunately, cost‐effectiveness research is generally underfunded, in part because while the federal government and the NIH have recently apportioned a significant sum of money for comparative‐effectiveness research, implementation of the results of cost‐effectiveness research are strictly forbidden by the funding sources (an example of the lack of political will) [55,56]. Additionally, the quality of cost‐effectiveness research varies significantly, despite concerted efforts from experts and leaders in the field to standardize research methodologies [57,58]. And lastly, an accurate cost component of cost‐effectiveness research is often difficult to obtain. Whether from Medicare/Medicaid, insurance claims, or other public sources of cost data, there are frequently discrepancies (and outright confusion) between charges, costs, etc. Recognizing and overcoming these barriers to reforming health care spending are critical. Some may argue that medical care is the most important expenditure that exists, and that increased taxation and out‐of‐ pocket payment for this “commodity” is completely appropriate. Most, though, would agree that current trends cannot be maintained indefinitely. Herein lies the opportunity for researchers, especially clinicians interested in policy shifts for health care delivery. The discipline of surgery, in particular, with its disproportionately high contribution to all health care expenses is both a target and an opportunity for cost‐effectiveness research and rationale implementation of fiscally and ethically responsible guidelines [35].

Techniques in Cost‐Effectiveness Research Model types. As with any research methodology, there are varying levels of sophistication within cost‐effectiveness research. Below we describe a few of the major model types that are traditionally used for these types of analyses, though this list is in no way comprehensive, and does not fully explain the complexity that can—and should—be included to make a cost‐effectiveness investigation sound. The simplest model is that of a “flat” tree (named for the many “branches” that result as a model is fleshed out), which incorporates a number of branching points representing subsequent outcomes and their attendant probabilities whenever different clinical outcomes are possible. While this model can incorporate both cost and outcome, this linear modeling does not incorporate inherent changes in the study

Cost‐Effectiveness Research population over time. An example of a part of a simple decision tree is shown in Figure 1 [30]. A more sophisticated cost‐effectiveness approach is found in Markov modeling. A Markov decision tree not only incorporates potential clinical outcomes and costs, but includes changes in health states that inevitably occur. By modeling transitions between health states over time, with their attendant quality of life, cost and survival differences, a much more accurate, long‐term assessment of cost‐effectiveness can be made (Fig. 2) [59]. Monte Carlo simulation is yet another technique that can either be used within other models, or as a stand‐alone method. In a Monte Carlo simulation, a model is constructed and “run” with many (thousands or even millions) of hypothetical and random patients working through various potential clinical courses. This technique can highlight changes in the distributions of outcomes that may dramatically impact the conclusions or policy implications of the study. Metrics and robustness of modeling. The most common metrics that result from cost‐effectiveness are (1) cost and (2) survival (preferably quality‐of‐life adjusted), though other outcomes such as presence or absence of disease may be the final outcome measured. When cost and survival, for example, are considered together, a number of results are possible. One intervention may be both less costly and associated with better outcomes, in which case the better strategy/intervention is clearly most cost‐effective, while the other is “dominated,” or clearly inferior. Frequently, however, one intervention is more effective but also costs more. Consider the situation of two interventions for a given clinical problem. Strategy A costs $100 to gain 3 quality‐adjusted life years (QALYs) while strategy B costs $200 to gain 5 QALYs. This situation allows the investigator to determine an incremental cost‐effectiveness ratio (ICER), calculated by dividing the differences in cost ($200  $100) by the differences in survival (5  3 QALYs). The ICER for strategy B would then be $50/QALY, and in graphical terms is the slope of the line between two points on a cost‐effectiveness plot (Fig. 3) [60]. The challenge of ICERs is not necessarily calculating them, but rather the implications of their numbers. How much is 1 quality adjusted life year worth in the United States? Despite attempts to define this (e.g., what Medicare will pay for 1 year of dialysis), societal “willingness to pay” has not been agreed upon. This lack of direction by public policy makers to resolutely define what the upper limit of payment should be for a given situation is a hindrance to meaningful implantation of many cost‐effectiveness analyses.

Fig. 1. Simple “flat” tree demonstrating potential outcomes after bringing a patient to the operative room for pancreatic resection. Red triangles represent “terminal” nodes (no further branching points) while green circles represent “chance” nodes, that further branch until a terminal node is reached. Journal of Surgical Oncology

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Fig. 2. An example of a Markov model investigating various surveillance strategies following pancreatic resection. The second column of text outlines the 4 distinct health states of the model: alive with no detected recurrence, alive with recurrence on treatment, alive with recurrence not on treatment and dead.

