Framework For Extrapolation Research Articles

Medicines in pregnancy: A clinical pharmacology extrapolation framework to address knowledge gaps.

Medicines in pregnancy: A clinical pharmacology extrapolation framework to address knowledge gaps.

Extrapolation Framework and Characteristic Analysis of Load Spectrum for Agriculture General Power Machinery

As a crucial step in food production, tillage and land preparation play a pivotal role in achieving sustainable crop production and improving the soil environment. However, accurate assessment of the load that agricultural machinery implements during the operation process has always been a vexing problem that needs urgent solutions. In this paper, an extrapolation and reconstruction framework for the time-domain load is constructed based on the probability-weighted moments (PWM) estimation and the peaks-over-threshold function, and the load spectrum is obtained for agriculture general power machinery. Firstly, the load acquisition system was developed, the traction resistance and output torque of the tractor were measured, and the collected load signals were preprocessed. Next, the mean excess function and PWM estimation are introduced to select the optimal threshold and generalized Pareto distribution (GPD) fitting parameters and the extreme load distribution that exceeds the threshold range is fitted. The extreme points in the original data are replaced by generating new extreme points that follow the GPD distribution, and the extrapolation of the load spectrum is achieved. Finally, the real extrapolated load spectrum was validated based on statistical characteristics and rainflow counting analysis, and the correlation coefficient between the fitting data and the extreme load samples was greater than 0.99. It can retain the load sequence characteristics of the original load to a great extent, truly reflecting the load state during the operation of agricultural machinery. Meanwhile, the characteristics of the load spectrum can be accurately obtained, such as extreme, mean, and amplitude values, and the real load during deep loosening and rotary tillage are accurately described. The values provide more authentic and reliable data support for the subsequent selection of optimal operating parameters, reliability design of the power transmission system, and the life assessment of the agricultural implements.

Methods for Spatial Extrapolation of Methane Measurements in Constructing Regional Estimates from Sample Populations.

Methane emission estimates for oil and gas facilities are typically based on estimates at a subpopulation of facilities, and these emission estimates are then extrapolated to a larger region or basin. Basin-level emission estimates are then frequently compared with basin-level observations. Methane emissions from oil and gas systems are inherently variable and intermittent, which make it difficult to determine whether a sample population is sufficiently large to be representative of a larger region. This work develops a framework for extrapolation of emission estimates using the case study of an operator in the Green River Basin. This work also identifies a new metric, the capture ratio, which quantifies the extent to which sources are represented in the sample population, based on the skewness of emissions for each source. There is a strong correlation between the capture ratio and extrapolation error, which suggests that understanding source-level emissions distributions can mitigate error when sample populations are selected and extrapolating measurements. The framework and results from this work can inform the selection and extrapolation of site measurements when developing methane emission inventories and establishing uncertainty bounds to assess whether inventory estimates are consistent with independent large spatial-scale observations.

A Novel Load Extrapolation Method for Multiple Non-Stationary Loads on the Drill Pipe of a Rotary Rig

The drill pipe of a rotary rig is subject to the dynamic influence of non-stationary loads, including rotation torque and applied force. In order to address the challenge of simultaneously extrapolating multiple non-stationary loads, a novel extrapolation framework is proposed. This framework utilizes rainflow counting to obtain mean and amplitude sequences of the loads. The extreme values of the amplitude sequence are fitted using the Generalized Pareto Distribution (GPD), while the median values are fitted using the Double Kernel Density Estimation (DKDE). By extrapolating the Inverse Cumulative Distribution Function (ICDF) based on the fitted distribution, a new amplitude sequence can be derived. The combination of this extrapolated amplitude sequence with the original mean sequence forms a new load spectrum. The results of applying the proposed extrapolation method to the drill pipe of a rotary rig demonstrate the ability of the method to yield conservative extrapolation results and accurately capture the variations in damage under the original working conditions.

Criteria for assessing evidence for biomarker-targeted therapies in rare cancers-an extrapolation framework.

