Emotions and decisions in the real world: What can we learn from quasi-field experiments?
Ontology highlight
ABSTRACT:
SUBMITTER: Bhanot SP
PROVIDER: S-EPMC7744061 | biostudies-literature | 2020
REPOSITORIES: biostudies-literature
Publications
Emotions and decisions in the real world: What can we learn from quasi-field experiments?
Bhanot Syon P SP Chang Daphne D Lee Cunningham Julia J Ranson Matthew M
PloS one 20201216 12
Researchers in the social sciences have increasingly studied how emotions influence decision-making. We argue that research on emotions arising naturally in real-world environments is critical for the generalizability of insights in this domain, and therefore to the development of this field. Given this, we argue for the increased use of the "quasi-field experiment" methodology, in which participants make decisions or complete tasks after as-if-random real-world events determine their emotional ...[more]
Similar Datasets
Project description:How humans resolve non-trivial tradeoffs in their navigational choices between the social interactions (e.g., the presence and movements of others) and the physical factors (e.g., spatial distances, route visibility) when escaping from threats in crowded confined spaces? The answer to this question has major implications for the planning of evacuations and the safety of mass gatherings as well as the design of built environments. Due to the challenges of collecting behavioral data from naturally-occurring evacuation settings, laboratory-based virtual-evacuation experiments have been practiced in a number of studies. This class of experiments faces the traditional question of contextual bias and generalizability: How reliably can we infer humans' behavior from decisions made in hypothetical settings? Here, we address these questions by making a novel link between two different forms of empirical observations. We conduct hypothetical emergency exit-choice experiments framed as simple pictures, and then mimic those hypothetical scenarios in more realistic fashions through staging mock evacuation trials with actual crowds. Econometric choice models are estimated based on the observations made in both experimental contexts. The models are contrasted with each other from a number of perspectives including their predictions as well as the sign, magnitude, statistical significance, person-to-person variations (reflecting individuals' perception/preference differences) and the scale (reflecting context-dependent decision randomness) of their inferred parameters. Results reveal a surprising degree of resemblance between the models derived from the two contexts. Most strikingly, they produce fairly similar prediction probabilities whose differences average less than 10%. There is also unexpected consensus between the inferences derived from both experimental sources on many aspects of people's behavior notably in terms of the perception of social interactions. Results show that we could have elicited peoples' escape strategies with fair precision without observing them in action (i.e., simply by using only hypothetical-choice data as an inexpensive, practical and non-invasive experimental technique in this context). As a broader application, this offers promising evidence as to the potential applicability of the hypothetical-decision experiments to other decision contexts (at least for non-financial decisions) when field or real-world data is prohibitively unavailable. As a practical application, the behavioral insights inferred from our observations (reflected in the estimated parameters) can improve how accurately we predict the movement patterns of human crowds in emergency scenarios arisen in complex spaces. Fully-generic-in-parameters, our proposed models can even be directly introduced to a broad range of crowd simulation software to replicate navigation decision making of evacuees.
| S-EPMC5117746 | biostudies-literature
Project description:Survey experiments, like vignette and conjoint analyses, are widely used in the social sciences to elicit stated preferences and study how humans make multidimensional choices. However, there is a paucity of research on the external validity of these methods that examines whether the determinants that explain hypothetical choices made by survey respondents match the determinants that explain what subjects actually do when making similar choices in real-world situations. This study compares results from conjoint and vignette analyses on which immigrant attributes generate support for naturalization with closely corresponding behavioral data from a natural experiment in Switzerland, where some municipalities used referendums to decide on the citizenship applications of foreign residents. Using a representative sample from the same population and the official descriptions of applicant characteristics that voters received before each referendum as a behavioral benchmark, we find that the effects of the applicant attributes estimated from the survey experiments perform remarkably well in recovering the effects of the same attributes in the behavioral benchmark. We also find important differences in the relative performances of the different designs. Overall, the paired conjoint design, where respondents evaluate two immigrants side by side, comes closest to the behavioral benchmark; on average, its estimates are within 2% percentage points of the effects in the behavioral benchmark.
