The application of latent curve analysis to testing developmental theories in intervention research
The application of latent curve analysis to testing developmental theories in intervention research
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DOI：
10.1023/a:1022137429115
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发表时间：
1999-08-01
影响因子：
3.1
通讯作者：
Muthén, BO
中科院分区：
文献类型：
--
作者：
Curran, PJ;Muthén, BO
预防或干预计划的有效性传统上是通过治疗组和对照组之间的平均水平的特定时间上升比较来评估的。然而，许多时候，干预的目标行为是自然发展的，治疗的目标是改变这种自然或规范的发展轨迹。当感兴趣的行为随着时间的推移而系统地发展时，检查特定雾水的平均水平可能是有限的，并且可能具有误导性。IR在这里认为，在规范和改变的发育轨迹方面，重铸干预治疗效果具有理论和统计学上的优势。回顾了最近发展起来的潜伏期曲线(LC)分析技术，并将其扩展到真正的实验设计环境中，在该环境中，受试者被随机分配到处理干预或对照条件。LC模型被应用于人工生成的干预数据集和真实干预数据集，以评估干预计划的效果。与更传统的固定效应模型相比，LC模型不仅提供了对治疗组和对照组发育过程的更全面的了解，而且LC模型具有更强的统计能力来检测给定的治疗效果。最后，对LC模型进行了修改，以允许在各种条件和假设下计算特定功率估计，这可以为未来更强大的BUR成本效益干预计划的规划和设计提供急需的信息。
The effectiveness of a prevention or intervention program has traditionally been assessed rising time-specific comparisons of mean levels between the treatment and the control groups. However, many times the behavior targeted by the intervention is naturally developing over rime, and the goal of the treatment is to alter this natural or normative developmental trajectory. Examining rime-specific mean levels can be both limiting and potentially misleading when the behavior of interest is developing systematically over time. Ir is argued here that there are both theoretical and statistical advantages associated with recasting intervention treatment effects in terms of normative and altered developmental trajectories. The recently developed technique of latent curve (LC) analysis is reviewed and extended to a true experimental design setting in which subjects are randomly assigned to a treatment intervention or a control condition. LC models are applied to both artificially generated and real intervention data sets to evaluate the efficacy of an intervention program. Not only do die LC models provide a more comprehensive understanding of the treatment and control group developmental processes compared to more traditional fixed-effects models, but LC models have greater statistical power to detect a given treatment effect. Finally, the LC models are modified to allow for the computation of specific power estimates under a variety of conditions and assumptions that can provide much needed information for the planning and design of more powerful bur cost-efficient intervention programs for the future.