Two-stage Modeling Approach Research Articles

Retracted: Multi-objective grasshopper optimization algorithm for optimal energy scheduling by considering heat as integrated demand response

Due to the challenges associated with grid management, it is crucial to develop energy management systems that consider optimal resource performance within an integrated system and enable control of power exchange with the grid. Simultaneously, addressing the demand for multiple energy sources is a critical step towards achieving an economically and efficiently integrated energy system (IES), which plays a pivotal role in promoting sustainable IES development. Building upon these considerations, this article proposes a two-objective optimization model that incorporates the concept of demand response and integrated energy systems, while considering comprehensive economic and energy efficiency benefits. To ensure realism, a range of linear and nonlinear constraints are employed. To address such a problem, a two-stage modeling and solution approach is proposed. Initially, flexible electrical and thermal loads are combined to establish a mechanism for effectively responding to system behavior. Subsequently, a multi-objective model is presented to enhance energy generation and transfer efficiency, taking into account both demand response and economic aspects. Given the distinct properties of these objectives, a multi-objective grasshopper optimization algorithm (GOA) is developed. The Pareto concept is utilized to enhance search performance, and fuzzy theory is employed to select the optimal solution from the set of solutions. Finally, the proposed model and algorithm are evaluated under various operating conditions on the studied system. The results demonstrate that this model effectively improves economic aspects, enhances efficiency, and reduces pollutant emissions to an acceptable level.

Examining neighborhood effects on mental health utilizing a novel two-stage modeling approach

Neighourhood conditions may affect health, but health may also determine a preference where to live. This study estimates the effect of neighbourhood characteristics on mental health while aiming to adjust for this residential self-selection. A two-step method was implemented using register data from Statistics Netherlands from all residents of the city of Rotterdam relocating within the city in 2013 (N=12.456). First, using a conditional logit model, we estimated for each individual the probability of relocating to a neighbourhood over all other neighbourhoods in Rotterdam, based on personal and neighbourhood characteristics in 2013. Second, we corrected for this selection process in a model investigating the effects of neighbourhood characteristics in 2014 on reimbursed anti-depressant/anti-psychotic medication in 2016. Personal and neighbourhood characteristics predicted neighbourhood choice, indicating strong patterns of selection into neighbourhoods. Unadjusted for selection log neighbourhood income was associated with reimbursed medication (β=─0.040,95%CI:─0.060,─0.020), but the association strongly attenuated after controlling for self-selection into neighbourhoods (β=─0.010,95%CI:─0.030,0.011). The opposite was observed for contact with neighbors; unadjusted for self-selection there was no association (β=─0.020, 95%CI:─0.073,0.033), but after adjustment increased neighborhood contact was associated with an 8.5% relative reduction in reimbursed medication (β=-0.075,95%CI:=─0.126,─0.025). The method illustrated in this study offers new opportunities to disentangle selection from causation in neighbourhood health research.

Reliable Computationally Efficient Behavioral Modeling of Microwave Passives Using Deep Learning Surrogates in Confined Domains

The importance of surrogate modeling techniques has been steadily growing over the recent years in high-frequency electronics, including microwave engineering. Fast metamodels are employed to speed up design processes, especially those conducted at the level of full-wave electromagnetic (EM) simulations. The surrogates enable massive system evaluations at nearly EM accuracy and negligible costs, which is invaluable in parameter tuning, multiobjective optimization, or uncertainty quantification. Nevertheless, modeling of electrical characteristics of microwave components is impeded by nonlinearity of their electrical characteristics, the need for covering broad parameter ranges, as well as dimensionality issues. Recently, a two-stage modeling approach has been proposed, which addresses some of these issues by constraining the surrogate model domain to only include high-quality designs, thereby reducing the cardinality of the dataset required to establish an accurate metamodel. In this article, a novel technique is proposed, which combines the two-stage modeling concept with multihead deep regression network (MHDRN) surrogates customized to handle responses of microwave passives over wide ranges of operating frequencies and geometry parameters. Using three microstrip circuits, a superior performance of the proposed modeling framework is demonstrated with respect to multiple state-of-the-art benchmark methods. In particular, the relative rms error is shown to reach the level of less than 3% for the datasets consisting of just a few hundred samples.

