Monte Carlo Simulation Framework Research Articles

Decoupling the multi-drivers of urban extreme heat environment in urban agglomerations using ensemble learning

Urban extreme heat (UEH) has been imparting a formidable menace to various facets of sustainable urban and regional development. While increasing studies have examined the urban thermal environment, a gap exists in decoupling the nonlinear multi-drivers of UEH environment in intra-urban regions, especially across the urban agglomerations. In this study, we estimated UEH through the land surface temperature (LST) in the extended summer season exceeding a specific threshold computed through cross-regional statistics. Furthermore, an additive interpretable ensemble learning model enhanced via the Bayesian Optimization algorithm (BO) and Monte Carlo Simulation framework (MCS) was employed to dissect the intricate nonlinear interplay between UEH and intra-urban morphology, green-blue composition, and atmospheric determinants. Our results highlight the indispensably suppressive effect of green and blue factors on UEH, whereas urban morphological variables generally exhibit an opposite trend. Interestingly, we reveal heterogeneity responses of building heights, elevated urban structures mitigate UEH in temperate monsoon and subtropical monsoon zones and exhibit diminished marginal utility in temperate continental zones. Furthermore, the arid climatic characteristics are expected to exert an unexpectedly enhancing effect on water bodies' cooling capacity. These findings provide heterogeneous local guidance for landscape and urban planning towards the severe challenges of climate change.

Cellular automata-based simulators for the design of prescribed fire plans: the case study of Liguria, Italy

Abstract Background Socio-economic changes in recent decades have resulted in an accumulation of fuel within Mediterranean forests, creating conditions conducive to potential catastrophic wildfires intensified by climate change. Consequently, several wildfire management systems have integrated prescribed fires as a proactive strategy for land management and wildfire risk reduction. The preparation of prescribed fires involves meticulous planning, entailing the identification of specific objectives, verification of prescriptions, and the definition of various scenarios. During the planning phase, simulation models offer a valuable decision-support tool for the qualitative and quantitative assessment of different scenarios. In this study, we harnessed the capabilities of the well-established wildfire simulation tool , to identify areas where prescribed fires can be performed, optimizing the wildfire risk mitigation and the costs. We selected a case study in the Liguria region, Italy, where the model is utilized operationally by the regional wildfire risk management system in emergency situations. Results Initially, we employed the propagation model to simulate a historical wildfire event, showcasing its potential as an emergency response tool. We focused on the most significant fire incident that occurred in the Liguria region in 2022. Subsequently, we employed to identify optimal areas for prescribed fires with the dual objectives of maximizing the mitigation of wildfire risk and minimizing treatment costs. The delineation of potential areas for prescribed fires has been established in accordance with regional regulations and expert-based insights. The methodology put forth in this study is capable of discerning the most suitable areas for the implementation of prescribed burns from a preselected set. A Monte Carlo simulation framework was employed to evaluate the efficacy of prescribed burns in mitigating the spread of wildfires. This assessment accounted for a variety of conditions, including fuel loads, ignition points, and meteorological patterns. The model was utilized to simulate the progression of wildfire spread. Conclusions This study underscores the utility of in offering both quantitative and qualitative insights that can inform prescribed fire planning. Our methodology has been designed to involve active engagement with subject matter experts throughout the process, to develop scenarios grounded in their expert opinions. The ability to assess diverse scenarios and acquire quantitative information empowers decision-makers to make informed choices, thereby advancing safer and more efficient fire management practices.

A novel hybrid adaptive framework for support vector machine-based reliability analysis: A comparative study

This study presents an innovative hybrid Adaptive Support Vector Machine - Monte Carlo Simulation (ASVM-MCS) framework for reliability analysis in complex engineering structures. These structures often involve highly nonlinear implicit functions, making traditional gradient-based first or second order reliability algorithms and Monte Carlo Simulation (MCS) time-consuming. The application of surrogate models has proven effective in addressing computational challenges associated with a large number of simulations. Support Vector Machine (SVM), as an emerging machine learning method suitable for small-sample scenarios, offers a well-established theoretical foundation and presents an effective model substitution approach for reliability analysis in engineering structures. However, the existing literature lacks a comprehensive and thorough comparative analysis of SVM's hybrid adaptive modeling approach, encompassing initial sampling methods and learning functions, with regards to both computational efficiency and accuracy. Additionally, there is a gap in adaptive modeling methods capable of accommodating diverse types of input uncertainty, the nonlinearity of limit state functions, and various application scenarios. In response to these gaps, this article introduces the ASVM-MCS framework, which addresses these challenges by considering different types of input variables and various failure modes. Moreover, this study provides a comprehensive evaluation of the ASVM-MCS framework's performance, including its initial sampling methods and learning functions, across a range of application scenarios, such as scenarios involving only random variables, mixed variables, and the reliability of series-parallel systems.

