What Else do Epileptic Data Reveal
DOI : 10.3844/amjsp.2011.13.28
Current Research in Medicine
Volume 2, Issue 1
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 estimated and tested. The proximity of the patients could then be identified using the indices based on mapping the principal components of their data as demonstrated in the article.
© 2011 Ramalingam Shanmugam. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.