Flexible cost-penalized Bayesian model selection: Developing inclusion paths with an application to diagnosis of heart disease.

We propose a Bayesian model selection approach that allows medical practitioners to select among predictor variables while taking their respective costs into account. Medical procedures almost always incur costs in time and/or money. These costs might exceed their usefulness for modeling the outcome of interest. We develop Bayesian model selection that uses flexible model priors to penalize costly predictors a priori and select a subset of predictors useful relative to their costs. Our approach (i) gives the practitioner control over the magnitude of cost penalization, (ii) enables the prior to scale well with sample size, and (iii) enables the creation of our proposed inclusion path visualization, which can be used to make decisions about individual candidate predictors using both probabilistic and visual tools. We demonstrate the effectiveness of our inclusion path approach and the importance of being able to adjust the magnitude of the prior's cost penalization through a dataset pertaining to heart disease diagnosis in patients at the Cleveland Clinic Foundation, where several candidate predictors with various costs were recorded for patients, and through simulated data.

Bayesian model selection for generalized linear mixed models.

We propose a Bayesian model selection approach for generalized linear mixed models (GLMMs). We consider covariance structures for the random effects that are widely used in areas such as longitudinal studies, genome-wide association studies, and spatial statistics. Since the random effects cannot be integrated out of GLMMs analytically, we approximate the integrated likelihood function using a pseudo-likelihood approach. Our Bayesian approach assumes a flat prior for the fixed effects and includes both approximate reference prior and half-Cauchy prior choices for the variances of random effects. Since the flat prior on the fixed effects is improper, we develop a fractional Bayes factor approach to obtain posterior probabilities of the several competing models. Simulation studies with Poisson GLMMs with spatial random effects and overdispersion random effects show that our approach performs favorably when compared to widely used competing Bayesian methods including deviance information criterion and Watanabe-Akaike information criterion. We illustrate the usefulness and flexibility of our approach with three case studies including a Poisson longitudinal model, a Poisson spatial model, and a logistic mixed model. Our proposed approach is implemented in the R package GLMMselect that is available on CRAN.

Interest in and perceived effectiveness of contingency management among alcohol drinkers using behavioral economic purchase tasks.

Contingency management (CM), in which financial incentives are provided upon verification of abstinence from alcohol, cigarettes, and/or illicit substances, is one of the most highly effective and empirically supported treatments for substance use disorders. However, the financial cost of implementation has been identified as a major barrier to implementation of this treatment. The purpose of this study was to develop behavioral economic purchase tasks to assess interest in CM as a function of treatment cost and perceived effectiveness of CM as a function of abstinence incentive size in alcohol drinkers. Alcohol drinkers recruited from Amazon Mechanical Turk (MTurk) completed behavioral economic purchase tasks measuring demand for CM based on targeted abstinence intervals and treatment effectiveness and alcohol use disorder severity assessments. Nonlinear mixed-effects modeling was used to fit demand curves and assess the relationship between individual characteristics and demand metrics for CM. Results reveal that participants reported higher probability of remaining abstinent from drinking when offered larger incentives and required larger incentives when duration of abstinence required to earn the incentive was increased. Additionally, willingness to pay for treatment increased as effectiveness of treatment increased. Abstinence interval and treatment effectiveness are important features to consider when developing effective CM for widespread use, as these variables affected participants' likelihood of being abstinent and their interest in treatment. Future work will validate these assessments with actual treatment outcomes and determine predictors of CM treatment effectiveness. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A tribute to Howard Rachlin and his two-parameter discounting model: Reliable and flexible model fitting.

Delay discounting reflects the rate at which a reward loses its subjective value as a function of delay to that reward. Many models have been proposed to measure delay discounting, and many comparisons have been made among these models. We highlight the two-parameter delay discounting model popularized by Howard Rachlin by demonstrating two key practical features of the Rachlin model. The first feature is flexibility; the Rachlin model fits empirical discounting data closely. Second, when compared with other available two-parameter discounting models, the Rachlin model has the advantage that unique best estimates for parameters are easy to obtain across a wide variety of potential discounting patterns. We focus this work on this second feature in the context of maximum likelihood, showing the relative ease with which the Rachlin model can be utilized compared with the extreme care that must be used with other models for discounting data, focusing on two illustrative cases that pass checks for data validity. Both of these features are demonstrated via a reanalysis of discounting data the authors have previously used for model selection purposes.

