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

Area of Science:

• Biostatistics
• Epidemiology
• Data Science

Background:

• Individual studies often estimate causal effects with incomplete confounder adjustment.
• Integrating summary-level statistics from multiple datasets with diverse confounders presents a significant challenge in data fusion for causal inference.

Purpose of the Study:

• To propose a novel method for identifying and estimating the causal effect of a treatment or exposure on a continuous outcome.
• To address the challenge of incomplete confounder adjustment in individual studies by leveraging multiple datasets.

Main Methods:

• Developed a novel data fusion technique combining summary-level statistics.
• Utilized multiple datasets containing subsets of confounders.
• Incorporated an external dataset with complete confounding information to identify the causal effect.

Main Results:

• Demonstrated that the causal effect is identifiable using the proposed method.
• Simulation studies confirmed the unbiasedness of the causal effect estimates.
• Applied the method to investigate the effect of body mass index on fasting blood glucose.

Conclusions:

• The proposed method effectively integrates summary statistics from multiple datasets with varying confounders.
• This approach enables unbiased causal effect estimation, enhancing causal inference.
• The method shows practical utility in real-world applications, such as analyzing health-related exposures and outcomes.