STATISTICAL ISSUES IN MENTAL HEALTH EPIDEMIOLOGY

生物统计学研究提出了几种方法论。 心理健康流行病学家需要获得的问题 关于老年人精神障碍发生率的可靠和有效的数据 一般社区。这些问题来自以下三个阶段 精神病学流行病学研究：诊断性研究的建立 可靠性、流行率调查的设计和统计 多变量数据分析。具体目标是：1.发展 卡伯值及其标准误差的估算方法和构造方法 使用有效的测试-重测设计的置信度 是否包含在重新测试阶段取决于第一个阶段的结果 测试。2.制定选择诊断有效性案例的规则 基于两个或多个独立诊断的研究 诊断的预测力很差。与之前制定的规则不同， 这些都不会假设诊断灵敏度在所有情况下都是恒定的 病人的类型。3.评估各种设计的效率 罕见精神障碍的两阶段调查。我们将特别注意 适用于使用多变量筛选规则的设计，尤其是 那些试图为人们筛查不同精神障碍的机构。 4.制定估计患病率标准误差的方法 从两个阶段的调查中得出的估计数，并用于建立信心 在这些流行率估计范围内。5.批判性地审视 错误指定Logistic回归模型的影响，包括 独立变量，这些变量在结构上与 给定的结果。如果这样的影响不是微不足道的，就可以开发出实用的 在逻辑模型规范中指导数据分析师的规则。最后， 6.调查排列测试评估的有用性 当多变量结果不是多变量时的多变量假设 很正常。将开发一个FORTRAN计算机程序来实现 多变量置换测试，该方法的结果将是 相对于其他稳健的方法进行评估。不会有新的数据 在拟议的研究中收集的数据。来自心理健康调查的数据 智力迟缓和儿童残疾将被用来说明 在AIMS 3和4中发展的方法，以及来自于 抑郁的心理社会危险因素将被用来说明 方法在AIMS 5和AIMS 6中发展。

Biostatistical research is proposed to address several methodological problems that arise from mental health epidemiologists' need to obtain reliable and efficient data about the rates of mental disorders in the general community. The problems come from all three phases of epidemiologic research in psychiatry: the establishment of diagnostic reliability, the design of prevalence surveys, and the statistical analysis of multivariate data. The specific aims are:, 1. To develop methods for estimating kappa and its standard error, and for constructing confidence bounds using an efficient test-retest design in which inclusion in the retest phases is dependent on the outcome of the first test. 2. To develop rules for selecting cases for diagnostic validity studies based on two or more independent diagnoses when a single diagnosis has poor predictive power. Unlike previously developed rules, these will not assume that diagnostic sensitivity is constant across all types of patients. 3. To evaluate the efficiency of various designs for two-phase surveys of rare mental disorders. Special attention will be given to designs that employ multivariate screening rules, especially ones that attempt to screen persons for different psychiatric disorders. 4. To develop methods for estimating the standard error of prevalence estimates derived from two-phase surveys, and for constructing confidence bounds around these prevalence estimates. 5. To examine critically the effects mis-specifying a logistic regression model by including independent variables that are not structurally related to the odds of a given outcome. If such effects are nontrivial, to develop practical rules to guide data analysts in logistic model specification. Finally, 6. To investigate the usefulness of permutation tests to evaluate multivariate hypotheses when the multivariate outcome is not multivariate normal. A FORTRAN computer program will be developed to implement multivariate permutation tests, and the results of this method will be evaluated relative to other robust methods. No new data will be collected in the proposed research. Data from surveys of mental retardation and childhood disability will be used to illustrate the methods developed in aims 3 and 4, and data from a case-control study of psychosocial risk factors for depression will be used to illustrate methods developed in aims 5 and 6.