Location Allocation of Biorefineries for a Switchgrass-Based Bioethanol Supply Chain Using Energy Consumption and Emissions

Background: Due to the growing demand for energy and environmental issues related to using fossil fuels, it is becoming tremendously important to find alternative energy sources. Bioethanol produced from switchgrass is considered as one of the best alternatives to fossil fuels. Methods: This study develops a two-stage supply chain modeling approach that first determines feasible locations for constructing switchgrass-based biorefineries in the state of North Dakota by using Geographic Information Systems (GIS) analysis. In the second stage, the profit of the corresponding switchgrass-based bioethanol supply chain is maximized by developing a mixed-integer linear program that aims to commercialize the bioethanol production while impacts of energy use and carbon emission costs on the supply chain decisions and siting of biorefineries are included. Results: The numerical results show that carbon emissions and energy consumption penalties affect optimal biorefinery selections and supply chain decisions. Conclusions: We conclude that there is no need to penalize both emissions and energy use simultaneously to achieve desirable environmental benefits, otherwise, the supply chain becomes non-profitable. Moreover, imposing emissions or energy consumption penalties makes the optimization model closer to supply sources while having higher land rental costs. Such policies would promote sustainable second-generation biomass production, thus decreasing reliance on fossil fuels.

Does employee empowerment moderate the effect of situational and dispositional variables on emotional exhaustion differently? The COVID-19 crisis context

Purpose Building on the conservation of resources (COR) theory, this study aims to investigate employee empowerment’s moderation effect on the relationship of situational (job satisfaction, affective commitment) and dispositional (positive affectivity, emotional intelligence) variables toward the emotional exhaustion of service employees amidst the pandemic. Design/methodology/approach In total, 288 service employees from various sectors in Indonesia participate as the study’s respondents. This study applies a two-stage structural equation modeling approach to test the hypotheses. Findings The results show that employee empowerment moderates situational and dispositional variables differently. While employee empowerment significantly influences situational variables, a different situation is found on dispositional variables, that employee empowerment does not significantly influence these variables. This study’s findings portray the COR theory in practice and clarify the importance of employee empowerment for employees with particular attributions. Research limitations/implications The present study bears four limitations: the cross-sectional design; no exploration of dispositional and situational variables’ antecedents; the findings are limited to the service workers; and lastly, this study only takes Indonesian samples. Practical implications From a practical perspective, this study reveals which type of service employees are responsive to empowerment policy and which are prone to experience emotional exhaustion, particularly during a crisis. Social implications By understanding what factors determine employee empowerment’s effectiveness, managers could maximize the impacts of their empowerment policies. Subsequently, it will create better service deliveries which might benefit the broader societal scope. Originality/value This study contributes to both theoretical and practical understanding. Theoretically, this study adds and promotes using a categorical lens to examine the pattern of interactions between organizations and employees.

Educational Process Mining for Discovering Students' Problem-Solving Ability in Computer Programming Education

Educational process mining is now a promising method to provide decision-support information for the teaching–learning process via finding useful educational guidance from the event logs recorded in the learning management system. Existing studies mainly focus on mining students' problem-solving skills or behavior patterns and intervening in students' learning processes according to this information in the late course. However, educators often expect to improve the learning outcome in a proactive manner through dynamically designing instructional strategies prior to a course that are more appropriate to students' average ability. Therefore, in this article, we propose a two-stage problem-solving ability modeling approach to obtain students' ability in different learning stages, including the pre-problem-solving ability model and the post-problem-solving ability model. The models are trained with Gradient Boosting Decision Tree (GBDT) on the historical event logs of the prerequisite course and the target course, respectively. With the premodel, we establish the students' pre-problem-solving ability profiles that reflect their average knowledge level before starting a course. Then, the instructional design is dynamically chosen according to the profiles. After a course completes, the post-problem-solving ability profiles are generated by the postmodel to analyze the learning outcome and prompt the learning feedback, in order to complete the closed-loop teaching process. We study the modeling of coding ability in computer programming education to show our teaching strategy. The experimental results show that the generalizable problem-solving ability models yield high classification precision, while most students' abilities have been significantly improved by the proposed approach at the end of the course.

