College

Arts & Sciences

Department

Mathematics & Statistics

Rank

Professor

Biographical Statement

I am a professor of statistics in the department of mathematics and statistics. I have taught a wide variety of undergraduate and graduate courses. I have developed several courses and some minors in my discipline. I work closely with undergraduate and graduate students in my scholarship. For three semesters I guided fifteen undergraduate seniors on their projects for the statistical experience that they learnt through the courses taken at UNF. Over the years my interest in research has morphed from theoretical to applied mathematics and more recently to big data analytics. The submitted article is with one of my graduate students where we used different analytic tools for statistical analysis on a big data.

Type of Work

Journal Article

Publication Information

Journal of Basic and Applied Research International 19(3): 194-205, 2016.

Description of Work

Statistical methodology and data analytics have avenues of exploring relationships among observed variables that are qualitative and quantitative in nature. The main objective of this study is to show that there is not a single “best” model to predict the length of stay of elderly patients; but rather that there is a preferred model for different age groups with various health conditions. We investigate a large amount of public data that are collected for the Agency for Health Care Administration and suggest possible predictive models to interpret its outcomes. Our data consist of every Medicare inpatient hospital discharge record related in the state of Florida 2011 related to the following primary diagnoses: Acute Myocardial Infarction, Heart Failure, and Pneumonia. The response variable is duration of stay in days. The nature of the predictor variables is either categorical or ordinal. We use an Accelerated Failure Time model and a Cox Proportional Hazard model for the right-censored response time in order to analyze related distribution functions. We interpret the effect of sex, primary diagnosis, age, inclusion of respiratory charges, and severity of illness as explanatory variables and use these to rank the patients in terms of expected length of stay. We use an extensive amount of visual display to substantiate the outcomes. The result includes expected instantaneous rate of change on the hazard functions of Accelerated Failure Time and Cox Proportional Hazard models, as well as the Kaplan Meier estimates. The study results indicate the importance of using multiple model types when analyzing any data which incorporates failure time data.

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Analysis of Survival Functions In Predicting Length Of Stay In Florida Hospitals

Statistical methodology and data analytics have avenues of exploring relationships among observed variables that are qualitative and quantitative in nature. The main objective of this study is to show that there is not a single “best” model to predict the length of stay of elderly patients; but rather that there is a preferred model for different age groups with various health conditions. We investigate a large amount of public data that are collected for the Agency for Health Care Administration and suggest possible predictive models to interpret its outcomes. Our data consist of every Medicare inpatient hospital discharge record related in the state of Florida 2011 related to the following primary diagnoses: Acute Myocardial Infarction, Heart Failure, and Pneumonia. The response variable is duration of stay in days. The nature of the predictor variables is either categorical or ordinal. We use an Accelerated Failure Time model and a Cox Proportional Hazard model for the right-censored response time in order to analyze related distribution functions. We interpret the effect of sex, primary diagnosis, age, inclusion of respiratory charges, and severity of illness as explanatory variables and use these to rank the patients in terms of expected length of stay. We use an extensive amount of visual display to substantiate the outcomes. The result includes expected instantaneous rate of change on the hazard functions of Accelerated Failure Time and Cox Proportional Hazard models, as well as the Kaplan Meier estimates. The study results indicate the importance of using multiple model types when analyzing any data which incorporates failure time data.