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.

翻译：在健康研究中,我们经常遇到以基线和后续观测为特点的两点时间序列数据。我们研究一种新型的两点时间序列结构,没有控制组,没有控制组。我们研究一种新型的两点时间序列结构,其驱动力是一套观察性常规临床数据集,该数据集收集的目的是监测2美元糖尿病(T2D)和心血管疾病(CVD)的关键风险标志。我们提议一种名为“I-Rand”的抽取方法,用于独立抽样,对每个人的两个时间点之一进行独立抽样,并推断根据匹配方法估计的因果关系。我们发现,根据这种方法,肥胖是T2D和CVD的重要风险因素,而基于服务的饮食干预方法可以显著减轻T2D和CVD的风险。基线数据包含研究参与者的干预前健康记录,后续访问中记录了LCD干预后的健康数据,提供了一种两点时间序列模式,没有平行控制组。我们发现,肥胖是T2D和CVD的重要风险因素,而LCD方法可以大大减轻T2D和CVD的风险。我们提供我们的方法。