(untitled) (Journal of Statistics on Health Decision, 2021)
A large part of the known results and evidences in medical and social sciences come from a type of studies known as observational studies. This type of studies differ from the usual randomized experiences because, due to several different reasons, the researcher is not able to conduct a randomized experience, controlling for the effects on covariables under investigation to assess for some treatment effects. Some methodologies have been developed in order to reduce the imbalanced in the samples of treated and control units. Propensity Score Matching (PSM) is still one of the most common approaches applied but Coarsened Exact Matching appears to produce better results, most of the times in which it is used. This work illustrates the application of each of the two techniques to a set of data from Nepal population. Non-communicable disease are the leading causes of death in developed and developing countries worldwide. Nepal is an example of this paradigm - from 2005 to 2015, there was an increase in ischemic heart disease and brain vascular disease. The April and May 2015 earthquakes imposed critical social and epidemiological pressures, resulting in critical lifestyle changes, namely regarding cardiovascular disease (CVD) risk factors Some organizations were able to provided support to these people and engaged in various activities aiming to fight against the CVD threat.The aim of this study was to appraise the prevalence of CVD risk factors and health in two cohorts of a native Nepalese population, with a common origin from a remote village that were separated in two groups after the incidents of the May 2015 earthquake. The results obtained from the two approaches are generally in agreement but with CEM there is additional conclusions not perceptible with PSM.