Notes on demand: Conceptual and empirical benefits of applying Rachlin's discounting equation to demand data.

OBJECTIVE: Howard Rachlin wrote extensively on how value diminishes in a hyperbolic form, and he contributed to understanding choice processes between different commodities as a molar pattern of behavior. The field of behavioral economic demand has been dominated by exponential decay functions, indicating that decreases in consumption of a commodity are best fit by exponential functions. Because of the success of Rachlin's equation at describing how hyperbolic decay affects the value of a commodity across various factors (e.g., delay, probability, social distance), we attempted to extend his equation to behavioral economic demand data for alcohol and opioids. METHOD: Rachlin's discounting equation was applied to estimate consumption on alcohol purchase task data and nonhuman drug demand data. We compared results of his equation to the exponentiated demand equation using both a mixed-effects modeling approach and a two-stage approach. RESULTS: Rachlin's equation provided better fits to consumption data than the exponentiated equation for both mixed-effects and two-stage modeling. We also found that traditional demand metrics, such as Pmax, can be derived analytically when using Rachlin's equation. Certain metrics derived from Rachlin's equation appeared to be related to clinical covariates in ways similar to the exponentiated equation. CONCLUSIONS: Rachlin's equation better described demand data than did the exponentiated equation, indicating that demand for a commodity may decrease hyperbolically rather than exponentially. Other benefits of his equation are that it does not have the same pitfalls as the current exponential equations and is relatively straightforward in its conceptualization when applied to demand data. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Hyperbolic discounting and exponentiated demand: Modeling demand for cigarettes in three dimensions.

Behavioral economics has been a fruitful area of research in substance use. Mathematical descriptions of how individuals temporally discount the value of a commodity have been correlated with substance use and mathematical descriptions of drug consumption decreasing as a function of price (i.e., demand) predict maladaptive substance use. While there is a logical assumption that temporal factors affect demand for a drug, little has been done to merge these models. Thus, the purpose of this study was to combine models of discounting and demand, extending Howard Rachlin's work and contributions to novel areas of study. Data from 85 participants from Amazon Mechanical Turk (MTurk) who completed a hypothetical cigarette purchase task that included price of and delay to cigarettes were analyzed. Multilevel modeling was used to determine descriptive accuracy of combined additive and multiplicative models of discounting and demand. Of the discounting models used in conjunction with the exponentiated demand equation, the Rachlin hyperboloid best described the delay dimension of consumption. The multiplicative version of the Rachlin equation applied to both delay and price outperformed other models tested. Therefore, existing models of discounting and demand can be extended to modeling consumption data from complex multidimensional experimental arrangements.

Approximate Bayesian Techniques for Statistical Model Selection and Quantifying Model Uncertainty-Application to a Gait Study.

Frequently, biomedical researchers need to choose between multiple candidate statistical models. Several techniques exist to facilitate statistical model selection including adjusted R2, hypothesis testing and p-values, and information criteria among others. One particularly useful approach that has been slow to permeate the biomedical literature is the notion of posterior model probabilities. A major advantage of posterior model probabilities is that they quantify uncertainty in model selection by providing a direct, probabilistic comparison among competing models as to which is the "true" model that generated the observed data. Additionally, posterior model probabilities can be used to compute posterior inclusion probabilities which quantify the probability that individual predictors in a model are associated with the outcome in the context of all models considered given the observed data. Posterior model probabilities are typically derived from Bayesian statistical approaches which require specialized training to implement, but in this paper we describe an easy-to-compute version of posterior model probabilities and inclusion probabilities that rely on the readily-available Bayesian information criterion. We illustrate the utility of posterior model probabilities and inclusion probabilities by re-analyzing data from a published gait study investigating factors that predict required coefficient of friction between the shoe sole and floor while walking.

Behavioral economic demand modeling chronology, complexities, and considerations: Much ado about zeros.

