Medical Decision Making最新文献

Background: Discrete choice experiments (DCEs) are frequently used to study preferences and quantify tradeoffs in decision making. It is important to understand how stable their results are.

Objective: To investigate to what extent an extreme change in context, the COVID-19 pandemic, affected preferences for vaccine priority setting, as observed in an earlier DCE.

Methods: We replicated a DCE in which participants had to prioritize vaccination programs for public funding. The initial DCE was executed in Flanders (Belgium) right before the onset of the SARS-CoV-2 pandemic (December 2019, N = 1,636). The replicated DCE was executed 6 months later when the population was in lockdown (April 2020, N = 1,127). A total of 612 respondents participated in both waves of the DCE. We used panel mixed logit models to quantify attribute and level importance and compared utility estimates for consistency.

Results: The number of vaccine-preventable deaths became less important during the pandemic than before, whereas the influential attributes, the vaccine's contribution to disease eradication and certainty about vaccine effectiveness became even more important. Respondents attached equal importance to the number of patients with transient or permanent morbidity, to the disease's economic impact as well as to its equity profile.

Conclusion: Different preferences for vaccine priority setting were observed during the first COVID-19 lockdown as compared with before, although these differences were, given the extreme nature of the changing circumstances, relatively small.

Highlights: We replicated a discrete choice experiment (DCE) about vaccine priority setting during the first COVID-19 lockdown and compared results with those from the original setting.The major attributes, contribution to disease eradication, and scientific certainty about vaccine effectiveness became even more important than they already were, whereas avoidable mortality became less important.Respondents attached equal importance to the number of patients with transient or permanent morbidity, to the disease's economic impact as well as to its equity profile.An extreme change in directly related context to the choice assignment led to changes in stated preferences, although these changes were relatively small, given the extreme change in context.Priorities in the second DCE were even less aligned with cost-effectiveness analysis than those observed initially.

Introduction: Surgeons are entrusted with providing patients with information necessary for deliberation about surgical intervention. Ideally, surgical consultations generate a shared understanding of the treatment experience and determine whether surgery aligns with a patient's overall health goals. In-depth assessment of communication patterns might reveal opportunities to better achieve these objectives.

Methods: We performed a secondary analysis of audio-recorded consultations between surgeons and patients considering high-risk surgery. For 43 surgeons, we randomly selected 4 transcripts each of consultations with patients aged ≥60 y with at least 1 comorbidity. We developed a coding taxonomy, based on principles of informed consent and shared decision making, to categorize surgeon speech. We grouped transcripts by treatment plan and recorded the treatment goal. We used box plots, Sankey diagrams, and flow diagrams to characterize communication patterns.

Results: We included 169 transcripts, of which 136 discussed an oncologic problem and 33 considered a vascular (including cardiac and neurovascular) problem. At the median, surgeons devoted an estimated 8 min (interquartile range 5-13 min) to content specifically about intervention including surgery. In 85.5% of conversations, more than 40% of surgeon speech was consumed by technical descriptions of the disease or treatment. "Fix-it" language was used in 91.7% of conversations. In 79.9% of conversations, no overall goal of treatment was established or only a desire to cure or control cancer was expressed. Most conversations (68.6%) began with an explanation of the disease, followed by explanation of the treatment in 53.3%, and then options in 16.6%.

Conclusions: Explanation of disease and treatment dominate surgical consultations, with limited time spent on patient goals. Changing the focus of these conversations may better support patients' deliberation about the value of surgery.Trial registration: ClinicalTrials.gov Identifier: NCT02623335.

Highlights: In decision-making conversations about high-risk surgical intervention, surgeons emphasize description of the patient's disease and potential treatment, and the use of "fix-it" language is common.Surgeons dedicated limited time to eliciting patient preferences and goals, and 79.9% of conversations resulted in no explicit goal of treatment.Current communication practices may be inadequate to support deliberation about the value of surgery for individual patients and their families.

Background: Past studies have shown a commission bias for cancer treatment, a tendency to choose active treatment even when watchful waiting is less risky. This bias suggests motivations for action beyond mortality statistics, but recent evidence suggests that individuals differ in their emotional sensitivity to probabilities (ESP), the tendency to calibrate emotional reactions to probability. The current study aims to examine the role of ESP in the commission bias, specifically whether those higher in ESP are more likely to choose watchful waiting when risk probabilities align with that choice.

Methods: Participants (N = 1,055) read a scenario describing a hypothetical cancer diagnosis and chose between surgery and watchful waiting, with random assignment between versions where the mortality rate was either lower for surgery or for watchful waiting. We modeled choice using the Possibility Probability Questionnaire (PPQ), a measure of ESP, and several other individual differences in a logistic regression.

Results: We observed a commission bias as in past studies with most participants choosing surgery both when surgery was optimal (71%) and when watchful waiting was optimal (58%). An ESP × Condition interaction indicated that the predictive role of ESP depended on condition. Those higher in ESP were more likely to choose surgery when probabilities favored surgery, β = 0.57, P < 0.001, but when probabilities favored watchful waiting, ESP had a near-zero relationship with choice, β = 0.05, P < 0.99.

