SEARCH · Search NASA

Results for “dose response model”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2

All sources NASA NTRS DOE OSTI

NASA NTRS2003

Mortality of atomic bomb survivors predicted from laboratory animals

Exposure, pathology and mortality data for mice, dogs and humans were examined to determine whether accurate interspecies predictions of radiation-induced mortality could be achieved. The analyses revealed that (1) days of life lost per unit dose can be estimated for a species even without information on radiation effects in that species, and (2) accurate predictions of age-specific radiation-induced mortality in beagles and the atomic bomb survivors can be obtained from a dose-response model for comparably exposed mice. These findings illustrate the value of comparative mortality analyses and the relevance of animal data to the study of human health effects.

NASA Discipline Radiation Health↗

DOE OSTI2023

False Indications of Dose-Response Nonlinearity in Large Epidemiologic Cancer Radiation Cohort Studies; A Simulation Exercise

This study explores the likely prevalence of false indications of dose-response nonlinearity in large epidemiologic cancer radiation cohort studies (A-bomb survivors, INWORKS, Techa River). Reasons: Increasing numbers of tests of nonlinearity are being made in studies. Hypothesized nonlinear dose-response models have been justified to policy makers by analyses that rely in part on isolated findings that could be statistical fluctuations. After removing dose nonlinearity (linearization) by adjusting person-years of observation at each dose category, indications of nonlinearity, necessarily false, were counted in 5,000 randomized replications of six datasets. The average frequency of any false positive for five indicators of nonlinearity tested against a linear null was roughly 25% in Monte Carlo simulations per study, consistent with binomial calculations, increasing to ~50% within 6 studies assessed. Comparable frequencies were found using Akaike's information criterion (AIC) for model selection or multi-model averaging. False above-zero threshold doses were found more than 50% of the time, averaging to 0.05 Gy, consistent with findings in the 6 studies. Such bias, uncorrected, could distort meta-analyses of multiple studies, because meta-analyses can incorporate high P value findings. AIC-based correction for the extra threshold parameter lowered these false occurrences to 8 to 19%. Given the simulation rates, the possibility of false positives might be noted when isolated findings of nonlinearity are discussed in a regulatory context. When reporting a threshold dose with a P value > 0.05, it would be informative to note the expected high false prevalence rate due to bias.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2021

A Case Study on Pathogen Transport, Deposition, Evaporation and Transmission: Linking High-Fidelity Computational Fluid Dynamics Simulations to Probability of Infection

A high-fidelity, low-Mach computational fluid dynamics simulation tool that includes evaporating droplets and variable-density turbulent flow coupling is well-suited to ascertain transmission probability and supports risk mitigation methods development for airborne infectious diseases such as COVID-19. A multi-physics large-eddy simulation-based paradigm is used to explore droplet and aerosol pathogen transport from a synthetic cough emanating from a kneeling humanoid. For an outdoor configuration that mimics the recent open-space social distance strategy of San Francisco, maximum primary droplet deposition distances are shown to approach 8.1 m in a moderate wind configuration with the aerosol plume transported in excess of 15 m. In quiescent conditions, the aerosol plume extends to approximately 4 m before the emanating pulsed jet becomes neutrally buoyant. A dose–response model, which is based on previous SARS coronavirus (SARS-CoV) data, is exercised on the high-fidelity aerosol transport database to establish relative risk at eighteen virtual receptor probe locations.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRSDate not supplied