Economic performance evaluation of flexible centralised and decentralised blue hydrogen production systems design under uncertainty

Blue hydrogen is viewed as an important energy vector in a decarbonised global economy, but its large-scale and capital-intensive production displays economic performance vulnerabities in the face of increased market and regulatory uncertainty. This study analyses flexible (modular) blue hydrogen production plant designs and evaluates their effectiveness to enhance economic performance under uncertainty. The novelty of this work lies in the development of a comprehensive techno-economic evaluation framework that considers flexible centralised and decentralised blue hydrogen plant design alternatives in the presence of irreducible uncertainty, whilst explicitly considering the time value of money, economies of scale and learning effects. A case study of centralised and decentralised blue hydrogen production for the transport sector in the San Francisco area is developed to highlight the underlying value of flexibility. The proposed methodological framework considers various blue hydrogen plant designs (fixed, phased, and flexible) and compares them using relevant economic indicators (net present value (NPV), capex, value-at-risk/gain, etc.) through a detailed Monte Carlo simulation framework. Results indicate that flexible centralised hydrogen production yields greater economic value than alternative designs, despite the associated cost-premium of modularity. It is also shown that the value of flexibility increases under greater uncertainty, higher learning rates and weaker economies of scale. Moreover, sensitivity analysis reveals that flexible design remains the preferred investment option over a wide range of market and regulatory conditions except for high initial hydrogen demand. Finally, this study demonstrates that major regulatory and market uncertainties surrounding blue hydrogen production can be effectively managed through the application of flexible engineering system design that protects the investment from major downside risks whilst allowing access to favourable upside opportunities.

Genetic matching for time-dependent treatments: a longitudinal extension and simulation study

BackgroundLongitudinal matching can mitigate confounding in observational, real-world studies of time-dependent treatments. To date, these methods have required iterative, manual re-specifications to achieve covariate balance. We propose a longitudinal extension of genetic matching, a machine learning approach that automates balancing of covariate histories. We examine performance by comparing the proposed extension against baseline propensity score matching and time-dependent propensity score matching.MethodsTo evaluate comparative performance, we developed a Monte Carlo simulation framework that reflects a static treatment assigned at multiple time points. Data generation considers a treatment assignment model, a continuous outcome model, and underlying covariates. In simulation, we generated 1,000 datasets, each consisting of 1,000 subjects, and applied: (1) nearest neighbour matching on time-invariant, baseline propensity scores; (2) sequential risk set matching on time-dependent propensity scores; and (3) longitudinal genetic matching on time-dependent covariates. To measure comparative performance, we estimated covariate balance, efficiency, bias, and root mean squared error (RMSE) of treatment effect estimates. In scenario analysis, we varied underlying assumptions for assumed covariate distributions, correlations, treatment assignment models, and outcome models.ResultsIn all scenarios, baseline propensity score matching resulted in biased effect estimation in the presence of time-dependent confounding, with mean bias ranging from 29.7% to 37.2%. In contrast, time-dependent propensity score matching and longitudinal genetic matching achieved stronger covariate balance and yielded less biased estimation, with mean bias ranging from 0.7% to 13.7%. Across scenarios, longitudinal genetic matching achieved similar or better performance than time-dependent propensity score matching without requiring manual re-specifications or normality of covariates.ConclusionsWhile the most appropriate longitudinal method will depend on research questions and underlying data patterns, our study can help guide these decisions. Simulation results demonstrate the validity of our longitudinal genetic matching approach for supporting future real-world assessments of treatments accessible at multiple time points.