Conclusions: The role of ESP in decision making is context specific. Higher levels of ESP predict choosing action when that action is warranted but do not predict a shift away from surgery when watchful waiting offers better chances of survival. ESP does not overcome the commission bias.

Highlights: Past studies have identified a "commission bias," a tendency to choose active treatment over watchful waiting, even when mortality rate is lower for waiting.Evaluation of risk probabilities is related to individual differences in emotional sensitivity to probabilities (ESP) and has been shown to predict reactions to and decisions about health risk situations.ESP appears to be selectively factored into decision making. ESP was a robust predictor of choosing surgery when probability information supported surgery but did not predict decisions when probability information supported watchful waiting.Those who are most emotionally attuned to probabilities are just as susceptible to the commission bias as those who are less attuned.

Introduction: Informed decision making is recommended in breast cancer screening. Decision aids with balanced information on harms and benefits are recommended to support informed decision making. However, informed screening decision making may be challenged by overly positive attitudes toward cancer screening. We hypothesized that a substantial proportion of Danish women would want to participate in screening regardless of the presented information. Therefore, we aimed to estimate the prevalence of Danish women wanting to participate in a hypothetical breast cancer screening offering no reduction in breast cancer mortality but potential harms related to unnecessary treatment.

Methods: In a cross-sectional study, we invited a random sample of 751 women in the nonscreening population aged 44 to 49 y in the Central Denmark Region to an online questionnaire using the official digital mailbox system. The questionnaire included a description of a hypothetical screening and questions about thoughts on breast cancer, health literacy, and questions on the assessment of the hypothetical screening including intended participation, understanding, and belief in information. Data were linked to register data on sociodemographic factors.

Results: In total, 43.0% (323/751) responded to the questionnaire. Of these, 247 (82.3% [95% confidence interval: 77.5-86.5]) wanted to participate in the hypothetical breast cancer screening (participation group). More than two-thirds in both the participation group and nonparticipation group seemed to understand the presented information. Half of the women who understood the information disbelieved it.

Conclusions: Exceeding our expectations, a majority of women wanted to participate in a hypothetical screening with potential harms but no reduction in breast cancer mortality. A large proportion understood but disbelieved the screening information. This could indicate that Danish women make their screening decisions based on beliefs rather than presented screening information. This study was registered at ClinicalTrials.gov (Identifier: NCT04509063).

Highlights: The majority of Danish women wanted to participate in a hypothetical breast cancer screening with potential harms related to unnecessary treatment but no reduction in mortality.A large proportion of women understood but disbelieved the hypothetical screening information.Informed decision making may be challenging when women disbelieve the information they receive.Enthusiasm for cancer screening and potential disbelief in information are important factors when developing and improving screening information and invitation.

Introduction: Simulating individual-level trial data when only summary data are available is often useful for meta-analysis, forming external control arms and calibrating trial results to real-world data (RWD). The joint distribution of baseline characteristics in a trial is usually simulated by combining its summary data with RWD's correlations. However, RWD correlations may not be a perfect proxy for the trial. A misspecified correlation structure could bias any analysis in which the outcomes generating models are nonlinear or include effect modifiers.

Methods: We developed an iterative algorithm using copula and resampling, which was based on the estimated propensity score for the likelihood of enrollment in a trial given participants' characteristics. Validation was performed using Monte Carlo simulations under different scenarios in which the marginal and joint distributions of covariates differ between trial samples and RWD. Two applications were illustrated using an actual trial and the Surveillance, Epidemiology, and End Results-Medicare data. We calculated the standardized mean difference (SMD) to assess the generalizability of the trial and explored the feasibility of generating an external control by applying a parametric Weibull model trained in RWD to predict survival in the simulated trial cohort.

Results: Across all scenarios, approximated correlations derived from the algorithm were closer to the true correlations than the RWD's correlations. The algorithm also successfully reproduced the joint distribution of characteristics for the actual trial. A similar SMD was observed using simulated data and individual-level trial data. The 95% confidence intervals were overlapped between adjusted survival estimates from the simulated trial and actual trial Kaplan-Meier estimates.

Conclusions: The algorithm could be a feasible way to simulate individual-level data when only summary data are available. Further research is needed to validate our approach with larger sample sizes.

Highlights: The correlation structure is crucial to building the joint distribution of patient characteristics, and a misspecified correlation structure could potentially influence predicted outcomes.An iterative algorithm was developed to approximate a trial's correlation structure using published summary trial data and real-world data.The algorithm could be a feasible way to simulate individual-level trial data when only trial summary data are available.

Highlights: This article compares the relative preferences from stated preference (SP) questions requiring ordered categorical versus discrete choice responses. The approaches were contrasted for blood donation service characteristics that offer opportunities to donate blood.The estimates of relative preferences for alternative blood donation service characteristics were similar between the 2 forms of SP approach.This study illustrates how SP survey questions can be formulated to provide responses on an ordered categorical scale and to estimate marginal rates of substitution between different attributes, which can be compared with those derived from discrete choice experiment (DCE) choices.The article highlights the potential value of considering alternative choice framings rather than relying solely on DCEs.