Federated Adaptive Causal Estimation (FACE) of Target Treatment Effects

Han, Larry; Hou, Jingbo; Cho, Kelly; Duan, Rui; Cai, Tianxi

doi:10.48550/arxiv.2112.09313

Cited by 4 publications

(8 citation statements)

References 23 publications

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Section: Introductionmentioning

confidence: 80%

“…Our work is most closely related to the recent studies of privacy-preserving methods for causal inference by Vo et al 27 and Han et al 28,29 † † Vo et al 27 estimate treatment effects by modeling potential outcomes by Gaussian processes. Han et al 28,29 propose to estimate treatment effects for target populations by adaptively and optimally weighing source F I G U R E 1 Coefficient of the exposure to alpha blockers. This figure shows the estimated coefficient and its 95% confidence interval of the exposure to alpha blockers in a logit outcome model, where the outcome indicates whether the patient with acute respiratory distress (ARD) received mechanical ventilation and then had in-hospital death.…”

Section: Introductionmentioning

confidence: 80%

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Federated causal inference in heterogeneous observational data

Xiong

Koenecke

Powell

et al. 2023

Statistics in Medicine

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We are interested in estimating the effect of a treatment applied to individuals at multiple sites, where data is stored locally for each site. Due to privacy constraints, individual‐level data cannot be shared across sites; the sites may also have heterogeneous populations and treatment assignment mechanisms. Motivated by these considerations, we develop federated methods to draw inferences on the average treatment effects of combined data across sites. Our methods first compute summary statistics locally using propensity scores and then aggregate these statistics across sites to obtain point and variance estimators of average treatment effects. We show that these estimators are consistent and asymptotically normal. To achieve these asymptotic properties, we find that the aggregation schemes need to account for the heterogeneity in treatment assignments and in outcomes across sites. We demonstrate the validity of our federated methods through a comparative study of two large medical claims databases.

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Section: Introductionmentioning

confidence: 80%

Section: Introductionmentioning

confidence: 80%

Federated causal inference in heterogeneous observational data

Xiong

Koenecke

Powell

et al. 2023

Statistics in Medicine

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“…In the causal inference framework, these efforts have been carried out using direct standardization (Varewyck et al 2014) or indirect standardization (Daignault & Saarela 2017). Recent advances in causal inference can leverage summary-level data from federated data sources, but these approaches are either limited in their ability to specify a target population of interest (Vo et al 2021, Xiong et al 2021 or unable to examine treatment-specific outcomes (Han et al 2021, Xiong et al 2021, both of which are crucial for hospital quality measurement. These obstacles underscore a substantial need for privacy-preserving, communication-efficient integrative estimation and inferential methods that account for heterogeneity both within local hospitals and across systems.…”

Section: Causal Inference For Quality Measurementmentioning

confidence: 99%

Privacy-Preserving, Communication-Efficient, and Target-Flexible Hospital Quality Measurement

Han¹,

Li²,

Niknam³

et al. 2022

Preprint

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Data sharing can improve hospital quality measurement, but sharing patientlevel data between hospitals is often infeasible due to privacy concerns. Motivated by the problem of evaluating the quality of care provided by candidate Cardiac Centers of Excellence (CCE), we propose a federated causal inference framework to safely leverage information from peer hospitals to improve the precision of quality estimates for a target hospital. We develop a federated doubly robust estimator that is privacy-preserving (requiring only summary statistics be shared between hospitals) and communication-efficient (requiring only one round of communication between hospitals). We contribute to the quality measurement and causal inference literatures by developing a framework for assessing treatment-specific performance in hospitals without sharing patient-level data. We also propose a penalized regression approach on summary statistics of the influence functions for efficient estimation and valid inference. In so doing, the proposed estimator is data-adaptive, downweighting hospitals with different case-mixes from the target hospital for bias reduction and upweighting hospitals with similar case-mixes for efficiency gain. We show the improved performance of the federated global estimator in extensive simulation studies. Studying candidate CCE, we find that the federated global estimator improves precision of treatment effect estimates by 34% to 86% for target hospitals, qualitatively altering the evaluation of the percutaneous coronary intervention (PCI) treatment effect in 22 of 51 hospitals. Focusing on treatment-specific rankings, we find that hospitals rarely excel in both PCI and medical management (MM), stressing the importance of treatment-specific performance assessments.

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“…Recent work by Xiong et al (2021) considers producing global propensity scores from the locally estimated gradients in a federated learning setting. Han et al (2021) introduces an adaptive federated procedure of weighing the estimators locally estimated from source sites to augment average treatment effect estimate in a target site, meanwhile allowing potential heterogeneity in covariate distributions. Most of the newly proposed meta-analytic approaches adopt a divide-and-conquer strategy, similar to a parallelized operation that requires reliable local estimates and inferential quantities.…”

Section: Introductionmentioning

confidence: 99%

“…We plan to take advantage of the privacy-preserving nature of COLA and extend it to data fusion problems. Another interesting potential extension of our method is to transport our COLA estimation which targets the population underlying the current multi-center clinical trials to a new population(Dahabreh et al 2020, Han et al 2021…”

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confidence: 99%

Collaborative causal inference with a distributed data-sharing management

Hu¹,

Xu²,

Song³

2022

Preprint

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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.

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Federated Adaptive Causal Estimation (FACE) of Target Treatment Effects

Cited by 4 publications

References 23 publications

Federated causal inference in heterogeneous observational data

Federated causal inference in heterogeneous observational data

Privacy-Preserving, Communication-Efficient, and Target-Flexible Hospital Quality Measurement

Collaborative causal inference with a distributed data-sharing management

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