Behavioral economic demand has been shown to have high utility in quantifying the value or consumption of a commodity. Demand describes the relationship between cost and consumption of a commodity, and tends to be curvilinear with consumption approaching zero as the cost increases to a sufficiently high cost to suppress consumption completely. Over a period spanning greater than three decades, behavioral economists have made great strides in the modeling of demand and addressing analytical challenges, although this work is not complete and unresolved challenges remain. The analytical challenges associated with modeling zeros both when they arise as consumption values of zero and when consumption at zero cost is assessed have been a substantial part of this evolution in models. The goals of this methodological review are to provide a historical overview of the major behavioral economic demand models that have been proposed, describe some of the common difficulties with analyzing behavioral economic demand, and discuss general considerations for the analysis of demand. In an environment with evolving and multiple competing analytical practices, we conclude that researchers can maximize scientific rigor by embracing transparency in their analysis choices and employing techniques such as sensitivity analyses to determine if their analysis choices impact the conclusions of their experiments.

Blood Nicotine Predicts the Behavioral Economic Abuse Liability of Reduced-Nicotine Cigarettes.

BACKGROUND: Cigarette smoking continues to be a major health concern and remains the leading preventable cause of death in the US. Recent efforts have been made to determine the potential health and policy benefits of reducing nicotine in combustible cigarettes. The degree to which changes in blood nicotine relate to measures of the abuse liability of reduced-nicotine cigarettes is unknown. The current study examined the relation between blood nicotine and behavioral economic demand measures of cigarettes differing in nicotine content. METHODS: Using a within-subject design, participants smoked a single cigarette during each experimental session. Cigarettes included the participant's usual-brand cigarette and SPECTRUM investigational cigarette differing in nicotine level (mg of nicotine to g of tobacco; 15.8 mg/g, 5.2 mg/g, 2.4 mg/g, 1.3 mg/g, and 0.4 mg/g). During each session, blood was collected at multiple timepoints and behavioral economic demand was assessed. Nonlinear mixed-effects models were used to estimate differences in derived intensity (Q0) and change in elasticity (α). RESULTS: Measures of blood nicotine decreased in an orderly fashion related to nicotine level and significantly predicted change in elasticity (α), but not derived intensity. No differences in demand parameters between the usual brand and 15.8mg/g cigarettes were observed. However, α was significantly higher (lower valuation) for 0.4mg/g than 15.8mg/g cigarettes. CONCLUSIONS: The lowest nicotine level (0.4mg/g) corresponded with the lowest abuse liability (α) compared to the full-strength control (15.8mg/g), with the 1.3mg/g level also resulting in low abuse liability. IMPLICATIONS: This is the first study examining the relative contributions of nicotine content in cigarettes and blood nicotine levels on the behavioral economic demand abuse liability of cigarettes ranging in nicotine content. Our results suggest blood nicotine and nicotine content both predict behavioral economic demand abuse liability. In addition, our results suggest a nicotine content of 1.3mg/g or lower may be effective at reducing cigarette uptake among first-time (naïve) smokers. Our results largely conform to previous findings suggesting a very low nicotine content cigarette maintains lower abuse liability than full-strength cigarettes.

Applying Mixed-Effects Modeling to Behavioral Economic Demand: An Introduction.

Behavioral economic demand methodology is increasingly being used in various fields such as substance use and consumer behavior analysis. Traditional analytical techniques to fitting demand data have proven useful yet some of these approaches require preprocessing of data, ignore dependence in the data, and present statistical limitations. We term these approaches "fit to group" and "two stage" with the former interested in group or population level estimates and the latter interested in individual subject estimates. As an extension to these regression techniques, mixed-effect (or multilevel) modeling can serve as an improvement over these traditional methods. Notable benefits include providing simultaneous group (i.e., population) level estimates (with more accurate standard errors) and individual level predictions while accommodating the inclusion of "nonsystematic" response sets and covariates. These models can also accommodate complex experimental designs including repeated measures. The goal of this article is to introduce and provide a high-level overview of mixed-effects modeling techniques applied to behavioral economic demand data. We compare and contrast results from traditional techniques to that of the mixed-effects models across two datasets differing in species and experimental design. We discuss the relative benefits and drawbacks of these approaches and provide access to statistical code and data to support the analytical replicability of the comparisons. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40614-021-00299-7.

