CHOICE: Communicating Health Options Through Information and Cancer Education
Ontology highlight
ABSTRACT:
DISEASE(S): Rectal Neoplasms,Colorectal Neoplasms
PROVIDER: 2014585 | ecrin-mdr-crc |
REPOSITORIES: ECRIN MDR
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Project description:Realistic, everyday rewards contain multiple components. An apple has taste and size. However, we choose in single dimensions, simply preferring some apples to others. How can such single-dimensional preference relationships refer to multicomponent choice options? Here, we measured how stochastic choices revealed preferences for 2-component milkshakes. The preferences were intuitively graphed as indifference curves that represented the orderly integration of the 2 components as trade-off: parts of 1 component were given up for obtaining 1 additional unit of the other component without a change in preference. The well-ordered, nonoverlapping curves satisfied leave-one-out tests, followed predictions by machine learning decoders and correlated with single-dimensional Becker-DeGroot-Marschak (BDM) auction-like bids for the 2-component rewards. This accuracy suggests a decision process that integrates multiple reward components into single-dimensional estimates in a systematic fashion. In interspecies comparisons, human performance matched that of highly experienced laboratory monkeys, as measured by accuracy of the critical trade-off between bundle components. These data describe the nature of choices of multicomponent choice options and attest to the validity of the rigorous economic concepts and their convenient graphic schemes for explaining choices of human and nonhuman primates. The results encourage formal behavioral and neural investigations of normal, irrational, and pathological economic choices. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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Project description:BackgroundDecision curve analysis can be used to determine whether a personalized model for treatment benefit would lead to better clinical decisions. Decision curve analysis methods have been described to estimate treatment benefit using data from a single randomized controlled trial.ObjectivesOur main objective is to extend the decision curve analysis methodology to the scenario in which several treatment options exist and evidence about their effects comes from a set of trials, synthesized using network meta-analysis (NMA).MethodsWe describe the steps needed to estimate the net benefit of a prediction model using evidence from studies synthesized in an NMA. We show how to compare personalized versus one-size-fit-all treatment decision-making strategies, such as "treat none" or "treat all patients with a specific treatment" strategies. First, threshold values for each included treatment need to be defined (i.e., the minimum risk difference compared with control that renders a treatment worth taking). The net benefit per strategy can then be plotted for a plausible range of threshold values to reveal the most clinically useful strategy. We applied our methodology to an NMA prediction model for relapsing-remitting multiple sclerosis, which can be used to choose between natalizumab, dimethyl fumarate, glatiramer acetate, and placebo.ResultsWe illustrated the extended decision curve analysis methodology using several threshold value combinations for each available treatment. For the examined threshold values, the "treat patients according to the prediction model" strategy performs either better than or close to the one-size-fit-all treatment strategies. However, even small differences may be important in clinical decision making. As the advantage of the personalized model was not consistent across all thresholds, improving the existing model (by including, for example, predictors that will increase discrimination) is needed before advocating its clinical usefulness.ConclusionsThis novel extension of decision curve analysis can be applied to NMA-based prediction models to evaluate their use to aid treatment decision making.HighlightsDecision curve analysis is extended into a (network) meta-analysis framework.Personalized models predicting treatment benefit are evaluated when several treatment options are available and evidence about their effects comes from a set of trials.Detailed steps to compare personalized versus one-size-fit-all treatment decision-making strategies are outlined.This extension of decision curve analysis can be applied to (network) meta-analysis-based prediction models to evaluate their use to aid treatment decision making.
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Project description:Acute myeloid leukemia (AML) therapies are rapidly evolving with novel targeted therapies showing high-level responses in a notoriously difficult to treat group of patients - the elderly and unfit. This review will examine the outcomes of older AML patients (>60 years old) with conventional induction strategies, and published literature on risks of pursuit of induction. Low-intensity combination therapy response rates appear to be approaching that of induction regimens, and with lower toxicity, low-intensity therapy likely represents the future standard approach in this age group. Lastly, allogeneic transplant appears to have a role in increasing durable remissions regardless of age and should be considered in patients with limited comorbidities.
