Next Generation Missing Data Methods in HIV Research

摘要 这项提议在很大程度上是因为我们参与了博茨瓦纳的组合 预防项目(BCPP)--一种正在进行的大规模人类免疫缺陷病毒 (艾滋病毒)分组随机预防试验在博茨瓦纳全国30个社区进行。如中所示 大多数艾滋病毒预防研究，关于艾滋病毒状况的不完整数据，以及对关于 性行为是这项研究目前面临的一个重要挑战，数据很可能不会丢失 随机的，以复杂的模式存在于个体之间。认识到现有的统计数据 缺失数据的方法在很大程度上不适合完全解决艾滋病毒的这个重要问题 研究后，我们建议开发出下一代进展顺利的缺失数据方法 超越了当前的识别和推理理论。具体地说，我们建议(1)制定一个 统一的身份认同理论汇集了身份认同理论的最新发展 基于因果图和最近统计识别结果的识别 文学。这将允许我们建立条件，在复杂的缺失数据设置中 正如在BCPP中一样，人们可以解开潜在种群的特征，这些特征可能是 来自无反应过程的特征的科学兴趣不一定是科学的 兴趣；(2)在(1)的基础上发展相应的逆概率加权和倍增 在BCPP中数据可能丢失的情况下用于统计推断的稳健方法 在随机和复杂模式中；(3)开发新的半参数归算方法 仅依赖于在无响应过程中编码的假设，从而允许完整的 BCPP中的数据分布保持不受归责过程的影响；(4)开发 用户友好的软件，以促进在AIMS 1-3中开发的方法的广泛使用，并 在广泛的模拟研究中应用并展示其良好性能 回答BCPP中最感兴趣的科学问题。

Abstract This proposal is largely motivated by our involvement with the Botswana Combination Prevention Project (BCPP) which is an on-going large scale human immunodeficiency virus (HIV) cluster randomized prevention trial conducted in 30 communities across Botswana. As in most HIV prevention studies, incomplete data on HIV status and nonresponse to queries about sexual behavior is an important challenge the study currently faces, with data likely missing not at random and in complex patterns across individuals. Recognizing that existing statistical methods for missing data are largely ill-suited to fully address this important problem in HIV research, we propose to develop the next generation of missing data methods going well beyond current theory of identification and inference. Specifically, we propose (1) to develop a unified theory of identification bringing together recent developments in the theory of identification based on causal graphs with recent identification results from the statistics literature. This will allow us to establish conditions under which in complex missing data settings as in the BCPP, one can untangle features of the underlying population which may be of scientific interest from features of the non-response process not necessarily of scientific interest;(2) to build on (1) to develop corresponding inverse-probability-weighted and doubly robust methods for statistical inference in the BCPP where data are likely to be missing not at random and in complex patterns; (3) to develop novel semiparametric imputation methods that solely rely on assumptions encoded in the nonresponse process, thus allowing the complete data distribution in the BCPP to remain unscathed by the imputation process; (4) to develop user-friendly software to facilitate widespread use of the methods developed in Aims 1-3, and to apply and demonstrate their good performance in extensive simulation studies as well as in answering scientific queries of primary interest in the BCPP.