Abstract. Medical intervention encompasses a range of strategies, such as drug therapy, surgical procedures, and lifestyle modifications, aimed at enhancing the patients health or preventing diseases. The objective of assessing the efficacy of medical interventions is to determine whether these interventions are efficacious, safe, cost-effective, and ethical. Statistical methods serve as vital tools for evaluating the impacts of medical interventions, facilitating the development of rational research protocols, data collection and analysis, trustworthy conclusions, and the promotion and implementation of findings. This paper examines the application of four statistical methods in evaluating the effects of medical interventions using diabetes management as a case study. The analysis reveals that randomized controlled trials, meta-analyses, and causal inference all contribute to enhancing the reliability of assessment, while propensity score matching enhances the quality of matching. However, the drawback of randomized controlled trials is their substantial demand for subjects, time, resources, and personnel, making it challenging to generalize their findings to real-world clinical and social contexts. Causal inference may encounter issues such as randomization failure during implementation. Propensity score matching necessitates the selection of an appropriate propensity score model and matching method, which can impact the effectiveness and efficiency of the matching process.
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