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Project description:Irrational decision making in humans and other species challenges the use of optimality in behavioural biology. Here we show that such observations are in fact powerful tools to understand the adaptive significance of behavioural mechanisms. We presented starlings choices between probabilistic alternatives, receiving or not information about forthcoming, delayed outcomes after their choices. Subjects could not use this information to alter the outcomes. Paradoxically, outcome information induced loss-causing preference for the lower probability option. The effect depended on time under uncertainty: information given just after each choice caused strong preference for lower probability, but information just before the outcome did not. A foraging analysis shows that these preferences would maximize gains if post-choice information were usable, as when predators abandon a chase when sure of the prey escaping. Our study illustrates how experimentally induced irrational behaviour supports rather than weakens the evolutionary optimality approach to animal behaviour.
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Project description:IntroductionTreatment options for Waldenström's Macroglobulinemia (WM) have expanded rapidly in the last decades. However, there is no consensus on a preferred treatment. Therefore, patient preferences become increasingly important in making individualized treatment plans. Still, WM patients' priorities and perspectives regarding their treatment options are unknown. We evaluated treatment preferences of WM patients using a discrete choice experiment (DCE).MethodsA mixed-method approach was utilized for identification and selection of attributes/levels. The DCE questionnaire included five attributes: type of agent (targeted versus chemotherapy); frequency and route of administration; 5-year progression-free survival (PFS); adverse events; and risk of secondary malignancies. An orthogonal design and a mixed logit panel data model were used to construct choice tasks and assess patient preferences, respectively.ResultsThree hundred thirty WM patients participated in the project. In total, 214 (65%) complete questionnaires were included for data analysis. The 5-year PFS, followed by risk of secondary malignancies were the most important attributes for making treatment choices. Regarding side effects, patients chose to avoid neuropathy the most compared to nausea/vomiting and extreme fatigue. Patients preferred a fixed-duration treatment with IV/SC administration at the hospital over a continuous daily oral regimen at home.ConclusionThese are the first systematic data obtained on WM patient preferences for treatment. The results may help discussions with individual patients about their treatment choices. Also, these data can help design clinical trials in WM and inform health-care decision-making regarding outcomes that are most relevant to patients.
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Project description:Signals that reduce uncertainty can be valuable because well-informed decision-makers can better align their preferences to opportunities. However, some birds and mammals display an appetite for informative signals that cannot be used to increase returns. We explore the role that reward-predictive stimuli have in fostering such preferences, aiming at distinguishing between two putative underlying mechanisms. The 'information hypothesis' proposes that reducing uncertainty is reinforcing per se, somewhat consistently with the concept of curiosity: a motivation to know in the absence of tractable extrinsic benefits. In contrast, the 'conditioned reinforcement hypothesis', an associative account, proposes asymmetries in secondarily acquired reinforcement: post-choice stimuli announcing forthcoming rewards (S+) reinforce responses more than stimuli signalling no rewards (S-) inhibit responses. In three treatments, rats faced two equally profitable options delivering food probabilistically after a fixed delay. In the informative option (Info), food or no food was signalled immediately after choice, whereas in the non-informative option (NoInfo) outcomes were uncertain until the delay lapsed. Subjects preferred Info when (1) both outcomes were explicitly signalled by salient auditory cues, (2) only forthcoming food delivery was explicitly signalled, and (3) only the absence of forthcoming reward was explicitly signalled. Acquisition was slower in (3), when food was not explicitly signalled, showing that signals for positive outcomes have a greater influence on the development of preference than signals for negative ones. Our results are consistent with an elaborated conditioned reinforcement account, and with the conjecture that both uncertainty reduction and conditioned reinforcement jointly act to generate preference.
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