Dose-Response Data Considerations for the NASA Quesst Community Test Campaign

Key outcomes for NASA's Quesst mission are noise dose and perceptual response data to inform regulators on their decisions regarding noise certification standards for the future of overland commercial supersonic flight. Dose-response curves are commonly utilized in community noise studies to describe the annoyance of a community to a particular noise source. The X-59 aircraft utilizes shaped-boom technology to demonstrate low noise supersonic flight. For X-59 community studies, the sound level from X-59 overflights constitutes the dose, while the response is an annoyance rating selected from a verbal scale, e.g., “slightly annoyed” and “very annoyed.” Dose-response data will be collected from individual flyovers (single event dose) and an overall response to the accumulation of single events at the end of the day (cumulative dose). There are quantifiable sources of error in the noise dose due to uncertainty in microphone measurements of the sonic thumps and uncertainty in predicted noise levels at survey participant locations. Assessing and accounting for error in the noise dose is essential to obtain an accurate dose-response model. There is also a potential for error in the perceptual response. This error is due to the ability of participants to provide their response in a timely manner and participant fatigue after responding to up to one hundred surveys over the course of a month. This talk outlines various challenges in estimating noise dose and perceptual response and the methods considered in preparation for X-59 community tests.

dose-response↗

DOE OSTI2022

Dietary B group vitamin intake and the bladder cancer risk: a pooled analysis of prospective cohort studies

Abstract Purpose Diet may play an essential role in the aetiology of bladder cancer (BC). The B group complex vitamins involve diverse biological functions that could be influential in cancer prevention. The aim of the present study was to investigate the association between various components of the B group vitamin complex and BC risk. Methods Dietary data were pooled from four cohort studies. Food item intake was converted to daily intakes of B group vitamins and pooled multivariate hazard ratios (HRs), with corresponding 95% confidence intervals (CIs), were obtained using Cox-regression models. Dose–response relationships were examined using a nonparametric test for trend. Results In total, 2915 BC cases and 530,012 non-cases were included in the analyses. The present study showed an increased BC risk for moderate intake of vitamin B1 (HR B1 : 1.13, 95% CI: 1.00–1.20). In men, moderate intake of the vitamins B1, B2, energy-related vitamins and high intake of vitamin B1 were associated with an increased BC risk (HR (95% CI): 1.13 (1.02–1.26), 1.14 (1.02–1.26), 1.13 (1.02–1.26; 1.13 (1.02–1.26), respectively). In women, high intake of all vitamins and vitamin combinations, except for the entire complex, showed an inverse association (HR (95% CI): 0.80 (0.67–0.97), 0.83 (0.70–1.00); 0.77 (0.63–0.93), 0.73 (0.61–0.88), 0.82 (0.68–0.99), 0.79 (0.66–0.95), 0.80 (0.66–0.96), 0.74 (0.62–0.89), 0.76 (0.63–0.92), respectively). Dose–response analyses showed an increased BC risk for higher intake of vitamin B1 and B12. Conclusion Our findings highlight the importance of future research on the food sources of B group vitamins in the context of the overall and sex-stratified diet.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2022

Comparison of Two Statistical Models for Low Boom Dose-response Relationships with Correlated Responses

This study compares two statistical models to construct summary dose-response curves for low boom community noise surveys. Data from two NASA field surveys are used that consist of multiple responses per survey participant. These data require an approach that accounts for the correlation among repeated annoyance observations from the same participant. A multilevel model accounts for the correlation by allowing estimated parameters to vary with each survey participant. On the other hand, a population average model utilizes generalized estimating equations and accounts for the correlation via a userspecified within-subject correlation structure. A visual comparison of the dose-response curves for these two methods reveals similar results. When comparing specific points along the summary curves, the multilevel model yields more precise confidence bounds than the population average model. The similarity between the summary curves derived from each model lends validity to both approaches for approximating a population representative summary curve, though modeling assumptions may lend favor to the multilevel logistic modeling approach over the population average model.

dose-response↗

NASA NTRSDate not supplied

Comparing Two Statistical Models for Low Boom Dose-response Relationships with Correlated Responses

This study compares two statistical modeling approaches to construct summary dose-response curves for low boom community noise surveys. NASA field survey data were used that consist of multiple responses from a survey participant. These data require an approach that accounts for the correlation among repeated annoyance observations from the same participant. A multilevel model accounts for the correlation by allowing estimated parameters to vary with each survey participant. On the other hand, a population average model utilizes generalized estimating equations and accounts for the correlation via a user-specified within-subject correlation structure. A visual comparison of the dose-response curves for these two methods reveals similar results. When comparing specific points along the summary curves, the multilevel model yields more precise confidence bounds than the population average model. The similarity between the summary curves derived from each model lends validity to both approaches for approximating a population representative summary curve.

