Statistical Methods in Risk Science

风险科学中的统计方法

基本信息

  • 批准号:
    RGPIN-2015-04554
  • 负责人:
  • 金额:
    $ 1.24万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Discovery Grants Program - Individual
  • 财政年份:
    2019
  • 资助国家:
    加拿大
  • 起止时间:
    2019-01-01 至 2020-12-31
  • 项目状态:
    已结题

项目摘要

Advanced statistical methods are increasingly used in the quantitative characterization of risk, providing important evidence to support the development of risk decisions. The present research program focuses on six methodological issues of current interest, which will be addressed through the development and application of innovative statistical techniques. ***1. Environmental burden of disease (EBD). Estimation of the contribution of environmental agents to human disease is importing for establishing priorities for environmental health risk reduction. Statistical methods developed previously to characterize the EBD will be extended to incorporate reduction in quality of life, and illustrated using data from a recent meta-analysis of the risks of neurological disease associated with occupational exposure to lead.****2. Risks of radon in homes. Radon gas, an important cause of lung cancer, is naturally present in rocks and soils in the earth's crust, and enters homes through tiny cracks and fissures in the foundation. In order to guide further radon mitigation efforts, updated estimates of residential radon lung cancer risks will be developed based on new epidemiological and monitoring data from Canada and internationally.****3. Concordance between animal and human tumour sites. The International Agency for Research on Cancer (IARC) has recently completed a review an update of data on 109 cancer causing agents, including: pharmaceuticals; biological agents; arsenic, metals, fibres and dusts; radiation; personal habits and indoor combustions; and chemical agents and related occupations. These data will be used to evaluate the degree of concordance between tumour types seen in animals and humans that are caused by these agents****4. Biological mechanisms of cancer in animals and humans. The IARC data described in (3) above will also be used to compare the mechanisms by which carcinogenic agents operate in animals and humans.****5. Use of pharmacokinetic models for tissue dosimetry. The use of tissue concentrations predicted by pharmacokinetic models in dose-response modelling will be explored, with the expectation that tissue doses may lead to more accurate indicators of risk than dietary or ambient concentrations.****6. Optimal experimental designs for the Ames Salmonella assay. Because of the recent trend towards the use of in vitro data in toxicity testing, optimal experimental designs to estimate a point of departure on the dose-response curve (including both the traditional benchmark dose and the recently proposed signal-to-noise crossover dose) used in setting exposure guidelines for the Ames Salmonella assay will be developed.****Collectively, this work will expand the suite of techniques available for quantitative risk assessment, thereby contributing to better assessments of risk and sound risk-based decision making.******
先进的统计方法越来越多地用于风险的定量表征,为支持风险决策的发展提供重要证据。目前的研究计划侧重于当前感兴趣的六个方法问题,这将通过创新的统计技术的开发和应用来解决。*1。环境疾病负担(EBD)。 评估环境因子对人类疾病的影响对于确定减少环境健康风险的优先事项至关重要。 以前开发的表征EBD的统计方法将扩展到包括生活质量的降低,并使用最近的一项与职业暴露于铅相关的神经系统疾病风险的荟萃分析数据进行说明。2.家中氡的危害氡气是肺癌的一个重要原因,天然存在于地壳的岩石和土壤中,并通过地基的微小裂缝和裂缝进入家庭。 为了指导进一步的氡减排工作,将根据加拿大和国际上新的流行病学和监测数据,对住宅氡肺癌风险进行最新估计。3.动物和人类肿瘤部位之间的一致性。 国际癌症研究机构(癌症研究机构)最近完成了一项审查,更新了109种致癌物质的数据,包括:药物;生物制剂;砷、金属、纤维和灰尘;辐射;个人习惯和室内燃烧;化学制剂和相关职业。 这些数据将用于评价动物和人类中由这些药物引起的肿瘤类型之间的一致性程度 *4。动物和人类癌症的生物学机制。上述(3)中描述的IARC数据也将用于比较致癌物在动物和人类中的作用机制。**** 5.药代动力学模型在组织剂量测定中的应用。将探讨在剂量反应建模中使用药代动力学模型预测的组织浓度,预期组织剂量可能比饮食或环境浓度产生更准确的风险指标。6.艾姆斯沙门氏菌检测的最佳实验设计。 由于最近的趋势是在毒性试验中使用体外数据,因此将开发最佳实验设计,以估计用于设定艾姆斯沙门氏菌试验暴露指南的剂量-反应曲线(包括传统基准剂量和最近提出的信噪比交叉剂量)上的起点。*总的来说,这项工作将扩大可用于定量风险评估的一套技术,从而有助于更好地评估风险和基于风险的合理决策。