| S-EPMC4345583 | biostudies-literature
Project description:Randomized controlled trials (RCTs) are preferred by payers for health technology assessments and coverage decisions. However, the inclusion of a highly selective patient population and the rigorously controlled conditions in RCTs may not be reflective of real-world clinical practice. Real-world evidence (RWE) obtained from an analysis of real-world data (RWD) from observational studies can bridge gaps in evidence not addressed by RCTs and is thus valuable to public and private payers for decision-making. Through a broad literature search to obtain insights into payers' experience, we found that payers have concerns about real-world studies with respect to data quality, poor internal validity, potential bias, and lack of meaningful endpoints. However, they valued RWE to fill evidence gaps not addressed by RCTs, such as high-quality, real-world, long-term effectiveness and safety data; head-to-head drug comparisons; cost analyses for tiering formulary placement; medication use and adherence patterns; identification of relevant responder and non-responder patient subpopulations; and patient-reported outcomes (PROs). RWE can be used to assess clinically meaningful endpoints and gauge the impact of interventions on the quality of healthcare. Here, we review how payers use or can use RWD on the comparative effectiveness and safety of treatments, PROs, medication adherence and persistence, prescribing patterns, healthcare resource utilization, and patient characteristics and/or biomarkers associated with treatment response when making health technology assessments and payer coverage decisions across therapeutic areas.
| S-EPMC7895868 | biostudies-literature
Project description:Humans can rapidly discriminate complex scenarios as they unfold in real time, for example during law enforcement or, more prosaically, driving and sport. Such decision-making improves with experience, as new sources of information are exploited. For example, sports experts are able to predict the outcome of their opponent's next action (e.g., a tennis stroke) based on kinematic cues "read" from preparatory body movements. Here, we explore the use of psychophysical classification-image techniques to reveal how participants interpret complex scenarios. We used sport as a test case, filming tennis players serving and hitting ground strokes, each with two possible directions. These videos were presented to novices and club-level amateurs, running from 0.8 s before to 0.2 s after racquet-ball contact. During practice, participants anticipated shot direction under a time limit targeting 90% accuracy. Participants then viewed videos through Gaussian windows ("bubbles") placed at random in the temporal, spatial or spatiotemporal domains. Comparing bubbles from correct and incorrect trials revealed how information from different regions contributed toward a correct response. Temporally, only later frames of the videos supported accurate responding (from ~0.05 s before ball contact to 0.1 s afterwards). Spatially, information was accrued from the ball's trajectory and from the opponent's head. Spatiotemporal bubbles again highlighted ball trajectory information, but seemed susceptible to an attentional cuing artifact, which may caution against their wider use. Overall, bubbles proved effective in revealing regions of information accrual, and could thus be applied to help understand choice behavior in a range of ecologically valid situations.
| S-EPMC6256113 | biostudies-literature
Project description:The use of bone turnover marker (BTM) testing for patients with osteoporosis in the USA has not been well characterized. This retrospective US-based real-world data study found BTM testing has some association with treatment decision-making and lower fracture risk in patients with presumed osteoporosis, supporting its use in clinical practice.IntroductionThe purpose of this study was to characterize bone turnover marker (BTM) testing patterns and estimate their clinical utility in treatment decision-making and fragility fracture risk in patients with osteoporosis using a retrospective claims database.MethodsData from patients aged ≥ 50 years with newly diagnosed osteoporosis enrolled in the Truven MarketScan® Commercial Claims and Encounters and Medicare Supplemental and Co-ordination of Benefits databases from January 2008 to June 2018 were included. Osteoporosis was ascertained by explicit claims, fragility fracture events associated with osteoporosis, or prescribed anti-resorptive or anabolic therapy. BTM-tested patients were 1:1 propensity score matched to those untested following diagnosis. Generalized estimating equation models were performed to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for testing versus no testing on both treatment decision-making and fragility fracture.ResultsOf the 457,829 patients with osteoporosis, 6075 were identified with ≥ 1 BTM test following diagnosis; of these patients, 1345 had a unique treatment decision made ≤ 30 days from BTM testing. The percentage of patients receiving BTM tests increased significantly each year (average annual % change: + 8.1%; 95% CI: 5.6-9.0; p = 0.01). Patients tested were significantly more likely to have a treatment decision (OR: 1.14; 95% CI: 1.13-1.15), and testing was associated with lower odds of fracture versus those untested (OR: 0.87; 95% CI: 0.85-0.88).ConclusionIn this large, heterogeneous population of patients with presumed osteoporosis, BTM testing was associated with treatment decision-making, likely leading to fragility fracture reduction following use.