Two-stage credit scoring using Bayesian approach

Commercial banks are required to explain the credit evaluation results to their customers. Therefore, banks attempt to improve the performance of their credit scoring models while ensuring the interpretability of the results. However, there is a tradeoff between the logistic regression model and machine learning-based techniques regarding interpretability and model performance because machine learning-based models are a black box. To deal with the tradeoff, in this study, we present a two-stage logistic regression method based on the Bayesian approach. In the first stage, we generate the derivative variables by linearly combining the original features with their explanatory powers based on the Bayesian inference. The second stage involves developing a credit scoring model through logistic regression using these derivative variables. Through this process, the explanatory power of a large number of original features can be utilized for default prediction, and the use of logistic regression maintains the model's interpretability. In the empirical analysis, the independent sample t-test reveals that our proposed approach significantly improves the model’s performance compared to that based on the conventional single-stage approach, i.e., the baseline model. The Kolmogorov–Smirnov statistics show a 3.42 percentage points (%p) increase, and the area under the receiver operating characteristic shows a 2.61%p increase. Given that our two-stage modeling approach has the advantages of interpretability and enhanced performance of the credit scoring model, our proposed method is essential for those in charge of banking who must explain credit evaluation results and find ways to improve the performance of credit scoring models.

Why externalize failure? Social goals, engagement and self-handicapping in the concern of eastern cultural community

During this COVID-19 pandemic, the community, especially students, experienced anxiety due to the uncertainty that occurred. To cover it up, self-handicapping is the most common option. People in the eastern culture, such as Indonesia, prefer to achieve social achievement (SAch) goals than academic achievement (AAch) goals in motivation. This study aims to examine the model of the relationship between SAch goals with adaptive and maladaptive outcomes. This study used 904 respondents who filled out a questionnaire that had been tested for validity and reliability using confirmatory factor analysis and internal consistency respectively. The respondents of this research were young business practitioners who were studying in college. The results of the correlation test confirm the results of previous research that SAch goals consist of three interrelated but independent dimensions, so that students can pursue these three goals. Academic engagement also has two correlated dimensions, while self-handicapping has two uncorrelated and even contradictory dimensions. The results of testing the relationship model using a two-stage structural equation modeling approach further strengthen that behavioral self-handicapping was maladaptive. Social development goals (SDevGs) produced adaptive outcomes, while social demonstration goals (SDemGs) produced maladaptive outcomes. Eastern culture prefers did not make mistakes than showed social competence by appearing popular and having many friends. Need a curriculum that includes activities that encourage the achievement of social goals and feel an attachment to academic activities but minimizes behavior that externalizes failure.

An Integrated Quantitative Method Based on ArcGIS Evaluating the Contribution of Rural Straw Open Burning to Urban Fine Particulate Pollution

This study presents a GIS-based method integrating hourly transport pathways and wind-field grid reconstruction, straw open burning (SOB) source identification, and a two-stage spatiotemporal multi-box modeling approach to quantify the contribution of external sources of SOB to elevated urban PM2.5 concentrations during a specific pollution episode (PE) at a high temporal resolution of 1 h. Taking Jilin Province as an empirical study, the contribution of SOB in province-wide farmlands to urban haze episodes in Changchun during the SOB season of 2020–2021 was evaluated quantitatively using a combination of multi-source datasets. The results showed that Changchun experienced three severe PEs and one heavy PE during the study period, and the total PM2.5 contributions from SOB sources were 352 μg m−3, 872 μg m−3, and 1224 μg m−3 during the three severe PEs, respectively; these accounted for 7%, 27%, and 23% of the urban cumulative PM2.5 levels, which were more obvious than the contribution during the PE. The total PM2.5 contribution from SOB sources (4.9 μg m−3) was only 0.31% of the urban cumulative PM2.5 level during the heavy PE. According to the analysis of the impact of individual factors, some policy suggestions are put forward for refined SOB management, including control spatial scope, burning time interval, as well as burning area limit under different urban and transport pathways’ meteorological conditions and different transport distances.

