Multilevel and Longitudinal Study Sample Size Tools for Behavioral Scientists

描述(申请人提供)：目前用于多水平和纵向数据的样本量方法经常得到错误的答案。规模太小的研究无法实现科学目标。规模过大的研究会让人类研究参与者受到不必要的伤害。约束NIH资助的行为科学家与人类参与者进行研究的伦理准则要求正确的样本量选择。当前样本大小方法的缺陷是取得进展的关键障碍。行为科学家使用社区试验、纵向数据和多水平模型来比较不同的人群。了解成瘾和衡量头颈部癌症筛查的知识需要随着时间的推移衡量结果。复杂的数据会产生复杂的方差模式。当前的软件(如最优设计)对方差模式做了简化的假设，这可能导致错误的样本大小。没有可用的软件总是给出正确的样本量分析；现有的范例失败了。在设计多水平和纵向研究方面有三个障碍：1)当当前样本量方法失败时，评估工具不足；2)选择样本量的方法和软件有缺陷；3)培训不足，无法找到正确的样本量。两个目标的成功完成将消除这三个障碍。目标是1)创建新的样本量方法和软件，以准确反映在研究不同人群健康行为中常见的正常、二进制和泊松数据的多水平和纵向模型，以及2)培训行为科学家使用新方法和软件。培训将包括模板样本量分析、教程出版物、短期课程、网络研讨会和在线教程。更好的样本量计算将产生更好的设计研究。设计更好的研究将加快道德、高效和有效的行为研究。行为研究是预防上瘾和癌症的核心。正确的样本量选择不仅仅是统计上的精确度。新方法将直接改善数百万美国人的健康。 公共卫生相关性：人类研究的伦理和成本需要准确的样本量。对于多水平和纵向数据，当前的样本量选择方法可能会失败。我们将为行为科学家开发新的样本量工具，在人口多样化的社区进行预防研究。

DESCRIPTION (provided by applicant): Current sample size methods for multilevel and longitudinal data often get the wrong answer. Studies that are too small cannot achieve scientific goals. Studies that are too large expose human research participants to unnecessary harm. The ethical codes that bind NIH funded behavioral scientists doing research with human participants demand correct sample size selection. Flaws of current sample size methods are a critical barrier to progress. Behavioral scientists use community trials, longitudinal data, and multilevel models to compare diverse populations. Understanding addiction and gauging knowledge about head and neck cancer screening require measuring outcomes over time. The complex data yield complex variance patterns. Current software (such as Optimal Design) makes simplifying assumptions about the variance patterns, which may lead to the wrong sample size. No available software always gives the correct sample size analysis; the existing paradigm fails. There are three barriers to progress in designing multilevel and longitudinal studies: 1) inadequate tools to evaluate when current sample size methods fail; 2) flawed methods and software to select sample size; and 3) insufficient training to find correct sample sizes. The successful completion of two aims will remove the three barriers. The aims are 1) Create new sample size methods and software needed to accurately mirror multilevel and longitudinal models of normal, binary, and Poisson data common in studying health behavior in diverse populations and 2) Train behavioral scientists to use the new methods and software. Training will include template sample size analyses, tutorial publications, short courses, webinars and online tutorials. Better sample size calculations will produce better designed studies. Better designed studies will speed ethical, efficient, and effective behavioral research. Behavioral research lies at the heart of preventing addiction and cancer. Correct sample size selection is not merely a statistical nicety. The new methods will directly improve the health of millions of Americans. PUBLIC HEALTH RELEVANCE: The ethics and cost of human research demands accurate sample size. Current sample size selection methods can fail with multilevel and longitudinal data. We will develop new sample size tools for behavioral scientists conducting prevention research in communities with diverse populations.