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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

Author

Listed:
  • 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
    University Of Ulster
    University of Cambridge)

  • Jeffrey Bohn

    (University of California)

  • Lisa Goldberg

    (University of California)

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.

Suggested Citation

  • Xiaowu Dai & Saad Mouti & Marjorie Lima do Vale & Sumantra Ray & Jeffrey Bohn & Lisa Goldberg, 2025. "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," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 17(1), pages 78-131, April.
    • Handle: RePEc:spr:stabio:v:17:y:2025:i:1:d:10.1007_s12561-023-09390-w
    DOI: 10.1007/s12561-023-09390-w
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    References listed on IDEAS

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