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Results for “Decision Theory”

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At least 19 recordsLinked to original sources
PubMed2004

Epidemiological theory, decision theory and mental health services research.

BACKGROUND: Mathematical models describing the epidemiology of major depression are potentially useful for epidemiological analyses, as decision support tools and in economic analyses. The objective of this project was to develop a Markov model based on epidemiological theory that may be useful for decision analysis and health services research. METHODS: Longitudinal data from a Canadian national survey, the National Population Health Survey (NPHS), were used. The NPHS has collected longitudinal data on a cohort of 17,262 subjects since 1994. The analysis employed a Markov tunnel in order to model the dependence of recovery probabilities on episode duration. RESULTS: Episode incidence ranged between 6.2 % per year in women under 35 to 0.26 % in men over the age of 65.A greater proportion of subjects over 35 years old reported episodes lasting more than 26 weeks. The probability of recovery declined with increasing episode duration, independently of sex. Under steady-state assumptions, a Markov model integrating these parameters predicted a point prevalence of approximately 2% in women and 1% in men under the age of 55. In older age groups, the predicted point prevalence declined in both sexes. CONCLUSIONS: These models support the hypothesis that sex differences in major depression prevalence are due primarily to differences in incidence rather than episode length. These results also indicate that there is no meaningful "central tendency" describing the distribution of episode length in major depression episode. Estimates of mean episode duration represent an intermixing of frequent brief episodes with infrequent protracted episodes. This finding may have important policy implications.

Adolescent↗
PubMedDate not supplied

Focusing technology assessment using medical decision theory.

Combining medical decision theory and epidemiologic information, the authors have developed a strategy to assess diagnostic technologies. For any patient, patient utilities with new diagnostic information are compared with the preferred fallback action absent that diagnostic information. After determination of whether the expected value of diagnostic information (EVDI) justifies its cost, the method adds across the eligible population to determine whether the global EVDI justifies the technology's deployment, employing a screen (Hurdle 1) that assumes that the diagnostic device has perfect accuracy. This preliminary evaluation relies on published data on treatment efficacy, population probabilities of illness, etc., but not on new clinical trials. If the technology is not sufficiently cost-effective, even with this optimistic assumption, the strategy recommends against its use. Otherwise, the next step is Hurdle II, in which the critical clinical studies, identified by the decision-theory model, are undertaken. These commonly include measuring the actual diagnostic accuracy of a device, with which the cost-effectiveness is recalculated. These studies in general do not require randomized controlled trials.

Algorithms↗
PubMed1985

A microcomputer controlled snow ski binding system--II. Release decision theories.

A hierarchy of release decision theories for both tibia fracture and knee ligamentous injury are defined and simulated on a computer. Moment loading data, recorded during actual skiing by the microcomputer-based ski binding system described in Part I, are processed by the various release decision theories. At the bottom of the hierarchy is the simplest theory which treats boot loading as quasi-static and compares moment components to threshold levels. Another stage of the hierarchy defines an analytic expression for a combined loading failure locus. Note that this is the first formulation of a combined loading release decision theory. Yet another stage of the hierarchy computes bone moments via dynamic system leg models. The various release decision theories are evaluated by comparing processed results to both pain and bone failure limits. For the data generated by the field tests conducted to date, the simplest release decision theory satisfied the retention requirement for pain limits in the presence of muscle activity for both torsion and forward bending. For pain limits in the absence of muscle activity the retention requirement was not satisfied however. Another result is that leg dynamics are significant. A final result is that combined loading considerations lead to a more conservative theory.

Athletic Injuries↗
PubMedDate not supplied

Design of phase II clinical trials in cancer using decision theory.

An application of decision theory to the design of phase II clinical trials in cancer is presented. A risk function which depends on the prior distribution of the response rate, the costs of treating a patient, and the gains or losses resulting from the decision taken upon completion of the study is derived and optimal restricted sampling plans for a response rate of 20% are provided.

Decision Theory↗
PubMed2002

An extension of the continual reassessment method using decision theory.

