Causal data fusion methods using summary‐level statistics for a continuous outcome

Li, Hongkai; Wang, Miao; Cai, Zheng; Liu, Xinhui; Zhang, Tao; Xue, Fuzhong; Geng, Zhi

doi:10.1002/sim.8461

Cited by 6 publications

(12 citation statements)

References 14 publications

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“…Here we assume that multiple confounders not jointly observed are independent of each other or conditional independence given the common confounders

C_{s}

(eg, age and gender). Otherwise, we need an external data set including

C_{1}, C_{2}, \dots, C_{K}

to obtain the joint distributions 17 . For instance, one interest is the causal effect of personal smoking on adult asthma, the chronic bronchits and socioeconomic status (SES) are two independent confounders.…”

Section: Methodsmentioning

confidence: 99%

“…We account for clinical factor and endocrine index factors from data A and data B, respectively. The details about these two data sets can also be found in Li et al 17 …”

Section: Applicationmentioning

confidence: 99%

“…Li proposed the causal effect estimation by integrating two data sets with partial and mutually associated confounders and an external data set with full confounders under linear regression model. But this method fails to extend to more than two data sets as well as general exposure and outcome 17 …”

Section: Introductionmentioning

confidence: 99%

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A causal data fusion method for the general exposure and outcome

Jia

Yan

et al. 2021

Statistics in Medicine

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With the advent of the big data era, the need to combine multiple individual data sets to draw causal effects arises naturally in many medical and biological applications. Especially each data set cannot measure enough confounders to infer the causal effect of an exposure on an outcome. In this article, we extend the method proposed by a previous study to causal data fusion of more than two data sets without external validation and to a more general (continuous or discrete) exposure and outcome. Theoretically, we obtain the condition for identifiability of exposure effects using multiple individual data sources for the continuous or discrete exposure and outcome. The simulation results show that our proposed causal data fusion method has unbiased causal effect estimate and higher precision than traditional regression, meta‐analysis and statistical matching methods. We further apply our method to study the causal effect of BMI on glucose level in individuals with diabetes by combining two data sets. Our method is essential for causal data fusion and provides important insights into the ongoing discourse on the empirical analysis of merging multiple individual data sources.

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“…Here we assume that multiple confounders not jointly observed are independent of each other or conditional independence given the common confounders

C_{s}

(eg, age and gender). Otherwise, we need an external data set including

C_{1}, C_{2}, \dots, C_{K}

Section: Methodsmentioning

confidence: 99%

“…We account for clinical factor and endocrine index factors from data A and data B, respectively. The details about these two data sets can also be found in Li et al 17 …”

Section: Applicationmentioning

confidence: 99%

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A causal data fusion method for the general exposure and outcome

Jia

Yan

et al. 2021

Statistics in Medicine

Self Cite

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show abstract

“…A more challenging problem is when there are no complete data at any data source. This setting has been referred to as data combination [2,60] or data fusion [61,62,63] in the literature. In the following, we will first introduce methods applicable to the general data combination problem in Section 4.1.…”

Section: No Single Sample Contains All Relevant Variablesmentioning

confidence: 99%

“…A more general setting is studied in [63] assuming K + 1 datasets. Specifically, let X = (X 1 , X 2 , .…”

Section: Other Causal Inference Problemsmentioning

confidence: 99%

Data Integration in Causal Inference

Xu¹,

Pan²,

Wang³

2021

Preprint

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Integrating data from multiple heterogeneous sources has become increasingly popular to achieve a large sample size and diverse study population. This paper reviews development in causal inference methods that combines multiple datasets collected by potentially different designs from potentially heterogeneous populations. We summarize recent advances on combining randomized clinical trial with external information from observational studies or historical controls, combining samples when no single sample has all relevant variables with application to two-sample Mendelian randomization, distributed data setting under privacy concerns for comparative effectiveness and safety research using real-world data, Bayesian causal inference, and causal discovery methods.

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Data integration in causal inference

Shi

Pan

Wang

2022

WIREs Computational Stats

Self Cite

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Integrating data from multiple heterogeneous sources has become increasingly popular to achieve a large sample size and diverse study population. This article reviews development in causal inference methods that combines multiple datasets collected by potentially different designs from potentially heterogeneous populations. We summarize recent advances on combining randomized clinical trials with external information from observational studies or historical controls, combining samples when no single sample has all relevant variables with application to two-sample Mendelian randomization, distributed data setting under privacy concerns for comparative effectiveness and safety research using real-world data, Bayesian causal inference, and causal discovery methods.

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Causal data fusion methods using summary‐level statistics for a continuous outcome

Cited by 6 publications

References 14 publications

A causal data fusion method for the general exposure and outcome

A causal data fusion method for the general exposure and outcome

Data Integration in Causal Inference

Data integration in causal inference

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