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Statistical Testing and Power for MH Research

MH 研究的统计测试和功效

基本信息

批准号：
7198032
负责人：
Dulal Kumar Bhaumik
金额：
$ 16.81万
依托单位：
UNIVERSITY OF ILLINOIS AT CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2005
资助国家：
美国
起止时间：
2005-05-01 至 2008-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7198032
关键词：
Address Adolescent Ally Analysis of Variance Area Australia Austria Behavior Behavioral Belgium Biological Brazil Canada Child Chile Class Clinic Cluster Analysis Communities Computer software Condition Count Country of Turkey County Daily Data Data Set Development Dimensions Disease regression Equation Equilibrium Finland Germany Goals Government Agencies Greece Health Health Services Research Homelessness Hospitals Hylobates Genus Individual Intercept Internet Intervention Italy Japan Linear Models Literature Logistic Regressions Logistics Longitudinal Studies Measurement Measures Mental Health Mental Health Services Methods Mexico Military Personnel Minority Modeling National Institute of Mental Health Nature Netherlands Neuro-Oncological Ventral Antigen 2 Non-linear Models Normal Statistical Distribution Numbers Outcome Performance Peru Plant Roots Poisson Distribution Poland Population Prevention Research Procedures Property Randomized Rate Recruitment Activity Relative (related person)Research Research Design Research Personnel Sample Size Sampling Schools Scientist Services Site Solutions Spain Specific qualifier value Speed Standards of Weights and Measures Statistical Computing Statistical Methods Structure Study Section Sweden Switzerland Testing Thailand Time United Kingdom Urination Visit Work base computer program cost day design distributed data impression interest member programs research study response service utilization simulation size social statistics suicidal risk theories tool trend

项目摘要

DESCRIPTION (provided by applicant): Over the last decade, mental health services researchers have made widespread use of generalized mixed-effects regression models for analysis of clustered and longitudinal data. Much of the work in this area has involved the development of efficient methods of statistical estimation, based on maximum marginal likelihood, empirical Bayes, and fully Bayesian estimation strategies. Generalization of the original model for continuous and normally distributed data to the case of non-linear mixed-effects regression models for binary, ordinal, nominal, and Poisson, distributions are now generally available and enjoy widespread use. Furthermore, computer software has now been developed and is either freely available over the Internet or commercially available. With the speed of this development and acceptance by the research community, it is therefore somewhat surprising that so little research has been conducted on the issue of hypothesis testing for generalized mixed-effects regression models. Indeed, traditional approaches of large sample tests based on likelihood ratios and Wald-type statistics are all that are generally available. These approaches are limited due to their large sample properties in addition to well-known limitations for testing models with varying numbers of random effects. Furthermore, in addition to the absence of an arsenal of tools for statistical testing, the literature is also quite limited with respect to statistically rigorous approaches to computing statistical power for clustered and longitudinal designs. For non-linear mixed-models (e.g., binary and ordinal cases), the literature on statistical power is virtually nonexistent, and gross oversimplification of the study design, estimation, and testing procedures must be used to obtain any estimates of the number of measurements needed at each level of nesting that are required to test a hypothesis with a reasonable balance of Type I and II errors. The primary goal of this proposal is to fill this void by (1) studying the large and small sample properties of various existing and new tests suitable for generalized linear and non-linear mixed-effects regression models, (2) to develop statistically rigorous approaches to computing statistical power for this class of models that is now so widely used by behavioral, social, and biological scientists in general, and health and mental health services researchers in particular, and (3) to develop a computer program for computing statistical power for linear and non-linear mixed-effects regression models (MIXPWR), and to incorporate these new tests into the existing programs (MIXREG, MIXOR, MIXPREG, MIXNO), which are distributed freely from ww.uic.edu/Iabs/biostat. Preliminary results reveal that the new small sample tests that we have derived provide the ability to detect dramatically smaller effects in small samples and increased statistical power over traditional large sample tests even when sample sizes are large. The net result is the ability to use rigorous statistical methods for analysis of longitudinal and clustered data, even in small and difficult to recruit populations such as minorities, homeless, and those at high risk for suicide.

描述（由申请人提供）：在过去的十年中，精神卫生服务研究人员广泛使用广义混合效应回归模型分析聚类和纵向数据。这一领域的大部分工作都涉及开发基于最大边际似然、经验贝叶斯和完全贝叶斯估计策略的有效统计估计方法。将连续和正态分布数据的原始模型推广到二元、有序、名义和泊松分布的非线性混合效应回归模型，现在已经普遍可用并得到广泛使用。此外，计算机软件现已开发出来，可通过互联网免费获得或通过商业渠道获得。随着这一发展的速度和接受的研究界，因此，这是有点令人惊讶的是，这么少的研究已经进行了广义混合效应回归模型的假设检验的问题。事实上，基于似然比和Wald型统计量的大样本检验的传统方法是所有通常可用的方法。这些方法是有限的，因为它们的大样本属性，除了众所周知的限制，测试模型与不同数量的随机效应。此外，除了缺乏统计检验的工具库，文献也相当有限，在统计上严格的方法来计算聚类和纵向设计的统计能力。对于非线性混合模型（例如，二进制和有序的情况下），统计功效的文献几乎是不存在的，研究设计，估计和检验程序的严重过度简化必须使用获得任何估计的测量数量需要在每个嵌套水平，需要检验一个假设与I型和II型错误的合理平衡。该提案的主要目标是通过以下方式填补这一空白：（1）研究适用于广义线性和非线性混合效应回归模型的各种现有和新测试的大样本和小样本特性，（2）开发统计上严格的方法来计算这类模型的统计功效，这类模型现在被行为，社会和生物科学家广泛使用，特别是健康和心理健康服务研究人员，以及（3）开发用于计算线性和非线性混合效应回归模型（MIXPWR）统计功效的计算机程序，并将这些新测试纳入现有程序（MIXREG、MIXOR、MIXPREG、MIXNO），这些程序可从www.example.com免费分发ww.uic.edu/Iabs/biostat。初步结果表明，新的小样本测试，我们已经得到了提供的能力，以检测显着较小的影响，在小样本和传统的大样本测试，即使当样本量很大的统计功率增加。最终的结果是能够使用严格的统计方法来分析纵向和集群数据，即使是在小而难以招募的人群中，如少数民族，无家可归者和自杀风险高的人群。