X-59↗

NASA NTRSDate not supplied

Effects of Dose Error and Sample Size on Sonic Boom Dose-response Curves

NASA will soon be collecting noise-annoyance community survey data as the X-59 aircraft flies supersonically over several communities in the USA. Sparse measurements of the X-59 sonic thumps will be used together with physics-based simulations to estimate noise doses at survey participant locations. These dose estimates have associated error that affects the accuracy of modeled dose-response curves, which can result in misestimation of annoyance. The precision in dose-response curves is also a consideration in selecting the number of survey participants. To enable pretest studies of dose error and precision, simulated dose-response data were generated based on NASA’s Quiet Supersonic Flights 2018 test. The data included various degrees of dose error and sample size. Frequentist multilevel logistic regression models were fit to the true and perturbed dose-response data. Simple proportional relationships were identified between the model parameters and the perturbation standard deviation. The summary dose-response curves illustrate the impact on accuracy if dose error is not accounted for in the model. The precision in the dose-response curves is also shown as the number of participants and degree of participation is varied. Finally, sampling variability is illustrated by showing the dose-response curves for several replicates with random draws of participants and errors.

X-59↗

NASA NTRSDate not supplied

Effects of Dose Error and Sample Size on Sonic Boom Dose-Response Curves

X-59↗

NASA NTRSDate not supplied

Dose Error Impacts on A Collection of Realistic Dose-Response Curves Based on A NASA Sonic Boom Community Noise Survey

The National Aeronautics and Space Administration (NASA) plans to conduct surveys of community response to quiet supersonic flight to collect dose-response data for international regulators. Previous models of noise dose versus annoyance response ignored uncertainty in the noise dose experienced by survey respondents. This dose error causes attenuation bias and results in inaccurate dose-response relationships. The impacts of dose errors can be explored by introducing error into the dose estimates of a simulated community noise survey. The simulated population annoyance response is determined by specific noise response characteristics, including the onset of annoyance intercept, rate of increasing annoyance, participant intercept variability, and response rates. This paper explores the effects of dose errors on notional populations created by perturbing the noise response characteristics observed in participants in a recent NASA flight test, QSF18. The QSF18-based population parameters were shifted up to ±20% to create the study populations. For the anticipated dose range of the X-59, changes to the onset of annoyance intercept have the greatest impact on erroneous model results. Quantifying point estimate errors illustrates the impact of dose error, with errors up to 14 dB observed for a fixed high annoyance percentage.

dose-response modeling↗

NASA NTRSDate not supplied

Dose Error Impacts on A Collection of Realistic Dose-Response Curves Based on A NASA Sonic Boom Community Noise Survey

dose-response modeling↗

NASA NTRSDate not supplied

Infusing Statistical Thinking into the NASA Quesst Community Test Campaign

Statistical thinking permeates many important decisions as NASA plans its Quesst mission, which will culminate in a series of community overflights using the X-59 aircraft to demonstrate low-noise supersonic flight. Month-long longitudinal surveys will be deployed to assess human perception and annoyance to this new acoustic phenomenon. NASA works with a large contractor team to develop systems and methodologies to estimate noise doses, to test and field socio-acoustic surveys, and to study the relationship between the two quantities, dose and response, through appropriate choices of statistical models. This latter dose-response relationship will serve as an important tool as national and international noise regulators debate whether overland supersonic flights could be permitted once again within permissible noise limits. In this presentation we highlight several areas where statistical thinking has come into play, including issues of sampling, classification and data fusion, and analysis of longitudinal survey data that are subject to rare events and the consequences of measurement error. We note several operational constraints that shape the appeal or feasibility of some decisions on statistical approaches, and we identify several important remaining questions to be addressed.