The primary goal of a phase I trial is to find the maximally tolerated dose (MTD) of a treatment. The MTD is usually defined in terms of a tolerable probability, q(*), of toxicity. Our objective is to find the highest dose with toxicity risk that does not exceed q(*), a criterion that is often desired in designing phase I trials. This criterion differs from that of finding the dose with toxicity risk closest to q(*), that is used in methods such as the continual reassessment method. We use the theory of decision processes to find optimal sequential designs that maximize the expected number of patients within the trial allocated to the highest dose with toxicity not exceeding q(*), among the doses under consideration. The proposed method is very general in the sense that criteria other than the one considered here can be optimized and that optimal dose assignment can be defined in terms of patients within or outside the trial. It includes as an important special case the continual reassessment method. Numerical study indicates the strategy compares favourably with other phase I designs.

Clinical Trials, Phase I as Topic↗
PubMed1987

Autoimmunity: a decision theory model.

Concepts from statistical decision theory were used to analyse the detection problem faced by the body's immune system in mounting immune responses to bacteria of the normal body flora. Given that these bacteria are potentially harmful, that there can be extensive cross reaction between bacterial antigens and host tissues, and that the decisions are made in uncertainty, there is a finite chance of error in immune response leading to autoimmune disease. A model of ageing in the immune system is proposed that is based on random decay in components of the decision process, leading to a steep age dependent increase in the probability of error. The age incidence of those autoimmune diseases which peak in early and middle life can be explained as the resultant of two processes: an exponentially falling curve of incidence of first contact with common bacteria, and a rapidly rising error function. Epidemiological data on the variation of incidence with social class, sibship order, climate and culture can be used to predict the likely site of carriage and mode of spread of the causative bacteria. Furthermore, those autoimmune diseases precipitated by common viral respiratory tract infections might represent reactions to nasopharyngeal bacterial overgrowth, and this theory can be tested using monoclonal antibodies to search the bacterial isolates for cross reacting antigens. If this model is correct then prevention of autoimmune disease by early exposure to low doses of bacteria might be possible.

Age Factors↗
PubMed1996

Application of decision theory to DUI assessment.

The application of decision theory to screening of driving under the influence (DUI) offenders is illustrated in an evaluation study that investigated the validity of a structured interview and a survey instrument. These findings are examined graphically using the relative operating characteristic (ROC). This graph relates the proportion of true-positive cases to the proportion of false-positive cases for various placements of the decision cut-off score. The ROC's two principal parameters, test sensitivity and examiner bias, are used to provide a complete quantitative description of the performance of two screening instruments. A major goal of this study is to show how a new measure of examiner bias, the "cost ratio," improves the evaluation of DUI screening programs.

Accidents, Traffic↗
PubMed1985

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.

Alkylating Agents↗
PubMedDate not supplied

Patient satisfaction and normative decision theory.

This article explores the application of normative decision theory (NDT) to the challenge of facilitating and measuring patient satisfaction. Patient satisfaction is the appraisal, by an individual, of the extent to which the care provided has met that individual's expectations and preferences. Classic decision analysis provides a graphic and computational strategy to link patient preferences for outcomes to the treatment choices likely to produce the outcomes. Multiple criteria models enable the complex judgment task of measuring patient satisfaction to be decomposed into elemental factors that reflect patient preferences, thus facilitating evaluation of care in terms of factors relevant to the individual patient. Through the application of NDT models, it is possible to use patient preferences as a guide to the treatment planning and care monitoring process and to construct measures of patient satisfaction that are meaningful to the individual. Nursing informatics, with its foundations in both information management and decision sciences, provides the tools and data necessary to promote care provided in accord with patient preferences and to ensure appraisal of satisfaction that aptly captures the complex, multidimensional nature of patient preferences.

Decision Theory↗
PubMed1998

Sample size determination for phase II clinical trials based on Bayesian decision theory.

This paper describes an application of Bayesian decision theory to the determination of sample size for phase II clinical studies. The approach uses the method of backward induction to obtain group sequential designs that are optimal with respect to some specified gain function. A gain function is proposed focussing on the financial costs of, and potential profits from, the drug development programme. On the basis of this gain function, the optimal procedure is also compared with an alternative Bayesian procedure proposed by Thall and Simon. The latter method, which tightly controls type I error rate, is shown to lead to an expected gain considerably smaller than that from the optimal test. Gain functions with respect to which Thall and Simon's boundary is optimal are sought and it is shown that these can only be of the form considered, that is, with constant cost for phase III study and cost of the phase II study proportional to the sample size, if potential profit increases over time.

