Eaton-Lambert syndrome and small cell lung cancer: side effects and certainty

Decision analysis in the clinical neurosciences: a systematic review of the literature

Clinical decision analysis can be a useful scientific tool for individual patient management, for planning of clinical research and for reaching consensus about clinical problems. We systematically reviewed the decision analytic studies in the clinical neurosciences that were published between 1975 and July 1994. All studies were assessed on aspects of clinical applicability: presence of case and context description, completeness of the analysed strategies from a clinical point of view, extendibility of the analyses to different patient profiles, and up-to-date-ness. Fifty-nine decision analyses of twenty-eight different clinical problems were identified. Twenty-eight analyses were based on the theory of subjective expected utility, twelve on cost-effectiveness analysis. Four studies used ROC analysis, and fifteen were risk-, or risk-benefit analyses. At least six studies could have been improved by more elaborately disclosing the context of the clinical problem that was addressed. In eleven studies, the effect of different, yet plausible assumptions was not explored, and in eighteen studies the reader was not informed how to extend the results of the analysis to patients with (slightly) different clinical characterisitics. All studies had, by nature, the potential to promote insight into the clinical problem and focus the discussion on clinically important aspects, and gave clinically useful advice. We conclude that clinical decision analysis, as an explicit, quantitative approach to uncertainty in decision making in the clinical neurosciences will fulfill a growing need in the near future.

Inaccuracies in estimates of life expectancies of patients with bronchial cancer in clinical decision making

Approximations of life expectancy in clinical decision making frequently assume constant disease-specific ("excess") mortality hazards over age at diagnosis and over time from diagnosis. This assumption is inconsistent with the longer relative survival of younger patients with bladder cancer and with the declines in mortality hazards from bladder and breast cancers over time from diagnosis. To estimate the error that may result from these assumptions, the authors derived excess mortality hazards from the Surveillance, Epidemiology and End Result (SEER) tumor registry for bronchial cancers stratified by age at diagnosis and time from diagnosis. They compared the life expectancies calculated by a model using an average constant annual cancer-specific mortality hazard over time from diagnosis with those calculated using data-derived cancer-specific annual mortality hazards that varied as a function of time from diagnosis. For younger patients with less advanced disease, the constant-average-mortality model underestimated life expectancies by up to 50% relative to those predicted by the time-variant model. For those over 75 years old at diagnosis, and for all patients with advanced disease, the constant-average-mortality model overestimated life expectancies by up to 65% relative to those predicted by the time-variant model. The authors conclude that predictions of life expectancy with bronchial cancer, and probably with other neoplasms, are limited by the widespread use of oversimplified methods of calculation and by the lack of data describing mortality hazards as a function of time from diagnosis.(ABSTRACT TRUNCATED AT 250 WORDS)

Decision analysis--a helpful tool for clinicians to establish diagnostic-therapeutic guidelines?

In this paper we focus on the question: Does decision analysis provide a framework to assess the value of diagnostic tests in clinical practice and how can it be used by clinicians in establishing diagnostic-therapeutic guidelines. To study this question we performed two analyses concerning the use of pelvic lymphadenectomy and pedal lymphography for staging prostate cancer. Both analyses yielded similar results as far as the preferred strategy was concerned, yet the approach and set up of the two analyses were different. The first analysis was performed in accordance with the textbooks on decision analysis. However, using this traditional approach we encountered some difficulties: in structuring the decision tree, in eliciting values for the quality of life parameters, and in interpreting the results. These difficulties urged us to modify the approach, presented in the second analysis. In this second analysis, the decision problem was split into several consecutive decision problems which corresponded to the questions posed by the clinicians. Longevity and quality of life were considered separately and the consequences of treatment and testing, which affect the quality of life of the patients, were indicated by just two parameters. Finally, the result of the analysis was expressed in clinically meaningful terms. The second analysis is compared with different approaches presented in the literature for analyzing decision problems involving diagnostic tests. Despite some unresolved methodological problems it is concluded that decision analysis provides a good framework for clinicians to structure and analyze complex decision problems.

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.

Stochastic trees: a new technique for temporal medical decision modeling

This paper introduces stochastic trees, a new modeling approach for the class of medical decision problems in which risks of mortality and morbidity may extend over time. A stochastic tree may be regarded as a continuous-time version of a Markov-cycle tree, or alternately, as a multi-state DEALE model. Optimal decisions in stochastic trees can be determined by rollback, much in the same fashion as decision trees. The author discusses how age-dependent mortality rates and declining incidence rates may be modeled using stochastic trees. Concepts are illustrated using examples from the medical literature. It is argued that stochastic trees possess important advantages over Markov-cycle trees for medical decision modeling.

Whose utilities for decision analysis?

The goal of this study was to examine sources of variation in the utilities assigned to health states. The authors selected a common clinical problem, carcinoma of the rectum, and examined the utilities assigned to colostomy, a common outcome of treatment for that disease. After preparing and validating a description of colostomy and its effects on patients' lives, utilities for the state were obtained from five groups of individuals. These comprised two groups of patients who received treatment for rectal cancer, a group of physicians and surgeons specializing in the treatment of this disease, and two groups of healthy subjects, none of whom were health professionals. Of the patients who had been treated for rectal cancer, one group had been treated surgically with the formation of colostomies and the other had been treated with radiotherapy and none had a colostomy. Utilities for colostomy were elicited using the standard gamble, category rating, and a treatment choice questionnaire. The groups differed substantially in the utilities assigned to colostomy. In general, patients with colostomies and physicians assigned significantly higher utilities than did patients who did not themselves have a colostomy. The clinical significance of these differences was examined in a simplified clinical decision problem that compared surgery (with colostomy) and radiotherapy (without colostomy) as primary treatment. The expected clinical value of these treatment alternatives was substantially influenced by the differences observed in utilities for colostomy. These results emphasize the importance of patient utilities in clinical decision making and the need to gain greater understanding of the factors that influence the utilities that patients assign to health states.

Expert systems and the pancreatic cancer problem: decision support in the pre-operative diagnosis

In this paper, after reviewing the main issue in artificial intelligence, decision support systems, medical decision-making, expert systems and some of their applications in medicine, we focus on the diagnostic aspect of pancreatic cancer. We briefly examine the most significant applications both from the oncological and from the diagnostic point of view. We discuss the medical problems mentioning incidence and mortality, aetiological factors and diagnosis, considering the roles of surgery and adjuvant therapies. Finally we justify the decision to develop an expert system in such a medical domain and discuss the SPES (Surgical Pancreatic Expert System) project, its parts dealing with the different medical phases of pancreatic cancer diagnosis and therapy: pre-operative, intra-operative and adjuvant therapies. In particular we discuss diagnostic aspects of pancreatic cancer disease, pointing out the aims of the project, methodologies, tools used and future developments.