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


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 inference 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.

Marjorie Lima Do Vale
Sumantra Ray
Publication date: 
March 12, 2021
Publication type: 
Journal Article