A Bayesian framework for incorporating exposure uncertainty into health analyses with application to air pollution and stillbirth.

Studies of the relationships between environmental exposures and adverse health outcomes often rely on a two-stage statistical modeling approach, where exposure is modeled/predicted in the first stage and used as input to a separately fit health outcome analysis in the second stage. Uncertainty in these predictions is frequently ignored, or accounted for in an overly simplistic manner when estimating the associations of interest. Working in the Bayesian setting, we propose a flexible kernel density estimation (KDE) approach for fully utilizing posterior output from the first stage modeling/prediction to make accurate inference on the association between exposure and health in the second stage, derive the full conditional distributions needed for efficient model fitting, detail its connections with existing approaches, and compare its performance through simulation. Our KDE approach is shown to generally have improved performance across several settings and model comparison metrics. Using competing approaches, we investigate the association between lagged daily ambient fine particulate matter levels and stillbirth counts in New Jersey (2011-2015), observing an increase in risk with elevated exposure 3 days prior to delivery. The newly developed methods are available in the R package KDExp.

344-OR: Body Composition Trajectories from Birth to 5 Years and Hepatic Fat in Early Childhood

Adiposity is an established risk factor for pediatric nonalcoholic fatty liver disease (NAFLD) , but little is known about the influence of body composition changes earlier in life on future NAFLD risk. Here, we examined associations of body composition trajectories from birth to early childhood with hepatic fat (HF) in early childhood in the Colorado Healthy Start cohort. Fat-free mass index (FFMI) , fat mass index (FMI) , and percent fat mass (%FM) were assessed at birth and/or ∼5 yrs in 1,235 children by air displacement plethysmography. At ∼5 yrs, HF was assessed by magnetic resonance imaging in a sub-sample of 285 children. We used a two-stage modeling approach: first, FFMI, FMI, and %FM trajectories were estimated using mixed models with random intercepts and slopes; second, associations of participant-specific deviations with HF were assessed by linear regression adjusted for confounders. As shown in Table 1, random intercepts for FFMI, FMI, and %FM at birth were all inversely associated with HF in early childhood; whereas %FM slope (i.e., velocity) from birth to ∼5 years was associated with higher HF. These findings suggest that smaller weight at birth, followed by greater gains in %FM are associated with higher HF in early childhood. This supports an influence of early life body composition patterns on HF susceptibility and the importance of perinatal strategies aiming to promote healthy adiposity in infancy/childhood. Disclosure C.C. Cohen: None. S.P. Gilley: None. W. Perng: None. K.A. Sauder: None. K. Shankar: None. D.H. Glueck: None. D. Dabelea: None.

Generalizable predictive modeling of semantic processing ability from functional brain connectivity

Semantic processing (SP) is one of the critical abilities of humans for representing and manipulating conceptual and meaningful information. Neuroimaging studies of SP typically collapse data from many subjects, but its neural organization and behavioral performance vary between individuals. It is not yet understood whether and how the individual variabilities in neural network organizations contribute to the individual differences in SP behaviors. We aim to identify the neural signatures underlying SP variabilities by analyzing functional connectivity (FC) patterns based on a large‐sample Human Connectome Project (HCP) dataset and rigorous predictive modeling. We used a two‐stage predictive modeling approach to build an internally cross‐validated model and to test the model's generalizability with unseen data from different HCP samples and other out‐of‐sample datasets. FC patterns within a putative semantic brain network were significantly predictive of individual SP scores summarized from five SP‐related behavioral tests. This cross‐validated model can be used to predict unseen HCP data. The model generalizability was enhanced in the language task compared with other tasks used during scanning and was better for females than males. The model constructed from the HCP dataset can be partially generalized to two independent cohorts that participated in different semantic tasks. FCs connecting to the Perisylvian language network show the most reliable contributions to predictive modeling and the out‐of‐sample generalization. These findings contribute to our understanding of the neural sources of individual differences in SP, which potentially lay the foundation for personalized education for healthy individuals and intervention for SP and language deficits patients.