Effect of iron thicknesses on spin transport in a Fe/Au bilayer system

This paper is concerned with a theoretical analysis of the behavior of optically excited spin currents in bilayer and multilayer systems of ferromagnetic and normal metals. As the propagation, control, and manipulation of the spin currents created in ferromagnets by femtosecond optical pulses is of particular interest, we examine the influence of different thicknesses of the constituent layers for the case of electrons excited several electronvolts above the Fermi level. Using a Monte-Carlo simulation framework for such highly excited electrons, we first examine the spatiotemporal characteristics of the spin current density driven in a Fe layer, where the absorption profile of the light pulse plays an important role. Further, we examine how the combination of light absorption profile, spin-dependent transmission probabilities, and iron layer thickness affects spin current density in a Fe/Au bilayer system. For high-energy electrons studied here, the interface and secondary electron generation have a small influence on spin transport in the bilayer system. However, we find that spin injection from one layer to another is most effective within a certain range of iron layer thicknesses.

Fiber Orientation Estimation from X-ray Dark Field Images of Fiber Reinforced Polymers Using Constrained Spherical Deconvolution

The properties of fiber reinforced polymers are strongly related to the length and orientation of the fibers within the polymer matrix, the latter of which can be studied using X-ray computed tomography (XCT). Unfortunately, resolving individual fibers is challenging because they are small compared to the XCT voxel resolution and because of the low attenuation contrast between the fibers and the surrounding resin. To alleviate both problems, anisotropic dark field tomography via grating based interferometry (GBI) has been proposed. Here, the fiber orientations are extracted by applying a Funk-Radon transform (FRT) to the local scatter function. However, the FRT suffers from a low angular resolution, which complicates estimating fiber orientations for small fiber crossing angles. We propose constrained spherical deconvolution (CSD) as an alternative to the FRT to resolve fiber orientations. Instead of GBI, edge illumination phase contrast imaging is used because estimating fiber orientations with this technique has not yet been explored. Dark field images are generated by a Monte Carlo simulation framework. It is shown that the FRT cannot estimate the fiber orientation accurately for crossing angles smaller than 70∘, while CSD performs well down to a crossing angle of 50∘. In general, CSD outperforms the FRT in estimating fiber orientations.

Preliminary debris risk assessment for mega-constellations in low and medium earth orbit due to satellite breakup

This paper presents a theoretical analysis of the potential risk posed by artificial debris clouds in low Earth orbit (LEO) from mega-constellations, modeled after current communication constellations such as Starlink and OneWeb with 750 satellites each. The analysis examines three different constellation designs: a low-altitude LEO, a high-altitude LEO, and a medium Earth orbit (MEO) constellation, which will be positioned using the Walker-Delta design. The study is based on physics-based digital mission engineering and a Monte Carlo simulation framework. The simulation involves debris generated from a single breakup of one randomly selected satellite per run, but does not consider cascading debris events. This debris cloud is propagated for 1 week and how it interacts with the mega-constellation is recorded. The results show an average of 705.65 potential conjunctions within the LEO constellation, with 14.40% of those being considered catastrophic, and an average of 165.5 conjunctions in the MEO constellation, with 0.72% considered catastrophic.

Seismic resilience assessment of Small Modular Reactors by a Three-loop Monte Carlo Simulation

We develop a three-loop Monte Carlo Simulation (MCS) framework for the seismic resilience assessment of Small Modular Reactors (SMRs), embedding Probabilistic Seismic Hazard Analysis (PSHA), seismic fragility evaluation and multiple SMR units accident sequence analysis. A set of metrics are computed to capture different aspects of SMR resilience to earthquakes, specifically the ability to withstand seismic disruption, mitigate consequences and restore normal operation. The MCS framework allows accounting for the aleatory and epistemic uncertainties of the PSHA and fragility parameters. An application is given with regards to an advanced Nuclear Power Plant (aNPP) consisting of four reactor units of NuScale SMR design. A comparison is made to a conventional NPP (cNPP), i.e., a typical large reactor of equivalent generation capacity. Both plants are fictitiously located on the Garigliano nuclear site (southern Italy). The results show that resilient features of SMRs overcome cNPPs in terms of post-accident scenario mitigation and restoration capabilities.