Advances in targeted therapy development and tumor sequencing technology are reclassifying cancers into smaller biomarker-defined diseases. Randomized controlled trials (RCTs) are often impractical in rare diseases, leading to calls for single-arm studies to be sufficient to inform clinical practice based on a strong biological rationale. However, without RCTs, favorable outcomes are often attributed to therapy but may be due to a more indolent disease course or other biases. When the clinical benefit of targeted therapy in a common cancer is established in RCTs, this benefit may extend to rarer cancers sharing the same biomarker. However, careful consideration of the appropriateness of extending the existing trial evidence beyond specific cancer types is required. A framework for extrapolating evidence for biomarker-targeted therapies to rare cancers is needed to support transparent decision-making. To construct a framework outlining the breadth of criteria essential for extrapolating evidence for a biomarker-targeted therapy generated from RCTs in common cancers to different rare cancers sharing the same biomarker. A series of questions articulating essential criteria for extrapolation. The framework was developed from the core topics for extrapolation identified from a previous scoping review of methodological guidance. Principles for extrapolation outlined in guidance documents from the European Medicines Agency, the US Food and Drug Administration, and Australia's Medical Services Advisory Committee were incorporated. We propose a framework for assessing key assumptions of similarity of the disease and treatment outcomes between the common and rare cancer for five essential components: prognosis of the biomarker-defined cancer, biomarker test analytical validity, biomarker actionability, treatment efficacy, and safety. Knowledge gaps identified can be used to prioritize future studies. This framework will allow systematic assessment, standardize regulatory, reimbursement and clinical decision-making, and facilitate transparent discussions between key stakeholders in drug assessment for rare biomarker-defined cancers.

MSR132 Integrating Relative Survival Framework for Survival Extrapolation in Multi-State Models—An Application to Predicting Quality-Adjusted Life Years for Chronic Myeloid Leukemia Patients

Continuous-time multi-state models provide a framework for estimating the probability of an individual transitioning from one health state to another together with time spent in each health state. In cost-utility studies, effectiveness often estimated using quality-adjusted life years (QALYs) over a lifetime horizon. Accurately estimating long-term QALYs is crucial in health technology assessment, but this usually requires extrapolating short-term follow-up data. Results are highly sensitive to how the extrapolation is performed. Survival extrapolation using a relative survival framework (i.e., extrapolating after partitioning the all-cause hazard into the expected hazard and the excess hazard) has proven to be superior to direct extrapolation of all-case survival for estimating life expectancy after a cancer diagnosis. However, this approach has not yet been implemented in multi-state models for predicting QALYs.

Modelling the biodistribution of inhaled gold nanoparticles in rats with interspecies extrapolation to humans

The increasing intentional and non-intentional exposure to nanoparticles (NPs) has raised the interest concerning their fate and biodistribution in the body of animals and humans after inhalation. In this context, Physiologically Based (pharmaco)Kinetic (PBK) modelling has emerged as an in silico approach that simulates the biodistribution kinetics of NPs in the body using mathematical equations. Due to restrictions in data availability, such models are first developed for rats or mice. In this work, we present the interspecies extrapolation of a PBK model initially developed for rats, in order to estimate the biodistribution of inhaled gold NPs (AuNPs) in humans. The extrapolation framework is validated by comparing the model results with experimental data from a clinical study performed on humans for inhaled AuNPs of two different sizes, namely 34 nm and 4 nm. The novelty of this work lies in the extrapolation of a PBK model for inhaled AuNPs to humans and comparison with clinical data. The extrapolated model is in good agreement with the experimental data, and provides insights for the mechanisms of inhaled AuNP translocation to the blood circulation, after inhalation. Finally, the biodistribution of the two sizes of AuNPs in the human body after 28 days post-exposure is estimated by the model and discussed.

An extrapolation framework for torque spectrum of excavator internal combustion engine via bivariate diffusion-based kernel density estimation

The torque extrapolation of the excavator internal combustion engine (ICE) is unknown and challenging. In this paper, an extrapolation framework for ICE torque of excavator is proposed. Data pre-processing, distribution estimation, load extrapolation, and damage evaluation are included in the framework. In estimating measurements, bivariate diffusion-based kernel density estimation is used to obtain the probability density. The cumulative cycle frequency and pseudo damage are compared with the results obtained by the other extrapolation methods. The results show that the proposed method can realize reasonable extrapolation. This extrapolation process can provide a reference for other engineering equipment in cyclic operation.

Extrapolating evidence for molecularly targeted therapies from common to rare cancers: a scoping review of methodological guidance

ObjectivesCancer is increasingly classified according to biomarkers that drive tumour growth and therapies developed to target them. In rare biomarker-defined cancers, randomised controlled trials to adequately assess targeted therapies may...

Quantitative system pharmacology as a legitimate approach to examine extrapolation strategies used to support pediatric drug development.