Bayes Theorem↗
PubMedDate not supplied

Application of statistical decision theory to treatment choices: implications for the design and analysis of clinical trials.

This paper explores the application of statistical decision theory to treatment choices in cancer which involve difficult value judgements in weighing beneficial and deleterious outcomes of treatment. Strengths and weaknesses of using decision theory are illustrated by considering the problem of selecting chemotherapy in advanced ovarian cancer. The paper includes an assessment of individual preferences in 27 volunteers and a discussion of some problems in utility assessment. An alternative approach, using threshold analysis, is presented in which the results of the decision analysis are expressed as a function of utility parameters. By knowing what sets of utilities favour each treatment, the assessment of patient preferences can then be focused on important differences of treatment options. The implications of these results for the design and analysis of clinical trials are discussed.

Antineoplastic Combined Chemotherapy Protocols↗
PubMed1985

[Importance of thyroid hormone determination in the diagnosis of hyperthyroidism--significant improvement in medical assessment using decision theory results].

A "schematic" classification of the results of serum thyroid hormone measurements on the basis of mathematical decision theory proves to be superior to the physicians' conventional evaluation. That has been shown by comparison with the evaluations made by three experienced doctors. When using no control regions (but the results of serum T4, FT4 and T3 simultaneously) the physicians' rates of misrecognition (0-2% false positives, 20-27% false negatives) are only a little lower than when classifying schematically on the basis of only one thyroid hormone. The construction of a suitable control region, however, results when T3 alone is used, in only 7% false negative and 0% false positive results, while these rates remain nearly unchanged for the physicians' evaluations. The optimum application of mathematical decision theory is only possible with the knowledge of the statistical distributions of the thyroid hormone data, both for a representative collective of normals and that of patients with proven hyperthyroidism. Thus, quantitative criteria for the effectiveness of each set of thyroid hormones can be constructed and a multivariate analysis can be performed, in our example - when applying suitable control regions - even with vanishing rates of misrecognition.

Decision Theory↗
PubMed1978

Decision theory in medicine: a review and critique.

Decision theory's role in medicine will lie between the extremes of naive optimism ("a Rosetta stone") and unmitigated pessimism ("a computerized Ouija board"). Its application to public health policy, research, and administrative problem-solving is established; experience offers some guidelines for more limited use in clinical practice.

Cost-Benefit Analysis↗
PubMed2006

Bayesian decision theory in sensorimotor control.

Action selection is a fundamental decision process for us, and depends on the state of both our body and the environment. Because signals in our sensory and motor systems are corrupted by variability or noise, the nervous system needs to estimate these states. To select an optimal action these state estimates need to be combined with knowledge of the potential costs or rewards of different action outcomes. We review recent studies that have investigated the mechanisms used by the nervous system to solve such estimation and decision problems, which show that human behaviour is close to that predicted by Bayesian Decision Theory. This theory defines optimal behaviour in a world characterized by uncertainty, and provides a coherent way of describing sensorimotor processes.

Brain↗
PubMed1992

Assessment of diagnostic ECG results using information and decision theory. Results from the CSE diagnostic study.

Information and decision theory were applied to assess the interpretation results of 15 computer programs (9 electrocardiogram and 6 vectorcardiogram) and 9 cardiologists; 8 of whom analyzed the electrocardiogram and 5 the vectorcardiogram, using a database of 1,220 clinically validated cases. The study demonstrates that information content and utility indices, by providing a comprehensive view of diagnostic performance, can enhance the insight given by the more classic statistical performance measures.

Cardiology↗
PubMed1975

Acupuncture compared with 33 per cent nitrous oxide for dental analgesia: A sensory decision theory evaluation.

Responses to electrical stimulation of the tooth pulp were obtained in both baseline and test sessions for subjects receiving acupuncture, 33 per cent nitrous oxide, or control conditions. A signal-detection analysis across sessions showed that both treatment groups demonstrated reduced sensitivity to stimulation, and increases in bias against reporting strong stimuli as painful. (Key words: Acupuncture; Anesthetics, gases, nitrous oxide; Measurement techniques, sensory decision theory; Pain, sensory decision theory).

Acupuncture Therapy↗