SIMO-Underwater Visible Light Communication (UVLC) system

The advent of visible light communication (VLC) towards 5G and beyond (5GB) networks opened new doors to unveil the mysteries of unguided mediums such as ocean. The varying physicochemical properties of water introduces the underwater optical turbulence (UOT). The UOT of UVLC link is modelled as unified Gamma-Gamma (GG) distribution fading under moderate-to-strong turbulence channel conditions as well as the misalignment of transceivers following exponential fading are included and studied. This study also contains the effect of UOT which is characterized by multiple water layers within the Monte Carlo simulation framework. The end-to-end (E2E) performances of a Single-Input and Multi-Output (SIMO) underwater VLC (UVLC) system is in consideration of Intensity-Modulation/Direct-Detection (IM/DD) technique which is obtained to fulfill the current communication requirements in hostile aqueous channels. More specifically, the probability distribution function (PDF) and cumulative distribution function (CDF) are derived to obtain the E2E performance metrics of the SIMO-UVLC system in terms of Meijer-G function. Consequently, novel closed-form expressions for average bit-error-rate (ABER) and outage probability of the system are formulated. Finally, the numerical and simulation results are obtained based on the experimental data in mixed water layers of the oceanic environments, and analytically verified through the Monte Carlo simulation approach in harsh channel conditions.

Investigation of Infiltration Loss in North Central Texas by Retrieving Initial Abstraction and Constant Loss from Observed Rainfall and Runoff Events

Accurate modeling of infiltration losses is vital for runoff estimation and thus the development of flood design/protection criteria and water management schemes, etc. In design flood practices, the initial abstraction and constant loss (IACL) method has been widely applied due to its simplicity. However, due to a lack of physical equivalent properties, the IACL method is often subject to issues in parametrization and has large dependency on calibration efforts for storm events. Despite the wide range/variability of IACL values, a single set of IA and CL values is normally adopted for specific flood frequency, which may introduce uncertainty and bias in resulting peak streamflow. In this study, we identified a total of 2,036 rainfall-runoff events for 18 watersheds in North Central Texas to estimate the total losses with their IA and CL components based on time-series of mean areal precipitation (MAP) and streamflow data. Threshold behavior is found for all studied subbasins between the summation of gross rainfall and antecedent soil moisture versus runoff depth: below the threshold, runoff depth is minimal; whereas above it, runoff is largely linearly correlated with the summation of rainfall and antecedent soil moisture. This finding provides a convenient way to estimate/predict total loss or runoff depth given MAP and antecedent soil moisture. In addition, this study shows that the IA and CL values can be approximated by the gamma and Weibull distributions, respectively. The fitted distributions of IA and CL values can be applied in a Monte Carlo simulation framework to stochastically simulate numerous rainfall-runoff events for a flood frequency analysis.

Integrated analysis of soil-structure interaction for statically indeterminate beams on spatially random soils

ABSTRACT This study aimed to develop an integrated approach to analyse the interaction between soils and beams. The research methods involved placing a statically indeterminate beam on spatially random soils, where soil springs acted as supports for the beam. The soil spring stiffness values were simulated using random field theory. This integrated approach was embedded in the Monte Carlo simulation framework to facilitate probabilistic assessment. This study concluded that the force method solution accurately determined the bending moment and shear diagrams for a beam supported by soil springs. Additionally, soil spatial variability had a significant impact on the beam responses, including the variations in footing settlements, support reactions, bending moment, and shear force. This study also identified a critical scale of soil fluctuation that coincides with the beam span, which resulted in the highest probability of structural bending failure. Overall, this study highlights the importance of accounting for soil spatial variability in an integrated geotechnical and structural design approach.

Toward a Plausible Methodology to Assess Rock Slope Instabilities at a Regional Scale

Slope failures along road cuts and highways, occurring due to heavy rainfalls or earthquakes, pose significant threats to people, vehicles, and emergency plans. In the present study, a methodology to assess the stability of rock slopes at a regional scale is proposed using a kinematic analysis and a probabilistic limit equilibrium analysis for plane sliding and wedge failure modes. The workflow adopted is described through its implementation along the main road network of the island of Thasos, located in northern Greece. On-site investigations and measurements along the island’s road network formed the basis of the present study. The results of the kinematic analysis showed that the joint sets, which were identified during the on-site investigations, formed critical intersections that could lead to wedge and plane sliding failures. The on-site measurements and the results of the kinematic analysis were utilized to perform limit equilibrium back-analyses at sites of identified failures due to the water pressure effects to probabilistically estimate the material strength properties of the joints. Subsequently, numerous limit equilibrium analyses were executed within a Monte Carlo simulation framework to produce representative fragility curves of rock slopes against plane sliding and wedge failures along the main road network, due to earthquake loading and water pressures.

