  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.