Extrapolation strategies from adult data for designing pediatric drug development programs are explored using the quantitative systems pharmacology (QSP) modeling approach, a mechanistic drug and disease modeling framework that can predict clinical response and guide pediatric drug development in general. This innovative model‐informed drug discovery and development approach can leverage adult‐pediatric pharmacology and disease similarity metrics to validate extrapolation assumptions. We describe the QSP model strategy and framework for extrapolation to design pediatric drug development programs by leveraging adult data across a wide range of therapeutic areas and illustrating stage‐gate decisions informed by such an approach.

Next-generation sequencing, should I use anti-HER2 therapy for HER2-amplified tumors off-label? Illustrating an extrapolation framework.

Background:Next-generation sequencing is used to increase targeted treatment opportunities, particularly for patients who have exhausted standard options. Where randomized controlled trial evidence for a targeted therapy is available for molecular alterations in one tumor type, the dilemma for the clinician is whether ‘matching’ targeted agents should be recommended off-label for the same molecular alterations detected in other tumor types, for which no trial data are available to guide practice. To judge the likely benefits, it may be possible to extrapolate evidence from cancers where treatment benefits have been established.Methods:We present a framework for assessing the appropriateness of extrapolation using trastuzumab, an anti-HER2 antibody, for HER2-amplified tumors where trastuzumab use would be off-label as an illustrative example.Results:The following should be considered for the tumor type where trastuzumab would be off-label: (a) reliability of the NGS assay for detecting HER2 amplification; (b) criteria for defining HER2 positivity; (c) strength of evidence supporting the actionability of HER2 amplification and trastuzumab; (d) whether better clinical outcomes with trastuzumab are due to a more favorable natural history rather than trastuzumab effect; (e) signals of trastuzumab activity and whether it translates to clinically meaningful benefit; (f) whether the safety profile of trastuzumab differs from established indications; and (g) discussion points for shared decision making (SDM) to facilitate informed consent.Conclusion:We present a systematic approach for appraising evidence to support extrapolating trastuzumab benefits from established indications to off-label applications. Extrapolation criteria and areas of uncertainty to inform SDM are outlined. This framework is potentially generalizable to other tumor-agnostic biomarker-targeted therapy scenarios. It is a practical approach for clinicians to apply in routine practice and should be considered by molecular tumor boards who make off-label recommendations.

Numerical Uncertainty in Density Estimation for Background Oriented Schlieren

Background Oriented Schlieren (BOS) is an image-based density measurement technique. BOS estimates the density gradient from the apparent distortion of a target pattern viewed through a medium with varying density using cross-correlation, tracking, or optical flow algorithms. The density gradient can then be numerically integrated to yield a spatially resolved estimate of the density [1]. A method was recently proposed to estimate the a-posteriori instantaneous and spatially resolved density uncertainty for BOS [2] and showed good agreement between the propagated uncertainties and the random error. However, the density uncertainty quantification method could not account for the systematic uncertainty in the density field due to the discretization errors introduced during the numerical integration, which could be much larger than the displacement random errors [2]. In this work, we propose a method to estimate the numerical uncertainty introduced by the density integration in BOS measurements, using a Richardson extrapolation framework. A procedure is also introduced to combine this systematic uncertainty with the random uncertainty from the previous work to provide an instantaneous, spatially-resolved total uncertainty on the density estimates. The method will be tested with synthetic fields and synthetic BOS images.

Resolution Enhancement in Terahertz Digital In-line Holography by Sparsity-Based Extrapolation

We introduce a generalized extrapolation framework as a constrained optimization problem to enhance the resolution in terahertz digital in-line holography. The alternating minimization method is employed to iteratively extrapolate the hologram beyond the actually detecting area and to reconstruct the complex amplitude distribution of the object wavefront. Within this framework, we propose a sparsity-based extrapolation model based on L1-norm, where the object mask is not required and the generalized positive absorption constraint can be utilized. This work can achieve super-resolution reconstruction completely by numerical postprocessing without any modification to the imaging system. Both the simulation and experiments on the terahertz band demonstrate the feasibility of the proposed algorithms, and the limit of resolution can be extended by a factor of 1.67.