A Hybrid Grant NOMA Random Access for Massive MTC Service

Nonorthogonal multiple access (NOMA) is classified into grant-based (GB) random access (RA) and grant-free (GF) RA in 5G systems. GF NOMA RA allows user equipments (UE) to directly transmit data packet over uplink resources without grant information so that the signaling overhead is substantially reduced. This approach is suitable for small-size data packet IoT services. However, power collision interference resulting from uncoordinated resource selection leads to serious transmission failure in massive MTC scenarios. As a result, the number of transmissions and access delay are sharply increased. In this article, we make the best of the advantages of both GB RA and GF RA and propose a hybrid grant (HG) NOMA RA scheme to address the signaling overhead and power collision issues. The objective of the proposed scheme is to achieve an acceptable tradeoff among signaling overhead, access failure probability, and access delay. The UE selects the access resource without uplink grant, and the BS assists to avoid inter-NOMA power collision by limited interference resolution (IR) signaling. To comprehensively analyze the performance, the closed-form expression of outage probability is derived by adopting the order statistic theory and the sum of independent exponential distributions, and the maximum number of transmissions and access delay are theoretical analyzed. A Monte Carlo simulation framework is built to verify the theoretical analysis. Finally, the analytical and simulation results show that HG NOMA RA outperforms GB NOMA RA and GF NOMA RA in massive access scenarios.

Preliminary measurements of optical properties in human abscess cavities prior to methylene blue photodynamic therapy.

As part of our ongoing Phase 1 clinical trial to establish the safety and feasibility of methylene blue photodynamic therapy (MB-PDT) for human deep tissue abscess cavities, we have shown that determination of abscess wall optical properties is vital for the design of personalized treatment plans aiming to optimize light dose. To that end, we have developed and validated an optical spectroscopy system for the assessment of optical properties at the cavity wall, including a compact fiber-optic probe that can be inserted through the catheter used for the standard of care abscess drainage. Here we report preliminary findings from the first three human subjects to receive these optical spectroscopy measurements. We observed wide variability in concentrations of oxy- and deoxy-hemoglobin prior to MB administration, ranging from 7.3-213 μM and 0.1-47.2 μM, respectively. Reduced scattering coefficients also showed inter-patient variability, but recovered values were more similar between subjects (5.5-10.9 cm-1 at 665 nm). Further, methylene blue uptake was found to vary between subjects, and was associated with a reduction in oxygen saturation. These measured optical properties, along with pre-procedure computed tomography (CT) images, will be used with our previously developed Monte Carlo simulation framework to generate personalized treatment plans for individual patients, which could significantly improve the efficacy of MB-PDT while ensuring safety.

A Monte Carlo simulation and sensitivity analysis framework demonstrating the advantages of probabilistic forecasting over deterministic forecasting in terms of flood warning reliability

Despite the significant progress in probabilistic forecasting science in the last two decades, particularly in the quantification of predictive uncertainty (PU), most operational flood early warning systems (FEWSs) continue to be based on deterministic forecasts. Thereupon, additional work is needed to demonstrate the advantages of using PU over deterministic forecasting to enhance the uptake of probabilistic forecasts in flood warning decision-making. In this paper, a Monte-Carlo (MC)-based sensitivity analysis is done to explore how the outcomes of flood peak water level-based deterministic and -probabilistic warning strategies behave when factors controlling the forecast quality are perturbed. The flood warning reliability is evaluated through the probability of detection (POD) and false alarm ratio (FAR) based on two criteria: a flooding threshold-based criterion (FTC), and a new floodplain property-based criterion (FPC) based on inundation level forecasting. The results of this work show that the advantage of a probabilistic strategy over a deterministic one is greater when PU is relatively high. The probabilistic strategy is robust to biases in the mean and variance of forecasts by maintaining POD and FAR stability, while this is not the case for a deterministic warning strategy. Likewise, it was concluded that if inundation level forecasting is undertaken (FPC), improved forecasts would be needed to achieve the same reliability level as for FTC, reflecting the more demanding FPC criterion. Also, the levels of correlation needed to achieve acceptable operational values of POD and FAR are shown. These results provide new insight into the advantages of a probabilistic forecasting strategy under several forecast quality scenarios and guidance for the design of operational FEWSs.