Effects of Reduced-Nicotine Cigarettes Across Regulatory Environments in the Experimental Tobacco Marketplace: A Randomized Trial.

INTRODUCTION: Cigarette smoking remains the leading preventable cause of death in the United States. Recent efforts have explored the potential health and policy benefits of reducing nicotine, an addictive component, in combustible cigarettes. To date, an experimental, prospective analysis directly comparing the effects of varying regulatory environments on purchases of multiple products has yet to be conducted. The present study compared real purchasing of conventional cigarettes, reduced-nicotine cigarettes, and a variety of other nicotine and tobacco products across a range of regulatory environments. METHODS: Participants were assigned to one of five groups, each associated with a different nicotine level (mg of nicotine to g of tobacco) in SPECTRUM investigational cigarettes (15.8, 5.2, 2.4, 1.3, and 0.4 mg/g). Across sessions, participants made real purchases for nicotine/tobacco products in an Experimental Tobacco Marketplace. Each session corresponded with a distinct regulatory environment wherein different nicotine/tobacco products were available for purchase. RESULTS: Our results suggest that the primary drivers of cigarette and nicotine purchasing are regulatory environment and the presence/absence of alternative nicotine and tobacco products. Perhaps surprisingly, nicotine level does not appear to be such a driver of purchasing behavior under these experimental conditions. Investigational cigarette purchasing is lowest when other preferred combustible products are available and highest when investigational cigarettes are the only combustible product available for purchase. CONCLUSIONS: If a reduced-nicotine policy is implemented, great care should be taken in determining and making available less-harmful nicotine/tobacco products as the availability of preferred combustible products may result in undesirable levels of purchasing. IMPLICATIONS: This is the first experimental study investigating different potential regulatory effects related to a reduced-nicotine policy by examining purchasing across a range of nicotine/tobacco products. Our results suggest the presence of affordable, highly preferred combustible products is likely to maintain tobacco purchasing at undesirable levels. To promote switching to less-harmful products, affordable alternate nicotine and tobacco products should be readily available. Finally, our results suggest that the availability of noncigarette products, not cigarette nicotine level, will most likely affect purchasing of reduced-nicotine cigarettes.

An overview of Bayesian reasoning in the analysis of delay-discounting data.

Statistical inference (including interval estimation and model selection) is increasingly used in the analysis of behavioral data. As with many other fields, statistical approaches for these analyses traditionally use classical (i.e., frequentist) methods. Interpreting classical intervals and p-values correctly can be burdensome and counterintuitive. By contrast, Bayesian methods treat data, parameters, and hypotheses as random quantities and use rules of conditional probability to produce direct probabilistic statements about models and parameters given observed study data. In this work, we reanalyze two data sets using Bayesian procedures. We precede the analyses with an overview of the Bayesian paradigm. The first study reanalyzes data from a recent study of controls, heavy smokers, and individuals with alcohol and/or cocaine substance use disorder, and focuses on Bayesian hypothesis testing for covariates and interval estimation for discounting rates among various substance use disorder profiles. The second example analyzes hypothetical environmental delay-discounting data. This example focuses on using historical data to establish prior distributions for parameters while allowing subjective expert opinion to govern the prior distribution on model preference. We review the subjective nature of specifying Bayesian prior distributions but also review established methods to standardize the generation of priors and remove subjective influence while still taking advantage of the interpretive advantages of Bayesian analyses. We present the Bayesian approach as an alternative paradigm for statistical inference and discuss its strengths and weaknesses.

Limb asymmetry during recovery from anterior cruciate ligament reconstruction.