FoV-Net: Field-of-View Extrapolation Using Self-Attention and Uncertainty

The ability to make educated predictions about their surroundings, and associate them with certain confidence, is important for intelligent systems, like autonomous vehicles and robots. It allows them to plan early and decide accordingly. Motivated by this observation, in this letter we utilize information from a video sequence with a narrow field-of-view to infer the scene at a wider field-of-view. To this end, we propose a temporally consistent field-of-view extrapolation framework, namely FoV-Net, that: (1) leverages 3D information to propagate the observed scene parts from past frames; (2) aggregates the propagated multi-frame information using an attention-based feature aggregation module and a gated self-attention module, simultaneously hallucinating any unobserved scene parts; and (3) assigns an interpretable uncertainty value at each pixel. Extensive experiments show that FoV-Net does not only extrapolate the temporally consistent wide field-of-view scene better than existing alternatives, but also provides the associated uncertainty which may benefit critical decision-making downstream applications. Project page is at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://charliememory.github.io/RAL21_FoV</uri> .

Assessing professional competence in optometry \u2013 a review of the development and validity of the written component of the competency in optometry examination (COE)

BackgroundCredentialing assessment for overseas-educated optometrists seeking registration in Australia and New Zealand is administered by the Optometry Council of Australia and New Zealand. The aim was to review the validation and outcomes of the written components of this exam to demonstrate credentialing meets entry-level competency standards.MethodsThe Competency in Optometry Examination consists of two written and two clinical parts. Part 1 of the written exam comprises multiple choice questions (MCQ) covering basic and clinical science, while Part 2 has 18 short answer questions (SAQ) examining diagnosis and management. Candidates must pass both written components to progress to the clinical exam. Validity was evaluated using Kane’s framework for scoring (marking criteria, item analysis), generalization (blueprint), extrapolation (standard setting), and implications (outcome, including pass rates). A competency-based blueprint, the Optometry Australia Entry-level Competency Standards for Optometry 2014, guided question selection with the number of items weighted towards key competencies. A standard setting exercise, last conducted in 2017, was used to determine the minimum standard for both written exams. Item response theory (Rasch) was used to analyse exams, produce reliability metrics, apply consistent standards to the results, calibrate difficulty across exams, and score candidates.ResultsData is reported on 12 administrations of the written examination since 2014. Of the 193 candidates who sat the exam over the study period, 133 (68.9%) passed and moved on to the practical component. Ninety-one (47.2%) passed both the MCQ and SAQ exams on their first attempt. The MCQ exam has displayed consistently high reliability (reliability index range 0.71 to 0.93, average 0.88) across all 12 administrations. Prior to September 2017 the SAQ had a set cutscore of 50%, and the difficulty of the exam was variable. Since the introduction of Rasch analysis to calibrate difficulty across exams, the reliability and power of the SAQ exam has been consistently high (separation index range 0.82 to 0.93, average 0.86).ConclusionsThe findings from collective evidence support the validity of the written components (MCQ and SAQ) of the credentialing of the competency of overseas-educated optometrists in Australia and New Zealand.

Correlation energies of the high-density spin-polarized electron gas to meV accuracy

We present a novel combination of quantum Monte Carlo methods and a finite size extrapolation framework with which we calculate the thermodynamic limit of the exact correlation energy of the polarized electron gas at high densities to meV accuracy, $-40.44(5)$ and $-31.70(4)$ mHa at $r_{\rm s}=0.5$ and $1$, respectively. The fixed-node error is characterized and found to exceed $1$ mHa, and we show that the magnitude of the correlation energy of the polarized electron gas is underestimated by up to $6$ meV by the Perdew-Wang parametrization, for which we suggest improvements.

Extrapolation Bias and the Predictability of Stock Returns by Price-Scaled Variables

Using survey data on expectations of stock returns, we recursively estimate the degree of extrapolative weighting in investors' beliefs (DOX). In an extrapolation framework, DOX determines the relative weight investors place on recent-versus-distant returns. DOX varies considerably over time. The ability of price-scaled variables to predict the year-ahead equity premium is contingent on DOX. High price-scaled variables are followed by lower returns only when DOX is high. Our findings support extrapolation-based theories of the stock market and the interpretation of price-scaled variables as mispricing proxies. Our results help answer a critical question: when will an overvalued asset experience a correction?

Application of the FDA Biosimilar Extrapolation Framework to Make Off-Label Determinations.

