Statistical Methods And Applications For Laboratory Animal Studies

实验动物研究的统计方法和应用

基本信息

批准号：
8336550
负责人：
Gregg Dinse
金额：
$ 3.07万
依托单位：
NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
资助国家：
美国
起止时间：
至
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/8336550
关键词：
Accounting Animals Area Biological Assay Biological Products Cause of Death Cells Chemicals Chinese People Chronic Collaborations Control Groups Data Data Analyses Databases Drops Failure Female Fibroadenoma Gland Goals Incidence Individual Institutes International Laboratories Laboratory Animals Left Malignant Neoplasms Mammary gland Mathematics Modeling Mononuclear National Institute of Environmental Health Sciences National Toxicology Program Neoplasm Metastasis New York City Normalcy Paper Participant Pathologist Pathology Probability Procedures Property Publishing Rat Strains Rattus Records Regression Analysis Reporting Research Research Personnel Rodent Role Sampling Scientist Solutions Sprague-Dawley Rats Statistical Methods Structure of thyroid parafollicular cell Techniques Testing Thyroid carcinoma Time Toxicology Weight Work Writing adenoma animal data base carcinogenicity diet route hazard immunotoxicity leukemia method development novel reproductive research study sex simulation statistics symposium tumor

项目摘要

Over the past year, the majority of my research on this project was performed in two areas: (1) censored failure-time analysis when some censoring indicators are missing and (2) analysis of historical control data in rodent carcinogenicity experiments. These two areas of research are described in more detail below. I also revised an entry in the Encyclopedia of Environmetrics about the general problem of using a 3-state stochastic model to analyze data from animal carcinogenicity studies. Area 1: Failure-time data are typically subject to censoring, such as when a study ends before all participants fail. Additionally, when multiple causes of failure are operating, the time to failure from one cause can be censored by a failure from another cause. In some situations, the censoring indicator is missing for a subset of individuals, such as in a cancer bioassay when the pathologist is not able to determine the role of a tumor in causing death or when some records are incomplete. The analysis of failure-time data typically focuses on hazard functions. Under an accelerated failure-time model, we derived three nonparametric hazard estimators that are appropriate when some failure times are right censored and some censoring indicators are missing. Specifically, we developed a regression surrogate estimator, an imputation estimator, and an inverse probability weighted estimator. All three estimators use kernel smoothing techniques and enjoy certain large-sample properties such as uniform strong consistency and asymptotic normality. A simulation study showed that the proposed hazard estimators also performed well in small samples. Under the same accelerated failure-time model, we also developed a regression analysis, which allows us to evaluate the effects of various explanatory variables on the hazard functions. We wrote two articles describing our work in this general area. For the homogeneous estimation problem, our first paper is in press at the Annals of the Institute of Statistical Mathematics. For the regression analysis, our second paper was published in Lifetime Data Analysis. I also gave an invited talk about this research at the International Chinese Statistical Association Applied Statistics Symposium in New York City on June 27, 2011. Area 2: When evaluating the carcinogenicity of a chemical, researchers often assess tumor incidence rates from the current rodent bioassay within the context of a historical database of control tumor rates from similar studies using animals of the same species, strain, and sex. If the tumor rate in the control group of the current study is outside the range of historical control rates, there may be concern about the validity of the conclusions drawn from the current study. When comparing tumor rates in current and historical control groups, our research demonstrated that a decision rule based on the historical range does not maintain the Type I error rate at the usual 5% signiﬁcance level and can, in fact, be as high as 67% in some real-world situations. In other cases, the power can go to zero. We developed a simple alternative procedure that controls Type I errors, adjusts for animal survival, and accounts for extra variability between studies. Extensive simulations showed that our test operated at or below the nominal level, whereas the range-based decision rule often resulted in extremely high Type I error rates. In other research related to historical control data, we compared tumor incidence rates in two strains of rats used in NTP studies. Specifically, in 2008 the NTP switched from using Fischer 344/N (F344/N) rats to using Harlan Sprague Dawley (SD) rats in its carcinogenicity, reproductive and immunotoxicity bioassays. The NTP had previously used female SD rats in nine chronic bioassays. We compared historical control tumor data from these nine SD studies with historical control tumor data from matched NTP chronic bioassays that used F344/N rats. Our goal was to identify similarities and differences in tumor incidence rates across the two strains. Matching on sex, diet, route, and laboratory led to nine comparable F344/N studies. All tumor types with fewer than 3 occurrences in the entire historical control database were excluded, as were metastases and combinations of tumors, leaving a total of 82 tumor types. Statistically significant strain differences in incidence rates were identified for several tumor types, including clitoral gland adenoma, mammary gland fibroadenoma, mammary gland carcinoma, thyroid gland C-Cell adenoma, and mononuclear cell leukemia. When vehicle was included as an additional matching criterion, the number of comparable F344/N studies dropped to four, but similar results were obtained. Our paper about the comparison of current and historical control tumor rates was published in Statistics in Biopharmaceutical Research and our paper regarding the comparison of control tumor rates in SD and F344/N rats was published in Toxicologic Pathology. This research was conducted in collaboration with Dr. Shyamal Peddada and is also mentioned in the report for his project entitled 'Statistical Methods with Applications to Toxicology and Microarray Data' (ES101744).