| S-EPMC8043891 | biostudies-literature
Project description:PurposeResults from active surveillance trials for ductal carcinoma in situ (DCIS) will not be available for > 10 years. A model based on real-world data (RWD) can demonstrate the comparative impact of non-intervention for women with low-risk features.MethodsMulti-state models were developed using Surveillance, Epidemiology, and End Results Program (SEER) data for three treatment strategies (no local treatment, breast conserving surgery [BCS], BCS + radiotherapy [RT]), and for women with DCIS low-risk features. Eligible cases included women aged ≥ 40 years, diagnosed with primary DCIS between 1992 and 2016. Five mutually exclusive health states were modelled: DCIS, ipsilateral invasive breast cancer (iIBC) ≤ 5 years and > 5 years post-DCIS diagnosis, contralateral IBC, death preceded by and death not preceded by IBC. Propensity score-weighted Cox models assessed effects of treatment, age, diagnosis year, grade, ER status, and race.ResultsData on n = 85,982 women were used. Increased risk of iIBC ≤ 5 years post-DCIS was demonstrated for ages 40-49 (Hazard ratio (HR) 1.86, 95% Confidence Interval (CI) 1.34-2.57 compared to age 50-69), grade 3 lesions (HR 1.42, 95%CI 1.05-1.91) compared to grade 2, lesion size ≥ 2 cm (HR 1.66, 95%CI 1.23-2.25), and Black race (HR 2.52, 95%CI 1.83-3.48 compared to White). According to the multi-state model, propensity score-matched women with low-risk features who had not died or experienced any subsequent breast event by 10 years, had a predicted probability of iIBC as first event of 3.02% for no local treatment, 1.66% for BCS, and 0.42% for BCS+RT.ConclusionRWD from the SEER registry showed that women with primary DCIS and low-risk features demonstrate minimal differences by treatment strategy in experiencing subsequent breast events. There may be opportunity to de-escalate treatment for certain women with low-risk features: Hispanic and non-Hispanic white women aged 50-69 at diagnosis, with ER+, grade 1 + 2, < 2 cm DCIS lesions.
| S-EPMC8062323 | biostudies-literature
Project description:Background:Propensity score adjustment is a popular approach for confounding control in observational studies. Reliable frameworks are needed to determine relative propensity score performance in large-scale studies, and to establish optimal propensity score model selection methods. Methods:We detail a propensity score evaluation framework that includes synthetic and real-world data experiments. Our synthetic experimental design extends the 'plasmode' framework and simulates survival data under known effect sizes, and our real-world experiments use a set of negative control outcomes with presumed null effect sizes. In reproductions of two published cohort studies, we compare two propensity score estimation methods that contrast in their model selection approach: L1-regularized regression that conducts a penalized likelihood regression, and the 'high-dimensional propensity score' (hdPS) that employs a univariate covariate screen. We evaluate methods on a range of outcome-dependent and outcome-independent metrics. Results:L1-regularization propensity score methods achieve superior model fit, covariate balance and negative control bias reduction compared with the hdPS. Simulation results are mixed and fluctuate with simulation parameters, revealing a limitation of simulation under the proportional hazards framework. Including regularization with the hdPS reduces commonly reported non-convergence issues but has little effect on propensity score performance. Conclusions:L1-regularization incorporates all covariates simultaneously into the propensity score model and offers propensity score performance superior to the hdPS marginal screen.