Lastly, the notion of sensitivity analyses deserves special mention. In many cases, the probabilities of outcomes and certain costs may be vague or uncertain. In these cases, a best estimate is used as the base case —the most likely value that is used initially. However, with the potential variability or uncertainty that remains around that point estimate, different probabilities or costs must be entertained, and inputting this spectrum of variability tests the robustness of our conclusions. If the perioperative mortality in strategy A is changed from 3% to 9%, how does that change our conclusions? If the cost of surgery for a different strategy doubles depending on insurer type or hospital, are our results still valid? These sensitivity analyses can become quite sophisticated by addressing variability with different statistical distributions, but suffice it to say that no cost‐effectiveness analysis is complete without testing the model with thoughtful sensitivity analysis, of any kind.

Cost‐Effectiveness Research in Surgical Oncology A majority of cost‐effectiveness studies have been performed in non‐ surgical disciplines [61–66]. This is likely due to the acceptance of these research methods by non‐surgeons at an earlier time and the paucity of surgeons who have become experts in this field. Furthermore, when medications—a discrete and quantitative intervention—can be easily

Fig. 3. Cost‐effectiveness plot with survival on the x‐axis and cost on the y‐axis, comparing various treatment strategies for potentially resectable pancreatic cancer. The ICER (incremental cost‐effectiveness ratio) is the slope of the line between the “surgery and adjuvant therapy” strategy and the “chemotherapy only” strategy.

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accounted for and ascribed a cost, analysis is often simpler. The challenge of cost‐effectiveness in the surgical realm is the frequency of hospital admission, complication rates, and the lack of clarity about the costs associated with these data points. Within all of surgery, surgical oncology is unique in cost‐ effectiveness research for a number of reasons. Firstly, it combines two of the most expensive disciplines found in all of health care delivery—administration of medicines (and/or radiation) for cancer, and surgical intervention [35,46,67,68]. Secondly, a high percentage of patients with malignancies die, bringing to the fore the moral dilemma of life worth. Lastly, patients (and family members of those) who are dying frequently base decisions on hopes and promises that may not even be considered under different circumstances. The result of these three forces is frequently expensive, sometimes efficacious care that may or may not have any impact on duration or quality of life. But this last fact is precisely why cost‐effectiveness in surgical oncology is so important. For common diseases, the relative benefit of a particular intervention for a particular cohort is almost always known [69–71]. Additionally, with cooperation from pharmaceutical companies and hospitals, the cost of such interventions can also be known. If these two data fields are aligned, expertly and responsibly, the power of the results should directly influence health care policy and delivery. Consider the sequence of diagnosis and therapy for a given disease. This, seemingly, is simple. Make the diagnosis, ensure the patient is appropriately selected and fit for the intervention, perform the intervention, and survey the patient to find recurrence, if any. But within each of these steps, there are a myriad of decisions that directly impact patient time and expense. Are all tests ordered at once? Do the results of one test determine whether another is appropriate, and if so what are the added costs of sequential, rather than simultaneous, testing? And what is the best sequence of treatments if multidisciplinary care is required? [72] Finally, how frequently should surveillance be performed, if at all? Optimally, each major decision in the course of treating a patient with a particular cancer would be based on high quality cost‐effectiveness research. And while each disease process is spotted with some cost‐effectiveness studies, the sphere of knowledge that considers both outcomes and costs is remarkably incomplete. There are some examples in the literature of cost‐effectiveness research pertaining to the first tenet of surgical oncology—making the diagnosis. At The University of Texas MD Anderson Cancer Center, we reviewed our experience with the sequence of macrosatellite instability (MSI) and immunohistochemistry (IHC) testing to make the diagnosis of Hereditary Non‐Polyposis Colorectal Cancer (HNPCC) (unpublished data). By comparing five different sequences, we determined that IHC testing followed by MSI analysis was the most effective, least costly strategy to detect HNPCC. Others have examined the cost‐effectiveness of repeat FNA for thyroid lesions initially read as atypia of undetermined significance, finding that repeat FNA as dictated by NCI recommendations is cost‐effective until the cost of FNA increases to $6,091 [73]. And still others, have examined the cost‐ effectiveness of colorectal cancer screening. Sonnenberg et al. [74] found that colonoscopy is a very cost‐effective strategy when compared to fecal occult blood testing or flexible sigmoidoscopy, costing approximately $11,000 per life year saved when compared to non‐procedural screening. Staging and localizing studies too have been studied with respect to cost‐ effectiveness. Our research group found that the addition of 4D‐CT to more standard localizing imaging techniques for primary hyperparathyroidism adds cost but does not decrease failure rates substantially [75]. In prostate cancer, Dutch researchers have shown that for patients who are at intermediate or high risk for harboring nodal metastases, MR lymphography was the least costly technique for accurate staging [76]. Melanoma researchers have evaluated the cost‐effectiveness of ultrasound guided FNA of regional lymph nodes as opposed to sentinel lymph node biopsy, and suggest that FNA is a more cost‐effective approach [77]. And Journal of Surgical Oncology