Bayesian model↗

NASA NTRS2024

Summary and Annotated Bibliography of Measurement Error Corrections with Potential Application in Future Quesst Mission Community Noise Studies

This document is motivated by likely needs of the Quesst mission community response tests, which will culminate in data collection and estimation of dose-response regression relationships for consideration by domestic and international aviation regulators. Furthermore, basic research questions evaluating interactions between rates of community annoyance, dose levels, and indicators of the presence of rattle, vibration, and startle hinge on hypothesis testing in the context of regression models. For a variety of reasons, noise doses may be known only imprecisely and may not reflect the actual level experienced by responding subjects. These differences between true dose and estimated dose, be they systematic or random, constitute covariate measurement error. Available statistics literature speaks to the impacts of measurement error on regression models, both in terms of bias in estimated coefficients and predicted values, and in terms of the loss of statistical power for hypothesis testing. Given the particulars of a categorical annoyance response variable and a continuous noise dose predictor variable subject to measurement error during testing, the emphasis of this report is on findings and methods pertinent to generalized linear (and mixed) models likely to be employed during the Quesst mission community tests. We reach the following conclusions: 1. Of four reviewed methods, structural Bayesian measurement error models and simulation extrapolation (SIMEX) may be the most readily applicable to Quesst mission community noise study objectives. 2. If warranted, a linear measurement model can help model systematic sources of measurement error that the classical measurement error does not. 3. For its ready implementation and small additional input requirements, simulation extrapolation may be ideally suited for addressing secondary research questions involving interactions between annoyance, noise dose, and other factors through hypothesis testing. 4. For their flexibility and ability to propagate uncertainty, structural Bayesian hierarchical models have great appeal for mission purposes; some care may be needed in developing appropriate probability models describing actual noise exposure during testing. An annotated bibliography logs additional papers and resources that may be of value to analysts in other projects and disciplines.

Dose-Response Model↗

NASA NTRS1980

An airport community noise-impact assessment model

A computer model was developed to assess the noise impact of an airport on the community which it serves. Assessments are made using the Fractional Impact Method by which a single number describes the community aircraft noise environment in terms of exposed population and multiple event noise level. The model is comprised of three elements: a conventional noise footprint model, a site specific population distribution model, and a dose response transfer function. The footprint model provides the noise distribution for a given aircraft operating scenario. This is combined with the site specific population distribution obtained from a national census data base to yield the number of residents exposed to a given level of noise. The dose response relationship relates noise exposure levels to the percentage of individuals highly annoyed by those levels.

Deloach, R.↗

NASA NTRS1994

An initiation-promotion model of tumour prevalence from high-charge and energy radiations

A repair/misrepair kinetic model for multiple radiation-induced lesions (mutation inactivation) is coupled to a two-mutation model of initiation-promotion in tissue to provide a parametric description of tumour prevalence in the mouse Harderian gland from high-energy and charge radiations. Track-structure effects are considered using an action-cross section model. Dose-response curves are described for gamma rays and relativistic ions, and good agreement with experiment is found. The effects of nuclear fragmentation are also considered for high-energy proton and alpha-particle exposures. The model described provides a parametric description of age-dependent cancer induction for a wide range of radiation fields. Radiosensitivity parameters found in the model for an initiation mutation (sigma 0 = 7.6 x 10(-10) cm2 and D0 = 148.0 Gy) are somewhat different than previously observed for neoplastic transformation of C3H10T1/2 cell cultures (sigma 0 = 0.7 x 10(-10) cm2 and D0 = 117.0 Gy). We consider the two hypotheses that radiation acts solely as an initiator or as both initiator and promoter and make model calculations for fractionation exposures from gamma rays and relativistic Fe ions. For fractionated Fe exposures, an inverse-dose-rate effect is provided by a promotion hypothesis with an increase of 30% or more, dependent on the dose level and fractionation schedule, using a mutation rate for promotion similar to that of single-gene mutations.