There is limited literature that follows a population of Anterior Cruciate Ligament Reconstruction (ACLR) patients through recovery. Our aim was to examine differences in movement and loading patterns across time and between limbs over four visits during 12 months post-ACLR. We hypothesized that kinematic and kinetic data during a stop-jump would have time- and limb-dependent differences through 12 months post-surgery. Twenty-three ACLR athletes performed five vertical stop-jumps at 4, 5, 6, and 12 months post-op with motion capture and force plate data collection. The peak knee flexion (PKF) was different between the 4 and 12, 5 and 6, and the 5 and 12 month visits with earlier months exhibiting higher PKF. The peak vertical ground reaction force (vGRF) was lower at 4 than at 5 and 6 months. The peak posterior ground reaction force (pGRF) was lower at 4 months than all other visits. Frontal knee and sagittal hip range of motion (ROM) were different between 12 months and each previous visit. Asymmetries were present in peak vGRF, peak knee extension moment and impulse up to 12 months. The loading rate and peak pGRF demonstrated between limb differences up to 6 months; limb stiffness demonstrated differences up to 5 months post-ACLR. PKF was only asymmetric at the 4 month visit. While some variables improved in the 12 months post-ACLR, limb asymmetries in peak knee extension moment, peak vGRF and impulse persisted up to 12 months. Additionally, frontal plane knee and sagittal hip ROM had not normalized at 12 months. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:1887-1893, 2018.

Preventing relapse to smoking with transcranial magnetic stimulation: Feasibility and potential efficacy.

Many smokers attempt to quit every year, but 90% relapse within 12 months. Converging evidence suggests relapse is associated with insufficient activation of the prefrontal cortex. Delay discounting rate reflects relative activity in brain regions associated with relapse. High-frequency repetitive transcranial magnetic stimulation (rTMS) of the left dorsolateral prefrontal cortex (LDLPFC) increases cortical excitability and reduces delay discounting rates, but little is known about feasibility, tolerability, and potential efficacy for smoking cessation. We hypothesized that 8 sessions of 20Hz rTMS of the LDLPFC combined with an evidence-based self-help intervention will demonstrate feasibility, tolerability, and potential efficacy in a limited double-blind randomized control trial. Smokers (n=29), abstinent for 24h, motivated to quit, and not using cessation medications, were randomized to active 20Hz rTMS at 110% of Motor Threshold or sham stimulation that replicated the look and sound of active stimulation. Stimulation site was located using the 6cm rule and neuro-navigation. Multiple clinical, feasibility, tolerability, and efficacy measures were examined. Active rTMS decreased delay discounting of $100 (F (1, 25.3694)=4.14, p=.05) and $1000 (F (1, 25.169)=8.42, p<.01), reduced the relative risk of relapse 3-fold (RR 0.29, CI 0.10-0.76, Likelihood ratio χ2 with 1 df=6.40, p=.01), increased abstinence rates (active 50% vs. sham 15.4%, Χ2 (df=1)=3.80, p=.05), and increased uptake of the self-help intervention. Clinical, feasibility, and tolerability assessments were favorable. Combining 20Hz rTMS of the LDLPFC with an evidence-based self-help intervention is feasible, well-tolerated, and demonstrates potential efficacy.

Improving tobacco dependence treatment outcomes for smokers of lower socioeconomic status: A randomized clinical trial.

INTRODUCTION: Evidence-based treatments for tobacco dependence are significantly less effective for smokers of lower socioeconomic status which contributes to socioeconomic disparities in smoking prevalence rates and health. We aimed to reduce the socioeconomic gradient in treatment outcomes by systematically adapting evidence-based, cognitive-behavioral treatment for tobacco dependence for diverse lower socioeconomic smokers. METHODS: Participants were randomized to adapted or standard treatment, received six 1-h group treatment sessions, and were followed for six months. We examined the effectiveness of the adapted treatment to improve treatment outcomes for lower socioeconomic groups. RESULTS: Participants (n=227) were ethnically, racially, and socioeconomically diverse. The adapted treatment significantly reduced the days to relapse for the two lowest socioeconomic groups: SES1: M=76.6 (SD 72.9) vs. 38.3 (SD 60.1) days to relapse (RR=0.63 95% CI, 0.45, 0.88, p=0.0013); SES2: M=88.2 (SD 67.3) vs. 40.1 (SD 62.6 days to relapse (RR=0.57 95% CI, 0.18, 0.70, p=0.0024). Interactions between socioeconomic status and condition were significant for initial abstinence (OR=1.26, 95% CI 1.09, 1.46, p=0.002), approached significance for 3-month abstinence (OR=0.90, 95% CI 0.80, 1.01, p<0.071), and were not significant for 6-month abstinence (OR=0.99 95% CI 0.88, 1.10, p=0.795). No significant differences in long-term abstinence were observed. CONCLUSION: Systematic adaption of evidence-based treatment for tobacco dependence can significantly improve initial and short-term treatment outcomes for diverse lower socioeconomic smokers and reduce inequities in days to relapse. Novel methods of providing targeted extended support are needed to improve long-term outcomes.