another interesting report has shown that replacing sentinel lymph node biopsy with either PET or MRI may be the most cost‐effective strategy to stage the axilla, though the sensitivity and specificity of MRI must be better understood to translate this finding into clinical practice [78]. As one would intuitively suppose, a significant proportion of the cost‐ effectiveness literature in surgery has focused on treatment. These studies include recommendations for sequence of therapy, stage‐specific treatment, and extent of therapy. A group from Massachusetts General Hospital determined that radiofrequency ablation (RFA) for Barrett’s esophagus (BE) was not cost‐effective for BE without dysplasia, but is more effective and less costly than surveillance for high‐grade dysplasia [79]. In breast cancer, evaluation of adding a boost to tangential radiation therapy after lumpectomy with negative margins came at a cost of over $300,000 per quality adjusted life years (QALYs), well above the commonly cited threshold of $50,000/QALY in the United States [80]. In esophageal cancer, researchers from the United Kingdom considered treatment, cost and quality of life, and determined that resection is at least as cost‐effective, if not more, than other non‐ surgical approaches [81]. Pancreatic cancer, a particular interest of our center and research group, is an excellent model for cost‐effectiveness analysis since interventions for this disease are expensive, and outcomes are uniformly poor. This nihilism prompted an evaluation of various treatment strategies including surgery, radiation therapy, and chemotherapy with respect to cost and outcome. Our analysis demonstrated that surgery plus adjuvant therapy resulted in a cost of $7,663 per quality adjusted life month when compared to no treatment, a cost that is likely acceptable in the current climate [60]. A second analysis, also from our research group, examined the sequence of therapy for pancreatic cancer. When comparing a surgery‐first approach to neoadjuvant therapy followed by surgery, and incorporating rates of failure to complete therapy in each arm, the neoadjuvant strategy was both more effective and less costly than a surgery‐first approach [30]. A recent study from Stanford evaluated four treatment strategies for the treatment of locally advanced pancreatic cancer using Markov modeling. When evaluating different permutations of chemotherapy and radiation therapy, the authors determined that stereotactic body radiotherapy (SBRT) plus gemcitabine demonstrated a cost‐effectiveness profile that is potentially acceptable by current societal standards, but that intensity‐modulated radiotherapy (IMRT) was not advisable, being both more expensive and less effective than SBRT [82]. Complications are an important component of cost‐effectiveness research, as the inherent repercussions of adverse events—poorer survival, worse quality of life, and increased costs—can profoundly impact the cost‐effectiveness profile of any intervention(s) [18,83]. Some current data address this issue directly. The Accordion Severity Grading System, in use at the Virginia Mason Medical Center in Seattle, is an ongoing effort to capture the impact of complications (and overall quality) on cost to provide real time feedback on areas for improvement. At their institution, of 285 esophageal cancer patients undergoing resection, a higher Accordion grade was significantly related to length of stay and cost [84]. John Birkmeyer [85] and his renowned group at the University of Michigan recently published their work demonstrating that—based on CABG, hip replacement, AAA repair, and colectomy—Medicare payments were substantially higher at higher complication hospitals. While intuitive, these are tangible, hard data that clearly show the need— and opportunity—in decreasing complications and decreasing cost. But more granular work needs to be done at the community, city and state level to determine exactly which patients, centers, and procedures are appropriately matched. Surveillance, with its substantial resources required for imaging, visits and subsequent treatment is a final topic that is especially ripe for cost‐effectiveness analysis. We have addressed this issue for pancreas cancer, using a Markov model to compare five surveillance strategies following pancreaticoduodenectomy. Our analysis revealed that 6‐

