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首页> 外文期刊>Prevention science: the official journal of the Society for Prevention Research >Random Assignment of Schools to Groups in the Drug Resistance Strategies Rural Project: Some New Methodological Twists
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Random Assignment of Schools to Groups in the Drug Resistance Strategies Rural Project: Some New Methodological Twists

机译：在农村地区耐药策略中学校的随机分组：一些新的方法论上的转折

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Random assignment to groups is the foundation for scientifically rigorous clinical trials. But assignment is challenging in group randomized trials when only a few units (schools) are assigned to each condition. In the DRSR project, we assigned 39 rural Pennsylvania and Ohio schools to three conditions (rural, classic, control). But even with 13 schools per condition, achieving pretest equivalence on important variables is not guaranteed. We collected data on six important school-level variables: rurality, number of grades in the school, enrollment per grade, percent white, percent receiving free/assisted lunch, and test scores. Key to our procedure was the inclusion of school-level drug use data, available for a subset of the schools. Also, key was that we handled the partial data with modern missing data techniques. We chose to create one composite stratifying variable based on the seven school-level variables available. Principal components analysis with the seven variables yielded two factors, which were averaged to form the composite inflate-suppress (CIS) score which was the basis of stratification. The CIS score was broken into three strata within each state; schools were assigned at random to the three program conditions from within each stratum, within each state. Results showed that program group membership was unrelated to the CIS score, the two factors making up the CIS score, and the seven items making up the factors. Program group membership was not significantly related to pretest measures of drug use (alcohol, cigarettes, marijuana, chewing tobacco; smallest p > .15), thus verifying that pretest equivalence was achieved.
机译：分组随机分配是科学严谨的临床试验的基础。但是，当每个条件只分配几个单位（学校）时，在小组随机试验中分配是具有挑战性的。在DRSR项目中，我们为39所宾夕法尼亚州和俄亥俄州的乡村学校分配了三种条件（乡村，经典，对照）。但是，即使每个条件有13所学校，也不能保证在重要变量上达到测验前的等值。我们收集了有关六个重要的学校级别变量的数据：乡村程度，学校的年级数量，每个年级的入学人数，白人比例，免费/辅助午餐的百分比以及考试成绩。我们程序的关键是纳入学校级别的药物使用数据，该数据可用于部分学校。同样，关键是我们用现代的缺失数据技术处理了部分数据。我们选择根据七个可用的学校级别变量创建一个复合分层变量。用七个变量进行主成分分析得出两个因子，将其平均以形成复合充气抑制指数（CIS），这是分层的基础。 CIS评分在每个州内分为三个层次;在每个州，每个州内，学校被随机分配到三个计划条件。结果表明，程序组成员资格与CIS分数，构成CIS分数的两个因素以及构成因素的七个项目无关。计划组成员资格与药物使用的前测指标（酒精，香烟，大麻，嚼烟;最小p> .15）没有显着相关性，因此证明了达到了前测等效性。

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