Neurocognitive dysfunction in addiction: Testing hypotheses of diffuse versus selective phenotypic dysfunction with a classification-based approach.

Neurocognitive dysfunctions are frequently identified in the addictions. These dysfunctions may indicate either diffuse dysfunction or may represent separate facets that have differential importance to the addiction phenotype. In a sample (n = 260) of alcohol and/or stimulant users and controls we measured responses across 7 diverse neurocognitive measures. These measures were Continuous Performance, Delay Discounting, Iowa Gambling, Stroop, Tower, Wisconsin Card Sorting, and Letter Number Sequencing. Comparisons were then made between the drug-dependent groups and controls using analysis of variance and also using a machine learning approach to classify participants based on task performance as substance-dependent or controls in 1 tree and as alcohol and/or stimulant users or controls in a second tree. The analysis of variance showed significant differences between groups on the Delay Discounting (p < .001), Iowa Gambling (p < .001), Letter Number Sequencing (p < .001), and Wisconsin Card Sorting (p < .05) tasks. The first classification tree correctly classified between substance-dependent or controls for 88.3% of participants and classified between alcohol and/or stimulant users or controls for 63.9% of participants. Delay discounting was the first split in both trees and in the substance-dependent and control tree. The analysis of variance results largely replicate previous findings. The machine learning classification tree analysis provides evidence to support the hypothesis that different measures of neurocognitive dysfunction represent different processes. Among them, delay discounting was the most robust in categorizing drug dependence. (PsycINFO Database Record

Are There Differences in Gait Mechanics in Patients With A Fixed Versus Mobile Bearing Total Ankle Arthroplasty? A Randomized Trial.

BACKGROUND: Total ankle arthroplasty (TAA) is an alternative to arthrodesis, but no randomized trial has examined whether a fixed bearing or mobile bearing implant provides improved gait mechanics. QUESTIONS/PURPOSES: We wished to determine if fixed- or mobile-bearing TAA results in a larger improvement in pain scores and gait mechanics from before surgery to 1 year after surgery, and to quantify differences in outcomes using statistical analysis and report the standardized effect sizes for such comparisons. METHODS: Patients with end-stage ankle arthritis who were scheduled for TAA between November 2011 and June 2013 (n = 40; 16 men, 24 women; average age, 63 years; age range, 35-81 years) were prospectively recruited for this study from a single foot and ankle orthopaedic clinic. During this period, 185 patients underwent TAA, with 144 being eligible to participate in this study. Patients were eligible to participate if they were able to meet all study inclusion criteria, which were: no previous diagnosis of rheumatoid arthritis, a contralateral TAA, bilateral ankle arthritis, previous revision TAA, an ankle fusion revision, or able to walk without the use of an assistive device, weight less than 250 pounds (114 kg), a sagittal or coronal plane deformity less than 15°, no presence of avascular necrosis of the distal tibia, no current neuropathy, age older than 35 years, no history of a talar neck fracture, or an avascular talus. Of the 144 eligible patients, 40 consented to participate in our randomized trial. These 40 patients were randomly assigned to either the fixed (n = 20) or mobile bearing implant group (n = 20). Walking speed, bilateral peak dorsiflexion angle, peak plantar flexion angle, sagittal plane ankle ROM, peak ankle inversion angle, peak plantar flexion moment, peak plantar flexion power during stance, peak weight acceptance, and propulsive vertical ground reaction force were analyzed during seven self-selected speed level walking trials for 33 participants using an eight-camera motion analysis system and four force plates. Seven patients were not included in the analysis owing to cancelled surgery (one from each group) and five were lost to followup (four with fixed bearing and one with mobile bearing implants). A series of effect-size calculations and two-sample t-tests comparing postoperative and preoperative increases in outcome variables between implant types were used to determine the differences in the magnitude of improvement between the two patient cohorts from before surgery to 1 year after surgery. The sample size in this study enabled us to detect a standardized shift of 1.01 SDs between group means with 80% power and a type I error rate of 5% for all outcome variables in the study. RESULTS: This randomized trial did not reveal any differences in outcomes between the two implant types under study at the sample size collected. In addition to these results, effect size analysis suggests that changes in outcome differ between implant types by less than 1 SD. Detection of the largest change score or observed effect (propulsive vertical ground reaction force [Fixed: 0.1 ± 0.1; 0.0-1.0; Mobile: 0.0 ± 0.1; 0.0-0.0; p = 0.0.051]) in this study would require a future trial to enroll 66 patients. However, the smallest change score or observed effect (walking speed [Fixed: 0.2 ± 0.3; 0.1-0.4; Mobile: 0.2 ± 0.3; 0.0-0.3; p = 0.742]) requires a sample size of 2336 to detect a significant difference with 80% power at the observed effect sizes. CONCLUSIONS: To our knowledge, this is the first randomized study to report the observed effect size comparing improvements in outcome measures between fixed and mobile bearing implant types. This study was statistically powered to detect large effects and descriptively analyze observed effect sizes. Based on our results there were no statistically or clinically meaningful differences between the fixed and mobile bearing implants when examining gait mechanics and pain 1 year after TAA. LEVEL OF EVIDENCE: Level II, therapeutic study.