在过去的一年中，我对该项目的大多数研究是在两个领域进行的：（1）当缺少某些审查指标时进行审查的失败时间分析以及（2）对啮齿动物致癌性实验中历史控制数据的分析。这两个研究领域在下面更详细地描述。我还修改了环境百科全书中的一个条目，内容涉及使用三态随机模型来分析动物致癌性研究数据的总体问题。区域1：失败时间数据通常受到审查的约束，例如研究在所有参与者失败之前结束时。此外，当失败的多个原因运行时，可能会因另一个原因的失败而审查失败的时间。在某些情况下，一部分个体缺少检查指标，例如在癌症生物测定中，病理学家无法确定肿瘤在造成死亡或某些记录不完整时的作用。故障时间数据的分析通常集中在危险功能上。在加速故障时间模型下，我们得出了三个非参数危害估计器，这些估计值适用于某些故障时间是正确的审查，并且缺少某些审查指标。具体而言，我们开发了回归替代估计量，插补估计器和一个反概率加权估计器。所有三个估计器都使用内核平滑技术，并享受某些大样本特性，例如均匀的强一致性和渐近正态性。一项模拟研究表明，提出的危险估计器在小样品中也表现良好。在相同的加速故障时间模型下，我们还开发了回归分析，该分析使我们能够评估各种解释变量对危险功能的影响。我们撰写了两篇文章，描述了我们在这个一般领域的作品。对于同质估计问题，我们的第一篇论文是在统计数学研究所的媒体上媒体。对于回归分析，我们的第二篇论文发表在终生数据分析中。我还于2011年6月27日在纽约市国际中国统计协会应用统计专题讨论会上发表了有关这项研究的邀请。区域2：在评估化学物质的致癌性时，研究人员经常在使用同一物种，菌株和性别的动物的类似研究的对照肿瘤率的历史数据库中评估当前啮齿动物生物测定的肿瘤发病率。如果当前研究的对照组中的肿瘤率超出了历史控制率的范围，则可能会担心当前研究得出的结论的有效性。在比较当前和历史对照组中的肿瘤率时，我们的研究表明，基于历史范围的决策规则并不能将I型错误率保持在通常的5％意义水平，实际上在某些现实情况下可以高达67％。在其他情况下，功率可以零。我们开发了一个简单的替代程序，该程序控制I型错误，调整动物生存，并解释研究之间的额外可变性。广泛的模拟表明，我们的测试在标称级别下进行或低于标称级别，而基于范围的决策规则通常会导致I型错误率极高。在与历史控制数据有关的其他研究中，我们比较了NTP研究中使用的两种大鼠的肿瘤发病率。具体而言，在2008年，NTP从使用Fischer 344/N（F344/N）大鼠转变为使用Harlan Sprague Dawley（SD）大鼠的致癌性，生殖和免疫毒性生物测定。 NTP以前曾在九个慢性生物测定中使用了雌性SD大鼠。我们将这9个SD研究的历史对照肿瘤数据与使用F344/N大鼠的NTP慢性生物测定的历史对照肿瘤数据进行了比较。我们的目标是确定两种菌株中肿瘤发病率的相似性和差异。性别，饮食，路线和实验室的匹配导致9项可比的F344/N研究。在整个历史控制数据库中，所有肿瘤类型都少于3，肿瘤的转移和组合都排除在外，总共剩下82种肿瘤类型。在几种肿瘤类型中鉴定出发病率的统计学意义，包括阴蒂腺腺瘤，乳腺纤维纤维瘤，乳腺癌，甲状腺C细胞C细胞腺瘤和单核细胞白血病。当将车辆作为额外的匹配标准包括在内时，可比的F344/N研究的数量下降到四个，但获得了相似的结果。我们的有关当前和历史对照肿瘤率比较的论文在生物制药研究的统计数据中发表了有关SD和F344/N大鼠对照肿瘤率比较的论文。这项研究是与Shyamal Peddada博士合作进行的，在报告中也提到了其题为“统计方法的统计方法，并适用于毒理学和微阵列数据”（ES101744）。