Medical decision making : an international journal of the Society for Medical Decision Making
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Many patients have low numeracy, which impedes their understanding of important information about health (e.g., benefits and harms of screening). We investigated whether physicians adapt their risk communication to accommodate the needs of patients with low numeracy, and how physicians' own numeracy influences their understanding and communication of screening statistics. ⋯ Most physicians know how to appropriately tailor risk communication for patients with low numeracy (i.e., with visual aids). However, physicians who themselves have low numeracy are likely to misunderstand the risks and unintentionally mislead patients by communicating incomplete information. High-quality risk communication and shared decision making can depend critically on factors that improve the risk literacy of physicians.
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When using risk prediction models, an important consideration is weighing performance against the cost (monetary and harms) of ascertaining predictors. ⋯ These approximations are suitable for back-of-the-envelope calculations. For example, in a study predicting the risk of invasive breast cancer, Model 2 adds to the predictors in Model 1 a set of 7 single nucleotide polymorphisms (SNPs). Based on the AUCs and the Tangent Condition, an MTT of 7200 was computed, which indicates that 7200 sets of SNPs are needed for every correct prediction of breast cancer to yield a positive overall expected utility. If ascertaining the SNPs costs $500, this MTT suggests that SNP ascertainment is not likely worthwhile for this risk prediction.
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Resource-constrained countries have difficulty conducting large EQ-5D valuation studies, which limits their ability to conduct cost-utility analyses using a value set specific to their own population. When estimates of similar but related parameters are available, shrinkage estimators reduce uncertainty and yield estimators with smaller mean square error (MSE). We hypothesized that health utilities based on shrinkage estimators can reduce MSE and mean absolute error (MAE) when compared to country-specific health utilities. ⋯ Health utilities derived from shrinkage estimation allow valuation studies with small sample size to "borrow strength" from other valuation studies to reduce uncertainty.
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Comprehension of risks, benefits, and alternative treatment options has been shown to be poor among patients referred for cardiac interventions. Patients' values and preferences are rarely explicitly sought. An increasing proportion of frail and older patients are undergoing complex cardiac surgical procedures with increased risk of both mortality and prolonged institutional care. We sought input from patients and caregivers to determine the optimal approach to decision making in this vulnerable patient population. ⋯ Identifying the barriers and facilitators to patient and caretaker engagement in decision making is a key step in the development of a structured, patient-centered SDM approach. Intervention early in the decision process, the use of individualized decision aids that employ graphic risk presentations, and a dedicated decisional coach were identified by patients and providers as approaches with a high potential for success. The impact of such a formalized shared decision making process in cardiac surgery on decisional quality will need to be formally assessed. Given the trend toward older and frail patients referred for complex cardiac procedures, the need for an effective shared decision making process is compelling.
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Parameter uncertainty in value sets of multiattribute utility-based instruments (MAUIs) has received little attention previously. This false precision leads to underestimation of the uncertainty of the results of cost-effectiveness analyses. The aim of this study is to examine the use of multiple imputation as a method to account for this uncertainty of MAUI scoring algorithms. ⋯ Ignoring uncertainty of the predicted health utilities derived from MAUIs could lead to substantial underestimation of the variance of mean utilities. Multiple imputation corrects for this underestimation so that the results of cost-effectiveness analyses using MAUIs can report the correct degree of uncertainty.