Predicting in-hospital length of stay: a two-stage modeling approach to account for highly skewed data

BackgroundIn the early stages of the COVID-19 pandemic our institution was interested in forecasting how long surgical patients receiving elective procedures would spend in the hospital. Initial examination of our models indicated that, due to the skewed nature of the length of stay, accurate prediction was challenging and we instead opted for a simpler classification model. In this work we perform a deeper examination of predicting in-hospital length of stay.MethodsWe used electronic health record data on length of stay from 42,209 elective surgeries. We compare different loss-functions (mean squared error, mean absolute error, mean relative error), algorithms (LASSO, Random Forests, multilayer perceptron) and data transformations (log and truncation). We also assess the performance of two stage hybrid classification-regression approach.ResultsOur results show that while it is possible to accurately predict short length of stays, predicting longer length of stay is extremely challenging. As such, we opt for a two-stage model that first classifies patients into long versus short length of stays and then a second stage that fits a regresssor among those predicted to have a short length of stay.DiscussionThe results indicate both the challenges and considerations necessary to applying machine-learning methods to skewed outcomes.ConclusionsTwo-stage models allow those developing clinical decision support tools to explicitly acknowledge where they can and cannot make accurate predictions.

The Mediating Effect of Parenting Stress on the Relationship Between Social Support and Quality of Life in Parents of Children With Autistic Spectrum Disorder: A Meta-Analytic Structural Equation Modeling.

ObjectiveThe aim of the present study is to investigate whether parenting stress mediate the relationship between social support and quality of life in parents of children with Autistic Spectrum Disorder. In addition, we examined what other variables moderate the relationships in this mediation model.MethodsUsing the two-stage meta-analytic structural equation modeling approach (MASEM), 44 correlation matrices were synthesized from 28 empirical studies (N = 13,270) and fitted to the hypothesized mediation model.ResultsThere is a significant partial mediation effect of parenting stress on the relationship between social support and quality of life. Subgroup analysis through the first stage analysis suggested that social support measurements, parental role, and child's age moderated the relationship between social support and parenting stress, and that the focus of quality of life moderated the relationship between social support and quality of life. Subgroup analysis through the second stage analysis indicated that parenting stress had a significantly stronger predictive effect on quality of life in Western culture, while the predictive effect of social support on quality of life was significantly stronger in Eastern culture.ConclusionHaving more social support can reduce parents' stress and then improving their quality of life, which can help them cope more positively and effectively with their autistic children.

Two-Stage Approaches to Accounting for Patient Heterogeneity in Machine Learning Risk Prediction Models in Oncology.

Machine learning models developed from electronic health records data have been increasingly used to predict risk of mortality for general oncology patients. But these models may have suboptimal performance because of patient heterogeneity. The objective of this work is to develop a new modeling approach to predicting short-term mortality that accounts for heterogeneity across multiple subgroups in the presence of a large number of electronic health record predictors. We proposed a two-stage approach to addressing heterogeneity among oncology patients of different cancer types for predicting their risk of mortality. Structured data were extracted from the University of Pennsylvania Health System for 20,723 patients of 11 cancer types, where 1,340 (6.5%) patients were deceased. We first modeled the overall risk for all patients without differentiating cancer types, as is done in the current practice. We then developed cancer type-specific models using the overall risk score as a predictor along with preselected type-specific predictors. The overall and type-specific models were compared with respect to discrimination using the area under the precision-recall curve (AUPRC) and calibration using the calibration slope. We also proposed metrics that characterize the degree of risk heterogeneity by comparing risk predictors in the overall and type-specific models. The two-stage modeling resulted in improved calibration and discrimination across all 11 cancer types. The improvement in AUPRC was significant for hematologic malignancies including leukemia, lymphoma, and myeloma. For instance, the AUPRC increased from 0.358 to 0.519 (∆ = 0.161; 95% CI, 0.102 to 0.224) and from 0.299 to 0.354 (∆ = 0.055; 95% CI, 0.009 to 0.107) for leukemia and lymphoma, respectively. For all 11 cancer types, the two-stage approach generated well-calibrated risks. A high degree of heterogeneity between type-specific and overall risk predictors was observed for most cancer types. Our two-stage modeling approach that accounts for cancer type-specific risk heterogeneity has improved calibration and discrimination than a model agnostic to cancer types.