项目成果

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Krewski, Daniel其他文献

Public health benefits of strategies to reduce greenhouse-gas emissions: health implications of short-lived greenhouse pollutants.
  • DOI:
    10.1016/s0140-6736(09)61716-5
  • 发表时间:
    2009-12-19
  • 期刊:
  • 影响因子:
    168.9
  • 作者:
    Smith, Kirk R.;Jerrett, Michael;Anderson, H. Ross;Burnett, Richard T.;Stone, Vicki;Derwent, Richard;Atkinson, Richard W.;Cohen, Aaron;Shonkoff, Seth B.;Krewski, Daniel;Pope, C. Arden, III;Thun, Michael J.;Thurston, George
  • 通讯作者:
    Thurston, George
Thiazolidinedione drugs in the treatment of type 2 diabetes mellitus: past, present and future.
  • DOI:
    10.1080/10408444.2017.1351420
  • 发表时间:
    2018-01-01
  • 期刊:
  • 影响因子:
    5.9
  • 作者:
    Davidson, Melissa A;Mattison, Donald R;Krewski, Daniel
  • 通讯作者:
    Krewski, Daniel
Public perception of terrorism threats and related information sources in Canada: Implications for the management of terrorism risks
  • DOI:
    10.1080/13669870600924477
  • 发表时间:
    2006-10-01
  • 期刊:
  • 影响因子:
    5.1
  • 作者:
    Lemyre, Louise;Turner, Michelle C.;Krewski, Daniel
  • 通讯作者:
    Krewski, Daniel
The structure of Canadians' health risk perceptions: Environmental, therapeutic and social health risks
  • DOI:
    10.1080/13698570600677399
  • 发表时间:
    2006-06-01
  • 期刊:
  • 影响因子:
    2.1
  • 作者:
    Lemyre, Louise;Lee, Jennifer E. C.;Krewski, Daniel
  • 通讯作者:
    Krewski, Daniel
A class of non-linear exposure-response models suitable for health impact assessment applicable to large cohort studies of ambient air pollution.
  • DOI:
    10.1007/s11869-016-0398-z
  • 发表时间:
    2016
  • 期刊:
  • 影响因子:
    5.1
  • 作者:
    Nasari, Masoud M.;Szyszkowicz, Mieczyslaw;Chen, Hong;Crouse, Daniel;Turner, Michelle C.;Jerrett, Michael;Pope, C. Arden, III;Hubbell, Bryan;Fann, Neal;Cohen, Aaron;Gapstur, Susan M.;Diver, W. Ryan;Stieb, David;Forouzanfar, Mohammad H.;Kim, Sun-Young;Olives, Casey;Krewski, Daniel;Burnett, Richard T.
  • 通讯作者:
    Burnett, Richard T.

Krewski, Daniel的其他文献

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{{ truncateString('Krewski, Daniel', 18)}}的其他基金

Statistical Methods in Evidence-based Risk Assessment
循证风险评估中的统计方法
  • 批准号:
    RGPIN-2022-05034
  • 财政年份:
    2022
  • 资助金额:
    $ 1.24万
  • 项目类别:
    Discovery Grants Program - Individual
NSERC Industrial Research Chair in Risk Science
NSERC 风险科学工业研究主席
  • 批准号:
    394852-2013
  • 财政年份:
    2020
  • 资助金额:
    $ 1.24万
  • 项目类别:
    Industrial Research Chairs
NSERC Industrial Research Chair in Risk Science
NSERC 风险科学工业研究主席
  • 批准号:
    394852-2013
  • 财政年份:
    2019
  • 资助金额:
    $ 1.24万
  • 项目类别:
    Industrial Research Chairs
Statistical Methods in Risk Science
风险科学中的统计方法
  • 批准号:
    RGPIN-2015-04554
  • 财政年份:
    2018
  • 资助金额:
    $ 1.24万
  • 项目类别:
    Discovery Grants Program - Individual
NSERC Industrial Research Chair in Risk Science
NSERC 风险科学工业研究主席
  • 批准号:
    394852-2013
  • 财政年份:
    2017
  • 资助金额:
    $ 1.24万
  • 项目类别:
    Industrial Research Chairs
Statistical Methods in Risk Science
风险科学中的统计方法
  • 批准号:
    RGPIN-2015-04554
  • 财政年份:
    2017
  • 资助金额:
    $ 1.24万
  • 项目类别:
    Discovery Grants Program - Individual
Statistical Methods in Risk Science
风险科学中的统计方法
  • 批准号:
    RGPIN-2015-04554
  • 财政年份:
    2016
  • 资助金额:
    $ 1.24万
  • 项目类别:
    Discovery Grants Program - Individual
Statistical Methods in Risk Science
风险科学中的统计方法
  • 批准号:
    RGPIN-2015-04554
  • 财政年份:
    2015
  • 资助金额:
    $ 1.24万
  • 项目类别:
    Discovery Grants Program - Individual
NSERC Industrial Research Chair in Risk Science
NSERC 风险科学工业研究主席
  • 批准号:
    394852-2013
  • 财政年份:
    2015
  • 资助金额:
    $ 1.24万
  • 项目类别:
    Industrial Research Chairs
NSERC Industrial Research Chair in Risk Science
NSERC 风险科学工业研究主席
  • 批准号:
    394852-2007
  • 财政年份:
    2013
  • 资助金额:
    $ 1.24万
  • 项目类别:
    Industrial Research Chairs

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