Decision theory in cancer therapy

Cost-Effectiveness of Continuous Support From a Layperson During a Woman's First Two Births

OBJECTIVE

To evaluate the cost-effectiveness and health outcomes related to continuous support from a layperson during a woman's first two births in a theoretical population.

DESIGN

Cost-effectiveness analysis.

PARTICIPANTS

A theoretical cohort of 1.2 million women based on an approximation of annual low-risk, nulliparous, term, singleton births in the United States with the assumption that these women have second births. This reflects the average number of births per woman in the United States.

METHODS

We designed a cost-effectiveness model to compare outcomes in women with continuous support from relatives, friends, or community members with minimal to no training (excluding trained doulas) during labor and birth compared with outcomes for women with no continuous support. Outcomes included mode of birth, uterine rupture, hysterectomy, maternal death, cost, and quality-adjusted life years (QALYs). We derived probabilities from the literature and set a cost-effectiveness threshold at $100,000/QALY.

RESULTS

In this theoretical model, continuous support by a layperson during the first birth resulted in fewer cesarean births, decreased costs, and increased QALYs for the first and subsequent births. Women with support from laypersons had 71,090 fewer cesarean births, 35 fewer uterine ruptures, 9 fewer hysterectomies, and 16 fewer maternal deaths, which saved $364 million with 2,673 increased QALYs. Sensitivity analyses showed that continuous support in the first birth was cost-effective even when varying the estimate of lost wages of the support person up to $708.

CONCLUSION

Continuous labor support from a layperson leads to fewer cesarean births, improved outcomes, decreased costs, and increased QALYs. This highlights the need to increase women's access to continuous layperson support during labor and birth uninhibited by financial and institutional barriers.

Applying evidence to patient care: from population health to individual patient values

What are the health status and health needs of people with epilepsy? How do clinicians and patients choose between alternative interventions for the same condition? Are health interventions used effectively in the community, and do they improve health? How can we translate findings from regulatory clinical trials to the real world? These and similar questions are the subject of applied translational research. This evolving and broad-ranging area of research involves the application of basic sciences such as epidemiology, biostatistics, economics, and behavioral science to the assessment of health, health interventions, and outcomes. However, despite its palpable importance, applied translational research remains underfunded and underutilized. Using their own innovative research as a prototype, two young and promising investigators provide insights not only into the enormous potential but also the gaps and hurdles of two specific areas of applied translational research, i.e., clinical decision analysis and health services research. The message is clear that if we are to understand and improve the health of people with epilepsy in clinics, hospitals, and communities, we must substantially increase research capacity to address the many gaps that thwart our progress in applied research in epilepsy.

Adding decision models to systematic reviews: informing a framework for deciding when and how to do so

OBJECTIVE

Decision models are sometimes used alongside systematic reviews to synthesize evidence. Clarity, however, is lacking about when and how to conduct modeling studies in tandem with systematic reviews, as well as about how to evaluate and present model results. The objective of this study was to collect and analyze information from various sources to inform the development of a framework for deciding when and how a decision model should be added to a systematic review.

METHODS

We collected data through 1) review and analysis of evidence reports that used decision models; 2) review and synthesis of current best practices for the development of decision models; 3) interviews of Evidence-Based Practice Center directors and selected staff, United States Preventive Services Task Force members, and decision modelers who developed models used by the United States Preventive Services Task Force; and 4) a focus group of expert modelers.

RESULTS

Models are well suited to address gaps in the literature, better suited for certain types of research questions, and essential for determining the value of information relating to future research. Opinions differ regarding whether model outputs constitute evidence, but interviewees expressed concern over the lack of standards and directions in grading and reporting such "evidence." Interviews of stakeholders and modelers revealed the importance of communication and presentation of model results as well as the importance of model literacy and involvement of stakeholders.

CONCLUSIONS

The study demonstrates the need for a framework for deciding when and how to use models alongside systematic reviews and provides information to develop such a framework.

Assessing the benefit: risk ratio of a drug--randomized and naturalistic evidence

Randomized evidence from clinical trials and naturalistic evidence collected from pharmacoepidemiology and pharmacovigilance activities both contribute to the initial and continuous assessment of the benefits and risks of a drug, ie, the balance between therapeutic efficacy and safety risks. Benefit-risk assessment (BRA) mainly relies on a qualitative assessment of quantitative data. Current attempts to quantify BRA are reviewed and discussed, along with the expectations of regulatory authorities such as the Food and Drug Administration and the European Medicines Agency. No method provides a fully satisfactory solution regarding BRA, because it is difficult to reduce its multidimensional aspect to simple metrics, in a context where other therapeutic alternatives play a role. Consistency and transparency are key in this assessment, which is performed throughout the whole drug life cycle. BRA is mainly based on randomized clinical studies during clinical development, and it is continued and consolidated by naturalistic data once the drug is on the market.