NASA Discipline Radiation Health↗

NASA NTRS1999

Predicting cancer rates in astronauts from animal carcinogenesis studies and cellular markers

The radiation space environment includes particles such as protons and multiple species of heavy ions, with much of the exposure to these radiations occurring at extremely low average dose-rates. Limitations in databases needed to predict cancer hazards in human beings from such radiations are significant and currently do not provide confidence that such predictions are acceptably precise or accurate. In this article, we outline the need for animal carcinogenesis data based on a more sophisticated understanding of the dose-response relationship for induction of cancer and correlative cellular endpoints by representative space radiations. We stress the need for a model that can interrelate human and animal carcinogenesis data with cellular mechanisms. Using a broad model for dose-response patterns which we term the "subalpha-alpha-omega (SAO) model", we explore examples in the literature for radiation-induced cancer and for radiation-induced cellular events to illustrate the need for data that define the dose-response patterns more precisely over specific dose ranges, with special attention to low dose, low dose-rate exposure. We present data for multiple endpoints in cells, which vary in their radiosensitivity, that also support the proposed model. We have measured induction of complex chromosome aberrations in multiple cell types by two space radiations, Fe-ions and protons, and compared these to photons delivered at high dose-rate or low dose-rate. Our data demonstrate that at least three factors modulate the relative efficacy of Fe-ions compared to photons: (i) intrinsic radiosensitivity of irradiated cells; (ii) dose-rate; and (iii) another unspecified effect perhaps related to reparability of DNA lesions. These factors can produce respectively up to at least 7-, 6- and 3-fold variability. These data demonstrate the need to understand better the role of intrinsic radiosensitivity and dose-rate effects in mammalian cell response to ionizing radiation. Such understanding is critical in extrapolating databases between cellular response, animal carcinogenesis and human carcinogenesis, and we suggest that the SAO model is a useful tool for such extrapolation.

Non-NASA Center↗

NASA NTRS2022

Comparison of Likelihood Methods for Generalized Linear Mixed Models with Application to Quiet Supersonic Flights 2018 Data

Repeated measurement will be a feature of the survey data collected during the Quesst missionX-59 community response tests (CRT). Since each participant will report his or her categorical level of annoyance in response to multiple events, the responses from any single individual may be correlated with one another. Several models within the class of generalized linear mixed models (GLMM) are pertinent to the analysis of correlated categorical outcomes; the random intercept logistic regression model is one example. Both Bayesian and frequentist methods for fitting these models are available, with frequentist methods relying on some form of approximation (of either an integral or the integrand) that appears in the marginal likelihood function. Given several anticipated similarities of the X-59 CRT data to data collected during a past risk reduction, Quiet Supersonic Flights 2018 (QSF18), this short note is intended to create awareness. It documents an instance in which a reported population average dose-response relationship derived from QSF18 single event data was distorted by the integral approximation applied in likelihood-based methods. We review some of the available literature on the topic, compare the outputs of several different computational approaches implemented in available statistical software, and present simple corrective actions that may be useful during the Quesst mission.

dose-response model↗

NASA NTRS2023

Accounting for Dose Uncertainty in Dose-Response Curve Estimation Using Hierarchical Bayes Models

As part of their development of the technology for low-boom supersonic flight, the National Aeronautics and Space Administration (NASA) is planning to conduct a set of supersonic aircraft annoyance surveys in select communities in the United States, to measure public perception of the reduced sonic boom. The relationship between a noise exposure level of a supersonic flight event and the probability of an individual being highly annoyed by the event is quantified by a dose-response curve, which is usually based on logistic regression modeling. Unavoidable amounts of estimation error are expected in the upcoming noise measurements, which can bias the results of the logistic regression if ignored. Hence, the development of an estimation approach that accounts for such error when estimating the dose-response curve is imperative. In this paper, an evaluation study is conducted to assess the impact of measurement error on dose-response curve estimation. For this, hierarchical Bayes models using different specifications are fit to data collected by NASA in 2018, as part of a risk-reduction study of supersonic flights affecting Galveston, Texas. It is observed that the estimated dose-response curve appears sensitive to uncertainty in dose measurements, being subject to attenuation bias.

community response↗