A method for analyzing longitudinal outcomes with many zeros.
A method for analyzing longitudinal outcomes with many zeros.
复制标题
DOI：
10.1023/b:mhsr.0000044749.39484.1b
复制
发表时间：
2004-12-01
期刊：
影响因子：
--
通讯作者：
Drake, Robert
中科院分区：
文献类型：
--
作者：
Xie, Haiyi;McHugo, Gregory;Drake, Robert
医疗保健利用率和成本数据给分析师带来了挑战，因为它们通常随着时间的推移而相关，高度倾斜，并且集中在 0。传统方法不能解决所有这些问题，心理健康和药物滥用干预措施的评估者经常面临如何以准确代表项目影响的方式分析这些数据的问题。最近，传统的两部分模型已扩展到具有相关随机效应的混合效应混合分布模型，以同时处理多余的零、偏度和相关观测值。我们通过分析自信社区治疗（ACT）研究的数据，向心理健康服务研究人员和评估人员介绍并演示这种新方法。响应变量是住院天数，在 3 年内每 6 个月收集一次。解释变量是组：ACT 与标准病例管理。诊断（精神分裂症与双相情感障碍）、时间和住院天数的基线值是协变量。结果表明，ACT 组的客户入院的可能性较高，但住院时间往往较短。混合分布模型提供了更详细的模型来拟合这些数据，并导致对结果的更精确的解释。
Health care utilization and cost data have challenged analysts because they are often correlated over time, highly skewed, and clumped at 0. Traditional approaches do not address all these problems, and evaluators of mental health and substance abuse interventions often grapple with the problem of how to analyze these data in a way that accurately represents program impact. Recently, the traditional 2-part model has been extended to mixed-effects mixed-distribution model with correlated random effects to deal simultaneously with excess zeros, skewness, and correlated observations. We introduce and demonstrate this new method to mental health services researchers and evaluators by analyzing the data from a study of assertive community treatment (ACT). The response variable is the number of days of hospitalization, collected every 6 months over 3 years. The explanatory variable is group: ACT vs. standard case management. Diagnosis (schizophrenia vs. bipolar disorder), time, and the baseline values of hospital days are covariates. Results indicate that clients in the ACT group have a higher probability of hospital admission, but tend to have shorter lengths of stay. The mixed-distribution model provides greater specification of a model to fit these data and leads to more refined interpretation of the results.