Decision making in head and neck cancer care

OBJECTIVES/HYPOTHESIS

To describe patterns of patient involvement in head and neck cancer decision making.

STUDY DESIGN

Prospective longitudinal ethnography of otolaryngology patients making treatment decisions.

METHODS

Grounded theory analysis of verbatim transcripts and original voice recordings from: 1) participant-driven diaries, 2) participants' office visits with their physicians, and 3) semistructured interviews completed after a treatment decision had been made.

RESULTS

Patients with serious illness and experiencing considerable pain, discomfort, or alteration in the ability to perform activities of daily living, and who fear for their life, do not make decisions in a way that adheres to the conventional model of decision making, which presumes a sequential, office-based interaction with clear patient autonomy. These patients have the ability to interpret information they receive during office visits, but they describe making a treatment decision as "deciding to do something" not choosing a specific treatment. This group also describes "trust" or "confidence" in the physician as the most important factor in making a decision, not the type or amount of information received. They move through providers toward treatment in a linear fashion, from one physician specialty to the next, usually without doubling back to revisit previous decisions or discussions.

CONCLUSIONS

Decision making in serious illness unfolds differently than in less serious problems. The conventional model does not fit this patient population, and reliance on trust of the physician figures prominently. Decision support should be aimed at physician decision making, promoting explicit incorporation of patient-specific data into the process.

The use of clinical utility assessments in early clinical development

A quickly realizable benefit of model-based drug development is in reducing uncertainty in risk/benefit, comprising individually of safety and effectiveness, two key attributes of a product evaluated for regulatory approval, marketing, and use. In this review, we investigate gaps and opportunities in using fundamental decision analytic principles in drug development and present a quantitative clinical pharmacology framework for the application of such aids for early clinical development decision making. We anticipate that implementation of such emerging tools will enable sufficient scientific understanding of the two attributes to facilitate the early termination of compounds with less than desirable risk/benefit profiles and continuance of compounds with acceptable risk/benefit profiles.

Decision analysis and Markov modeling in urology

PURPOSE

The process of decision making in medicine has become increasingly complex. This has developed as the result of increasing amounts of data, often without direct information or answers regarding a specific clinical problem. The use of mathematical models has grown and they are commonly used in all areas. We describe and discuss the application of decision analysis and Markov modeling in urology.

MATERIALS AND METHODS

We define decision analysis and Markov models, providing a background and primer to educate the urologist. In addition, we performed a complete MEDLINE database search for all decision analyses in all disciplines of urology, serving as a reference summarizing the current status of the literature.

RESULTS

The review provides urologists with the ability to critically evaluate studies involving decision analysis and Markov models. We identified 107 publications using decision analysis or Markov modeling in urology. A total of 36 studies used Markov models, whereas the remainder used standard decision analytical models. All areas of urology, including oncology, pediatrics, andrology, endourology, reconstruction, transplantation and erectile dysfunction, were represented.

CONCLUSIONS

Decision analysis and Markov modeling are widely used approaches in the urological literature. Understanding the fundamentals of these tools is critical to the practicing urologist.

Predictive value of fetal pulmonary venous flow patterns in identifying the need for atrial septoplasty in the newborn with hypoplastic left ventricle

BACKGROUND

Pulmonary venous Doppler (PVD) flow patterns in the fetus with hypoplastic left heart syndrome (HLHS) have been correlated with restrictive interatrial communication or intact atrial septum (RAS) postnatally; however, the ability of PVD to identify the neonate requiring emergent atrial septoplasty (EAS) for severe left atrial hypertension and hypoxemia has not been critically evaluated. It was the purpose of this study to determine the predictive power of fetal PVD in identifying the need for EAS in newborns with HLHS and RAS.