The FDA's extrapolation framework allows for a biosimilar to obtain licensure for indications that were not explicitly studied in the context of a clinical trial by extending conclusions from studies in 1 population to make inferences in other populations. Within routine clinical care, drugs and biologics are routinely used for medically accepted off-label indications. The appropriateness of these products for off-label indications are typically curated by compendia and guidelines, which have established processes and criteria for reviewing and evaluating the evidence to make such determinations. The evidence paradigm for biosimilars is different from originator biologics and is one of comparability to a reference product, not to reestablish clinical benefit. Thus, this paradigm shift can be applied to the exercise of making off-label determinations for biosimilars, and the FDA's framework of extrapolation can be used by clinicians and coverage policy decision makers to determine appropriate off-label indications for biosimilars. To highlight how the FDA's biosimilar extrapolation framework can be used to make off-label policy decisions, using to 2 approved biosimilars for filgrastim and infliximab as case studies. This study describes the FDA extrapolation framework for evaluating whether there are any differences in the mechanism of action, pharmacokinetics/biosdistribution, immunogenicity, and toxicity between on-label and off-label indications. Two case studies are presented that evaluate the biosimilars filgrastim-sndz and infliximab-dyyb for the offlabel indications of treating symptomatic anemia in patients with myelodysplastic syndromes and immune-mediated colitis, respectively. The analytical, nonclinical, and clinical pharmacology, along with clinical studies demonstrating that filgrastim-sndz and infliximab-dyyb are biosimilar to their respective reference products, are reviewed and discussed in context with the extrapolation framework to ascertain whether use of the biosimilar within the off-label indications is scientifically justified. The mechanism of action of filgrastim and infliximab between their FDA-approved and off-label indications are the same. In addition, there is a high degree of similarity with the analytical and nonclinical characteristics of filgrastim-sndz and infliximab-dyyb and their respective reference products. There is no expectation of differences in safety and immunogenicity across the patient populations. Thus, some decision makers may determine that filgrastim-sndz and infliximab-dyyb be used for the off-label indications of treating symptomatic anemia in patients with myelodysplasia and immune-mediated colitis, respectively. In some cases, the use of biosimilars for off-label indications can be scientifically justified. Since coverage policy decisions are intimately tied to compendia and guideline listings, it is incumbent upon these groups to conduct formal assessments of biosimilar off-label indications using the FDA extrapolation framework. No outside funding supported this study. Li discloses that he has received honoraria and/or paid travel expenses as an advisory board and speaker's bureau participant for Pfizer; for speaking on behalf of Mylan and Apobiologix; and for participating on advisory boards for Eli Lilly and Mylan. Lobaina has nothing to disclose. Li was responsible for study design and manuscript revision. Li took the lead in data collection and interpretation and manuscript preparation, along with Lobaina.

Development and application of a population physiologically based pharmacokinetic model for penicillin G in swine and cattle for food safety assessment

Penicillin G is a widely used antimicrobial in food-producing animals, and one of the most predominant drug residues in animal-derived food products. Due to reduced sensitivity of bacteria to penicillin, extralabel use of penicillin G is common, which may lead to violative residues in edible tissues and cause adverse reactions in consumers. This study aimed to develop a physiologically based pharmacokinetic (PBPK) model to predict drug residues in edible tissues and estimate extended withdrawal intervals for penicillin G in swine and cattle. A flow-limited PBPK model was developed with data from Food Animal Residue Avoidance Databank using Berkeley Madonna. The model predicted observed drug concentrations in edible tissues, including liver, muscle, and kidney for penicillin G both in swine and cattle well, including data not used in model calibration. For extralabel use (5× and 10× label dose) of penicillin G, Monte Carlo sampling technique was applied to predict times needed for tissue concentrations to fall below established tolerances for the 99th percentile of the population. This model provides a useful tool to predict tissue residues of penicillin G in swine and cattle to aid food safety assessment, and also provide a framework for extrapolation to other food animal species.

Data Association and Prediction for Tracking Multiple Objects

Objectives: Tracking moving objects is essential for high level computer vision analysis, like object behavior interpretation or gait recognition etc. In this paper, a new method to track multiple moving objects in the surveillance video sequence is proposed. Methods/Statistical Analysis: Object tracking is done by extracting color moments feature from the segmented foreground object and associating individual objects in the successive frames using nearest neighbor classifier and Chi- Square dissimilarity measure. Further, object tracking during occlusion has been addressed by predicting the missing state of the occluded object by applying Lagrange’s polynomial extrapolation using the apriori knowledge. Findings: The proposed method is evaluated using several challenging sequences of the benchmark IEEE PETS, IEEE CHANGE DETECTION, and EPFL dataset. Further, comparative evaluation with contemporary methods has been carried out to corroborate the efficacy of the proposed method. Application/Improvements: This work focuses on devising a new method to track multiple moving objects using color moments and Lagrange’s extrapolation framework which works for a complex environment and in the presence of occlusions. Further, the shadow elimination task after the motion segmentation could be considered as an improvement step in this work which will aid in better tracking results.