Causal Inferences for treatment moderators on Zero-Inflated outcomes of HIV risk

治疗调节者对艾滋病毒风险零膨胀结果的因果推论

基本信息

批准号：
8719417
负责人：
Robert Gallop
金额：
$ 14.88万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-09-15 至 2017-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8719417
关键词：
Address Adopted Age Binomial Model Clinical Clinical Trials Network Cocaine Collaborations Computer software Data Data Set Databases Development Diagnostic Diagnostic Procedure Drug usage Educational Intervention Effect Modifiers (Epidemiology)Effectiveness Gender Goals Greenland HIV HIV risk HIV/STD Individual Intervention Investigation Knowledge Male Condoms Methods Modeling National Institute of Drug Abuse Negative Binomial Distributions Outcome Outcome Measure Outcome Study Outpatients Pharmaceutical Preparations Population Procedures Psychotherapy Randomized Randomized Clinical Trials Relative (related person)Research Research Personnel Robin bird Severities Simulate Statistical Methods Statistical Models Structure Substance abuse problem Target Populations Testing Treatment Effectiveness Universities base clinically relevant follow-up interest men mental state methadone maintenance primary outcome public health relevance response safer sex simulation skills software development treatment as usual treatment program web site

项目摘要

DESCRIPTION (provided by applicant): This proposal addresses two statistical issues in substance abuse research (1) the development of causal inference model for count response variable with a high preponderance of zeros (i.e. zero-inflated outcomes), and (2) accommodation of treatment moderators to the causal inference modeling paradigm for zero-inflated outcomes. Our recent collaborations with the University of Rochester has exposed us to data consisting of zero-inflated outcomes as examined within two NIDA Clinical Trials Network (CTN) studies: CTN0018 ("HIV/STD Safer Sex Skills Groups For Men In Methadone Maintenance Or Drug-free Outpatient Treatment Programs") and CTN0019 ("HIV/STD Safer Sex Skills Groups For Men In Methadone Maintenance Or Drug-free Outpatient Treatment Programs"). These studies examined the effectiveness of a five session gender-specific HIV sexual skill intervention relative to a one session "treatment as usual" standard HIV education intervention. Our previous research focused on standard modeling methods for assessment of moderation within a combined CTN0018 and CTN0019 database. Our colleagues at Rochester are focused on development of distribution-free models to assess moderation for zero-inflated outcomes in the combined database. These standard and distribution-free methods, however, do not address potential confounding between the moderator and other unmeasured variables. This in turn, has led to our interest in extending the causal inference modeling structure to accommodate moderation investigations with zero-inflated outcomes. Our general goals in this proposal are to (1) develop a causal inference statistical model for analysis of zero-inflated count data using Rubin's counterfactual framework under a specific Structural mean models (SMM; Robins, 1994; Robins, 2003) called the rank preserving model (RPM; Ten Have et al., 2007), (2) implement methods of the analysis of moderation under this causal inference framework, and (3) create software for implementing these new causal inference methods for zero-inflated outcomes. Within the context of the CTN0018/19 pooled database, we will test the new models by examining several clinically relevant moderators that were evident using standard moderation analyses: drug use severity, poly-drug use, whether or not the primary drug abused was cocaine, age, knowledge and understanding of male condom usage, and mini-mental status score. The new statistical methods and associated software will be disseminated so that other investigators can test for causal effects of moderator variables when study outcomes are zero-inflated.

描述（由申请人提供）：该提案解决了药物滥用研究中的两个统计问题（1）开发零占优势的计数响应变量（即零膨胀结果）的因果推理模型，以及（2）适应零膨胀结果的因果推理建模范式的治疗调节器。我们最近与罗切斯特大学的合作使我们接触到了由零膨胀结果组成的数据，这些数据在两项 NIDA 临床试验网络 (CTN) 研究中进行了检查：CTN0018（“美沙酮维持或药物治疗中男性的 HIV/STD 安全性行为技能组”）免费门诊治疗计划”）和 CTN0019（“针对接受美沙酮维持或无药物门诊治疗的男性的 HIV/STD 安全性行为技能小组”）程序”）。这些研究检验了五次针对特定性别的艾滋病毒性技能干预相对于一次“照常治疗”标准艾滋病毒教育干预的有效性。我们之前的研究重点是在 CTN0018 和 CTN0019 组合数据库中评估审核的标准建模方法。我们在罗彻斯特的同事专注于开发无分布模型，以评估组合数据库中零膨胀结果的调节。然而，这些标准和无分布的方法并没有解决调节变量和其他未测量变量之间潜在的混淆问题。这反过来又引起了我们对扩展因果推理模型结构的兴趣，以适应零膨胀结果的适度调查。我们在该提案中的总体目标是（1）开发一个因果推理统计模型，用于在特定的结构平均模型（SMM；Robins，1994；Robins，2003）下使用 Rubin 的反事实框架来分析零膨胀计数数据，称为排名保留模型（RPM；Ten Have et al., 2007），（2）在此因果推理框架下实施调节分析方法， (3) 创建软件来实施这些新的因果推理方法，以实现零膨胀的结果。在 CTN0018/19 汇集数据库的背景下，我们将通过检查使用标准调节分析明显的几个临床相关调节器来测试新模型：药物使用严重程度、多种药物使用、主要滥用药物是否是可卡因、年龄、对男用安全套使用的知识和理解以及迷你精神状态评分。新的统计方法和相关软件将得到传播，以便其他研究人员可以在研究结果零膨胀时测试调节变量的因果效应。