The discounting model selector: Statistical software for delay discounting applications.

Original, open-source computer software was developed and validated against established delay discounting methods in the literature. The software executed approximate Bayesian model selection methods from user-supplied temporal discounting data and computed the effective delay 50 (ED50) from the best performing model. Software was custom-designed to enable behavior analysts to conveniently apply recent statistical methods to temporal discounting data with the aid of a graphical user interface (GUI). The results of independent validation of the approximate Bayesian model selection methods indicated that the program provided results identical to that of the original source paper and its methods. Monte Carlo simulation (n = 50,000) confirmed that true model was selected most often in each setting. Simulation code and data for this study were posted to an online repository for use by other researchers. The model selection approach was applied to three existing delay discounting data sets from the literature in addition to the data from the source paper. Comparisons of model selected ED50 were consistent with traditional indices of discounting. Conceptual issues related to the development and use of computer software by behavior analysts and the opportunities afforded by free and open-sourced software are discussed and a review of possible expansions of this software are provided.

Stuck in Time: Negative Income Shock Constricts the Temporal Window of Valuation Spanning the Future and the Past.

Insufficient resources are associated with negative consequences including decreased valuation of future reinforcers. To determine if these effects result from scarcity, we examined the consequences of acute, abrupt changes in resource availability on delay discounting-the subjective devaluation of rewards as delay to receipt increases. In the current study, 599 individuals recruited from Amazon Mechanical Turk read a narrative of a sudden change (positive, neutral, or negative) to one's hypothetical future income and completed a delay discounting task examining future and past monetary gains and losses. The effects of the explicit zero procedure, a framing manipulation, was also examined. Negative income shock significantly increased discounting rates for gains and loses occurring both in the future and the past. Positive income windfalls significantly decreased discounting to a lesser extent. The framing procedure significantly reduced discounting under all conditions. Negative income shocks may result in short-term choices.

Initial examination of priming tasks to decrease delay discounting.

Steep discounting of delayed rewards is linked with a variety of unhealthy behaviors that contribute to the major causes of preventable death and disease. Growing evidence suggests that decreases in delay discounting contribute to healthier preferences. This study sought to provide preliminary evidence for the viability of developing a brief priming task to reduce delay discounting in a large, diverse group of individuals. Participants (n=1,122) were randomized to one of three conditions: Future Focus (FF), Present Focus (PF), and Non-Temporal Focus (NTF) intended respectively to decrease, increase, or have no effect on delay discounting. Participants then completed the Monetary Choice Questionnaire, a brief assessment of delay discounting rate. Participants randomized to FF exhibited significantly lower discounting rates than those randomized to PF or NTF conditions. Race, Hispanic background, social self-monitoring, education, and cigarette smoking also accounted for a significant amount of variance in the discounting model. These findings provide support for the development of a brief priming intervention that might be examined in clinical or public health contexts to decrease discounting and support healthy choices.