Biomedical subjects
Publications and source records attributed to J Hilden.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
From the public health point of view, several formal attempts have been made to measure the impact of prenatal diagnosis (PND) on the incidence of Down's Syndrome (DS), but the results have varied widely. The impact of PND (reduction in the birth rate of chromosomally abnormal neonates) is related to utilization rates but quantitative estimates of this have not been established. In a three-year (1981-1983) total population study from Queensland, Australia, we present results to measure the impact of a voluntary PND programme on the birth incidence of DS, and also other chromosomally abnormal births. Utilization rates for the PND service were 15.5 per cent in that population of mothers 35 years and over. Numbers and rates of all cases of chromosomal abnormalities are presented, subclassified by type of diagnosis--either by PND or by clinical diagnosis after birth. For the total population, 7.3 per cent of cases of DS were detected prenatally, and 15.4 per cent of all chromosome abnormalities. (A method for measuring the impact of PND is described.) Using this in conjunction with our demographic data, we estimate that with a 15 per cent utilization rate of PND by older mothers, 14 per cent of DS births can be prevented in this age group, or a 5 per cent overall reduction can be achieved if mothers of all ages are considered. One index--the ratio of the percentage of DS births which are preventable compared with the population utilization rates of PND--has potential for widespread use. Queensland data for this ratio is 0.34, a figure consistent with that from other studies. Thus a 3.5 per cent drop in the overall DS birth rate may be expected for each 10 per cent increase in the utilization rates of PND for mothers of 35 years and over. A diagram is presented which may serve as a model for improved data collection and better impact estimates in the future.
Explore the source record for details and available documents.
Those who report a clinical trial should acknowledge the right of the 'consumer' to make decisions based on his own valuation of the beneficial and adverse effects which rival treatments may have. Suppose a new patient is inclined to trade one unit of benefit for c units of complication. Then he should (should not) be given the treatment if his estimated utility gain, x1-cx2, is positive (negative) and statistically significant according to the data of the trial; here x1 (x2) denotes the observed average benefit (complication level). If the estimated gain is not statistically significant, the data do not allow any firm recommendation. This c-dependent recommendation in general cannot be determined from inspection of a joint confidence region for the two means concerned. Therefore investigators should present the outcome of the significance test as a function of c (inverted inference). Typically there are several types of adverse effect or benefit, in which case the quantity c must be generalized into a vector of personal relative utility weights.
PURPOSE: The aim of the therapeutic trials was to optimize the treatment of severe aplastic anemia (SAA) and moderate aplastic anemia in children who lack a suitable bone marrow donor, using immunosuppressive therapy in the most effective combination and dose. PATIENTS AND METHODS: Two sequential therapeutic trials for the treatment of severe and moderate aplastic anemia in children were conducted by 10 institutions. The treatment protocols included antithymocyte globulin (ATG), prednisone, and cyclosporine A (CSA); patients entered on the first protocol, 0190 (ATG X 2), were given two courses of ATG, and those enrolled on the second protocol, 0190B (ATG X 1), were given only one course of ATG. Ten patients were evaluable on ATG X 2. All patients had SAA; three had hepatitis-induced severe aplastic anemia (HI-SAA). Twelve patients were evaluable on ATG X 1; all had SAA, one of whom had HI-SAA. RESULTS: Seven of 10 patients on ATG X 2 responded, and eight of 12 patients treated on ATG X 1 responded. CONCLUSION: Treatment with immunosuppressive therapy using ATG, CSA, and prednisone was very well tolerated. The response rates in both protocols were similar, and results compare favorably with those of previous therapeutic trials, suggesting that a second course of ATG is not necessary.
Unmanageable bushy decision trees result when a decision analysis involves several investigations. They can be simplified for riskless tests by deriving the maximum expected utility decision table for the problem as an intermediate step. This table can be logically summarized as Boolean expressions involving the tests. A minimum-cost testing sequence may then be found by manipulation of the Boolean formulas. The relationship between the resulting decision criteria and the receiver operating characteristic is shown.
When a decision table is used to find a maximum expected utility testing strategy, it is based on a given prior probability distribution of diseases. In the two-disease situation, a threshold analysis over all prior probabilities can be done using threshold transformations of the points of indifference between treatments. This results in a set of prior probability intervals each with its own unique decision rule. The Boolean expression for the table indicates the acceptable testing strategies. A decision table analysis may then be extended to include invasive or costly investigations. The technique represents a saving in time and effort compared with standard decision tree approaches, especially where investigative recommendations are to be made for a broad range of prior probabilities, e.g., where initial symptoms and signs are considered before the investigations.
Explore the source record for details and available documents.
Usually, when a patient is being investigated, only a small subset of all available tests is performed. Most selection methods for making this choice fail to account for the risk and cost of the test. By attempting to approximate a decision-analytic ideal, via the concept of quasi-utility, the authors developed the information-to-cost ratio and related measures, which balance the utility of the information gained against the price paid. Measurement of the relative importances of diseases is used to further refine the method.
The area under the receiver operating characteristic (ROC) curve is a popular measure of the power of a (two-disease) diagnostic test, but it is shown here to be an inconsistent criterion: tests of indistinguishable clinical impacts may have different areas. A class of diagnosticity measures (DMs) of proven optimality is proposed instead. Once a regret(-like) measure of diagnostic uncertainty is agreed upon, the associated DM is uniquely defined and, indeed, calculable from the ROC curve configuration. Two scaled variants of the ROC are introduced and used to advantage in the analysis. They may also be helpful to students of medical decision making.
Among those decisions that may be made by a patient in response to an illness, the authors single out a certain class: contingent investment decisions. They are characterized by the patient's committing him- or herself, on the basis of prognostic counseling, to a certain action or non-action that he or she may regret in retrospect. Examples show that, when assessing utilities, the decision analyst runs a risk of handling such investment decisions incorrectly, unless they are made explicit and incorporated into the medical decision process. The anomaly is explained as a violation of the structural rules for decision trees and is also interpreted in terms of "the price of prognostic ignorance," a quantity closely related to the expected utility value of perfect information.
Explore the source record for details and available documents.
Validation and clinical testing of medical decision aids is a hot topic at present. We state a few main points which, in our opinion, have not received the attention they deserve. Our main concerns have to do with the statistical design and analysis of field evaluation studies; with long-term effects; and with the interplay between medicine and the software industry. The points represent our reaction to a recent symposium on the topic, published in this special issue. We take the opportunity to supplement the papers at hand in various ways and, where we see a risk of misconceptions or poor practice becoming cemented, try to point out the proper course.