METHODS AND RESULTS

Forty-one patients with fetal PVD flow analysis and postnatally confirmed HLHS were studied. Pulsed-wave assessment of PVD flow included S-, D-, and A-wave velocity, time-velocity integral (VTI) of forward and reverse flow, and S/D velocity and forward/reverse VTI ratio. Neonatal EAS was used as the primary clinical outcome variable. Receiver operating characteristic curves were used to determine cutpoints at which PVD indices best predicted EAS. Cutpoints were evaluated for clinical accuracy and usefulness by use of Bayesian analysis. Eight of 41 subjects underwent EAS. Need for EAS was most accurately predicted by forward/reverse VTI ratio <5 (sensitivity, 0.88, 95% CI, 0.49 to 0.99; specificity, 0.97, 95% CI, 0.82 to 0.99), which, when present, increases the posttest likelihood of EAS to 74%, assuming a pretest prevalence of 10%. Accuracy and usefulness of other PVD indices were affected by false-positive results.

CONCLUSIONS

In the fetus with HLHS, a PVD forward/reverse VTI ratio of <5 is the strongest predictor of the need for EAS in the newborn period. These observations should improve our ability to identify and expectantly manage the fetus with HLHS and RAS.

Decision analysis and economic modelling: a primer

In this primer, the reader is introduced to the concepts governing decision analysis and cost-effectiveness analysis. The construction of decision trees and Markov models is presented to provide the necessary background to critique research articles in published literature. Specific sub-topics related to cost-effectiveness analysis are discussed including quality adjustment and utilities (patient preferences for health states), discounting, and sensitivity analysis including Monte Carlo simulation. Evidence based methods to critique decision and cost-effectiveness analysis are provided, and limitations to these analytic methods are examined. In summary, the major functions of decision analysis and cost-effectiveness analysis are to provide: (1) a quantitative summary of existing data, and (2) hypothesis generation for further research.

A prospective evaluation of patients undergoing surgery for the palliation of an advanced malignancy

BACKGROUND

Decisions regarding the use of surgical procedures for the palliation of symptoms caused by advanced malignancies require the highest level of surgical judgment. Prospective analysis of palliative surgical care may facilitate a more effective and representative evaluation of these patients.

METHODS

Patients requiring surgery planned solely for the palliation of an advanced malignancy were offered entry onto this study. Outcome measurements were made before surgery and monthly thereafter until the patient's death. Accepted techniques of pain assessment, quality of life, and functional status were used.

RESULTS

Between May 1997 and December 1999, 26 patients were enrolled. Although 46% (12 of 26) of patients demonstrated improvement in pain control or quality of life after palliative surgery, these benefits lasted a median of only 3.4 months. Palliative surgery was associated with significant postoperative complications in 35% (9 of 26) patients.

CONCLUSIONS

Although many patients had no apparent demonstrable benefit from surgery, surgeons were able to identify a group of patients who experienced significant benefits after a palliative procedure. The relationships between the patient and family members and the surgeon play an important role in decision-making throughout the palliative phase of cancer treatment.

Clinical economics of head and neck malignancies

With the continued increase in medical expenditures and the growing awareness that resources are not limitless, there is increasing pressure to curb health care costs and to establish priorities. As potential solutions are proposed and implemented, there is understandable concern that policy choices may adversely affect both the access to and the quality of care. Economic analyses are one tool used to optimize resource allocation decisions. The primary goal of these analyses is to maximize value and efficiency, not necessarily to decrease spending. The current focus on cost cutting is often associated with a more truncated, nonsocietal perspective (e.g., that of the payer or provider). To be most useful, these analyses must be methodologically rigorous. Standard guidelines, such as those established by Eisenberg, are helpful. As shown in the reports applicable to head and neck malignancies that have been discussed here, many articles published in the clinical literature must be interpreted cautiously, because fundamental methodological concerns (e.g., using costs rather than charges, discounting to a common base year) were frequently not addressed. Economic investigations are one aspect of the broader fields of outcomes and health services research. It is easy to underestimate how greatly economic studies depend on the availability of high quality noneconomic data. In that context, current initiatives in evidence-based medicine (EBM), using the best available evidence (considering for example, the type of trial, the quality of the research, and the credentials of the researcher) to help clinicians practice in situations where doubt may exist in the diagnosis, treatment, or prognosis of patients, will likely grow in importance. Evidence-based clinical practice guidelines and systematic literature reviews are manifestations of this trend. Historically, disease control measures and survival have been the primary and points in clinical cancer studies. Economic analyses and studies evaluating other end points (e.g., function, quality of life) will likely play a larger role in the future in evaluating the diagnosis, treatment, and follow-up of head, neck and other malignancies.