Two-Echelon Routing Problem for Parcel Delivery by Cooperated Truck and Drone

A new variant of the two-echelon routing problem is investigated, where the truck and the drone are used to cooperatively complete the deliveries of all parcels. The truck not only acts as a tool for parcel delivery but also serves as a moving depot for the drone. The drone can carry several parcels and take off from the truck, while returning to the truck after completing the delivery. The energy consumption model for the routing process of the drone is analyzed, when it is utilized to deliver multiple parcels. A two-stage route-based modeling approach is proposed to optimize both the truck’s main route and the drone’s adjoint flying routes. A hybrid heuristic integrating nearest neighbor and cost saving strategies is developed to quickly construct a feasible solution. The simulated annealing algorithm is integrated with Tabu search, to improve the quality of the solution as well as the search efficiency. Random instances at different scales are used to test the performance of the proposed algorithm. A case study based on the practical road network in Changsha, China, is presented, through which the sensitivity analysis is conducted with respect to some critical factors.

Identifying low-PM2.5 exposure commuting routes for cyclists through modeling with the random forest algorithm based on low-cost sensor measurements in three Asian cities

Cyclists can be easily exposed to traffic-related pollutants due to riding on or close to the road during commuting in cities. PM2.5 has been identified as one of the major pollutants emitted by vehicles and associated with cardiopulmonary and respiratory diseases. As routing has been suggested to reduce the exposures for cyclists, in this study, PM2.5 was monitored with low-cost sensors during commuting periods to develop models for identifying low exposure routes in three Asian cities: Taipei, Osaka, and Seoul. The models for mapping the PM2.5 in the cities were developed by employing the random forest algorithm in a two-stage modeling approach. The land use features to explain spatial variation of PM2.5 were obtained from the open-source land use database, OpenStreetMap. The total length of the monitoring routes ranged from 101.36 to 148.22 km and the average PM2.5 ranged from 13.51 to 15.40 μg/m³ among the cities. The two-stage models had the standard k-fold cross-validation (CV) R2 of 0.93, 0.74, and 0.84 in Taipei, Osaka, and Seoul, respectively. To address spatial autocorrelation, a spatial cross-validation approach applying a distance restriction of 100 m between the model training and testing data was employed. The over-optimistic estimates on the predictions were thus prevented, showing model CV-R2 of 0.91, 0.67, and 0.78 respectively in Taipei, Osaka, and Seoul. The comparisons between the shortest-distance and lowest-exposure routes showed that the largest percentage of reduced averaged PM2.5 exposure could reach 32.1% with the distance increases by 37.8%. Given the findings in this study, routing behavior should be encouraged. With the daily commuting trips expanded, the cumulative effect may become significant on the chronic exposures over time. Therefore, a route planning tool for reducing the exposures shall be developed and promoted to the public.

Prediction of speech recognition in background noise and competing speech from suprathreshold auditory and cognitive measures

