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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.
期刊论文(35)
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会议论文
Semiparametric Inference for Nonmonotone Missing-Not-at-Random Data: The No Self-Censoring Model.
非单身酮丢失 - 非狂热数据的半参数推断:无自审查模型。
DOI: 10.1080/01621459.2020.1862669
发表时间: 2022
期刊: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
影响因子: 3.7
作者: [Malinsky, Daniel, Shpitser, Ilya, Tchetgen, Eric J. Tchetgen]
通讯作者: Tchetgen, Eric J. Tchetgen
Identification of Personalized Effects Associated With Causal Pathways.
识别与因果路径相关的个性化效应。
DOI: --
发表时间: 2018
期刊: Uncertainty in artificial intelligence : proceedings of the ... conference. Conference on Uncertainty in Artificial Intelligence
影响因子: --
作者: [Shpitser,Ilya, Sherman,Eli]
通讯作者: Sherman,Eli
Identification and inference with nonignorable missing covariate data.
识别和推断不可签名的丢失协变量数据。
DOI: 10.5705/ss.202016.0322
发表时间: 2018-10
期刊: Statistica Sinica
影响因子: 1.4
作者: [Miao W, Tchetgen Tchetgen E]
通讯作者: Tchetgen Tchetgen E
General Identification of Dynamic Treatment Regimes Under Interference.
干扰下动态治疗方案的一般识别。
DOI: --
发表时间: 2020
期刊: Proceedings of machine learning research
影响因子: --
作者: [Sherman,EliS, Arbour,David, Shpitser,Ilya]
通讯作者: Shpitser,Ilya
24
    Novel Designs and Methods to Remove Hidden Confounding Bias in Health Sciences
    • 批准号:
      10447817
    • 项目类别:
    • 资助金额:
      $47.22万
    • 财政年份:
      2020
    • 负责人:
      Eric Joel Tchetgen Tchetgen
    • 依托单位:
    Novel Designs and Methods to Remove Hidden Confounding Bias in Health Sciences
    • 批准号:
      10678962
    • 项目类别:
    • 资助金额:
      $46.95万
    • 财政年份:
      2020
    • 负责人:
      Eric Joel Tchetgen Tchetgen
    • 依托单位:
    Novel Designs and Methods to Remove Hidden Confounding Bias in Health Sciences
    • 批准号:
      10159821
    • 项目类别:
    • 资助金额:
      $46.9万
    • 财政年份:
      2020
    • 负责人:
      Eric Joel Tchetgen Tchetgen
    • 依托单位:
    Theory and methods for mediation and interaction
    • 批准号:
      10092817
    • 项目类别:
    • 资助金额:
      $44.72万
    • 财政年份:
      2018
    • 负责人:
      Eric Joel Tchetgen Tchetgen
    • 依托单位:
    海外基金