Currently, assessing superiority of a diagnostic test requires cumbersome calculations and confusing interpretations because of nonlinear Receiver Operating Characteristic Curve (ROC). To remedy these difficulties, this article prepares, provides an easier alternate geometric approach. That is, a simple but more effective visual approach with two complementary mosaic masonries are constructed with their properties, superimposed and utilized. First masonry is conditional on disease status. The second masonry is conditional on test outcomes. By superimposing both masonries and visualizing their overlap territory, our approach utilizes an angle, θ to assess superiority of a diagnostic test. A hypothesis testing procedure is also devised to compute the significance level of an estimated angle. The statistical power to accept a true angle is developed in the procedure. In the end, the concepts, properties and advantages of mosaic masonries are illustrated using medical and public health data sets.
Globalization in this 21st century unifies nations. The globalization impacts positively to promote trade and services among the nations but it could also harm economic growth and technological innovations in nations as their capital are drained to elsewhere. On its own merits, the globalization is a true manifestation of an open economy. The medical profession is not immune to this phenomenon: globalization. Mainly for the sake of affordability, many patients in much advanced nations, often voluntarily, seek medical services in far less underdeveloped nations. In other times, some patients are incited by their health insurance industries to practice medical tourism. The medical tourism creates an economic turmoil. Several medical economists are quite puzzled by the advantages as much as by the damaging impacts by the booming popularity of the medical tourism. It is time that all consequences of the medical tourism are examined in details as the findings would benefit the patients, the health policy makers and health industries. Such are the aims of this article. This article applies a data mining approach to compute and interpret economic indices, in a novel manner, to portray the economic booming in the hosting nations which render the medical treatments to the patients who emanate from the guest nations. Using the variations in the medical tourism data, a principal components analysis is performed to group the medical treatments and separately to group the hosting nations in terms of their closer proximities. According to the principal component analysis of the medical tourism data, there are three groups of nations which provide the treatments. The first group consists of USA, Columbia, Costa Rica and Nicaragua. The second group consists of India, Israel and South Africa. The third group consists of Jordan, Thailand and Malaysia. Also, an analysis of variance is performed to assess the significance of the factors on the cost of the medical treatment. Performing likewise on the type of the medical treatments which the medical tourists seek, there are three groups. The first group consists of medical treatments: heart by-pass, angioplasty, heart valve replacement, hip replacement, hip resurfacing, knee replacement, spinal fusion, gastric sleeve and gastric by-pass and lap band and liposuction and ivy treatments. The second group consists of tummy tuck, breast implants, rhinoplasty, facelift, hysterectomy, cornea and retina. The third group consists of dental implant and Lasik eyesurgery. The analysis of variance reveals that the nation Israel differs significantly from the hosting nations Columbia, India, Jordan, Malaysia and South Africa. The nation India differs significantly from the hosting nations South Korea. Furthermore, the Gini's index of heterogenity and entropy index of heterogenity are computed as these indices capture the cost data volatility. These indices are then standardized and interpreted. Among the nations from which the medical tourists seek treatment in other nations, the nation U...
Problem statement: Aggregating and analyzing data of all patients using statistical methodologies as often done in macro sense would be not useful when physician's professional interest was only to provide the best medical care to the patient. For this purpose, individual data of the involved patient should be analyzed and modeled in a micro sense for the physician to notice whether the treatment was helping the particular patient as demonstrated in this article. Understandably, a medical treatment would work in some patients but not in all patients. The physician would be more helped to know whether the treatment worked in a patient. Otherwise, the physician might switch to another treatment for the patient. No appropriate methodology existed in the literature to perform such a profile analysis. Hence, this article introduced a new statistical methodology and demonstrated the methodology using epileptic data. Approach: A probabilistic approach was necessary, as the number of epilepsy seizure in a patient happened to involve a degree of uncertainty. In some patient, the chance for a large number of seizures might be more depending on his/her proneness. The proneness would be a latent and non-measurable factor and hence, it could be captured only as a parameter. The traditional Poisson distribution was not suitable as it assumed homogeneous patients with respect to the proneness. The probability model should match the reality. A generalized Poisson model with an additional parameter to describe individual patient's proneness was necessary as the article demonstrated. The author introduced such a model and investigated several statistical properties before in another article A new methodology with that probability was devised in this article for assessing the efficacy of a treatment for a chosen patient in epilepsy study. Results: Physicians pondered over whether epilepsy seizure incidences data support their hunch that their treatment was successful for a patient. This kind of case-by-case profiling was necessary to exercise the option of switching to another treatment for the patient. Aggregated medical data analysis of all patients did not help in making decision for a particular patient. The results of this article demonstrated about how the new methodology worked in epilepsy data to confirm when the treatment was successful. Patients, nurses and physicians were eager to develop an early warning system about how successful the treatment was in a patient. Such an early warning system was feasible, after finding the probability pattern of the data, because of the new methodology in this article. The discussions in this article could be emulated for other medical data analysis to address patient's profile. Conclusions/Recommendations: As demonstrated with an example using epilepsy data, other medical data could be fit, analyzed and interpreted using the incidence rate restricted Poisson model. Not only the incidence rate but also the restriction level on the incidence rate due to the treatment could be ...
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