Stochastic numerical analysis for static performance of composite laminates based on resampling method

Due to the unstable properties of composite materials, it is of great significance to obtain the strength and its mechanical dispersion of composite laminates. The dispersion can be simply simulated using a recently developed Stochastic Numerical Analysis Architecture based on resampling method. This paper attempts to quantify the inconsistency of mechanical uncertainty between the composite mechanical test panels and the components by resampling method in two scales. Researchers have given different scale distribution prediction methods based on manufacturing and experiments for this phenomenon. By introducing resampling method and 3D random field into the Monte-Carlo simulation framework, a fast prediction method for structural uncertainty is established. Three sets of laminate strength experiment were done to verify the analysis architecture, and the results showed that the predicted strength and distribution are in good agreement with the experimental ones. Some intelligent algorithms regarding applications of the 3D random field are also addressed.

Development of a Plastic Scintillator-Based Active Shield for the ICARE-NG Radiation Monitor

An active shield using a scintillator and silicon photo-multipliers (SiPMs) has been developed to operate with space environment particle detectors sensitive to both protons and electrons, such as Influence sur les Composants Avancés des Radiations de l’Espace-Nouvelle Génération (ICARE-NG). The method shows a reduction in electron contamination through the sides of the detector, thus increasing energy resolution. Two geometries are studied, one working in coincidence mode with the main detector and the other in anti-coincidence. Performances of both the geometries are simulated in proton and electron environments in energy ranges typical of a space environment. Experimental measurements then aimed to validate the Monte Carlo simulation framework for scintillating materials. Finally, a case study of electron decontamination is carried out, as well as an error rate estimation in flux reconstruction.

Dose coefficients for organ dosimetry in tomosynthesis imaging of adults and pediatrics across diverse protocols.

The gold-standard method for estimation of patient-specific organ doses in digital tomosynthesis (DT) requires protocol-specific Monte Carlo (MC) simulations of radiation transport in anatomically accurate computational phantoms. Although accurate, MC simulations are computationally expensive, leading to a turnaround time in the order of core hours for simulating a single exam. This limits their clinical utility. The purpose of this study is to overcome this limitation by utilizing patient- and protocol-specific MC simulations to develop a comprehensive database of air-kerma-normalized organ dose coefficients for a virtual population of adult and pediatric patient models over an expanded set of exam protocols in DT for retrospective and prospective estimation of radiation dose in clinical tomosynthesis. A clinically representative virtual population of 14 patient models was used, with pediatric models (M and F) at ages 1, 5, 10, and 15 and adult patient models (M and F) with body mass index (BMIs) at 10th, 50th, and 90th percentiles of the US population. A graphics processing unit (GPU)-based MC simulation framework was used to simulate organ doses in the patient models, incorporating the scanner-specific configuration of a clinical DT system (VolumeRad, GE Healthcare, Waukesha, WI, USA) and an expanded set of exam protocols, including 21 distinct acquisition techniques for imaging a variety of anatomical regions (head and neck, thorax, spine, abdomen, and knee). Organ dose coefficients (hn ) were estimated by normalizing organ dose estimates to air kerma at 70cm (X70cm ) from the source in the scout view. The corresponding coefficients for projection radiography were approximated using organ doses estimated for the scout view. The organ dose coefficients were further used to compute air-kerma-normalized patient-specific effective dose coefficients (Kn ) for all combinations of patients and protocols, and a comparative analysis examining the variation of radiation burden across sex, age, and exam protocols in DT, and with projection radiography was performed. The database of organ dose coefficients (hn ) containing 294 distinct combinations of patients and exam protocols was developed and made publicly available. The values of Kn were observed to produce estimates of effective dose in agreement with prior studies and consistent with magnitudes expected for pediatric and adult patients across the different exam protocols, with head and neck regions exhibiting relatively lower and thorax and C-spine (apsc, apcs) regions relatively higher magnitudes. The ratios (r=Kn /Kn ,rad ) quantifying the differences air-kerma-normalized patient-specific effective doses between DT and projection radiography were centered around 1.0 for all exam protocols, with the exception of protocols covering the knee region (pawk, patk). This study developed a database of organ dose coefficients for a virtual population of 14 adult and pediatric XCAT patient models over a set of 21 exam protocols in DT. Using empirical measurements of air kerma in the clinic, these organ dose coefficients enable practical retrospective and prospective patient-specific radiation dosimetry. The computation of air-kerma-normalized patient-specific effective doses further enables the comparison of radiation burden to the patient populations between protocols and between imaging modalities (e.g., DT and projection radiography), as presented in this study.