Decision making on surgical palliation based on patient outcome data

BACKGROUND

Strategies for the effective application of palliative procedures are infrequently standardized and incompletely understood. The effect on patient outcome as determined by elements such as resolution of chief complaints, quality of life, pain control, morbidity of therapy, and resource utilization should predominate decisions regarding surgical palliative care.

METHODS

Articles published between 1990 and 1996 on the surgical palliation of cancer were identified by a MEDLINE search and reviewed for designated parameters considered important for good palliative care.

RESULTS

A total of 348 citations were included. Entries considered these fundamental elements: cost (2%); pain control (12%); quality of life (17%); need to repeat the intervention (59%); morbidity and mortality (61 %); survival (64%); and physiologic response (69%). Established methods for quality of life and pain assessment were sporadically utilized.

CONCLUSIONS

In the current surgical literature, there is uncommon reporting of the range of data required to recommend sound palliative surgical choices.

Decision analysis in surgery

BACKGROUND

The technique of decision analysis is often applied to clinical policy and economic issues in surgery. Because surgeons may be unfamiliar with such work, this article catalogues decision analysis studies in the surgical specialties.

METHODS

We reviewed the medical literature (1966 to 1994) to identify surgical decision analysis studies and to assess trends over time. Each article was categorized according to the type of journal (surgical, other clinical, or technical) in which it was published and content, including surgical specialty, clinical topic, article focus (individual patient decision making, clinical policy, or cost-effectiveness), and primary findings.

RESULTS

Publication rates of surgical decision analysis have increased dramatically over time. Of the 86 total studies only six were published before 1980. In contrast, 44 studies appeared between 1990 and 1994. Although 77% were published in nonsurgical journals, decision analyses have begun to appear more regularly in surgical forums. Studies addressing all of the surgical specialties were found, although more than one half addressed topics in general surgery (34%) or cardiothoracic surgery (22%). The most frequent topics were gallstones (11 articles), head and neck cancer (five articles), coronary artery disease (four articles), and cerebral arteriovenous malformations (four articles). Articles focusing on clinical policy (i.e., those assessing surgical efficacy for broad groups of patients) now account for large majority of published decision analyses.

CONCLUSIONS

The use of decision analysis in surgery is growing steadily. Because decision analysis is being used to influence clinical policy, it is important for surgeons to be aware of these studies and to be able to review them critically.

Decision analysis in oncology: panacea or chimera?

A review of the literature on the use of decision analysis in clinical oncology shows that, although these techniques have been available for more than 25 years, they have not been widely applied: only 19 decision analyses of therapeutic management in clinical oncology were found. The main disadvantages concern the difficulty of accurately assessing probabilities and defining measures of outcome. Time-consuming analysis may produce results that are either equivocal or simply confirm the expectations of common sense. If the basic design fails to include all relevant factors then any conclusions will be of little value. The main advantages are that, by demanding that problems be explicitly stated and analysed in a logical fashion, deficiencies in current knowledge, belief and practice are identified. The usefulness of these techniques lies in formulating management guidelines, either for treatment or for follow-up. They have only a limited role in decision making for individual patients.

Histopathological prediction of liver metastasis after curative resection of colorectal cancer

To estimate the risk of liver metastasis after curative resection of colorectal cancer, resected specimens from 290 patients (45 with metachronous liver metastasis) were examined and the relationships between 10 histopathological variables and liver metastasis were analysed using our application of the Akaike information criterion (AIC). Of the 10 variables examined, the depth of venous invasion (Vd) had the greatest prognostic value for metastasis, followed by the number of venous invasions, the number of lymphovascular invasions, lymph node metastasis and type of infiltration. The prediction of liver metastasis was further improved by combining Vd with lymphocyte infiltration, mucinous production, interstitial fibrosis or depth of penetration, although these four variables per se were minimally informative for metastasis. We conclude that the prediction of liver metastasis is best achieved by combining Vd with other variables. Our risk group classification, and the estimated probability of liver metastasis for each group, are shown.

Decision-making studies in patient management: twenty years later

This article surveys the contribution of decision analysis to diagnosis and treatment in medicine, beginning with a review of Dr. Lee B. Lusted's 1971 paper, "Decision-making studies in patient management." From this beginning methods of decision analysis were developed that were particularly appropriate to medical problems. The author reviews the effects of Dr. Lusted's interests in the field on subsequent research and publications, and critiques prophetic statements found in the early paper.