Previous studies have struggled to identify measures beyond the audiogram to reliably predict speech-in-noise scores. This may owe to: (i) different mechanisms mediate performance depending on materials and task; and (ii) effects are not reproducible. Here, 38 listeners with normal/near-normal audiograms completed batteries of temporal auditory and cognitive tests, and speech recognition (“Theo-Victor-Michael” test) in speech-shaped noise (SSN), speech-envelope modulated noise (envSSN), one (1T) and two (2T) competing talkers. A two-stage Bayesian modeling approach was employed. In Stage 1, speech scores were corrected for target-word frequency/neighborhood density, psychometric function parameters were extracted from temporal tests, and cognitive measures were reduced to three composite variables. Stage 2 then applied Gaussian process models to predict speech scores from temporal and cognitive measures. Leave-one-out cross-validation and model stacking determined the best combination of predictive models. Performance in SSN/envSSN was best predicted by temporal envelope measures (forward masking, gap duration discrimination), while performance in 1T was best predicted by cognitive measures (executive function, processing speed). Temporal fine structure measures (frequency-modulation, interaural-phase-difference detection) predicted the number of 1T distractor responses. All models failed on 2T. These results show that prediction of speech-in-noise scores from suprathreshold “process” measures is highly task dependent.

Modeling tumor measurement data to predict overall survival (OS) in cancer clinical trials

IntroductionLongitudinal tumor measurements (TM) are commonly recorded in cancer clinical trials of solid tumors. To define patient response to treatment, the Response Evaluation Criteria in Solid Tumors (RECIST) categorizes the otherwise continuous measurements, which results in substantial information loss. We investigated two modeling approaches to incorporate all available cycle-by-cycle (continuous) TM to predict overall survival (OS) and compare the predictive accuracy of these two approaches to RECIST. Material and methodsJoint modeling (JM) for longitudinal TM and OS and two-stage modeling with potential time-varying coefficients were utilized to predict OS using data from three trials with cycle-by-cycle TM. The JM approach incorporates TM data collected throughout the course of the clinical trial. The two-stage modeling approach incorporates information from early assessments (before 12 weeks) to predict subsequent OS outcome. The predictive accuracy was quantified by c-indices. ResultsData from 577, 337, and 126 patients were included for the analysis (from two stage IV colorectal cancer trials (N9741, N9841) and an advanced non-small cell lung cancer trial (N0026), respectively). Both the JM and two-stage modeling reached a similar conclusion, i.e. the baseline covariates (age, gender, and race) were mostly not predictive of OS (p-value > 0.05). Quantities derived from TM were strong predictors of OS in the two colorectal cancer trials (p < 0.001 for both association in JM and two-stage modeling parameters); but less so in the lung cancer trial (p = 0.053 for association in JM and p = 0.024 and 0.160 for two-stage modeling parameters). The c-indices from the two-stage modeling were higher than those from a model using RECIST (range: 0.611–0.633 versus 0.586–0.590). The dynamic c-indices from the JM were in the range of 0.627–0.683 indicating good predictive accuracy. ConclusionBoth modeling approaches provide highly interpretable and clinical meaningful results; the improved predictive performance compared with RECIST indicates the possibility of deriving better trial endpoints from these approaches.

A two-stage diffusion modeling approach to the compelled-response task.

The issue of how perception and motor planning interact to generate a given choice between actions is a fundamental question in both psychology and neuroscience. Salinas and colleagues have developed a behavioral paradigm, the compelled-response task, where the signal that instructs the subject to make an eye movement is given before the cue that indicates which of two possible target choices is the correct one. When the cue is given rather late, the participant must guess and make an uninformed random choice. Perceptual performance can be tracked as a function of the amount of time during which sensory information is available. In Salinas' accelerated race-to-threshold model, two variables race against each other to a threshold, at which a saccade is initiated. The source of random variability is in the initial state of information buildup across trials. This implies that incorrect decisions are due to the inertia of the racing variables that have, at the start, sampled a constant buildup in the "wrong" direction. Here we suggest an alternative, non-time-homogeneous two-stage-diffusion model that is able to predict both response time distributions and choice probabilities with a few easy-to-interpret parameters and without assuming cross-trial parameter variability. It is falsifiable already at the level of qualitative features, for example, predicting bimodal reaction time (RT) distributions for particular gap times. It connects the compelled-response paradigm with an approach to decision making that has been uniquely successful in describing both behavioral and neural data in a variety of experimental settings for the last 40 years. (PsycInfo Database Record (c) 2021 APA, all rights reserved).