P-56 Estimating endpoint correlation between surrogate measures and overall survival using reconstructed survival data: Case studies from adjuvant and metastatic gastric cancer trials

Validation of intermediate endpoints such as disease-free survival (DFS) and progression-free survival (PFS) as surrogate predictors for overall survival (OS) in randomized controlled trials (RCTs) requires establishing their association at the individual-level. In the absence of individual-level patient data (IPD), this study developed an analytical framework to estimate this association between DFS/PFS and OS using reported Kaplan-Meier (KM) curves from the RCTs and demonstrated its predictive performance in adjuvant and metastatic gastric cancer (GC) treatment settings. Assuming a three-state illness-death model for cancer survival, we developed a linear optimization model to elicit the underlying pre-progression death probability as well as post-progression survival (PPS) distribution using pseudo-patient level DFS/PFS and OS data reconstructed from the published KM curves. In the adjuvant setting, pre-progression death probability was bounded below by the cure rates which were estimated by fitting mixture cure models (MCMs) to the DFS data. In the MCMs, time-to-event outcomes for the uncured subpopulation were modeled using parametric survival functions suggested by National Institute for Health and Care Excellence (NICE) and non-disease-related mortality rates were derived from the age- and sex-adjusted local life-table data from World Health Organization. Reconstructed DFS/PFS distributions were extrapolated via parametric- and spline-based models suggested by NICE and adjusted with estimated background mortality rates whereas elicited PPS distributions were extrapolated assuming constant hazard rate over time. Estimated pre-progression death probabilities and modeled DFS/PFS/PPS distributions governed a Monte-Carlo simulation framework which generated paired pseudo pre- and post-progression data to predict Spearman’s rank and Pearson’s product moment correlation coefficients. Model performance was tested on two correlation meta-analyses in GC (14 RCTs with 3371 patients on adjuvant chemotherapy; 20 RCTs with 4069 patients on metastatic treatments) published in 2013 by the GASTRIC group. For each test case, model-predicted OS rates and Spearman rank correlation coefficients were compared against their reported counterparts and corresponding 95% CIs. Predicted OS curves laid within the 95% CIs of the reported OS KM-curves 96% and 100% of the time in the adjuvant and metastatic setting, respectively, where the average deviation between the restricted mean survival times under the model-predicted OS curves and the statistically best-fitting OS curves to the reported data was < 1% in both settings. Average deviation between the estimated and reported Spearman rank correlation coefficients was no more than 0.01 (reported: 0.97 [95% CI:0.97-0.98] vs. predicted: 0.96 [95% CI:0.96-0.96]) and 0.13 (reported: 0.85 [95% CI:0.85-0.85] vs. predicted: 0.72 [95% CI:0.71-0.72]) in both settings. Predicted Pearson correlation coefficients were 0.95 [95% CI:0.95-0.95] and 0.94 [95% CI:0.94-0.95] in the adjuvant and metastatic setting, respectively. Our study offers a useful approach for an indirect endpoint correlation assessment in the absence of IPD. Results indicate the model to be precise in adjuvant but conservative in metastatic GC setting which should be approached with caution due to independent simulation of paired DFS/PFS and PPS durations from the illness-death model and the lack of data-driven lower bounds on pre-progression death probability in the metastatic setting.