| S-EPMC6280944 | biostudies-literature
Project description:Quantum mechanics allows distribution of intrinsically secure encryption keys by optical means. Twin-field quantum key distribution is one of the most promising techniques for its implementation on long-distance fiber networks, but requires stabilizing the optical length of the communication channels between parties. In proof-of-principle experiments based on spooled fibers, this was achieved by interleaving the quantum communication with periodical stabilization frames. In this approach, longer duty cycles for the key streaming come at the cost of a looser control of channel length, and a successful key-transfer using this technique in real world remains a significant challenge. Using interferometry techniques derived from frequency metrology, we develop a solution for the simultaneous key streaming and channel length control, and demonstrate it on a 206 km field-deployed fiber with 65 dB loss. Our technique reduces the quantum-bit-error-rate contributed by channel length variations to <1%, representing an effective solution for real-world quantum communications.
| S-EPMC8748954 | biostudies-literature
Project description:BACKGROUND:Oncology therapy is becoming increasingly more expensive and challenging the affordability and sustainability of drug programmes around the world. When new drugs are evaluated, health technology assessment organisations rely on clinical trials to inform funding decisions. However, clinical trials are not able to assess overall survival and generalises evidence in a real-world setting. As a result, policy makers have little information on whether drug funding decisions based on clinical trials ultimately yield the outcomes and value for money that might be expected. OBJECTIVE:The Canadian Real-world Evidence for Value of Cancer Drugs (CanREValue) collaboration, consisting of researchers, recommendation-makers, decision makers, payers, patients and caregivers, are developing and testing a framework for Canadian provinces to generate and use real-world evidence (RWE) for cancer drug funding in a consistent and integrated manner. STRATEGY:The CanREValue collaboration has established five formal working groups (WGs) to focus on specific processes in the generation and use of RWE for cancer drug funding decisions in Canada. The different RWE WGs are: (1) Planning and Drug Selection; (2) Methods; (3) Data; (4) Reassessment and Uptake; (5) Engagement. These WGs are acting collaboratively to develop a framework for RWE evaluation, validate the framework through the multiprovince RWE projects and help to integrate the final RWE framework into the Canadian healthcare system. OUTCOMES:The framework will enable the reassessment of cancer drugs, refinement of funding recommendations and use of novel funding mechanisms by decision-makers/payers across Canada to ensure the healthcare system is providing clinical benefits and value for money.
| S-EPMC6955501 | biostudies-literature
Project description:BackgroundNatural or quasi experiments are appealing for public health research because they enable the evaluation of events or interventions that are difficult or impossible to manipulate experimentally, such as many policy and health system reforms. However, there remains ambiguity in the literature about their definition and how they differ from randomized controlled experiments and from other observational designs. We conceptualise natural experiments in the context of public health evaluations and align the study design to the Target Trial Framework.MethodsA literature search was conducted, and key methodological papers were used to develop this work. Peer-reviewed papers were supplemented by grey literature.ResultsNatural experiment studies (NES) combine features of experiments and non-experiments. They differ from planned experiments, such as randomized controlled trials, in that exposure allocation is not controlled by researchers. They differ from other observational designs in that they evaluate the impact of events or process that leads to differences in exposure. As a result they are, in theory, less susceptible to bias than other observational study designs. Importantly, causal inference relies heavily on the assumption that exposure allocation can be considered 'as-if randomized'. The target trial framework provides a systematic basis for evaluating this assumption and the other design elements that underpin the causal claims that can be made from NES.ConclusionsNES should be considered a type of study design rather than a set of tools for analyses of non-randomized interventions. Alignment of NES to the Target Trial framework will clarify the strength of evidence underpinning claims about the effectiveness of public health interventions.
| S-EPMC7879679 | biostudies-literature