Cost‐Effectiveness Research month surveillance with CT imaging only for elevated Ca 19‐9 or abnormal signs/symptoms in the office was the most cost‐effective strategy, and that more intense surveillance added significant cost with essentially no clinical benefit [59]. This type of analysis has been performed in colon cancer surveillance as well, demonstrating that carcinoembryonic antigen (CEA) assessment was the most cost‐ effective test when compared to physical exam, chest X‐ray, and colonoscopy; the costs per resectable recurrence were $5,696, $10,078, and $45,810 for CEA testing, chest xray and colonoscopy, respectively. Interestingly, physical exam did not detect a single recurrence [86]. And finally, innovative means of post‐operative patient visits are being studied with respect to cost‐effectiveness. From a single surgeon experience in Wisconsin, tele‐rounding saved patients, on average, $357 and 119 miles traveled with no adverse events from not having an in– person physician visit [87]. Limitations. There are, of course, limitations to cost‐effectiveness research in its current form. Accurate cost data, for one—whether from an institutional or societal perspective—is continually problematic. Medicare reimbursement data, for example, are publicly available and reliable, but generally apply only to patients over 65 and are considered very low compared to “real world” private payer reimbursement. Unfortunately, hospitals and insurance companies alike are reticent to share their fiscal information, considering such data strategically important to their business plan. Other methodologic issues in comparative and cost‐effectiveness research also deserve mention. Markov modeling, for example, is heavily reliant on time‐specific rates, outcomes and costs, and many of these often occur far in the future. Such granular detail is often times not available and must be estimated, which may lead to inaccurate conclusions, particularly if a long‐term time horizon is being modeled. Furthermore, much of the data used in this modeling are based on aggregate statistics which may be ignorant of important patient‐ specific disease characteristics or utilities. Awareness of this limitation, and attempts to address it by detailed and meticulous modeling, is critical for the integrity of the research.

FUTURE DIRECTIONS Clearly, cost‐effectiveness research in surgery is just beginning. More sophisticated analysis with larger and more mature datasets will, and need to be, performed. For every study discussed in this review, there are follow‐up studies that should be done to expand on and validate their findings. Examples of important future endeavors include assessing the costs of recommendations from guidelines such as those set forth by the National Comprehensive Cancer Network, or those associated with the introduction of new pharmaceutical agents. Once cost is considered, comparing various diagnostic, treatment, and surveillance strategies for specific disease sites will be a responsible and useful contribution. The field should also perform a more granular examination of individual institutions and practitioners, determining where and who can provide the least costly, most effective care. At our institution, we are beginning to evaluate surgeons and hospitals on a city and statewide bases. Specifically, our research group is interested in demonstrating the cost and complication implications of doing moderately complex gastrointestinal complications at high versus low volume hospitals. Using our analysis, we will be able to inform payers—both private and public—about the most appropriate matching of hospital and practitioner for a given procedure. Furthermore, these data should provide patients with unbiased evidence about where care should be pursued. Yet another opportunity in cost‐effectiveness research lies in analyzing specific subsets of patients to determine which patient populations are served in the most cost‐effective manner. The CRYSTAL trial, demonstrating that only K‐ras wild‐type patients benefit from cetuximab, is an excellent example of such personalized Journal of Surgical Oncology

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diagnostics and therapeutics [71]. As clarity increases about the molecular profile of tumors, the evaluation of the costs of these assays, and their implications on subsequent treatment, provide a new horizon for cost‐effectiveness research. Including demographics such as age, comorbidities, and expected quality of life into such analysis should also refine our understanding of what our society acknowledges as cost‐ effective therapy. This progress, however, will only be made by addressing the aforementioned limitations. More accurate cost estimates for these kinds of analyses will be dependent on increased transparency by both hospitals and insurance companies, and this sharing of data must be demanded at the public policy level. An additional benefit of such a willingness to share what is often deemed “proprietary” information will be the incorporation of patient‐specific data, such as specific tumor markers and treatment information, that will minimize the bluntness of using aggregate data. And perhaps most importantly, it will take a concerted effort by all parties involved to ensure that attempts at cost‐ savings do not stifle important research and innovation investments. Progress in health care delivery will be made on many fronts, and over‐ zealous cost‐effectiveness researchers must not hamper that advancement. In summary, cost‐effectiveness research is a small but increasingly important aspect of clinical outcomes research. This discipline has been established in many other genres, but is relatively new to the practice and evolution of surgery. As increasing awareness and recognition of the fiscal crisis looming over health care permeates surgery, cost‐ effectiveness research needs to be funded, expertly performed, and most importantly implemented to ensure health care delivery is as efficient and cost‐conscious as possible.

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Making the case for cost-effectiveness research.

Cost-effectiveness research is a component of clinical outcomes that addresses both cost and outcomes simultaneously, providing an understanding of wh...
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