A multiple robust propensity score method for longitudinal analysis with intermittent missing data

Chen, Chixiang; Shen, Biyi; Liu, Aiyi; Wu, Rongling; Wang, Ming

doi:10.1111/biom.13330

Cited by 18 publications

(14 citation statements)

References 33 publications

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“…Throughout this work, we assumed that all the main data points were observed or missing completely at random, which may not be the case in some applications. However, our method can easily be embedded into a well‐developed scheme, such as the inverse probability weight technique (Chen et al., 2021; Enders, 2010). More details of the estimation procedure are given in Section F of the Supporting Information.…”

Section: Discussionmentioning

confidence: 99%

An Efficient Data Integration Scheme for Synthesizing Information from Multiple Secondary Datasets for the Parameter Inference of the Main Analysis

2023

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Many observational studies and clinical trials collect various secondary outcomes that may be highly correlated with the primary endpoint. These secondary outcomes are often analyzed in secondary analyses separately from the main data analysis. However, these secondary outcomes can be used to improve the estimation precision in the main analysis. We propose a method called multiple information borrowing (MinBo) that borrows information from secondary data (containing secondary outcomes and covariates) to improve the efficiency of the main analysis. The proposed method is robust against model misspecification of the secondary data. Both theoretical and case studies demonstrate that MinBo outperforms existing methods in terms of efficiency gain. We apply MinBo to data from the Atherosclerosis Risk in Communities study to assess risk factors for hypertension.

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Section: Discussionmentioning

confidence: 99%

An Efficient Data Integration Scheme for Synthesizing Information from Multiple Secondary Datasets for the Parameter Inference of the Main Analysis

2023

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“…Another interesting work is to extend ELW to the case with competing risks (Austin et al, 2016;Lau et al, 2009). The techniques, such as inverse probability weight that are commonly adopted in the missing data problem, can be applied to address the issue of biased sampling (Chen et al, 2019(Chen et al, , 2021b. Also, it is highly likely that multiple secondary outcomes that are associated with the time-to-event are available in the study, so how to extend our proposal to aggregate various datasets would be of great interest.…”

Section: Discussionmentioning

confidence: 99%

Synthesizing secondary data into survival analysis to improve estimation efficiency

Chen

Shen

et al. 2022

Biometrical J

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The accelerated failure time (AFT) model and Cox proportional hazards (PH) model are broadly used for survival endpoints of primary interest. However, the estimation efficiency from those models can be further enhanced by incorporating the information from secondary outcomes that are increasingly available and highly correlated with primary outcomes. Those secondary outcomes could be longitudinal laboratory measures collected from doctor visits or cross‐sectional disease‐relevant variables, which are believed to contain extra information related to primary survival endpoints to a certain extent. In this paper, we develop a two‐stage estimation framework to combine a survival model with a secondary model that contains secondary outcomes, named as the empirical‐likelihood‐based weighting (ELW), which comprises two weighting schemes accommodated to the AFT model (ELW‐AFT) and the Cox PH model (ELW‐Cox), respectively. This innovative framework is flexibly adaptive to secondary outcomes with complex data features, and it leads to more efficient parameter estimation in the survival model even if the secondary model is misspecified. Extensive simulation studies showcase more efficiency gain from ELW compared to conventional approaches, and an application in the Atherosclerosis Risk in Communities study also demonstrates the superiority of ELW by successfully detecting risk factors at the time of hospitalization for acute myocardial infarction.

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“…One direct extension is to address missing data problem in the main analysis. In presence of high missingness, our method can be easily adjusted by adopting some well-known scheme, such as inverse probability weight, 31,33,34 into the estimating function g(D u i ; 𝜷). More details about this extension is provided in section 3.2 in Appendix S1.…”

Section: Discussionmentioning

confidence: 99%

Improving main analysis by borrowing information from auxiliary data

Chen

Han

2021

Statistics in Medicine

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In many clinical and observational studies, auxiliary data from the same subjects, such as repeated measurements or surrogate variables, will be collected in addition to the data of main interest. Not directly related to the main study, these auxiliary data in practice are rarely incorporated into the main analysis, though they may carry extra information that can help improve the estimation in the main analysis. Under the setting where part of or all subjects have auxiliary data available, we propose an effective weighting approach to borrow the auxiliary information by building a working model for the auxiliary data, where improvement of estimation precision over the main analysis is guaranteed regardless of the specification of the working model. An information index is also constructed to assess how well the selected working model works to improve the main analysis. Both theoretical and numerical studies show the excellent and robust performance of the proposed method in comparison to estimation without using the auxiliary data. Finally, we utilize the Atherosclerosis Risk in Communities study for illustration.

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A multiple robust propensity score method for longitudinal analysis with intermittent missing data

Cited by 18 publications

References 33 publications

An Efficient Data Integration Scheme for Synthesizing Information from Multiple Secondary Datasets for the Parameter Inference of the Main Analysis

An Efficient Data Integration Scheme for Synthesizing Information from Multiple Secondary Datasets for the Parameter Inference of the Main Analysis

Synthesizing secondary data into survival analysis to improve estimation efficiency

Improving main analysis by borrowing information from auxiliary data

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