Treatment selection for cancer patients: application of statistical decision theory to the treatment of advanced ovarian cancer

Optimal treatment selection for patients with chronic disease, especially advanced cancer, requires careful consideration in weighing risks and benefits of each therapy. The application of statistical decision theory to such problems provides an explicit and systematic means of combining information on risks and benefits with individual patient preferences on quality-of-life issues. This paper evaluates the strengths and weaknesses of this methodology by using, as an example, treatment selection in advanced ovarian cancer. Possible treatment options and the major consequences of each are first outlined on a decision tree. The probability of various outcomes is estimated from the literature and methods for assessing the relative value or utility of each outcome are illustrated by interviews with 9 volunteers. Based on decision analysis, the recommended treatment for advanced ovarian cancer is found to be highly dependent on survival estimates but far less dependent on other probability estimates or the method of obtaining utilities. Individual preferences are also found to influence the treatment choice. The analysis illustrates that an important strength in using decision theory is its ability to identify key factors in the decision through sensitivity analysis. This may help both the physician selecting treatment and the investigator planning clinical trials which compare these therapies. In addition, this method can help in planning a trial's sample size by determining what survival difference between therapeutic strategies is worth detecting. Some problems identified with this methodology include the need for several simplifying assumptions and the difficulties in assessing individual preferences. On balance, we believe decision theory in this setting can play a useful role in complementing the physician's clinical judgement.

Should adjuvant chemotherapy be withheld from any patient with operable breast cancer?

We examined the clinical course of operable breast cancer and looked at what effect tumour size had on the probability of death from the disease. We analysed data from 1936 patients who were classified as having international stage I and II disease: decision theory was used to show a technique for determining the best strategy for adjuvant chemotherapy in the overall management of breast cancer.To evaluate this approach further, studies need to be designed to yield numerical values for the total morbidity of treatment on a scale from 0 to 100-the concept of utility loss-where 100 represents the maximum utility loss in patients in the early stages of disease. Such studies would contribute more to determining the best overall management of such patients than the current proliferation of clinical trials that are designed to evaluate either different combinations of adjuvant drugs or the effect of known combinations in selected subgroups of patients.

The role of modeling methods in medical diagnosis

Modeling methods in medical diagnosis are concerned with medical information processing as it pertains to utilizing biological modeling methods to facilitate patient care. Major considerations in this particular area are (1) the classification problem related to the establishment of disease entities-the taxonomy problem, and (2) the diagnosis of diseases. Available are properties, criteria, signs, symptoms, and manifestations of diseases that have been cumulated and categorized by clinicians and researchers. The problem is to optimally utilize the information content of a sign or set of signs in the practice of patient care as pertaining to the medical diagnosis problem. Some mathematical approaches implemented to facilitate such analyses include cluster analysis, discriminant analysis, Bayesian methods, computer approaches, game theory, information theory, stochastic representations, stepwise procedures, decision analysis, and pattern recognition techniques. Each of these has been studied in depth by numerous researchers advocating computer applications in medicine. Here we discuss the scope and limitations of utilizing modeling methods as a viable approach to interpreting vast amounts of biological data collected on a single patient during an encounter. We consider the following: (1) limitations associated with modeling methodologies; (2) levels of responsibilities, ranging over logging, summarizing, reporting, monitoring, and therapy selection; and (3) operational strategies and considerations as they affect hardware logistics, the actual algorithm utilized, and implementation of these sophisticated analysis systems.

The evaluation of the results of cancer therapy treatment utility

The results of cancer management are frequently presented in a way such that the only comparison that can be made between treatments is that of fixed term survival. Very few reports attempt to include an analysis of the effect of second line therapy or a comparison of the different morbidities of different treatments. Techniques now exist (one of these is the concept of treatment utility) which take into account not only the probability of the outcome of a treatment but also the quality of that outcome. To expand this concept requires research in cancer management to be directed to the production of numerical values for treatment toxicity, pain, disability and mutilation, in addition to the conventional survival rates. This paper examines the concept of treatment utility when a comparison is made between two treatments which produce differences in survival but only one of which is associated with significant permanent disability.

[The basis of diagnostic and therapeutic decisions (author's transl)]

The recent logical and psychological results of the theories of probability, utility and decision are valid also for medicine. Their principles and methods are briefly described. The review deals with the special conditions for their application in medicine. Diagnostic methods are derived from the principles of exclusion and pattern recognition. Concerning treatment the importance of the so called therapeutic index as a quotient of possible benefit and injury is stressed. From these elements decision rules are developed for the usual clinical situations.