A Resampling Approach for Causal Inference on Novel Two-Point Time-Series with Application to Identify Risk Factors for Type-2 Diabetes and Cardiovascular Disease

Xiaowu Dai (), Saad Mouti, Marjorie Lima do Vale, Sumantra Ray, Jeffrey Bohn and Lisa Goldberg
Additional contact information
Xiaowu Dai: University of California
Saad Mouti: University of California
Marjorie Lima do Vale: NNEdPro Global Centre for Nutrition and Health
Sumantra Ray: NNEdPro Global Centre for Nutrition and Health
Jeffrey Bohn: University of California
Lisa Goldberg: University of California

Statistics in Biosciences, 2025, vol. 17, issue 1, No 6, 78-131

Abstract: Abstract Two-point time-series data, characterized by baseline and follow-up observations, are frequently encountered in health research. We study a novel two-point time-series structure without a control group, which is driven by an observational routine clinical dataset collected to monitor key risk markers of type-2 diabetes (T2D) and cardiovascular disease (CVD). We propose a resampling approach called “I-Rand” for independently sampling one of the two-time points for each individual and making inferences on the estimated causal effects based on matching methods. The proposed method is illustrated with data from a service-based dietary intervention to promote a low-carbohydrate diet (LCD), designed to impact risk of T2D and CVD. Baseline data contain a pre-intervention health record of study participants, and health data after LCD intervention are recorded at the follow-up visit, providing a two-point time-series pattern without a parallel control group. Using this approach we find that obesity is a significant risk factor of T2D and CVD, and an LCD approach can significantly mitigate the risks of T2D and CVD. We provide code that implements our method.

Keywords: Resampling; Matching method; Causal inference; Two-point time-series; Synthetic control; Type-2 diabetes; Cardiovascular disease (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12561-023-09390-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stabio:v:17:y:2025:i:1:d:10.1007_s12561-023-09390-w

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/12561

DOI: 10.1007/s12561-023-09390-w

Access Statistics for this article

Statistics in Biosciences is currently edited by Hongyu Zhao and Xihong Lin

More articles in Statistics in Biosciences from Springer, International Chinese Statistical Association
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().