Data Integration in Causal Inference

Xu, Shengyuan; Pan, Ziyang; Wang, Miao

doi:10.48550/arxiv.2110.01106

Cited by 3 publications

(3 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Proof: The proof of Lemma 2 is as follows. We technically know 15 according to the exponential tightness theorem that there exists a 𝜍 2 ∈ [0, ∞) while for…”

Section: Appendix D Proof Of Theoremmentioning

confidence: 99%

How to Game-theoretically Disentangle & Control the Efficiency of Orbital Angular Momentum

Zamanipour¹

2022

Preprint

View full text Add to dashboard Cite

<p>How are we able to fix it so as to theoretically go over it, if the optimality and efficiency of the orbital angular momentum (OAM) multiplexing technique in a multi-input multi-output (MIMO) system fails in practice $-$ something that technically causes a considerable reduction in the maximum achievable value of the transceiver's degree-of-freedom? Is the aforementioned challenging issue theoretically interpretable?</p>

show abstract

“…Proof: The proof of Lemma 2 is as follows. We technically know 15 according to the exponential tightness theorem that there exists a 𝜍 2 ∈ [0, ∞) while for…”

Section: Appendix D Proof Of Theoremmentioning

confidence: 99%

How to Game-theoretically Disentangle & Control the Efficiency of Orbital Angular Momentum

Zamanipour¹

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…As forcefully argued in Eichler et al (2021), "the future is not about RCTs vs. RWE but RCTs and RWE." There are numerous opportunities in how the integration of RCTs and RWD can achieve fruitful results that using either RCT or RWD alone can not (Colnet et al, 2020;Degtiar and Rose, 2021;Shi et al, 2021). Among those, an important theme is on augmenting the RCT with RWD to increase efficiency (Yang et al, 2020a,b;Gagnon-Bartsch et al, 2021;Chen et al, 2021;Cheng and Cai, 2021;Li and Luedtke, 2021), and particularly, constructing an externally augmented control arm in the analysis of RCTs (Li et al, 2020;Harton et al, 2021;Gao et al, 2021;Liu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Testing for Treatment Effect Twice Using Internal and External Controls in Clinical Trials

Yi¹,

Zhang²,

Du³

et al. 2022

Preprint

View full text Add to dashboard Cite

Leveraging external controls -relevant individual patient data under control from external trials or real-world data -has the potential to reduce the cost of randomized controlled trials (RCTs) while increasing the proportion of trial patients given access to novel treatments. However, due to lack of randomization, RCT patients and external controls may differ with respect to covariates that may or may not have been measured. Hence, after controlling for measured covariates, for instance by * equal contribution †

show abstract

“…Future work can potentially relax those assumptions and focus on examining the model's robustness in estimation by flagging out incompatible or outlying local data sites and reducing communication burdens between sites by allowing a varying control of data privacy in different parallel problems. In recent causal inference method development, a line of research is primarily aimed at combining multiple datasets collected by different designs from potentially heterogeneous populations (Yang & Ding 2020, Wang et al 2020, Bareinboim & Pearl 2016,Shi et al 2021). Most data fusion methods estimate causal treatment effect by incorporating patient-level data from auxiliary data sources into the main data source without consideration of data privacy issues.…”

mentioning

confidence: 99%

Collaborative causal inference with a distributed data-sharing management

Hu¹,

Xu²,

Song³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Data sharing barriers are paramount challenges arising from multicenter clinical trials where multiple data sources are stored in a distributed fashion at different local study sites. Merging such data sources into a common data storage for a centralized statistical analysis requires a data use agreement, which is often time-consuming. Data merging may become more burdensome when causal inference is of primary interest because propensity score modeling involves combining many confounding variables, and systematic incorporation of this additional modeling in meta-analysis has not been thoroughly investigated in the literature. We propose a new causal inference framework that avoids the merging of subject-level raw data from multiple sites but needs only the sharing of summary statistics. The proposed collaborative inference enjoys maximal protection of data privacy and minimal sensitivity to unbalanced data distributions across data sources. We show theoretically and numerically that the new distributed causal inference approach has little loss of statistical power compared to the centralized method that requires merging the entire data. We present large-sample properties and algorithms for the proposed method. We illustrate its performance by simulation experiments and a real-world data example on a multicenter clinical trial of basal insulin treatment for reducing the risk of post-transplantation diabetes among kidney-transplant patients.

show abstract

Data Integration in Causal Inference

Cited by 3 publications

References 68 publications

How to Game-theoretically Disentangle & Control the Efficiency of Orbital Angular Momentum

How to Game-theoretically Disentangle & Control the Efficiency of Orbital Angular Momentum

Testing for Treatment Effect Twice Using Internal and External Controls in Clinical Trials

Collaborative causal inference with a distributed data-sharing management

Contact Info

Product

Resources

About